Lisa V. Alexander

We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1° latitude‐longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5° latitude‐longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two‐stage reduced‐space optimal interpolation procedure, followed by superposition of quality‐improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave‐based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well, containing SST fields with more uniform variance through time and better month‐to‐month persistence than those in GISST. HadMAT1 is more consistent with SST and with collocated land surface air temperatures than previous NMAT data sets.

A suite of climate change indices derived from daily temperature and precipitation data, with a primary focus on extreme events, were computed and analyzed. By setting an exact formula for each index and using specially designed software, analyses done in different countries have been combined seamlessly. This has enabled the presentation of the most up‐to‐date and comprehensive global picture of trends in extreme temperature and precipitation indices using results from a number of workshops held in data‐sparse regions and high‐quality station data supplied by numerous scientists world wide. Seasonal and annual indices for the period 1951–2003 were gridded. Trends in the gridded fields were computed and tested for statistical significance. Results showed widespread significant changes in temperature extremes associated with warming, especially for those indices derived from daily minimum temperature. Over 70% of the global land area sampled showed a significant decrease in the annual occurrence of cold nights and a significant increase in the annual occurrence of warm nights. Some regions experienced a more than doubling of these indices. This implies a positive shift in the distribution of daily minimum temperature throughout the globe. Daily maximum temperature indices showed similar changes but with smaller magnitudes. Precipitation changes showed a widespread and significant increase, but the changes are much less spatially coherent compared with temperature change. Probability distributions of indices derived from approximately 200 temperature and 600 precipitation stations, with near‐complete data for 1901–2003 and covering a very large region of the Northern Hemisphere midlatitudes (and parts of Australia for precipitation) were analyzed for the periods 1901–1950, 1951–1978 and 1979–2003. Results indicate a significant warming throughout the 20th century. Differences in temperature indices distributions are particularly pronounced between the most recent two periods and for those indices related to minimum temperature. An analysis of those indices for which seasonal time series are available shows that these changes occur for all seasons although they are generally least pronounced for September to November. Precipitation indices show a tendency toward wetter conditions throughout the 20th century.

Abstract Indices for climate variability and extremes have been used for a long time, often by assessing days with temperature or precipitation observations above or below specific physically‐based thresholds. While these indices provided insight into local conditions, few physically based thresholds have relevance in all parts of the world. Therefore, indices of extremes evolved over time and now often focus on relative thresholds that describe features in the tails of the distributions of meteorological variables. In order to help understand how extremes are changing globally, a subset of the wide range of possible indices is now being coordinated internationally which allows the results of studies from different parts of the world to fit together seamlessly. This paper reviews these as well as other indices of extremes and documents the obstacles to robustly calculating and analyzing indices and the methods developed to overcome these obstacles. Gridding indices are necessary in order to compare observations with climate model output. However, gridding indices from daily data are not always straightforward because averaging daily information from many stations tends to dampen gridded extremes. The paper describes recent progress in attribution of the changes in gridded indices of extremes that demonstrates human influence on the probability of extremes. The paper also describes model projections of the future and wraps up with a discussion of ongoing efforts to refine indices of extremes as they are being readied to contribute to the IPCC's Fifth Assessment Report. WIREs Clim Change 2011, 2:851–870. doi: 10.1002/wcc.147 This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change

We present a dataset of daily resolution climatic time series that has been compiled for the European Climate Assessment (ECA). As of December 2001, this ECA dataset comprises 199 series of minimum, maximum and/or daily mean temperature and 195 series of daily precipitation amount observed at meteorological stations in Europe and the Middle East. Almost all series cover the standard normal period 1961–90, and about 50% extends back to at least 1925. Part of the dataset (90%) is made available for climate research on CDROM and through the Internet (at http://www.knmi.nl/samenw/eca). A comparison of the ECA dataset with existing gridded datasets, having monthly resolution, shows that correlation coefficients between ECA stations and nearest land grid boxes between 1946 and 1999 are higher than 0.8 for 93% of the temperature series and for 51% of the precipitation series. The overall trends in the ECA dataset are of comparable magnitude to those in the gridded datasets. The potential of the ECA dataset for climate studies is demonstrated in two examples. In the first example, it is shown that the winter (October–March) warming in Europe in the 1976–99 period is accompanied by a positive trend in the number of warm-spell days at most stations, but not by a negative trend in the number of cold-spell days. Instead, the number of cold-spell days increases over Europe. In the second example, it is shown for winter precipitation between 1946 and 1999 that positive trends in the mean amount per wet day prevail in areas that are getting drier and wetter. Because of its daily resolution, the ECA dataset enables a variety of empirical climate studies, including detailed analyses of changes in the occurrence of extremes in relation to changes in mean temperature and total precipitation. Copyright © 2002 Royal Meteorological Society.

This chapter addresses changes in weather and climate events relevant to extreme impacts and disasters. An extreme (weather or climate) event is generally defined as the occurrence of a value of a weather or climate variable above (or below) a threshold value near the upper (or lower) ends (‘tails’) of the range of observed values of the variable. Some climate extremes (e.g., droughts, floods) may be the result of an accumulation of weather or climate events that are, individually, not extreme themselves (though their accumulation is extreme). As well, weather or climate events, even if not extreme in a statistical sense, can still lead to extreme conditions or impacts, either by crossing a critical threshold in a social, ecological, or physical system, or by occurring simultaneously with other events. A weather system such as a tropical cyclone can have an extreme impact, depending on where and when it approaches landfall, even if the specific cyclone is not extreme relative to other tropical cyclones. Conversely, not all extremes necessarily lead to serious impacts. [3.1]

Marine heatwaves (MHWs) have been observed around the world and are expected to increase in intensity and frequency under anthropogenic climate change. A variety of impacts have been associated with these anomalous events, including shifts in species ranges, local extinctions and economic impacts on seafood industries through declines in important fishery species and impacts on aquaculture. Extreme temperatures are increasingly seen as important influences on biological systems, yet a consistent definition of MHWs does not exist. A clear definition will facilitate retrospective comparisons between MHWs, enabling the synthesis and a mechanistic understanding of the role of MHWs in marine ecosystems. Building on research into atmospheric heatwaves, we propose both a general and specific definition for MHWs, based on a hierarchy of metrics that allow for different data sets to be used in identifying MHWs. We generally define a MHW as a prolonged discrete anomalously warm water event that can be described by its duration, intensity, rate of evolution, and spatial extent. Specifically, we consider an anomalously warm event to be a MHW if it lasts for five or more days, with temperatures warmer than the 90th percentile based on a 30-year historical baseline period. This structure provides flexibility with regard to the description of MHWs and transparency in communicating MHWs to a general audience. The use of these metrics is illustrated for three 21st century MHWs; the northern Mediterranean event in 2003, the Western Australia ‘Ningaloo Niño’ in 2011, and the northwest Atlantic event in 2012. We recommend a specific quantitative definition for MHWs to facilitate global comparisons and to advance our understanding of these phenomena.

Abstract Heatwaves are important climatic extremes in atmospheric and oceanic systems that can have devastating and long-term impacts on ecosystems, with subsequent socioeconomic consequences. Recent prominent marine heatwaves have attracted considerable scientific and public interest. Despite this, a comprehensive assessment of how these ocean temperature extremes have been changing globally is missing. Using a range of ocean temperature data including global records of daily satellite observations, daily in situ measurements and gridded monthly in situ-based data sets, we identify significant increases in marine heatwaves over the past century. We find that from 1925 to 2016, global average marine heatwave frequency and duration increased by 34% and 17%, respectively, resulting in a 54% increase in annual marine heatwave days globally. Importantly, these trends can largely be explained by increases in mean ocean temperatures, suggesting that we can expect further increases in marine heatwave days under continued global warming.

In this study, we present the collation and analysis of the gridded land‐based dataset of indices of temperature and precipitation extremes: HadEX2. Indices were calculated based on station data using a consistent approach recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices, resulting in the production of 17 temperature and 12 precipitation indices derived from daily maximum and minimum temperature and precipitation observations. High‐quality in situ observations from over 7000 temperature and 11,000 precipitation meteorological stations across the globe were obtained to calculate the indices over the period of record available for each station. Monthly and annual indices were then interpolated onto a 3.75° × 2.5° longitude‐latitude grid over the period 1901–2010. Linear trends in the gridded fields were computed and tested for statistical significance. Overall there was very good agreement with the previous HadEX dataset during the overlapping data period. Results showed widespread significant changes in temperature extremes consistent with warming, especially for those indices derived from daily minimum temperature over the whole 110 years of record but with stronger trends in more recent decades. Seasonal results showed significant warming in all seasons but more so in the colder months. Precipitation indices also showed widespread and significant trends, but the changes were much more spatially heterogeneous compared with temperature changes. However, results indicated more areas with significant increasing trends in extreme precipitation amounts, intensity, and frequency than areas with decreasing trends.

Evidence that extreme rainfall intensity is increasing at the global scale has strengthened considerably in recent years. Research now indicates that the greatest increases are likely to occur in short-duration storms lasting less than a day, potentially leading to an increase in the magnitude and frequency of flash floods. This review examines the evidence for subdaily extreme rainfall intensification due to anthropogenic climate change and describes our current physical understanding of the association between subdaily extreme rainfall intensity and atmospheric temperature. We also examine the nature, quality, and quantity of information needed to allow society to adapt successfully to predicted future changes, and discuss the roles of observational and modeling studies in helping us to better understand the physical processes that can influence subdaily extreme rainfall characteristics. We conclude by describing the types of research required to produce a more thorough understanding of the relationships between local-scale thermodynamic effects, large-scale atmospheric circulation, and subdaily extreme rainfall intensity.

The global ocean has warmed substantially over the past century, with far-reaching implications for marine ecosystems1. Concurrent with long-term persistent warming, discrete periods of extreme regional ocean warming (marine heatwaves, MHWs) have increased in frequency2. Here we quantify trends and attributes of MHWs across all ocean basins and examine their biological impacts from species to ecosystems. Multiple regions in the Pacific, Atlantic and Indian Oceans are particularly vulnerable to MHW intensification, due to the co-existence of high levels of biodiversity, a prevalence of species found at their warm range edges or concurrent non-climatic human impacts. The physical attributes of prominent MHWs varied considerably, but all had deleterious impacts across a range of biological processes and taxa, including critical foundation species (corals, seagrasses and kelps). MHWs, which will probably intensify with anthropogenic climate change3, are rapidly emerging as forceful agents of disturbance with the capacity to restructure entire ecosystems and disrupt the provision of ecological goods and services in coming decades. Marine heatwaves are increasing in frequency, but they vary in their manifestation. All events impact ecosystem structure and functioning, with increased risk of negative impacts linked to greater biodiversity, number of species near their thermal limit and additional human impacts.

Abstract This study investigates the presence of trends in annual maximum daily precipitation time series obtained from a global dataset of 8326 high-quality land-based observing stations with more than 30 years of record over the period from 1900 to 2009. Two complementary statistical techniques were adopted to evaluate the possible nonstationary behavior of these precipitation data. The first was a Mann–Kendall nonparametric trend test, and it was used to evaluate the existence of monotonic trends. The second was a nonstationary generalized extreme value analysis, and it was used to determine the strength of association between the precipitation extremes and globally averaged near-surface temperature. The outcomes are that statistically significant increasing trends can be detected at the global scale, with close to two-thirds of stations showing increases. Furthermore, there is a statistically significant association with globally averaged near-surface temperature, with the median intensity of extreme precipitation changing in proportion with changes in global mean temperature at a rate of between 5.9% and 7.7% K−1, depending on the method of analysis. This ratio was robust irrespective of record length or time period considered and was not strongly biased by the uneven global coverage of precipitation data. Finally, there is a distinct meridional variation, with the greatest sensitivity occurring in the tropics and higher latitudes and the minima around 13°S and 11°N. The greatest uncertainty was near the equator because of the limited number of sufficiently long precipitation records, and there remains an urgent need to improve data collection in this region to better constrain future changes in tropical precipitation.

Abstract Despite their adverse impacts, definitions and measurements of heat waves are ambiguous and inconsistent, generally being endemic to only the group affected, or the respective study reporting the analysis. The present study addresses this issue by employing a set of three heat wave definitions, derived from surveying heat-related indices in the climate science literature. The definitions include three or more consecutive days above one of the following: the 90th percentile for maximum temperature, the 90th percentile for minimum temperature, and positive extreme heat factor (EHF) conditions. Additionally, each index is studied using a multiaspect framework measuring heat wave number, duration, participating days, and the peak and mean magnitudes. Observed climatologies and trends computed by Sen's Kendall slope estimator are presented for the Australian continent for two time periods (1951–2008 and 1971–2008). Trends in all aspects and definitions are smaller in magnitude but more significant for 1951–2008 than for 1971–2008. Considerable similarities exist in trends of the yearly number of days participating in a heat wave and yearly heat wave frequency, suggesting that the number of available heat wave days drives the number of events. Larger trends in the hottest part of a heat wave suggest that heat wave intensity is increasing faster than the mean magnitude. Although the direct results of this study cannot be inferred for other regions, the methodology has been designed as such that it is widely applicable. Furthermore, it includes a range of definitions that may be useful for a wide range of systems impacted by heat waves.

Using the latest HadGHCND daily temperature dataset, global trends in observed summertime heatwaves and annually calculated warm spells for 1950–2011 are analysed via a multi‐index, multi‐aspect framework. Three indices that separately focus on maximum temperature (TX90pct), minimum temperature (TN90pct) and average temperature (EHF) were studied with respect to five characteristics of event intensity, frequency and duration. Despite which index is employed, increases in heatwave/warm spell intensity, frequency and duration are found. Furthermore, TX90pct and TN90pct trends are larger and exhibit more significance for warm spells, implying that non‐summer events are driving annual trends over some regions. Larger increases in TN90pct aspects relative to EHF and TX90pct are also observed. While qualitative information on event trends is similar across the indices, quantitative values vary. This result highlights the importance of employing the most appropriate index when assessing the impact of sustained extreme temperature events.

