P. K. Snyder

Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

Identifying and quantifying planetary boundaries that must not be transgressed could help prevent human activities from causing unacceptable environmental change, argue Johan Rockström and colleagues.

Rockström, J., W. Steffen, K. Noone, Å. Persson, F. S. Chapin, III, E. Lambin, T. M. Lenton, M. Scheffer, C. Folke, H. Schellnhuber, B. Nykvist, C. A. De Wit, T. Hughes, S. van der Leeuw, H. Rodhe, S. Sörlin, P. K. Snyder, R. Costanza, U. Svedin, M. Falkenmark, L. Karlberg, R. W. Corell, V. J. Fabry, J. Hansen, B. Walker, D. Liverman, K. Richardson, P. Crutzen, and J. Foley. 2009. Planetary boundaries:exploring the safe operating space for humanity. Ecology and Society 14(2): 32. https://doi.org/10.5751/ES-03180-140232

The Amazon Basin is one of the world's most important bioregions, harboring a rich array of plant and animal species and offering a wealth of goods and services to society. For years, ecological science has shown how large-scale forest clearings cause declines in biodiversity and the availability of forest products. Yet some important changes in the rainforests, and in the ecosystem services they provide, have been underappreciated until recently. Emerging research indicates that land use in the Amazon goes far beyond clearing large areas of forest; selective logging and other canopy damage is much more pervasive than once believed. Deforestation causes collateral damage to the surrounding forests – through enhanced drying of the forest floor, increased frequency of fires, and lowered productivity. The loss of healthy forests can degrade key ecosystem services, such as carbon storage in biomass and soils, the regulation of water balance and river flow, the modulation of regional climate patterns, and the amelioration of infectious diseases. We review these newly revealed changes in the Amazon rainforests and the ecosystem services that they provide.

Abstract Expansion of agricultural lands and inherent variability of climate can influence the water cycle in the Amazon basin, impacting numerous ecosystem services. However, these two influences do not work independently of each other. With two once-in-a-century-level droughts occurring in the Amazon in the past decade, it is vital to understand the feedbacks that contribute to altering the water cycle. The biogeophysical impacts of land cover change within the Amazon basin were examined under drought and pluvial conditions to investigate how land cover and drought jointly may have enhanced or diminished recent precipitation extremes by altering patterns and intensity. Using the Weather Research and Forecasting (WRF) Model coupled to the Noah land surface model, a series of April–September simulations representing drought, normal, and pluvial years were completed to assess how land cover change impacts precipitation and how these impacts change under varied rainfall regimes. Evaporative sources of water vapor that precipitate across the region were developed with a quasi-isentropic back-trajectory algorithm to delineate the extent and variability that terrestrial evaporation contributes to regional precipitation. A decrease in dry season latent heat flux and other impacts of deforestation on surface conditions were increased by drought conditions. Coupled with increases in dry season moisture recycling over the Amazon basin by ~7% during drought years, land cover change is capable of reducing precipitation and increasing the amplitude of droughts in the region.

Abstract Since World War II, the expansion of irrigation throughout the Great Plains has resulted in a significant decline in the water table of the Ogallala Aquifer, threatening its long-term sustainability. The addition of near-surface water for irrigation has previously been shown to impact the surface energy and water budgets by modifying the partitioning of latent and sensible heating. A strong increase in latent heating drives near-surface cooling and an increase in humidity, which has opposing impacts on convective precipitation. In this study, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation on precipitation. Using a satellite-derived fractional irrigation dataset, grid cells were divided into irrigated and nonirrigated segments and the near-surface soil layer within irrigated segments was held at saturation. Nine April–October periods (three drought, three normal, and three pluvial) were simulated over the Great Plains. Averaging over all simulations, May–September precipitation increased by 4.97 mm (0.91%), with localized increases of up to 20%. The largest precipitation increases occurred during pluvial years (6.14 mm; 0.98%) and the smallest increases occurred during drought years (2.85 mm; 0.63%). Precipitation increased by 7.86 mm (1.61%) over irrigated areas from the enhancement of elevated nocturnal convection. Significant precipitation increases occurred over irrigated areas during normal and pluvial years, with decreases during drought years. This suggests that a soil moisture threshold likely exists whereby irrigation suppresses convection over irrigated areas when soil moisture is extremely low and enhances convection when antecedent soil moisture is relatively high.

While the earth's climate can affect the structure and functioning of terrestrial ecosystems, the process also works in reverse. As a result, changes in terrestrial ecosystems may influence climate through both biophysical and biogeochemical processes. This two-way link between the physical climate system and the biosphere is under increasing scrutiny. We review recent developments in the analysis of this interaction, focusing in particular on how alterations in the structure and functioning of terrestrial ecosystems, through either human land-use practices or global climate change, may affect the future of the earth's climate.