A climate change workshop for the Middle East brought together scientists and data for the region to produce the first area‐wide analysis of climate extremes for the region. This paper reports trends in extreme precipitation and temperature indices that were computed during the workshop and additional indices data that became available after the workshop. Trends in these indices were examined for 1950–2003 at 52 stations covering 15 countries, including Armenia, Azerbaijan, Bahrain, Cyprus, Georgia, Iran, Iraq, Israel, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, Syria, and Turkey. Results indicate that there have been statistically significant, spatially coherent trends in temperature indices that are related to temperature increases in the region. Significant, increasing trends have been found in the annual maximum of daily maximum and minimum temperature, the annual minimum of daily maximum and minimum temperature, the number of summer nights, and the number of days where daily temperature has exceeded its 90th percentile. Significant negative trends have been found in the number of days when daily temperature is below its 10th percentile and daily temperature range. Trends in precipitation indices, including the number of days with precipitation, the average precipitation intensity, and maximum daily precipitation events, are weak in general and do not show spatial coherence. The workshop attendees have generously made the indices data available for the international research community.

Abstract Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system. This study analyses, to our knowledge for the first time, the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm. We find that growing season climate factors—including mean climate as well as climate extremes—explain 20%–49% of the variance of yield anomalies (the range describes the differences between crop types), with 18%–43% of the explained variance attributable to climate extremes, depending on crop type. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors, while irrigation partly mitigates negative effects of high temperature extremes. We developed a composite indicator to identify hotspot regions that are critical for global production and particularly susceptible to the effects of climate extremes. These regions include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production. Our study highlights the importance of considering climate extremes for agricultural predictions and adaptation planning and provides an overview of critical regions that are most susceptible to variations in growing season climate and climate extremes.

We analyze century‐long daily temperature and precipitation records for stations in Europe west of 60°E. A set of climatic indices derived from the daily series, mainly focusing on extremes, is defined. Linear trends in these indices are assessed over the period 1901–2000. Average trends, for 75 stations mostly representing Europe west of 20°E, show a warming for all temperature indices. Winter has, on average, warmed more (∼1.0°C/100 yr) than summer (∼0.8°C), both for daily maximum (TX) and minimum (TN) temperatures. Overall, the warming of TX in winter was stronger in the warm tail than in the cold tail (1.6 and 1.5°C for 98th and 95th, but ∼1.0°C for 2nd, 5th and 10th percentiles). There are, however, large regional differences in temperature trend patterns. For summer, there is a tendency for stronger warming, both for TX and TN, in the warm than in the cold tail only in parts of central Europe. Winter precipitation totals, averaged over 121 European stations north of 40°N, have increased significantly by ∼12% per 100 years. Trends in 90th, 95th and 98th percentiles of daily winter precipitation have been similar. No overall long‐term trend occurred in summer precipitation totals, but there is an overall weak (statistically insignificant and regionally dependent) tendency for summer precipitation to have become slightly more intense but less common. Data inhomogeneities and relative sparseness of station density in many parts of Europe preclude more robust conclusions. It is of importance that new methods are developed for homogenizing daily data.

Abstract Marine heatwaves (MHWs) can cause devastating impacts to marine life. Despite the serious consequences of MHWs, our understanding of their drivers is largely based on isolated case studies rather than any systematic unifying assessment. Here we provide the first global assessment under a consistent framework by combining a confidence assessment of the historical refereed literature from 1950 to February 2016, together with the analysis of MHWs determined from daily satellite sea surface temperatures from 1982–2016, to identify the important local processes, large-scale climate modes and teleconnections that are associated with MHWs regionally. Clear patterns emerge, including coherent relationships between enhanced or suppressed MHW occurrences with the dominant climate modes across most regions of the globe – an important exception being western boundary current regions where reports of MHW events are few and ocean-climate relationships are complex. These results provide a global baseline for future MHW process and prediction studies.

Abstract Multiple simulations from nine globally coupled climate models were assessed for their ability to reproduce observed trends in a set of indices representing temperature and precipitation extremes over Australia. Observed trends over the period 1957–1999 were compared with individual and multi‐modelled trends calculated over the same period. When averaged across Australia, the magnitude of trends and interannual variability of temperature extremes were well simulated by most models, particularly for the index for warm nights. The majority of models also reproduced the correct sign of trend for precipitation extremes although there was much more variation between the individual model runs. A bootstrapping technique was used to calculate uncertainty estimates and also to verify that most model runs produce plausible trends when averaged over Australia. Although very few showed significant skill at reproducing the observed spatial pattern of trends, a pattern correlation measure showed that spatial noise could not be ruled out as dominating these patterns. Two of the models with output from different forcings showed that the observed trends over Australia for one of the temperature indices was consistent with an anthropogenic response, but was inconsistent with natural‐only forcings. Future projected changes in extremes using three emissions scenarios were also analysed. Australia shows a shift towards warming of temperature extremes, particularly a significant increase in the number of warm nights and heat waves with much longer dry spells interspersed with periods of increased extreme precipitation, irrespective of the scenario used. Copyright © 2008 Royal Meteorological Society

Marine heatwaves (MHWs) are extreme climatic events in oceanic systems that can have devastating impacts on ecosystems, causing abrupt ecological changes and socioeconomic consequences. Several prominent MHWs have attracted scientific and public interest, and recent assessments have documented global and regional increases in their frequency. However, for proactive marine management, it is critical to understand how patterns might change in the future. Here we estimate future changes in MHWs to the end of the 21st century, as simulated by the CMIP5 global climate model projections. Significant increases in MHW intensity and count of annual MHW days are projected to accelerate, with many parts of the ocean reaching a near-permanent MHW state by the late 21st century. The two greenhouse gas emission scenarios considered (Representative Concentration Pathway 4.5 and 8.5) strongly affect the projected intensity of MHW events, the proportion of the globe exposed to permanent MHW states, and the occurrence of the most extreme MHW events. Comparison with simulations of a natural world, without anthropogenic forcing, indicate that these trends have emerged from the expected range of natural variability within the first half of the 21st century. This discrepancy implies a degree of “anthropogenic emergence”, with a departure from the natural MHW conditions that have previously shaped marine ecosystems for centuries or even millennia. Based on these projections we expect impacts on marine ecosystems to be widespread, significant and persistent through the 21st Century.

[1] A gridded land-only data set representing near-surface observations of daily maximum and minimum temperatures (HadGHCND) has been created to allow analysis of recent changes in climate extremes and for the evaluation of climate model simulations. Using a global data set of quality-controlled station observations compiled by the U.S. National Climatic Data Center (NCDC), daily anomalies were created relative to the 1961–1990 reference period for each contributing station. An angular distance weighting technique was used to interpolate these observed anomalies onto a 2.5° latitude by 3.75° longitude grid over the period from January 1946 to December 2000. We have used the data set to examine regional trends in time-varying percentiles. Data over consecutive 5 year periods were used to calculate percentiles which allow us to see how the distributions of daily maximum and minimum temperature have changed over time. Changes during the winter and spring periods are larger than in the other seasons, particularly with respect to increasing temperatures at the lower end of the maximum and minimum temperature distributions. Regional differences suggest that it is not possible to infer distributional changes from changes in the mean alone.

the results of HadEX.For these reasons, the authors set out to develop a new dataset to address these issues using the world's largest repository of daily in situ observations of temperature and precipitation-the National Climatic Data Center (NCDC)'s Global Historical Climatology Network (GHCN)-Daily dataset.This article describes the resulting dataset, termed GHCNDEX-an operationally updated, global land gridded dataset of climate extremes.We also demonstrate the application of the dataset for climate change and climate monitoring purposes in addition to assessing some issues regarding uncertainty by comparing the results with existing datasets.OBSERVATIONAL DATA.GHCN-Daily is the premier source of daily observations of maximum and minimum temperatures as well as daily precipitation amounts from various regions of the globe.The dataset is composed of observations from numerous data sources that have been integrated and undergone extensive quality assurance reviews.As of October 2012, GHCN-Daily contains roughly 29,000 stations with daily maximum and minimum temperature and more than 80,000 stations with daily precipitation amounts (version 3.00-upd-2012100507-see Fig. 1a,c,e).Although the database is updated regularly over Europe, North America, and Australia as well as at several hundred synoptic stations across numerous countries, many records from Asia, Africa, and South America do not contain data from the most recent years.In addition, while many records are short or incomplete, many others-especially in North America, Europe, and Australia-date back well into the nineteenth century.At present, there are no bias adjustments available for GHCN-Daily to account for historical changes in instrumentation, observing practice, station location, or site conditions.

The Intergovernmental Panel on Climate Change (IPCC) first attempted a global assessment of long-term changes in temperature and precipitation extremes in its Third Assessment Report in 2001. While data quality and coverage were limited, the report still concluded that heavy precipitation events had increased and that there had been, very likely, a reduction in the frequency of extreme low temperatures and increases in the frequency of extreme high temperatures. That overall assessment had changed little by the time of the IPCC Special Report on Extremes (SREX) in 2012 and the IPCC Fifth Assessment Report (AR5) in 2013, but firmer statements could be added and more regional detail was possible. Despite some substantial progress throughout the IPCC Assessments in terms of temperature and precipitation extremes analyses, there remain major gaps particularly regarding data quality and availability, our ability to monitor these events consistently and our ability to apply the complex statistical methods required. Therefore this article focuses on the substantial progress that has taken place in the last decade, in addition to reviewing the new progress since IPCC AR5 while also addressing the challenges that still lie ahead.

Using a global observational dataset of daily gridded maximum and minimum temperatures we investigate changes in the respective probability density functions of both variables using two 30‐year periods; 1951–1980 and 1981–2010. The results indicate that the distributions of both daily maximum and minimum temperatures have significantly shifted towards higher values in the latter period compared to the earlier period in almost all regions, whereas changes in variance are spatially heterogeneous and mostly less significant. However asymmetry appears to have decreased but is altered in such a way that it has become skewed towards the hotter part of the distribution. Changes are greater for daily minimum (night‐time) temperatures than for daily maximum (daytime) temperatures. As expected, these changes have had the greatest impact on the extremes of the distribution and we conclude that the distribution of global daily temperatures has indeed become “more extreme” since the middle of the 20th century.

Here we show and discuss the results of an assessment of changes in both area-averaged and station-based climate extreme indices over South America (SA) for the 1950–2010 and 1969–2009 periods using high-quality daily maximum and minimum temperature and precipitation series. A weeklong regional workshop in Guayaquil (Ecuador) provided the opportunity to extend the current picture of changes in climate extreme indices over SA. Our results provide evidence of warming and wetting across the whole SA since the mid-20th century onwards. Nighttime (minimum) temperature indices show the largest rates of warming (e.g. for tropical nights, cold and warm nights), while daytime (maximum) temperature indices also point to warming (e.g. for cold days, summer days, the annual lowest daytime temperature), but at lower rates than for minimums. Both tails of night-time temperatures have warmed by a similar magnitude, with cold days (the annual lowest nighttime and daytime temperatures) seeing reductions (increases). Trends are strong and moderate (moderate to weak) for regional-averaged (local) indices, most of them pointing to a less cold SA during the day and warmer night-time temperatures. Regionally-averaged precipitation indices show clear wetting and a signature of intensified heavy rain events over the eastern part of the continent. The annual amounts of rainfall are rising strongly over south-east SA (26.41 mm/decade) and Amazonia (16.09 mm/decade), but north-east Brazil and the western part of SA have experienced non-significant decreases. Very wet and extremely days, the annual maximum 5-day and 1-day precipitation show the largest upward trends, indicating an intensified rainfall signal for SA, particularly over Amazonia and south-east SA. Local trends for precipitation extreme indices are in general less coherent spatially, but with more general spatially coherent upward trends in extremely wet days over all SA.

Abstract The authors estimate the change in extreme winter weather events over Europe that is due to a long-term change in the North Atlantic Oscillation (NAO) such as that observed between the 1960s and 1990s. Using ensembles of simulations from a general circulation model, large changes in the frequency of 10th percentile temperature and 90th percentile precipitation events over Europe are found from changes in the NAO. In some cases, these changes are comparable to the expected change in the frequency of events due to anthropogenic forcing over the twenty-first century. Although the results presented here do not affect anthropogenic interpretation of global and annual mean changes in observed extremes, they do show that great care is needed to assess changes due to modes of climate variability when interpreting extreme events on regional and seasonal scales. How changes in natural modes of variability, such as the NAO, could radically alter current climate model predictions of changes in extreme weather events on multidecadal time scales is also discussed.

Abstract We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org .

Weather and climate extremes are identified as major areas necessitating further progress in climate research and have thus been selected as one of the World Climate Research Programme (WCRP) Grand Challenges. Here, we provide an overview of current challenges and opportunities for scientific progress and cross-community collaboration on the topic of understanding, modeling and predicting extreme events based on an expert workshop organized as part of the implementation of the WCRP Grand Challenge on Weather and Climate Extremes. In general, the development of an extreme event depends on a favorable initial state, the presence of large-scale drivers, and positive local feedbacks, as well as stochastic processes. We, therefore, elaborate on the scientific challenges related to large-scale drivers and local-to-regional feedback processes leading to extreme events. A better understanding of the drivers and processes will improve the prediction of extremes and will support process-based evaluation of the representation of weather and climate extremes in climate model simulations. Further, we discuss how to address these challenges by focusing on short-duration (less than three days) and long-duration (weeks to months) extreme events, their underlying mechanisms and approaches for their evaluation and prediction.

Outputs from new state-of-the-art climate models under the Coupled Model Inter-comparison Project phase 6 (CMIP6) promise improvement and enhancement of climate change projections information for Australia. Here we focus on three key aspects of CMIP6: what is new in these models, how the available CMIP6 models evaluate compared to CMIP5, and their projections of the future Australian climate compared to CMIP5 focussing on the highest emissions scenario. The CMIP6 ensemble has several new features of relevance to policymakers and others, for example, the integrated matrix of socioeconomic and concentration pathways. The CMIP6 models show incremental improvements in the simulation of the climate in the Australian region, including a reduced equatorial Pacific cold tongue bias, slightly improved rainfall teleconnections with large-scale climate drivers, improved representation of atmosphere and ocean extreme heat events, as well as dynamic sea level. However, important regional biases remain, evident in the excessive rainfall over the Maritime Continent and rainfall pattern biases in the nearby tropical convergence zones. Projections of Australian temperature and rainfall from the available CMIP6 ensemble broadly agree with those from CMIP5, except for a group of CMIP6 models with higher climate sensitivity and greater warming and increase in some extremes after 2050. CMIP6 rainfall projections are similar to CMIP5, but the ensemble examined has a narrower range of rainfall change in austral summer in Northern Australia and austral winter in Southern Australia. Overall, future national projections are likely to be similar to previous versions but perhaps with some areas of improved confidence and clarity.

Abstract Prolonged high-temperature extreme events in the ocean, marine heatwaves, can have severe and long-lasting impacts on marine ecosystems, fisheries and associated services. This study applies a marine heatwave framework to analyse a global sea surface temperature product and identify the most extreme events, based on their intensity, duration and spatial extent. Many of these events have yet to be described in terms of their physical attributes, generation mechanisms, or ecological impacts. Our synthesis identifies commonalities between marine heatwave characteristics and seasonality, links to the El Niño-Southern Oscillation, triggering processes and impacts on ocean productivity. The most intense events preferentially occur in summer, when climatological oceanic mixed layers are shallow and winds are weak, but at a time preceding climatological maximum sea surface temperatures. Most subtropical extreme marine heatwaves were triggered by persistent atmospheric high-pressure systems and anomalously weak wind speeds, associated with increased insolation, and reduced ocean heat losses. Furthermore, the most extreme events tended to coincide with reduced chlorophyll- a concentration at low and mid-latitudes. Understanding the importance of the oceanic background state, local and remote drivers and the ocean productivity response from past events are critical steps toward improving predictions of future marine heatwaves and their impacts.

This study expands previous work on climate extremes in Australia by investigating the simulation of a large number of extremes indices in the CMIP5 multi-model dataset and comparing them to multiple observational datasets over a century of observed data using consistent methods. We calculate 24 indices representing extremes of temperature and precipitation from 1911 to 2010 over Australia and show that there have been significant observed trends in temperature extremes associated with warming while there have been few significant observed trends in precipitation extremes. We compare the observed indices calculated from two mostly independent datasets with 22 CMIP5 models to determine how well global climate models are able to simulate observed climatologies, variability and trends. We find that generally temperature extremes are reasonably well simulated (climatology, variability and trend patterns) although the models tend to overestimate minimum temperature extremes and underestimate maximum temperature extremes. Some models stand out as being outliers and we exclude one model (INMCM4) entirely from the multi-model analysis as it simulates unrealistic minimum temperature extremes over the historical period. There is more spread between models for precipitation than temperature extremes but in most cases the observations sit within the model spread. Exceptions are consecutive wet days (CWD) where nearly all models overestimate the actual number of annual wet days and simple daily intensity (SDII) and one day precipitation maxima (Rx1day) where the models tend to underestimate precipitation intensity. However, some of these differences likely lie in observational uncertainty. Most models including the multi-model mean indicate that precipitation intensity has increased over the last century but the two observational datasets analysed disagree on the sign of change of precipitation intensity, one of them indicating a significant decrease. We use the CMIP5 simulations for two future Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5) to project changes in temperature and precipitation extremes across Australia. By the end of the century the number of cold temperature extremes substantially reduces and the number of warm temperature extremes substantially increases; changes scaling relative to the strength of emissions scenario. Changes in temperature extremes are often greatest in the tropics. While the results for precipitation extremes are less marked, simulations for the end of the century compared to present day indicate more periods of dryness while the most intense precipitation extremes increase substantially, with a separation becoming clear between emissions scenarios.

Abstract This paper provides an updated analysis of observed changes in extreme precipitation using high-quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of 1-day (Rx1day) and 5-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two-thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a covariate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percentage change in extreme precipitation per 1 K increase in GMST is 6.6% (5.1% to 8.2%; 5%–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0% to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–09 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records.

During austral summer (DJF) 2017/18, the New Zealand region experienced an unprecedented coupled ocean-atmosphere heatwave, covering an area of 4 million km2. Regional average air temperature anomalies over land were +2.2 °C, and sea surface temperature anomalies reached +3.7 °C in the eastern Tasman Sea. This paper discusses the event, including atmospheric and oceanic drivers, the role of anthropogenic warming, and terrestrial and marine impacts. The heatwave was associated with very low wind speeds, reducing upper ocean mixing and allowing heat fluxes from the atmosphere to the ocean to cause substantial warming of the stratified surface layers of the Tasman Sea. The event persisted for the entire austral summer resulting in a 3.8 ± 0.6 km3 loss of glacier ice in the Southern Alps (the largest annual loss in records back to 1962), very early Sauvignon Blanc wine-grape maturation in Marlborough, and major species disruption in marine ecosystems. The dominant driver was positive Southern Annular Mode (SAM) conditions, with a smaller contribution from La Niña. The long-term trend towards positive SAM conditions, a result of stratospheric ozone depletion and greenhouse gas increase, is thought to have contributed through association with more frequent anticyclonic 'blocking' conditions in the New Zealand region and a more poleward average latitude for the Southern Ocean storm track. The unprecedented heatwave provides a good analogue for possible mean conditions in the late 21st century. The best match suggests this extreme summer may be typical of average New Zealand summer climate for 2081–2100, under the RCP4.5 or RCP6.0 scenario.

Gridded trends of annual values of various climate extreme indices were estimated for 1950 to 1995, presenting a clearer picture of the patterns of trends in climate extremes than has been seen with raw station data. The gridding also allows one, for the first time, to compare these observed trends with those simulated by a suite of climate model runs forced by observed changes in sea surface temperatures, sea ice extent, and various combinations of human-induced forcings. Bootstrapping techniques are used to assess the uncertainty in the gridded trend estimates and the field significance of the patterns of observed trends. The findings mainly confirm earlier, less objectively derived, results based on station data. There have been significant decreases in the number of frost days and increases in the number of very warm nights over much of the Northern Hemisphere. Regions of significant increases in rainfall extremes and decreases in the number of consecutive dry days are smaller in extent. However, patterns of trends in annual maximum 5-day rainfall totals were not significant. Comparisons of the observed trend estimates with those simulated by the climate model indicate that the inclusion of anthropogenic effects in the model integrations, in particular increasing greenhouse gases, significantly improves the simulation of changing extremes in temperatures. This analysis provides good evidence that human-induced forcing has recently played an important role in extreme climate. The model shows little skill in simulating changing precipitation extremes.

We develop methods for adjusting grid box average temperature time series for the effects on variance of changing numbers of contributing data. Owing to the different sampling characteristics of the data, we use different techniques over land and ocean. The result is to damp average temperature anomalies over a grid box by an amount inversely related to the number of contributing stations or observations. Variance corrections influence all grid box time series but have their greatest effects over data sparse oceanic regions. After adjustment, the grid box land and ocean surface temperature data sets are unaffected by artificial variance changes which might affect, in particular, the results of analyses of the incidence of extreme values. We combine the adjusted land surface air temperature and sea surface temperature data sets and apply a limited spatial interpolation. The effects of our procedures on hemispheric and global temperature anomaly series are small.

A January 2001 workshop held in Kingston, Jamaica, brought together scientists and data from around the Caribbean region and made analysis of indices of extremes derived from daily weather observation in the region possible. The results of the analyses indicate that the percent of days having very warm maximum or minimum temperatures increased strongly since the late 1950s while the percent of days with very cold temperatures decreased. One measure of extreme precipitation shows an increase over this time period while the one analyzed measure of dry conditions, the maximum number of consecutive dry days, is decreasing. These changes generally agree with what is observed in many other parts of the world.

Abstract Up‐to‐date regional and local assessments of changing climate extremes are important to allow countries to make informed decisions on mitigation and adaptation strategies, and to put these changes into a global context. A workshop for countries from the Indo‐Pacific region has brought together daily observations from 13 countries for an analysis of climate extremes between 1971 and 2005. This paper makes use of the workshop outcomes and post‐workshop analyses to build on previous work in Southeast Asia to update the assessment of changing climate extremes using newly available station data. We utilise a consistent and widely tested methodology to allow a direct comparison of the results with those from other parts of the world. The relationship of inter‐annual variability in the climate extremes indices with sea surface temperature (SST) patterns has been investigated with a focus on the influence of the El Niño‐Southern Oscillation phenomenon. The results support findings from elsewhere around the globe that warm extremes, particularly at night, are increasing and cold extremes are decreasing. Trends in precipitation extremes are less spatially consistent across the region. © Royal Meteorological Society and Crown Copyright 2010.

Changes in climate extremes are often monitored using global gridded datasets of climate extremes based on in situ observations or reanalysis data. This study assesses the consistency of temperature and precipitation extremes between these datasets. Both the temporal evolution and spatial patterns of annual extremes of daily values are compared across multiple global gridded datasets of in situ observations and reanalyses to make inferences on the robustness of the obtained results. While normalized time series generally compare well, the actual values of annual extremes of daily data differ systematically across the different datasets. This is partly related to different computational approaches when calculating the gridded fields of climate extremes. There is strong agreement between extreme temperatures in the different in situ–based datasets. Larger differences are found for temperature extremes from the reanalyses, particularly during the presatellite era, indicating that reanalyses are most consistent with purely observational-based analyses of changes in climate extremes for the three most recent decades. In terms of both temporal and spatial correlations, the ECMWF reanalyses tend to show greater agreement with the gridded in situ–based datasets than the NCEP reanalyses and Japanese 25-year Reanalysis Project (JRA-25). Extreme precipitation is characterized by higher temporal and spatial variability than extreme temperatures, and there is less agreement between different datasets than for temperature. However, reasonable agreement between the gridded observational precipitation datasets remains. Extreme precipitation patterns and time series from reanalyses show lower agreement but generally still correlate significantly.

Abstract A 0.05° × 0.05° gridded dataset of daily observed rainfall is compared with high‐quality station data at 119 sites across Australia for performance in capturing extreme rainfall characteristics. A range of statistics was calculated and analysed for a selection of extreme indices representing the frequency and intensity of heavy rainfall events, and their contribution to total rainfall. As is often found for interpolated data, we show that the gridded dataset tends to underestimate the intensity of extreme heavy rainfall events and the contribution of these events to total annual rainfall as well as overestimating the frequency and intensity of very low rainfall events. The interpolated dataset captures the interannual variability in extreme indices. The spatial extent of significant trends in the frequency of extreme rainfall events is also reproduced to some degree. An investigation into the performance of this gridded dataset in remote areas reveals issues, such as the appearance of spurious trends, when stations come in and out of use. We recommend masking over areas of low station density for this particular gridded data. It is likely that in areas of low station density, gridded datasets will, in general, not perform as well. Therefore, caution should be exercised when examining trends and variability in these regions. We conclude that this gridded product is suitable for use in studies on trends and variability in rainfall extremes across much of Australia. The methodology employed in this study, to examine extreme rainfall over Australia in a gridded dataset, may be applied to other areas of the world. While our study indicates that, in general, gridded datasets can be used to investigate extreme rainfall trends and variability, the data should first be subjected to tests similar to those employed here.

Determining the time of emergence of climates altered from their natural state by anthropogenic influences can help inform the development of adaptation and mitigation strategies to climate change. Previous studies have examined the time of emergence of climate averages. However, at the global scale, the emergence of changes in extreme events, which have the greatest societal impacts, has not been investigated before. Based on state-of-the-art climate models, we show that temperature extremes generally emerge slightly later from their quasi-natural climate state than seasonal means, due to greater variability in extremes. Nevertheless, according to model evidence, both hot and cold extremes have already emerged across many areas. Remarkably, even precipitation extremes that have very large variability are projected to emerge in the coming decades in Northern Hemisphere winters associated with a wettening trend. Based on our findings we expect local temperature and precipitation extremes to already differ significantly from their previous quasi-natural state at many locations or to do so in the near future. Our findings have implications for climate impacts and detection and attribution studies assessing observed changes in regional climate extremes by showing whether they will likely find a fingerprint of anthropogenic climate change.

Abstract Knowledge about long‐term changes in climate extremes is vital to better understand multidecadal climate variability and long‐term changes and to place today's extreme events in a historical context. While global changes in temperature and precipitation extremes since the midtwentieth century are well studied, knowledge about century‐scale changes is limited. This paper analyses a range of largely independent observations‐based data sets covering 1901–2010 for long‐term changes and interannual variability in daily scale temperature and precipitation extremes. We compare across data sets for consistency to ascertain our confidence in century‐scale changes in extremes. We find consistent warming trends in temperature extremes globally and in most land areas over the past century. For precipitation extremes we find global tendencies toward more intense rainfall throughout much of the twentieth century; however, local changes are spatially more variable. While global time series of the different data sets agree well after about 1950, they often show different changes during the first half of the twentieth century. In regions with good observational coverage, gridded observations and reanalyses agree well throughout the entire past century. Simulations with an atmospheric model suggest that ocean temperatures and sea ice may explain up to about 50% of interannual variability in the global average of temperature extremes, and about 15% in the global average of moderate precipitation extremes, but local correlations are mostly significant only in low latitudes.