The US National Research Council (NRC, 2005) recommended the expansion of the climate change issue to include land use and land-cover processes as an important climate forcing. These processes have not been a major component of past Intergovernmental Panel on Climate Change (IPCC) reports. The NRC report states that beyond the change in mean atmospheric composition caused by increasing greenhouse gases, landscape variations may have important local, regional and potentially global climatic implications. In some cases, the climate response to land use and land-cover change may even exceed the contribution from increasing greenhouse gases. ‘Improving societally relevant projections of regional climate impacts will require a better understanding of the magnitudes of regional forcings and the associated climate responses.’ The International Geosphere Biosphere Programme (IGBP) and the Global Energy and Water Cycle Experiment (GEWEX) have also identified the importance of understanding the climate response to land use and land-cover change. As we move forward to the Fifth Assessment Report of the IPCC, there is growing impetus to address this aspect of anthropogenic impacts on the planet's environment. As a matter of fact, the CMIP5 suite of climate simulations that will be run for this report assessment (http://cmip-pcmdi.llnl.gov/cmip5/) now includes a new forcing dataset: the changes in land–surface areas used for agriculture, grazing activities, forestry, etc. This special issue is inspired by a recent National Science Foundation (NSF)-sponsored workshop titled ‘Detecting the Atmospheric Response to the Changing Face of the Earth: A Focus on Human-Caused Regional Climate Forcings, Land-Cover/Land Use Change, and Data Monitoring’ that was held in Boulder, Colorado, USA in August 2007. Workshop presentations are available online from the National Center for Atmospheric Research (NCAR) Joint Office for Science Support (JOSS; http://www.joss.ucar.edu/joss_psg/meetings/Meetings_2007/Detecting/Index.html), and an overview of workshop conclusions is presented by Mahmood et al. (2010). Presenters from this workshop and other interested researchers have contributed articles for two special issues. In addition to this special issue of the International Journal of Climatology, there is also a special issue of Boundary Layer Meteorology (Niyogi et al., 2009) focusing on the effects of land use and land-cover change on fluxes to the atmosphere, and subsequent impacts on weather and climate at the synoptic scales and the mesoscale. This special issue introduces a number of the studies presented at the Boulder workshop. Most of the studies examine regional impacts of land surface states on climate (Costa and Pires, 2010; Fall et al., 2010a, 2010b; Ge, 2010; Kishtawal et al., 2010; Mishra et al., 2010; Moore et al., 2010; Petchprayoon et al., 2010; Sertel et al., 2010; Takahashi et al., 2010; Tokairin et al., 2010; Xiao et al., 2010). However, several studies take a global perspective of land-cover consequences (Anantharaj et al., 2010; Kvalevåg et al., 2010; Lawrence and Chase, 2010; Strengers et al., 2010). Hibbard et al. (2010) sums up with a position paper on recommended future directions for research. Both observational and modelling studies are presented in this study. The observational studies use in situ climate data (Petchprayoon et al., 2010; Xiao et al., 2010), satellite measurements (Ge, 2010; Kishtawal et al., 2010) and data from regional reanalyses (Fall et al., 2010a, 2010b). The modelling studies use regional atmospheric models (Moore et al., 2010; Sertel et al., 2010; Takahashi et al., 2010; Tokairin et al., 2010; Xiao et al., 2010), global climate models (Anantharaj et al., 2010; Costa and Pires, 2010; Kvalevåg et al., 2010; Lawrence and Chase, 2010; Strengers et al., 2010) and one uses a land surface model run offline (Mishra et al., 2010). Several studies focus on the impact of urbanization on climate change. Kishtawal et al. (2010) look at the evidence of urbanization on precipitation trends and the occurrence of extreme rainfall events over India. Petchprayoon et al. (2010) search for evidence that increased runoff and flooding in the Yom River Basin of Thailand is connected to changes in land use, particularly the spread of urban areas. Sertel et al. (2010) find evidence that inaccurate specification of land cover in the default configuration of the Weather Research and Forecast (WRF) model, and in particular, poor representation of the extent of urban areas, impairs the simulation of surface temperature as compared to station reports. Tokairin et al. (2010) use a mesoscale model to examine the effects of urbanization on circulation over the island of Java, Indonesia. Other studies examine the consequences of regional vegetation change on climate. Costa and Pires (2010) model the precipitation response to future deforestation scenarios over South America, considering not only the tropical forests but also the cerrado to the south. Mishra et al. (2010) look at historic, current and future land use effects on surface fluxes over Wisconsin in offline simulations with the Variable Infiltration Capacity (VIC) land surface model driven by meteorological output from IPCC climate models. Ge (2010) examines the effect of agriculture, specifically the cultivation of winter wheat, compared to native vegetation, on surface temperature over the Southern Great Plains of the United States. Wet and dry soil conditions in a high-resolution model of Southeast Asia are used by Takahashi et al. (2010) to investigate the effect of extreme land use change on wet season climate. Xiao et al. (2010) appraise whether the flooding caused by construction of the Three Gorges Dam can be linked to changes in rainfall in the vicinity of the resulting reservoir. A number of studies take a global view of the impacts of land-cover change on climate that may be of great interest to the IPCC. Strengers et al. (2010) determine that anthropogenic changes to land cover have climate consequences that far outweigh the secondary feedbacks that vegetation responding to projected climate change will have on