Abstract Despite the availability of several observationally constrained data sets of daily precipitation based on rain gauge measurements, remote sensing, and/or reanalyses, we demonstrate a large disparity in the quasi‐global land mean of daily precipitation intensity. Surprisingly, the magnitude of this spread is similar to that found in the Coupled Model Intercomparison Project Phase 5 (CMIP5). A weakness of reanalyses and CMIP5 models is their tendency to over simulate wet days, consistent with previous studies. However, there is no clear agreement within and between rain gauge and remotely sensed data sets either. This large discrepancy highlights a shortcoming in our ability to characterize not only modeled daily precipitation intensities but even observed precipitation intensities. This shortcoming is partially reconciled by an appreciation of the different spatial scales represented in gridded data sets of in situ precipitation intensities and intensities calculated from gridded precipitation. Unfortunately, the spread in intensities remains large enough to prevent us from satisfactorily determining how much it rains over land.

Geophysical Research LettersVolume 38, Issue 14 ClimateFree Access Reanalysis suggests long-term upward trends in European storminess since 1871 M. G. Donat, M. G. Donat m.donat@unsw.edu.au Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia Institute of Meteorology, Freie Universität Berlin, Berlin, GermanySearch for more papers by this authorD. Renggli, D. Renggli Institute of Meteorology, Freie Universität Berlin, Berlin, Germany Swiss Reinsurance Company, Zurich, SwitzerlandSearch for more papers by this authorS. Wild, S. Wild Institute of Meteorology, Freie Universität Berlin, Berlin, GermanySearch for more papers by this authorL. V. Alexander, L. V. Alexander Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, AustraliaSearch for more papers by this authorG. C. Leckebusch, G. C. Leckebusch Institute of Meteorology, Freie Universität Berlin, Berlin, Germany School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UKSearch for more papers by this authorU. Ulbrich, U. Ulbrich Institute of Meteorology, Freie Universität Berlin, Berlin, GermanySearch for more papers by this author M. G. Donat, M. G. Donat m.donat@unsw.edu.au Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia Institute of Meteorology, Freie Universität Berlin, Berlin, GermanySearch for more papers by this authorD. Renggli, D. Renggli Institute of Meteorology, Freie Universität Berlin, Berlin, Germany Swiss Reinsurance Company, Zurich, SwitzerlandSearch for more papers by this authorS. Wild, S. Wild Institute of Meteorology, Freie Universität Berlin, Berlin, GermanySearch for more papers by this authorL. V. Alexander, L. V. Alexander Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, AustraliaSearch for more papers by this authorG. C. Leckebusch, G. C. Leckebusch Institute of Meteorology, Freie Universität Berlin, Berlin, Germany School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UKSearch for more papers by this authorU. Ulbrich, U. Ulbrich Institute of Meteorology, Freie Universität Berlin, Berlin, GermanySearch for more papers by this author First published: 26 July 2011 https://doi.org/10.1029/2011GL047995Citations: 76AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract [1] Regional trends of wind storm occurrence in Europe are investigated using the 20th Century Reanalysis (20CR). While based on surface observations only, this dataset produces storm events in good agreement with the traditional ERA40 and NCEP reanalyses. Time series display decadal-scale variability in the occurrence of wind storms since 1871, including a period of enhanced storm activity during the early 20th century. Still, significant upward trends are found in central, northern and western Europe, related to unprecedented high values of the storminess measures towards the end of the 20th century, particularly in the North Sea and Baltic Sea regions. Key Points We present robust upward trends in European storminess over the past 140 years The storminess measures show unprecedented high values in recent decades We show good agreement of the new reanalysis data with established reanalyses 1. Introduction [2] Severe wind storms are one of the major natural disasters in Europe, often causing heavy destruction over inland areas, rough sea conditions, and the risk of storm surges in coastal regions. Associated extreme wind speeds typically relate to strong North Atlantic extra-tropical cyclones, traveling eastwards towards the European continent. These storm events affect large areas and often cause high cumulated losses, attracting broad public attention. Climate modeling studies suggest a poleward shift of the mid-latitude storm tracks under future anthropogenic climate conditions [Meehl et al., 2007]. More intense cyclones may form under future climate conditions over the eastern North Atlantic (for a review see Ulbrich et al. [2009]), leading to enhanced extreme wind speeds over Europe [Gastineau and Soden, 2009; Donat et al., 2011] and more frequent storm occurrence [Donat et al., 2010b]. Note some uncertainties are related to circulation responses in climate projections particularly for the European region [Woollings, 2010]. Nevertheless, modeling results suggest that increases in storminess could already be detectable in data from the last century, reflecting related increases in observed greenhouse gas concentrations. Several studies estimating storminess from pressure records, however, could not find evidence for such trends in the recent past. They generally point at large decadal-scale variability, including enhanced occurrence of severe storms in the late 19th to early 20th century and at the end of the 20th century [Alexandersson et al., 1998, 2000; Bärring and von Storch, 2004; Hanna et al., 2008; Wang et al., 2009, 2011]. Wang et al. [2009], however, show that the early maximum in extreme wind speeds occurred during summer, whereas for winter upward trends in storminess can be found in the North Sea area. A recent extension of their study including western and central Europe confirms considerable seasonal differences of the calculated trends [Wang et al., 2011]. [3] Available studies on past storm trends over the last century make use of pressure records, calculating geostrophic wind speeds from pressure differences or considering local pressure tendencies as a proxy for storm occurrences. They do not use wind speed measurements, as this quantity is easily affected by inhomogeneities, e.g., through changes in station surroundings, instruments and location. For the present study we make use of the newly available 20th Century Reanalysis [Compo et al., 2011] covering the period from 1871 to 2008, which provides both pressure and simulated wind speed on the underlying model's grid. It is expected to be less affected by inhomogeneities for individual stations due to quality checks and the assimilation procedure, providing physically consistent fields. The results are validated against the well established but shorter NCEP and ERA40 reanalysis for the overlapping periods since about 1950, which have a considerably larger data basis for the assimilation. This study enhances previous assessments of long-term trends in storminess by analyzing trends in two different storminess measures (and considering different thresholds for each) in reanalysis data sets rather than analyzing station pressure records only. Note, however, that trends in reanalysis wind speeds may differ from observations [Smits et al., 2005], partly explained by surface roughness changes affecting the recorded wind speeds [Vautard et al., 2010]. 2. Data and Methods [4] The newly available Twentieth Century Reanalysis (20CR) covers the period 1871–2008. It assimilates surface pressure observations only [Compo et al., 2006, 2011] into an atmospheric model on a horizontal resolution of T62 (approximately 1.9°) and also uses observed monthly sea-surface temperatures and sea-ice distributions as boundary conditions. An Ensemble Kalman Filter is used to optimally combine the imperfect observations and estimates of current state, producing an ensemble of 56 realizations of the reanalysis [Compo et al., 2011]. This allows an investigation of the observational uncertainties in the data assimilation. Output data are provided on a regular 2° × 2° grid. [5] Two state-of-the-art reanalyses, assimilating a considerably larger set of 3-dimensional atmospheric observations, are used to validate the 20CR results for the recent five to six decades: NCEP reanalysis [Kistler et al., 2001] covering the years from 1948 to present and ERA40 [Uppala et al., 2005] available for the period 1958 to 2001. ERA40 reanalysis data are available on a spatial resolution of about 1.125° (N80); the NCEP model also works on a T62 spectral grid, the reanalysis output of NCEP is provided on a 2.5° × 2.5° grid. For the calculation of the different measures of storminess, we make use of simulated daily mean-sea-level pressure (MSLP) and near-surface wind speeds, i.e., 10 m (ERA40) or the lowest model layer (NCEP, 20CR). The MSLP fields of all data sets are interpolated onto a uniform 2.5° × 2.5° regular grid prior to the calculation of geostrophic gale indices. [6] Two measures of storminess, representing storm day frequency and local extreme wind speeds, are considered: [7] 1. For quantifying storm day frequency, a gale index (known as "Jenkinson-Collinson" [Jones et al., 1993]) is calculated using geostrophic approximation from the MSLP values at the grid points in a range of ±15 degrees around the central point of each investigation area. Terms for directional flow (F) and vorticity (Z) are derived from the spatial pressure differences and are subsequently used for calculating the gale index G = . Details of the calculation of the geostrophic flow indices are given by Jones et al. [1993]. For this study we identify annual (seasonal) sums of gale days (G > 35 hPa) and severe gale days (G > 40 hPa) as measures of storm frequency. However, as the findings are generally similar for both, we restrict the presentation of results to the first threshold. Recent studies have shown this gale index to be well suited to the investigation of synoptic-scale storm situations in both reanalysis [Donat et al., 2010a] and climate model data [Donat et al., 2010b]. In this study we present the gale day frequencies calculated from large-scale geostrophic flow for six investigation areas in central, western and northern Europe (Table 1). Table 1. Mean Annual Gale Day Frequencies and 95th Percentiles of Daily Maximum Wind Speeds During the Period 1960–2000 Are Shown for the Different Data Sets and Investigation Areas Used in This Study Region Abbreviation Central Point of Investigation Area Mean Annual Number of Gale Days G > 35 hPa (1960–2000) Mean Annual 95th Percentile of Daily Max Wind Speeds (1960–2000), (ms−1) 20CR NCEP1 ERA40 20CR NCEP1 ERA40 North Sea North NSN 60N, 5E 10.4 9.6 9.7 19.0 17.7 13.9 British Isles BI 55N, 5W 19.6 19.6 21.6 19.1 15.8 11.3 North Sea Central NSC 55N, 5E 10.7 11.3 13.4 17.3 16.1 13.0 Baltic Sea BS 55N, 15E 6.0 7.2 9.7 14.0 12.4 9.4 Channel CHA 50N, 0E 9.6 10.2 12.0 16.1 13.2 9.3 Central Europe CE 50N, 10E 3.0 4.2 5.5 10.1 9.8 6.0 [8] 2. The 95th, 98th and 99th annual (seasonal) percentiles of daily maximum wind speeds are analyzed to represent the local intensity of extreme storm events and ensure comparability with previous studies [Alexandersson et al., 1998, 2000; Wang et al., 2009]. We find that results are generally similar across the different thresholds (though slightly more significant for the lower) and we therefore only show results derived for the 95th percentile of daily maximum wind speeds. Wind speed percentiles are calculated from the daily maxima of simulated wind speeds for 00, 06, 12 and 18 UTC at each grid box, and time series are presented for the same regions as the gale day frequencies (Table 1). For the time series (Figure 2), the field averages of wind speeds in the 3 × 3 grid boxes around the central point of the investigation area are considered. Trend calculations are, however, almost identical if only one grid box representative of the central point is used. [9] Long-term trends are fitted with an ordinary least squares regression. For better comparison between the different investigation areas, the original time series are normalized prior to the trend fitting by removing the mean and dividing by the standard deviation, as by Wang et al. [2009]. The statistical significance of the trends is estimated using a Mann-Kendall-Test [Kendall, 1975]. Note that all evaluations cover the whole year unless otherwise specified. 3. Results [10] In all three datasets, both the frequency of gale days and the magnitude of extreme wind speeds show a pronounced southeast-northwest gradient over Europe (Table 1). The annual mean number of gale days is highest over north-western Europe and the North Sea region (BI, NSC, CHA, NSN), followed by Baltic Sea (BS) and Central Europe (CE). The spatial distribution of extreme wind speeds is generally similar; the simulated near-surface wind speeds are, however, subject to surface friction and thus stronger drag over land areas. Consequently the order is slightly modified when looking at absolute wind speeds, with slightly lower values over BI compared to NSN. [11] For all sub-areas considered, time series of gale day frequencies and high wind percentiles are in good agreement between the different reanalysis datasets for their overlapping periods (Figure 1), with Spearman correlation coefficients generally above 0.7 (and a maximum of 0.98). On average, the correlation coefficients are higher for the MSLP-based storm frequency measure than for the high wind speed percentile. These comparisons confirm that the 20CR reproduces the occurrence of European wind storms well in spite of only assimilating surface pressure observations compared to the much more comprehensive set of 3-dimensional variables assimilated by the other reanalysis products. Note that this result may be related to the high observation density over the European continent and adjacent sea areas. We assume that 20CR should also provide reasonable estimates of storm activity back to 1871, given that Europe is relatively observation-rich throughout the whole period [Compo et al., 2011]. X. L. Wang et al. (Extra-tropical cyclone activity in the ensemble of the Twentieth Century Reanalysis (20CRv2), 2010, available at http://www.joss.ucar.edu/events/2010/acre/agenda.html) found 20CR to be homogeneous during the past century in the areas of interest, whereas inhomogeneities are reported in data sparse regions. Considering the 56 ensemble members further allows estimation of observation-related uncertainties. Figure 1Open in figure viewerPowerPoint Spearman correlation coefficients of the storminess measures for each region between the different data sets. Correlations are calculated for the overlapping periods of the pairs of reanalysis data sets; 20CR and NCEP: 1948–2008 (blue); 20CR and ERA40: 1957–2002 (red); NCEP and ERA40: 1957–2002 (green). Each dark coloured (left) bar shows the correlation of annual frequency of gale days G > 35, the light coloured (right) bars are for the annual 95th percentile of daily maximum wind speeds. All correlations are significant at the 5% level. [12] Both storminess measures display pronounced variability on (multi-) decadal time scales. For most regions, phases of high storm activity are found during the first decades of the 20th century (Figure 2), although the specific magnitudes of the individual peaks differ for both storminess measures and in the different regions. A minimum in storm activity is generally found around 1960, followed by steep increase and a maximum storm activity during the 1990s, and a decline to average values in the first decade of the 21st century. Unprecedented high values of both storminess measures are found during the maximum in the late 20th century, around 1990, in the North Sea and Baltic Sea regions (BI, NSN, NSC, BS). Annual gale day frequencies are unprecedented in all regions during the latest maximum (Figure 2, left). Statistically significant positive trends over the whole 20C period are found for all investigation areas in central, western and northern Europe, with mean standardized trends ranging from 0.06 to 0.08 std/10 yr (gale days) and 0.04 to 0.10 std/10 yr (extreme wind speeds). This corresponds to absolute gale day frequency increases between 0.1/10 yr (CE) and 0.5/10 yr (BI) and respective for 95th percentile of wind speeds between 0.03 ms−1/10 yr (CHA) and 0.09 ms−1/10 yr (NSN). Trends tend to be even stronger for the most recent decades since about 1950, when a number of state-of-the-art reanalyses are available. Trends for winter (DJF) months only are similar on the whole (not shown), whereas slightly negative (and generally not significant) trends can be found for summer (JJA). We find enhanced extreme wind speeds during summer around 1920 in some regions (NSC, CH, CE), contributing to the early local maxima of the annual wind speed percentiles. Only occasional single occurrences of gale day events G > 35 hPa during summer are found throughout the investigation period. By far the majority of gale days and (synoptic-scale) high wind speeds occur during winter in Europe. Figure 2Open in figure viewerPowerPoint Time series of storm activity, (left) annual number of gale days and (right) annual 95th percentile of daily maximum wind speeds in the different regions based on the different reanalysis data sets. Solid lines show 11 yr running means of the normalized storminess measures in 20CR ensemble mean (blue), NCEP (green) and ERA40 (red), the grey shaded ranges indicate the spread between the maximum and minimum value of the 56 ensemble members of 20CR. Linear trends are indicated by the dashed lines, trend parameters slope (m, unit: standard deviations per 10 yr) and significance (p, Mann-Kendall-Test) are provided for each time series. [13] The storm activity measures are calculated for all 56 ensemble members and allow an estimation of the effect of observational uncertainties on the storm trend calculations. The spread (grey areas in Figure 2) of the gale day frequencies and extreme wind speeds in the 56 realizations is generally larger towards the beginning of the analysis period, as expected given the potentially lower quality and smaller number of stations available for assimilation. Nevertheless, all realizations exhibit significant trends (p ≤ 0.08 even for the weakest trend of extreme wind speeds in CHA). [14] Over southern Europe and the Mediterranean region, the correlations of the storminess measures in the different data sets are generally lower (not shown), suggesting a lower confidence in 20CR for trend estimations in these areas. Examining the trends of 20CR extreme wind speeds at each grid point (Figure 3) confirms the picture gained from the time series at individual sub-regions. Significant increases in extreme wind speeds over the 138 yr period are found over north (Scandinavia), northwest (British Isles, North Sea) and central Europe, whereas trends in the remaining regions are mostly not significant. A small region around the Adriatic Sea exhibits negative trends. Figure 3Open in figure viewerPowerPoint Trends in the annual 95th percentile of daily maximum wind speeds in 20CR ensemble mean during the period 1871–2008 (unit: std/10 yr). Trends are only plotted where significant (p ≤ 0.05, Mann-Kendall-Test). The circles indicate the respective gale day trends at the specific locations from Figure 2. 4. Summary, Discussion and Conclusions [15] Trends of European storminess are analyzed from three different reanalysis data sets, namely NCEP, ERA40 and 20CR. The newly available 20CR covers the past 138 years, back to 1871, allowing an examination of long-term trends beyond decadal-scale variability. Two different measures of storminess are examined, one quantifying the frequency of storm days, the other the magnitude of extreme wind speeds in relation to storm events. Intercomparison of the storminess measures calculated from the different data sets shows that 20CR, assimilating only surface pressure observations, is capable of reproducing the measures of storminess calculated from the NCEP and ERA40 reanalyses. Our results confirm previous studies in showing a high decadal-scale variability in the occurrence of wind storms in Europe [e.g., Alexandersson et al., 2000; Bärring and von Storch, 2004; Wang et al., 2009]. These previous works, using only station pressure records, identified periods of high storm activity during the late 19th / early 20th century, which was of similar magnitude to the recent maximum around 1990. In contrast, our results suggest that storminess was more intense during the recent maximum, in particular over the North Sea and Baltic Sea regions. We detect significant upward trends in both storminess measures since 1871 in many parts of western, central and northern Europe. It is not clear from this study what the causes of the identified trends are. On the one hand, climate model experiments often show more frequent and stronger wind storms in Europe under enhanced greenhouse gas forcing [e.g., Leckebusch et al., 2006; Pinto et al., 2007; Donat et al., 2010b]. This suggests that the identified trends may (at least partly) be a consequence of increasing GHG concentrations during the past century although uncertainties remain in future projections [Woollings, 2010]. On the other hand, enhanced natural variability cannot be excluded from the recent maximum of storm activity. [16] Some differences are apparent between storm trend estimations in reanalyses compared to station data. For wind speed measures, those differences [e.g., Smits et al., 2005] may at least partly be explained by land use change, resulting in increased surface roughness, and leading to stilling of wind speeds [Vautard et al., 2010]. However, studies using pressure-based proxies for storminess [e.g., Hanna et al., 2008; Wang et al., 2009] do not find as strong increases as we do and rather highlight the large decadal-scale variability of storminess. Nevertheless, Wang et al. [2011] report an unprecedented maximum in the early 1990s in the North Sea-British Isles area. Since the 20CR provides global physically consistent fields over 100+ years it is expected to be less affected by errors or inhomogeneities at individual stations. However, the 20CR is likely to suffer from some inhomogeneity due to the changing station density and the unknown quality of some early observations. Compared to other regions, the observation density is high in Europe throughout the investigation period [Compo et al., 2011]. Observational uncertainty is further accounted for by considering the 56 ensemble realizations of the 20CR, all of them showing robust upward trends in the western, central and northern European investigation areas. [17] Further in-depth analyses of 20CR are warranted, focusing on the variability and trends in atmospheric features related to storm events, such as storm tracks, extra-tropical cyclones, and teleconnection patterns. This may also help to understand the periods of high storm activity in the late 19th / early 20th century, partly occurring during summer months (compare to Wang et al. [2009]) and also to attribute mechanisms to explain the detected trends. Acknowledgments [18] Support for the Twentieth Century Reanalysis Project dataset is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office. We thank the European Centre for Medium-Range Weather Forecasts (ECMWF) and Deutscher Wetterdienst (DWD) for ERA40 reanalysis data availability. M.G. Donat and L.V. Alexander are supported by the Australian Research Council grants LP100200690 and CE110001028. We are also grateful to two anonymous reviewers for their helpful suggestions. 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Abstract. The impact of historical land use induced land cover change (LULCC) on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO2 from 280 ppmv to 375 ppmv. In general, consistent changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC-induced change in the extremes can be of similar magnitude to the response to the change in CO2, the impacts of LULCC are much more geographically isolated. For most models, the impacts of LULCC oppose the impact of the increase in CO2 except for one model where the CO2-caused changes in the extremes are amplified. While we find some evidence that individual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperatures to LULCC is relatively large, we conclude that unless this forcing is included, we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.

Abstract Leading patterns of observed monthly extreme rainfall variability in Australia are examined using an empirical orthogonal teleconnection (EOT) method. Extreme rainfall variability is more closely related to mean rainfall variability during austral summer than in winter. The leading EOT patterns of extreme rainfall explain less variance in Australia-wide extreme rainfall than is the case for mean rainfall EOTs. The authors illustrate that, as with mean rainfall, the El Niño–Southern Oscillation (ENSO) has the strongest association with warm-season extreme rainfall variability, while in the cool season the primary drivers are atmospheric blocking and the subtropical ridge. The Indian Ocean dipole and southern annular mode also have significant relationships with patterns of variability during austral winter and spring. Leading patterns of summer extreme rainfall variability have predictability several months ahead from Pacific sea surface temperatures (SSTs) and as much as a year in advance from Indian Ocean SSTs. Predictability from the Pacific is greater for wetter-than-average summer months than for months that are drier than average, whereas for the Indian Ocean the relationship has greater linearity. Several cool-season EOTs are associated with midlatitude synoptic-scale patterns along the south and east coasts. These patterns have common atmospheric signatures denoting moist onshore flow and strong cyclonic anomalies often to the north of a blocking anticyclone. Tropical cyclone activity is observed to have significant relationships with some warm-season EOTs. This analysis shows that extreme rainfall variability in Australia can be related to remote drivers and local synoptic-scale patterns throughout the year.

Abstract Understanding how climate extremes are sensitive to a changing climate requires characterization of the physical mechanisms behind such events. For this purpose, the application of self‐organizing maps (SOMs) has become popular in the climate science literature. One potential drawback, though not unique to SOMs, is that the background synoptic conditions represented by SOMs may be too generalized to adequately describe the atypical conditions that can co‐occur during the extreme event being considered. In this paper, using the Australian region as a case study, we illustrate how the commonly used SOM training procedure can be readily modified to produce both more accurate patterns and patterns that would otherwise occur too rarely to be represented in the SOM. Even with these improvements, we illustrate that without careful treatment, the synoptic conditions that co‐occur during some types of extreme events (i.e., heavy rainfall and midlatitudinal cyclone occurrence days) risk being poorly represented by the SOM patterns. In contrast, we find that during Australian heat wave events the circulation is indeed well represented by the SOM patterns and that this application can provide additional insight to composite analysis. While these results should not necessarily discourage researchers seeking to apply SOMs to study climate extremes, they highlight the importance of first critically evaluating the features represented by the SOM. This study has provided a methodological framework for such an evaluation which is directly applicable to other weather typing procedures, regions, and types of extreme events.

Abstract We explore uncertainties in observed daily precipitation extremes over the terrestrial tropics and subtropics (50°S–50°N) based on five commonly used products: the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset, the Global Precipitation Climatology Centre‐Full Data Daily (GPCC‐FDD) dataset, the Tropical Rainfall Measuring Mission (TRMM) multi‐satellite research product (T3B42 v7), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN‐CDR), and the Global Precipitation Climatology Project's One‐Degree Daily (GPCP‐1DD) dataset. We use the precipitation indices R10mm and Rx1day, developed by the Expert Team on Climate Change Detection and Indices, to explore the behavior of “moderate” and “extreme” extremes, respectively. In order to assess the sensitivity of extreme precipitation to different grid sizes we perform our calculations on four common spatial resolutions (0.25° × 0.25°, 1° × 1°, 2.5° × 2.5°, and 3.75° × 2.5°). The impact of the chosen “order of operation” in calculating these indices is also determined. Our results show that moderate extremes are relatively insensitive to product and resolution choice, while extreme extremes can be very sensitive. For example, at 0.25° × 0.25° quasi‐global mean Rx1day values vary from 37 mm in PERSIANN‐CDR to 62 mm in T3B42. We find that the interproduct spread becomes prominent at resolutions of 1° × 1° and finer, thus establishing a minimum effective resolution at which observational products agree. Without improvements in interproduct spread, these exceedingly large observational uncertainties at high spatial resolution may limit the usefulness of model evaluations. As has been found previously, resolution sensitivity can be largely eliminated by applying an order of operation where indices are calculated prior to regridding. However, this approach is not appropriate when true area averages are desired (e.g., for model evaluations).

This study investigates relationships between variability in the Pacific and extreme rainfall in eastern Australia. Using an index of extreme precipitation derived from a daily gridded precipitation data set from 1900 to 2011, we find that a nonlinear relationship between El Niño–Southern Oscillation and extreme rainfall exists. That is, the strength of a La Niña episode has a much greater influence on the intensity and duration of extreme rainfall than the magnitude of an El Niño episode. This relationship is found in both interpolated observations and reanalysis data and may be explained, in part, by shifts in the divergence of moisture flux. There is significant decadal variability in the relationship, such that the asymmetry is enhanced during Interdecadal Pacific Oscillation (IPO)–negative events and is nonexistent during IPO‐positive phases. This information has the potential to be of great use in the seasonal prediction of intense rainfall events that lead to flooding.

As part of a special issue on natural hazards, this paper reviews the current state of scientific knowledge of Australian heatwaves. Over recent years, progress has been made in understanding both the causes of and changes to heatwaves. Relationships between atmospheric heatwaves and large-scale and synoptic variability have been identified, with increasing trends in heatwave intensity, frequency and duration projected to continue throughout the 21st century. However, more research is required to further our understanding of the dynamical interactions of atmospheric heatwaves, particularly with the land surface. Research into marine heatwaves is still in its infancy, with little known about driving mechanisms, and observed and future changes. In order to address these knowledge gaps, recommendations include: focusing on a comprehensive assessment of atmospheric heatwave dynamics; understanding links with droughts; working towards a unified measurement framework; and investigating observed and future trends in marine heatwaves. Such work requires comprehensive and long-term collaboration activities. However, benefits will extend to the international community, thus addressing global grand challenges surrounding these extreme events.

Abstract A range of in situ , satellite and reanalysis products on a common daily 1° × 1° latitude/longitude grid were extracted from the Frequent Rainfall Observations on Grids database to help facilitate intercomparison and analysis of precipitation extremes on a global scale. 22 products met the criteria for this analysis, namely that daily data were available over global land areas from 50°S to 50°N since at least 2001. From these daily gridded data, 10 annual indices that represent aspects of extreme precipitation frequency, duration and intensity were calculated. Results were analysed for individual products and also for four cluster types: (i) in situ , (ii) corrected satellite, (iii) uncorrected satellite and (iv) reanalyses. Climatologies based on a common 13-year period (2001–2013) showed substantial differences between some products. Timeseries (which ranged from 13 years to 67 years) also highlighted some substantial differences between products. A coefficient of variation showed that the in situ products were most similar to each other while reanalysis products had the largest variations. Reanalyses however agreed better with in situ observations over extra-tropical land areas compared to the satellite clusters, although reanalysis products tended to fall into ‘wet’ and ‘dry’ camps overall. Some indices were more robust than others across products with daily precipitation intensity showing the least variation between products and days above 20 mm showing the largest variation. In general, the results of this study show that global space-based precipitation products show the potential for climate scale analyses of extremes. While we recommend caution for all products dependent on their intended application, this particularly applies to reanalyses which show the most divergence across results.

ABSTRACT Five gridded datasets containing measures of temperature and precipitation extremes over the past five decades over China are compared. Time series, spatial averages and trends in China's coldest and warmest days and nights and wettest days are estimated from these datasets that vary in their station network density, interpolation procedures and the order in which extremes are calculated. We find that country‐wide trends in temperature extremes are coherent irrespective of dataset choice although actual values are generally smaller in the dataset in which indices are calculated from daily interpolated grids rather than from station points. There are also some regional differences in trends especially over regions with sparse data networks, e.g. the Tibetan Plateau. Averaged across China, trends calculated over the period 1961–2009 vary from a minimum of 0.13 °C decade −1 (hottest day) to 0.65 °C decade −1 (coldest night). The coldest day and night have tended to warm faster than the warmest day and night. Trends in precipitation extremes are much less coherent spatially and can be positive or negative depending on the choice of dataset. The length of station record chosen is also vital to eliminate spurious trends. The results have important implications for detection and attribution and model evaluation studies.

Abstract Extreme precipitation often persists for multiple days with variable duration but has usually been examined at fixed duration. Here we show that considering extreme persistent precipitation by complete event with variable duration, rather than a fixed temporal period, is a necessary metric to account for the complexity of changing precipitation. Observed global mean annual‐maximum precipitation is significantly stronger (49.5%) for persistent extremes than daily extremes. However, both globally observed and modeled rates of relative increases are lower for persistent extremes compared to daily extremes, especially for Southern Hemisphere and large regions in the 0‐45°N latitude band. Climate models also show significant differences in the magnitude and partly even the sign of local mean changes between daily and persistent extremes in global warming projections. Changes in extreme precipitation therefore are more complex than previously reported, and extreme precipitation events with varying duration should be taken into account for future climate change assessments.

Abstract. Historical in situ sub-daily rainfall observations are essential for the understanding of short-duration rainfall extremes but records are typically not readily accessible and data are often subject to errors and inhomogeneities. Furthermore, these events are poorly quantified in projections of future climate change making adaptation to the risk of flash flooding problematic. Consequently, knowledge of the processes contributing to intense, short-duration rainfall is less complete compared with those on daily timescales. The INTENSE project is addressing this global challenge by undertaking a data collection initiative that is coupled with advances in high-resolution climate modelling to better understand key processes and likely future change. The project has so far acquired data from over 23 000 rain gauges for its global sub-daily rainfall dataset (GSDR) and has provided evidence of an intensification of hourly extremes over the US. Studies of these observations, combined with model simulations, will continue to advance our understanding of the role of local-scale thermodynamics and large-scale atmospheric circulation in the generation of these events and how these might change in the future.

Abstract Finer grids in global climate models could lead to an improvement in the simulation of precipitation extremes. We assess the influence on model performance of increasing spatial resolution by evaluating pairs of high‐ and low‐resolution forced atmospheric simulations from six global climate models (generally the latest CMIP6 version) on a common 1° × 1° grid. The differences in tuning between the lower and higher resolution versions are as limited as possible, which allows the influence of higher resolution to be assessed exclusively. We focus on the 1985–2014 climatology of annual extremes of daily precipitation over global land, and models are compared to observations from different sources (i.e., in situ‐based and satellite‐based) to enable consideration of observational uncertainty. Finally, we address regional features of model performance based on four indices characterizing different aspects of precipitation extremes. Our analysis highlights good agreement between models that precipitation extremes are more intense at higher resolution. We find that the spread among observations is substantial and can be as large as intermodel differences, which makes the quantitative evaluation of model performance difficult. However, consistently across the four precipitation extremes indices that we investigate, models often show lower skill at higher resolution compared to their corresponding lower resolution version. Our findings suggest that increasing spatial resolution alone is not sufficient to obtain a systematic improvement in the simulation of precipitation extremes, and other improvements (e.g., physics and tuning) may be required.

Abstract Extreme short-duration rainfall can cause devastating flooding that puts lives, infrastructure, and natural ecosystems at risk. It is therefore essential to understand how this type of extreme rainfall will change in a warmer world. A significant barrier to answering this question is the lack of sub-daily rainfall data available at the global scale. To this end, a global sub-daily rainfall dataset based on gauged observations has been collated. The dataset is highly variable in its spatial coverage, record length, completeness and, in its raw form, quality. This presents significant difficulties for many types of analyses. The dataset currently comprises 23 687 gauges with an average record length of 13 years. Apart from a few exceptions, the earliest records begin in the 1950s. The Global Sub-Daily Rainfall Dataset (GSDR) has wide applications, including improving our understanding of the nature and drivers of sub-daily rainfall extremes, improving and validating of high-resolution climate models, and developing a high-resolution gridded sub-daily rainfall dataset of indices.

Abstract Changes in diurnal temperature range (DTR) over global land areas are compared from a broad range of independent data sets. All data sets agree that global‐mean DTR has decreased significantly since 1950, with most of that decrease occurring over 1960–1980. The since‐1979 trends are not significant, with inter‐data set disagreement even over the sign of global changes. Inter‐data set spread becomes greater regionally and in particular at the grid box level. Despite this, there is general agreement that DTR decreased in North America, Europe, and Australia since 1951, with this decrease being partially reversed over Australia and Europe since the early 1980s. There is substantive disagreement between data sets prior to the middle of the twentieth century, particularly over Europe, which precludes making any meaningful conclusions about DTR changes prior to 1950, either globally or regionally. Several variants that undertake a broad range of approaches to postprocessing steps of gridding and interpolation were analyzed for two of the data sets. These choices have a substantial influence in data sparse regions or periods. The potential of further insights is therefore inextricably linked with the efficacy of data rescue and digitization for maximum and minimum temperature series prior to 1950 everywhere and in data sparse regions throughout the period of record. Over North America, station selection and homogeneity assessment is the primary determinant. Over Europe, where the basic station data are similar, the postprocessing choices are dominant. We assess that globally averaged DTR has decreased since the middle twentieth century but that this decrease has not been linear.

While there are obstacles to the exchange of long-term high temporal resolution precipitation data, there have been fewer barriers to the exchange of so-called 'indices'. These are derived from daily and sub-daily data and measure aspects of precipitation frequency, duration and intensity that could be used for the study of extremes. This paper outlines the history of the rationale and use of these indices, the types of indices that are frequently used and the advantages and pitfalls in analysing them. Moving forward, satellite precipitation products are now showing the potential to provide global climate indices to supplement existing products using longer-term in situ gauge records but we suggest that to advance this area differences between data products, limitations in satellite-based estimation processes, and the inherent challenges of scale need to be better understood.

Abstract. We present a new global land-based daily precipitation dataset from 1950 using an interpolated network of in situ data called Rainfall Estimates on a Gridded Network – REGEN. We merged multiple archives of in situ data including two of the largest archives, the Global Historical Climatology Network – Daily (GHCN-Daily) hosted by National Centres of Environmental Information (NCEI), USA, and one hosted by the Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst (DWD). This resulted in an unprecedented station density compared to existing datasets. The station time series were quality-controlled using strict criteria and flagged values were removed. Remaining values were interpolated to create area-average estimates of daily precipitation for global land areas on a 1∘ × 1∘ latitude–longitude resolution. Besides the daily precipitation amounts, fields of standard deviation, kriging error and number of stations are also provided. We also provide a quality mask based on these uncertainty measures. For those interested in a dataset with lower station network variability we also provide a related dataset based on a network of long-term stations which interpolates stations with a record length of at least 40 years. The REGEN datasets are expected to contribute to the advancement of hydrological science and practice by facilitating studies aiming to understand changes and variability in several aspects of daily precipitation distributions, extremes and measures of hydrological intensity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets.

Abstract Extreme precipitation occurring on consecutive days may substantially increase the risk of related impacts, but changes in such events have not been studied at a global scale. Here we use a unique global dataset based on in situ observations and multimodel historical and future simulations to analyze the changes in the frequency of extreme precipitation on consecutive days (EPCD). We further disentangle the relative contributions of variations in precipitation intensity and temporal correlation of extreme precipitation to understand the processes that drive the changes in EPCD. Observations and climate model simulations show that the frequency of EPCD is increasing in most land regions, in particular, in North America, Europe, and the Northern Hemisphere high latitudes. These increases are primarily a consequence of increasing precipitation intensity, but changes in the temporal correlation of extreme precipitation regionally amplify or reduce the effects of intensity changes. Changes are larger in simulations with a stronger warming signal, suggesting that further increases in EPCD are expected for the future under continued climate warming.

A climate model, coupled to a sophisticated land surface scheme, is used to explore the impact of land use induced land cover change (LULCC) on climate extremes indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The impact from LULCC is contrasted with the impact of doubling atmospheric carbon dioxide (CO 2 ). Many of the extremes indices related to temperature are affected by LULCC and the resulting changes are locally and field significant. Some indices are systematically affected by LULCC in the same direction as increasing CO 2 while for others LULCC opposes the impact of increasing CO 2 . We suggest that assumptions that anthropogenically induced changes in temperature extremes can be approximated just by increasing greenhouse gases are flawed, as LULCC may regionally mask or amplify the impact of increasing CO 2 on climate extremes. In some regions, the scale of the LULCC forcing is of a magnitude similar to the impact of CO 2 alone. We conclude that our results complicate detection and attribution studies, but also offer a way forward to a clearer and an even more robust attribution of the impact of increasing CO 2 at regional scales.

The HadISST1 data set was used to categorize seasonal patterns of observed global sea surface temperature (SST) variability between 1870 and 2006 using the method of Self‐Organizing Maps (SOM). Eight patterns represented the majority of global SST variations associated with the El Niño–Southern Oscillation (ENSO). Time series of the eight patterns exhibited periods with “preferred” SST states since the late 19th century, i.e., when one or more patterns occurred more frequently than in other periods. The eight patterns were used to investigate the global land‐based response of observed extreme temperature and precipitation indices from the HadEX data set to different nodes of SST variability between 1951 and 2003. Results showed very strong statistically significant opposite temperature and precipitation extremes associated with the first pattern (strong La Niña) and the last pattern (strong El Niño). Extreme maximum temperatures were significantly cooler during strong La Niña events than strong El Niño events over Australia, southern Africa, India, and Canada while the converse was true for United States and northeastern Siberia. These responses were larger when global warming was retained. Even intermediate patterns representing a shift from a weak El Niño to a weak La Niña with associated variability in the North Atlantic were linked with statistically significant increases in warm nights and warm days particularly across Scandinavia and northwest Russia. While the link between precipitation extremes and global SST patterns was less spatially coherent, there were large areas across North America and central Europe, which showed statistically significant differences in the response to opposite phases of the El Niño–Southern Oscillation. These results confirm that the variability of global SST anomaly patterns is important for the modulation of extreme temperature and precipitation globally.

The impact of the El Niño‐Southern Oscillation (ENSO) on temperature extremes is examined in both observations and coupled climate model simulations. HadEX2, a newly developed observed gridded dataset of climate extremes indices shows marked contrasts in seasonal composites of the monthly maximum value of daily maximum temperature during the cold and warm phases of ENSO. Extreme maximum temperatures are significantly cooler over Australia, southern Asia, Canada and South Africa during strong La Niña events compared to El Niño events and significantly warmer over the contiguous United States and southern South America. Two climate models are contrasted for their ability to capture these relationships given their very different simulations of ENSO. While both models capture some aspects of the observed patterns, the fidelity of the ENSO simulation appears to be crucial for simulating the magnitude and sign of the extreme maximum temperature relationships. The impact of future climate change on these patterns is also investigated.

Several regions of Australia are projected to experience an increase in the frequency, intensity and duration of heatwaves (HWs) under future climate change. The large-scale dynamics of HWs are well understood, however, the influence of soil moisture deficits—due for example to drought—remains largely unexplored in the region. Using the standardised precipitation evapotranspiration index, we show that the statistical responses of HW intensity and frequency to soil moisture deficits at the peak of the summer season are asymmetric and occur mostly in the lower and upper tails of the probability distribution, respectively. For aspects of HWs related to intensity, substantially greater increases are experienced at the 10th percentile when antecedent soil moisture is low (mild HWs get hotter). Conversely, HW aspects related to longevity increase much more strongly at the 90th percentile in response to low antecedent soil moisture (long HWs get longer). A corollary to this is that in the eastern and northern parts of the country where HW-soil moisture coupling is evident, high antecedent soil moisture effectively ensures few HW days and low HW temperatures, while low antecedent soil moisture ensures high HW temperatures but not necessarily more HW days.

A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averages over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.

Abstract. We introduce the Frequent Rainfall Observations on GridS (FROGS) database (Roca et al., 2019). It is composed of gridded daily-precipitation products on a common 1∘×1∘ grid to ease intercomparison and assessment exercises. The database includes satellite, ground-based and reanalysis products. As most of the satellite products rely on rain gauges for calibration, unadjusted versions of satellite products are also provided where available. Each product is provided over its length of record and up to 2017 if available. Quasi-global, quasi-global land-only, ocean-only and tropical-only as well as regional products (over continental Africa and South America) are included. All products are provided on a common netCDF format that is compliant with Climate and Forecast (CF) Convention and Attribute Convention for Dataset Discovery (ACDD) standards. Preliminary investigations of this large ensemble indicate that while many features appear robust across the products, the characterization of precipitation extremes exhibits a large spread calling for careful selection of the products used for scientific applications. All datasets are freely available via an FTP server and identified thanks to the DOI: https://doi.org/10.14768/06337394-73A9-407C-9997-0E380DAC5598.

Daily gridded precipitation data are needed for investigating spatiotemporal variability of precipitation, including extremes; however, uncertainties related to daily precipitation products are large. Here, we compare a range of precipitation grids for Australia. These datasets include products derived solely from in situ observations (interpolated datasets) and two products that combine both remote sensed data and in situ observations. We find that all precipitation grids have similar climatologies for annual aggregated precipitation totals and annual maximum precipitation. The temporal correlations of daily precipitation values are higher between the interpolated datasets, but the correlations between the most widely used interpolated product (AWAP) and the two remotely sensed products (TRMM and GPCP) are still reasonable. Our results, however, point to distinct structural uncertainties between those datasets gridding in situ observations and those datasets deriving precipitation estimates primarily from satellite measurements. All datasets analysed agree well for low to moderate daily precipitation amounts up to about 20 mm but diverge at upper quantiles, indicating that substantial uncertainty exists in gridded precipitation extremes over Australia.

Projections of changes in temperature extremes are critical to assess the potential impacts of climate change on agricultural and ecological systems. Statistical downscaling can be used to efficiently downscale output from a large number of general circulation models (GCMs) to a fine temporal and spatial scale, providing the opportunity for future projections of extreme temperature events. This paper presents an analysis of extreme temperature data downscaled from 7 GCMs selected from the Coupled Model Intercomparison Project phase 5 (CMIP5) using a skill score based on spatial patterns of climatological means of daily maximum and minimum temperature. Data for scenarios RCP4.5 and RCP8.5 for the New South Wales (NSW) wheat belt, south eastern Australia, have been analysed. The results show that downscaled data from most of the GCMs reproduces the correct sign of recent trends in all the extreme temperature indices (except the index for cold days) for 1961–2000. An independence weighted mean method is used to calculate uncertainty estimates, which shows that multi-model ensemble projections produce a consistent trend compared to the observations in most extreme indices. Great warming occurs in the east and northeast of the NSW wheat belt by 2061–2100 and increases the risk of exposure to hot days around wheat flowering date, which might result in farmers needing to reconsider wheat varieties suited to maintain yield. This analysis provides a first overview of projected changes in climate extremes from an ensemble of 7 CMIP5 GCM models with statistical downscaling data in the NSW wheat belt.

Abstract Observational evidence of precipitation extremes is vital to better understand how these events might change in a future warmer climate. Over the terrestrial regions of a quasi-global domain, we assess the representation of annual maxima of daily precipitation (Rx1day) in 22 observational products gridded at 1° × 1° resolution and clustered into four categories: station-based in situ , satellite observations with or without a correction to rain gauges, and reanalyses (5, 8, 4 and 5 datasets, respectively). We also evaluate the interproduct spread across the ensemble and within the four clusters, as a measure of observational uncertainty. We find that reanalyses present a heterogeneous representation of Rx1day in particular over the tropics, and their interproduct spread is the highest compared to any other cluster. Extreme precipitation in satellite data broadly compares well with in situ -based data. We find a general better agreement with in situ -based observations and less interproduct spread for the satellite products with a correction to rain gauges compared to the uncorrected products. Given the level of uncertainties associated with the estimation of Rx1day in the observations, none of the datasets can be thought of as the best estimate. Our recommendation is to avoid using reanalyses as observational evidence and to consider in situ and satellite data (the corrected version preferably) in an ensemble of products for a better estimation of precipitation extremes and their observational uncertainties. Based on this we choose a subsample of 10 datasets to reduce the interproduct spread in both the representation of Rx1day and its timing throughout the year, compared to all 22 datasets. We emphasize that the recommendations and selection of datasets given here may not be relevant for different precipitation indices, and other grid resolutions and time scales.

Abstract Estimates of observed long-term changes in daily precipitation globally have been limited due to availability of high-quality observations. In this study, a new gridded dataset of daily precipitation, called Rainfall Estimates on a Gridded Network (REGEN) V1–2019, was used to perform an assessment of the climatic changes in precipitation at each global land location (except Antarctica). This study investigates changes in the number of wet days (≥1 mm) and the entire distribution of daily wet- and all-day records, in addition to trends in annual and seasonal totals from daily records, between 1950 and 2016. The main finding of this study is that precipitation has intensified across a majority of land areas globally throughout the wet-day distribution. This means that when it rains, light, moderate, or heavy wet-day precipitation has become more intense across most of the globe. Widespread increases in the frequency of wet days are observed across Asia and the United States, and widespread increases in the precipitation intensity are observed across Europe and Australia. Based on a comparison of spatial pattern of changes in frequency, intensity, and the distribution of daily totals, we propose that changes in light and moderate precipitation are characterized by changes in precipitation frequency, whereas changes in extreme precipitation are primarily characterized by intensity changes. Based on the uncertainty estimates from REGEN, this study highlights all results in the context of grids with high-quality observations.

Abstract Marine cold‐spell (MCS) metrics—such as frequency and intensity—are decreasing globally, while marine heatwave (MHW) metrics are increasing due to sea surface temperature (SST) warming. However, the concomitant changes in MHW and MCS metrics, and whether SST warming can similarly explain the decreasing MCS metrics remain unclear. Here, we provide a comparative global assessment of these changes based on satellite SST observations over 1982–2020. Across the globe, we find distinct differences in mean MHW and MCS metrics. Furthermore, decreasing trends in MCS metrics are not necessarily aligned with increasing trends in MHW metrics. While differences in intensity trends are mainly explained by SST variance trends, differences in trends of annual days are less clear. Overall, decreasing MCS days and intensities are found to be largely driven by warming SST, rather than SST variance changes. Therefore, it is expected that MCS days and intensity will continue diminishing under global warming.

Severe storms defined as 3‐hourly pressure changes exceeding an extreme magnitude, were carefully manually quality‐controlled and analyzed at stations in the UK and Iceland which had at least 45 years of digitized data. Iceland showed significant distribution differences between the periods before and after 1980 with a tendency towards less extreme severe events in latter decades. In contrast, the UK regions have tended towards larger magnitude events in recent decades, particularly in the more southerly regions. There has been a significant increase in the number of severe storms over the UK as a whole since the 1950s, however, this may not be unusual in longer‐term variability. For both the UK and Iceland in winter these changes in severe storms appear to be related to changes in the North Atlantic Oscillation (NAO) but UK changes during October to December do not appear to be related to changes in the NAO.

In 1990 and 1992 the Intergovernmental Panel on Climate Change (IPCC), in its first assessment of climate change and its supplement, did not consider whether extreme weather events had increased in frequency and/or intensity globally, because data were too sparse to make this a worthwhile exercise. In 1995 the IPCC, in its second assessment, did examine this question, but concluded that data and analyses of changes in extreme events were ‘not comprehensive’and thus the question could not be answered with any confidence. Since then, concerted multinational efforts have been undertaken to collate, quality control, and analyse data on weather and climate extremes. A comprehensive examination of the question of whether extreme events have changed in frequency or intensity is now more feasible than it was 15 years ago. The processes that have led to this position are described, along with current understanding of possible changes in some extreme weather and climate events.

Abstract An examination of extreme storms across the British Isles over the last 85 years during the boreal autumn [October, November, December (OND)] and winter [January, February, March (JFM)] shows that large‐scale natural climate variability plays an important role in modulating the intensity and frequency of these events. Severe storms across the British Isles were most prominent in the 1920s and 1990s in OND, and in the 1920s, 1980s and 1990s in JFM. There is a significant correlation between JFM severe storminess across the British Isles and both the Gibraltar–South‐West (SW) Iceland and Azores–Iceland indices of the North Atlantic Oscillation (NAO), but this relationship fluctuates over the 85 years of data. Strongest NAO relationships occur during 1970–1990 and 1940–1960, with a weaker correlation in the 1920s–1940s, and effectively no correlation in 1950–1970. There is no significant relationship between the Gibraltar–SW Iceland NAO and severe storms in OND, but a significant correlation exists with the Azores–Iceland NAO and there is a clear link to a pattern in mean sea level pressure (MSLP) extending from the tropical Atlantic to higher latitudes of the North Atlantic. El Niño Southern Oscillation (ENSO) influences from the Pacific Ocean also appear to play a role in modulating OND severe storms over the British Isles. Importantly, severe storms in OND and JFM seasons respond to different physical mechanisms. Future work is needed to extend this study back into the late 19th century in order to evaluate fully any changes in severe storms across the British Isles using a longer instrumental record. This may be best achieved through long historical surface‐observations‐only global reanalyses, which can reconstruct tropospheric weather variables using longer instrumental records of daily to sub‐daily MSLP. Copyright © 2008 Royal Meteorological Society

Using daily station observations over the period 1951–2013 in a region of south-east Australia, we systematically compare how the horizontal resolution, interpolation method and order of operation in generating gridded data sets affect estimates of annual extreme indices of temperature and precipitation maxima (hottest and wettest days). Three interpolation methods (natural neighbors, cubic spline and angular distance weighting) are used to calculate grids at five different horizontal gridded resolutions ranging from 0.25° to 2.5°. In each case the order of operation in which the grid values of the hottest and wettest day are calculated is varied: either they are estimated from daily grids or from station points and then gridded. We find that the grid resolution-despite showing more regional detail at high resolution – has relatively limited effect when considering regional averages. However, the interpolation method and the order of operation can substantially influence the actual gridded values. And while the difference due to the order of operation is not substantial when using natural neighbor and cubic spline interpolation, it is particularly apparent for indices calculated from daily gridded estimates using the angular distance weighting method. As expected given the high spatial variability of precipitation fields, precipitation extremes are most sensitive to method, but temperature extremes also exhibit substantial differences. For the annual maximum values averaged over the study area, the differences may be up to 2.8 °C for temperature and 60 mm (about a factor 2) for precipitation. Differences are seen most prominently in return period estimates where a 1 in 100 year return value calculated using the angular distance weighting daily gridded method is equivalent to about a 1 in 5 year return value in most of the other methods. Despite substantial differences in the actual values of gridded extremes, analyses suggest that the impact on long-term trends and inter-annual variability is small.

Heatwaves have profound socio-economic impacts. Increases in temperature variability would exacerbate these impacts but debate rages in the literature about whether the climate has or will become more variable. There is currently no firm evidence that temperature variability has or will increase because questions have been raised about the methods used to reach this conclusion. However, irrespective of changing temperature variability, the impact from increases in the frequency and intensity of heatwaves will be a major problem for the future.

Abstract A warming climate is expected to intensify extreme precipitation, and climate models project a general intensification of annual extreme precipitation in most regions of the globe throughout the twenty-first century. We investigate the robustness of this future intensification over land across different models, regions, and seasons and evaluate the role of model interdependencies in the CMIP5 ensemble. Strong similarities in extreme precipitation changes are found between models that share atmospheric physics, turning an ensemble of 27 models into around 14 projections. We find that future annual extreme precipitation intensity increases in the majority of models and in the majority of land grid cells, from the driest to the wettest regions, as defined by each model’s precipitation climatology. The intermodel spread is generally larger over wet than over dry regions, smaller in the dry season compared to the wet season and at the annual scale, and largely reduced in extratropical compared to tropical regions and at the global scale. For each model, the future increase in annual and seasonal maximum daily precipitation amounts exceeds the range of simulated internal variability in the majority of land grid cells. At both annual and seasonal scales, however, there are a few regions where the change is still within the background climate noise, but their size and location differ between models. In extratropical regions, the signal-to-noise ratio of projected changes in extreme precipitation is particularly robust across models because of a similar change and background climate noise, whereas projected changes are less robust in the tropics.

It is commonly expected that the world's lowest income countries will face some of the worst impacts of global warming, despite contributing the least to greenhouse gas emissions. Using global atmospheric reanalyses we show that the world's lowest income countries are already experiencing greater increases in the occurrence of temperature extremes compared to the highest income countries, and have been for over two decades. Not only are low income countries less able to support mitigation and adaptation efforts, but their typically equatorial location predisposes them to lower natural temperature variability and thus greater changes in the occurrence of temperature extremes with global warming. This aspect of global warming is well known but overlooked in current international climate policy agreements and we argue that it is an important factor in reducing inequity due to climate impacts.

Abstract Trends in mean and extreme annual and seasonal temperature and precipitation over the 1951–2015 period were calculated for 57 stations in 20 western Pacific Ocean island countries and territories. The extremes indices are those of the World Meteorological Organization Expert Team on Sector-Specific Climate Indices. The purpose of the expert team and indices is to promote the use of globally consistent climate indices to highlight variability and trends in climate extremes that are of particular interest to socioeconomic sectors and to help to characterize the climate sensitivity of various sectors. Prior to the calculation of the monthly means and indices, the data underwent quality control and homogeneity assessment. A rise in mean temperature occurred at most stations, in all seasons, and in both halves of the study period. The temperature indices also showed strong warming, which for the majority was strongest in December–February and weakest in June–August. The absolute and percentile-based indices show the greatest warming at the upper end of the distribution. While changes in precipitation were less consistent and trends were generally weak at most locations, declines in both total and extreme precipitation were found in southwestern French Polynesia and the southern subtropics. There was a decrease in moderate- to high-intensity precipitation events, especially those experienced over multiple days, in southwestern French Polynesia from December to February. Strong drying trends have also been identified in the low- to moderate-extreme indices in the June–August and September–November periods. These negative trends contributed to an increase in the magnitude of meteorological drought in both subregions.

Using an expert consensus-based approach, a netball video analysis consensus (NVAC) group of researchers and practitioners was formed to develop a video analysis framework of descriptors and definitions of physical, technical and contextual aspects for netball research. The framework aims to improve the consistency of language used within netball investigations. It also aims to guide injury mechanism reporting and identification of injury risk factors. The development of the framework involved a systematic review of the literature and a Delphi process. In conjunction with commercially used descriptors and definitions, 19 studies were used to create the initial framework of key descriptors and definitions in netball. In a two round Delphi method consensus, each expert rated their level of agreement with each of the descriptors and associated definition on a 5-point Likert scale (1—strongly disagree; 2—somewhat disagree; 3—neither agree nor disagree; 4—somewhat agree; 5—strongly agree). The median (IQR) rating of agreement was 5.0 (0.0), 5.0 (0.0) and 5.0 (0.0) for physical, technical and contextual aspects, respectively. The NVAC group recommends usage of the framework when conducting video analysis research in netball. The use of descriptors and definitions will be determined by the nature of the work and can be combined to incorporate further movements and actions used in netball. The framework can be linked with additional data, such as injury surveillance and microtechnology data.

International Journal of ClimatologyVolume 29, Issue 3 p. 317-319 EditorialFree Access Climate extremes: progress and future directions Lisa V. Alexander, Corresponding Author Lisa V. Alexander School of Geography and Environmental Science, Monash University, Clayton, VIC 3800, AustraliaSchool of Geography and Environmental Science, Monash University, Clayton, VIC 3800, AustraliaSearch for more papers by this authorNigel Tapper, Nigel Tapper School of Geography and Environmental Science, Monash University, Clayton, VIC 3800, AustraliaSearch for more papers by this authorXuebin Zhang, Xuebin Zhang Climate Research Division, Environment Canada, Downsview, Ontario, M3H 5T4, CanadaSearch for more papers by this authorHayley J. Fowler, Hayley J. Fowler School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, UKSearch for more papers by this authorClaudia Tebaldi, Claudia Tebaldi Climate Central, Princeton, NJ 08542, USASearch for more papers by this authorAmanda Lynch, Amanda Lynch School of Geography and Environmental Science, Monash University, Clayton, VIC 3800, AustraliaSearch for more papers by this author Lisa V. Alexander, Corresponding Author Lisa V. Alexander School of Geography and Environmental Science, Monash University, Clayton, VIC 3800, AustraliaSchool of Geography and Environmental Science, Monash University, Clayton, VIC 3800, AustraliaSearch for more papers by this authorNigel Tapper, Nigel Tapper School of Geography and Environmental Science, Monash University, Clayton, VIC 3800, AustraliaSearch for more papers by this authorXuebin Zhang, Xuebin Zhang Climate Research Division, Environment Canada, Downsview, Ontario, M3H 5T4, CanadaSearch for more papers by this authorHayley J. Fowler, Hayley J. Fowler School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, UKSearch for more papers by this authorClaudia Tebaldi, Claudia Tebaldi Climate Central, Princeton, NJ 08542, USASearch for more papers by this authorAmanda Lynch, Amanda Lynch School of Geography and Environmental Science, Monash University, Clayton, VIC 3800, AustraliaSearch for more papers by this author First published: 16 February 2009 https://doi.org/10.1002/joc.1861Citations: 43AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. References Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank AMG, Haylock M, Collins D, Trewin B, Rahimzdeh F, Tagipour A, Kumar Kolli R, Revadekar JV, Griffiths G, Vincent L, Stephenson DB, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai P, Rusticucci M, Vazquez Aguirre JL. 2006. Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research—Atmospheres 111: D05109,DOI:10.1029/2005JD006290. Christidis N, Stott PA, Brown S, Hegerl GC, Caesar J. 2005. Detection of changes in temperature extremes during the second half of the 20th century. Geophysical Research Letters 32: L20716, DOI:10.1029/2005GL023885. Foullet A, Rey G, Laurent F, Pavillon G, Bellec S, Guihenneuc-Jouyaux C, Clavel J, Jougla E, Hemon D. 2007. Excess mortality related to the August 2003 heat wave in France. International Archives of Occupational and Environmental Health 80: 16– 24. Fowler HJ, Ekström M, Blenkinsop S, Smith AP. 2007. Estimating change in extreme European precipitation using a multi-model ensemble. Journal of Geophysical Research-Atmospheres 112: D18104, DOI:10.1029/2007JD008619. Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock M, Klein Tank AMG, Peterson T. 2002. Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Research 19: 193– 212. IPCC. 2007. Summary for policymakers. In: Climate Change 2007: The Physical Science Basis. S Solomon, D Qin, M Manning, Z Chen, M Marquis, KB Averyt, M Tignor, HL Miller (eds). Cambridge University Press: Cambridge, New York. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Lynch AH, Brunner RD. 2007. Context and climate change: an integrated assessment for barrow, Alaska. Climatic Change 82: 93– 111. Nicholls N, Alexander L. 2007. Has the climate become more variable or extreme? Progress in Physical Geography 32: 1– 11. Nicholls N, Gruza G, Jouzel J, Karl T, Ogallo L, Parker D. 1996. Observed Climate Variability and Change. Chapter 3 in Climate Change 1995: The Science of Climate Change. JT Houghton, LG Meira Filho, BA Callander, N Harris, A Kattenberg, K Maskell (Eds). Cambridge University Press: Cambridge, UK; 132– 192. Peterson TC, Manton MJ. 2008. Monitoring changes in climate extremes: a tale of international collaboration. Bulletin of the American Meteorological Society 89: 1266– 1271. DOI:10.1175/2008BAMS2501.1. Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA. 2006. Going to the extremes: an intercomparison of model-simulated historical and future changes in extreme events. Climatic Change 79: 185– 211. DOI:10.1007/s10584-006-9051-4. Trenberth KE, Shea DJ. 2006. Atlantic hurricanes and natural variability in 2005. Geophysical Research Letters 33: L12704. DOI:10.1029/2006GL026894. Walsh K, Karoly D, Nicholls N. 2008. The detection and attribution of climate change effects on tropical cyclones. 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Rainfall records for 23 countries and territories in the western Pacific have been collated for the purpose of examining trends in total and extreme rainfall since 1951. For some countries this is the first time that their data have been included in this type of analysis and for others the number of stations examined is more than twice that available in the current literature. Station trends in annual total and extreme rainfall for 1961–2011 are spatially heterogeneous and largely not statistically significant. This differs with the results of earlier studies that show spatially coherent trends that tended to reverse in the vicinity of the South Pacific Convergence Zone (SPCZ). We infer that the difference is due to the Interdecadal Pacific Oscillation switching to a negative phase from about 1999, largely reversing earlier rainfall changes. Trend analyses for 1981–2011 show wetter conditions in the West Pacific Monsoon (WPM) region and southwest of the mean SPCZ position. In the tropical North Pacific it has become wetter west of 160°E with the Intertropical Convergence Zone/WPM expanding northwards west of 140°E. Northeast of the SPCZ and in the central tropical Pacific east of about 160°E it has become drier. Our findings for the South Pacific subtropics are consistent with broader trends seen in parts of southern and eastern Australia towards reduced rainfall. The relationship between total and extreme rainfall and Pacific basin sea surface temperatures (SSTs) has been investigated with a focus on the influence of the El Niño-Southern Oscillation (ENSO). We substantiate a strong relationship between ENSO and total rainfall and establish similar relationships for the threshold extreme indices. The percentile-based and absolute extreme indices are influenced by ENSO to a lesser extent and in some cases the influence is marginal. Undoubtedly, larger-scale SST variability is not the only influence on these indices.

Hospitalisations are associated with ambient temperature, but little is known about responses in population sub-groups. In this study, heat responses for Indigenous and non-Indigenous people in two age groups were examined for two categories of cardiac diseases using daily hospital admissions from five Northern Territory hospitals (1992-2011). Admission rates during the hottest five per cent of days and the coolest five per cent of days were compared with rates at other times. Among 25-64 year olds, the Indigenous female population was more adversely affected by very hot days than the non-Indigenous female population, with admission rates for ischaemic heart disease (IHD) increasing by 32%. People older than 65 were more sensitive to cold, with non-Indigenous male admissions for heart failure increasing by 64%, and for IHD by 29%. For older Indigenous males, IHD admissions increased by 52% during cold conditions. For older non-Indigenous females, increases in admissions for heart failure were around 50% on these cold days, and 64% for older Indigenous females. We conclude that under projected climate change conditions, admissions for IHD amongst younger Indigenous people would increase in hot conditions, while admissions among elderly people during cold weather may be reduced. The responses to temperature, while showing significant relationships across the Northern Territory, may vary by region. These variations were not explored in this assessment.

Abstract The east coast of Australia is regularly influenced by midlatitude cyclones known as East Coast Lows. These form in a range of synoptic situations and are both a cause of severe weather and an important contributor to water security. This paper presents the first projections of future cyclone activity in this region using a regional climate model ensemble, with the use of a range of cyclone identification methods increasing the robustness of results. While there is considerable uncertainty in projections of cyclone frequency during the warm months, there is a robust agreement on a decreased frequency of cyclones during the winter months, when they are most common in the current climate. However, there is a potential increase in the frequency of cyclones with heavy rainfall and those closest to the coast and accordingly those with potential for severe flooding.

The oceans are a well-known source of natural variability in the climate system, although their ability to account for inter-annual variations of temperature and precipitation extremes over land remains unclear. In this study, the role of sea-surface temperature (SST)-forcing is investigated for variability and trends in a range of commonly used temperature and precipitation extreme indices over the period 1959 to 2013. Using atmospheric simulations forced by observed SST and sea-ice concentrations (SIC) from three models participating in the Climate of the Twentieth Century Plus (C20C+) Project, results show that oceanic boundary conditions drive a substantial fraction of inter-annual variability in global average temperature extreme indices, as well as, to a lower extent, for precipitation extremes. The observed trends in temperature extremes are generally well captured by the SST-forced simulations although some regional features such as the lack of warming in daytime warm temperature extremes over South America are not reproduced in the model simulations. Furthermore, the models simulate too strong increases in warm day frequency compared to observations over North America. For extreme precipitation trends, the accuracy of the simulated trend pattern is regionally variable, and a thorough assessment is difficult due to the lack of locally significant trends in the observations. This study shows that prescribing SST and SIC holds potential predictability for extremes in some (mainly tropical) regions at the inter-annual time-scale.

Abstract We present an automated method for updating existing long‐running precipitation series in near‐real time. Our analyses confirm the trend towards significantly drier summers in the south‐east of England and significantly wetter winters in the west of Scotland. In 2000 England and Wales saw the wettest April since records began in 1766 and record‐breaking daily precipitation in several regions in October led to the wettest autumn on record. Copyright © 2001 Royal Meteorological Society.

Abstract A high-quality daily dataset of in situ mean sea level pressure was collated for Australia for the period from 1907 to 2006. This dataset was used to assess changes in daily synoptic pressure patterns over Australia in winter using the method of self-organizing maps (SOMs). Twenty patterns derived from the in situ pressure observations were mapped to patterns derived from ERA-40 data to create daily synoptic pressure fields for the past century. Changes in the frequencies of these patterns were analyzed. The patterns that have been decreasing in frequency were generally those most strongly linked to variations in the southern annular mode (SAM) index, while patterns that have increased in frequency were more strongly correlated with variations in the positive phase of El Niño–Southern Oscillation. In general, there has been a reduction in the rain-bearing systems affecting southern Australia since the beginning of the twentieth century. Over the past century, reductions in the frequencies of synoptic patterns with a marked trough to the south of the country were shown to be linked to significant reductions in severe storms in southeast Australia and decreases in rainfall at four major Australian cities: Sydney, Melbourne, Adelaide, and Perth. Of these, Perth showed the most sustained decline in both the mean and extremes of rainfall linked to changes in the large-scale weather systems affecting Australia over the past century. The results suggest a century-long decline in the frequency of low pressure systems reaching southern Australia, consistent with the southward movement of Southern Hemisphere storm tracks. While most of these trends were not significant, associated changes in rainfall and storminess appear to have had significant impacts in the region.

The performance of five global coupled climate models in simulating temporal trends in annual indices of extremes in surface temperature and precipitation during the second half of the 20th century is examined. The selected models are all represented in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Linear trend patterns for individual model runs along with single‐ and multimodel ensemble mean trend patterns are objectively compared against corresponding observed trend fields. Some positive effects of the multimodel “super‐ensemble” approach were found when there was reasonable skill in contributing members.

Abstract. We assess the effects of different methodological choices made during the construction of gridded data sets of climate extremes, focusing primarily on HadEX2. Using global land-surface time series of the indices and their coverage, as well as uncertainty maps, we show that the choices which have the greatest effect are those relating to the station network used or that drastically change the values for individual grid boxes. The latter are most affected by the number of stations required in or around a grid box and the gridding method used. Most parametric changes have a small impact, on global and on grid box scales, whereas structural changes to the methods or input station networks may have large effects. On grid box scales, trends in temperature indices are very robust to most choices, especially in areas which have high station density (e.g. North America, Europe and Asia). The precipitation indices, being less spatially correlated, can be more susceptible to methodological choices, but coherent changes are still clear in regions of high station density. Regional trends from all indices derived from areas with few stations should be treated with care. On a global scale, the linear trends over 1951–2010 from almost all choices fall within the 5–95th percentile range of trends from HadEX2. This demonstrates the robust nature of HadEX2 and related data sets to choices in the creation method.

Abstract The Australian east coast low (ECL) is both a major cause of damaging severe weather and an important contributor to rainfall and dam inflow along the east coast, and is of interest to a wide range of groups including catchment managers and emergency services. For this reason, several studies in recent years have developed and interrogated databases of east coast lows using a variety of automated cyclone detection methods and identification criteria. This paper retunes each method so that all yield a similar event frequency within the ECL region, to enable a detailed intercomparison of the similarities, differences, and relative advantages of each method. All methods are shown to have substantial skill at identifying ECL events leading to major impacts or explosive development, but the choice of method significantly affects both the seasonal and interannual variation of detected ECL numbers. This must be taken into consideration in studies on trends or variability in ECLs, with a subcategorization of ECL events by synoptic situation of key importance.

Abstract. The International Surface Temperature Initiative (ISTI) is striving towards substantively improving our ability to robustly understand historical land surface air temperature change at all scales. A key recently completed first step has been collating all available records into a comprehensive open access, traceable and version-controlled databank. The crucial next step is to maximise the value of the collated data through a robust international framework of benchmarking and assessment for product intercomparison and uncertainty estimation. We focus on uncertainties arising from the presence of inhomogeneities in monthly mean land surface temperature data and the varied methodological choices made by various groups in building homogeneous temperature products. The central facet of the benchmarking process is the creation of global-scale synthetic analogues to the real-world database where both the "true" series and inhomogeneities are known (a luxury the real-world data do not afford us). Hence, algorithmic strengths and weaknesses can be meaningfully quantified and conditional inferences made about the real-world climate system. Here we discuss the necessary framework for developing an international homogenisation benchmarking system on the global scale for monthly mean temperatures. The value of this framework is critically dependent upon the number of groups taking part and so we strongly advocate involvement in the benchmarking exercise from as many data analyst groups as possible to make the best use of this substantial effort.