climate. Lawrence and Chase (2010) perform a classic global simulation of climate with current vegetation versus potential (no anthropogenic land use changes) vegetation in the NCAR climate model. They find the impact of vegetation change on the surface hydrologic cycle to outweigh radiative impacts (changes in albedo). Anantharaj et al. (2010) find in the same model significant errors in top-of-the-atmosphere and surface albedo. When surface albedo is corrected, the simulation of the atmospheric radiative budget is improved. Kvalevåg et al. (2010) use a climate model to separate the phenological component of land-cover change from the albedo changes, and find that albedo changes are keys in areas where cropland is the main land use change. However, changes in phenology are important contributors to the warming signal in the model. Fall et al. (2010a) corroborate this result examining the North American Regional Reanalysis (NARR), station temperature trends and time-varying satellite land-cover data. Moore et al. find that realistic vegetation parameters over East Africa improve simulations of temperature, and to a lesser extend precipitation, in a regional model. Fall et al. (2010b) use NARR data to compare variability and trends in equivalent temperature (including the impact of humidity) to conventional temperature, concluding that equivalent temperature trends include the signature of the underlying vegetation and capture more information than just temperature. These studies provide a sampling of the complexity involved in resolving the role of land use and land-cover change in climate change. These studies show that there can be significant local effects to observed changes in land use, and that models can represent these changes. Unlike the record of warming from increased greenhouse gases, which is most robust at the global scale yet often tenuous locally, impacts from land use change have their strongest signatures at small scales. Even in a controlled modelling framework, uncertainties are large and many questions remain, as shown in some of the studies presented in this study as well as the early results from the project on Land Use and Climate IDentification of robust impacts (LUCID; Pitman et al., 2009). The regional importance of land use induced land-cover changes on surface climate raises the question of the validity of detection/attribution studies in certain areas where Land Use and Land-Cover Changes have been consequent since pre-industrial times. These studies have shown that globally, land use does not introduce monotonic changes in either the surface energy or water balance, nor do all types of vegetation respond in the same manner to the thermal and radiative trends that are occurring. The broad vegetation categories used in global and regional climate models are often found to need adjustment or tuning at local scales, where we see the largest impacts. A more complete and comprehensive survey of vegetation phenology, radiative properties as well as soil, geomorphology and other properties relevant to surface hydrology and meteorology is required. Such a survey would greatly improve our ability to downscale climate change to useful regional and local projections, and to understand how land use changes alter local and regional climate. We also see from the results presented in this issue that the observational network monitoring climate change is not sampling, in proportion to their occurrences, the direct responses or feedbacks from vegetation. In many cases, natural variability in climate is sufficiently large to mask the early signs of the consequences of land use change at the global scale. Waiting for unequivocal climatic evidence frustrates if not scuttles the prospects for mitigation. Thus, we turn to models to project the results of land use changes. But we see again that locally models could verify better than they do. Even with high-quality input data and boundary conditions, the models used for simulating both climate change and land-cover change impacts still have much room for improvement. One way forward might be to focus the scientific community on a small number of pressing questions to which the panoply of observational and modelling potential could be brought to bear. This would provide the impetus to improve both the stream of observational data and the performance of models as each is confronted with the current limitations and needs of the other. These questions are not new. What has been the contribution of land use change to the observed surface temperature record over the last century? How will the partitioning of precipitation between evapotranspiration and runoff be modified with land use change and how will this affect the climate? How, in turn, is climate affected by modifications to the partitioning of precipitation between evapotranspiration and runoff caused by land use change? Are current land use changes exacerbating or ameliorating climate changes from other causes? Issues of land use and land-cover change in the climate context open what was once strictly a physical science to escalating complexity as many other disciplines are brought into play. As stated by Hibbard et al., ‘Process understanding, both from the socioeconomic as well as the natural science's side, will be important considerations.’ A concerted multidisciplinary effort is needed to address the issue properly.

Abstract Numerous studies have identified the regional-scale climate response to tropical deforestation through changes to water, energy, and momentum fluxes between the land surface and the atmosphere. There has been little research, however, on the role of tropical deforestation on the global climate. Previous studies have focused on the climate response in the extratropics with little analysis of the mechanisms responsible for propagating the signal out of the tropics. A climate modeling study is presented of the physical processes that are important in transmitting a deforestation signal out of the tropics to the Northern Hemisphere extratropics in boreal winter. Using the Community Climate System Model, version 3 Integrated Biosphere Simulator (CCM3–IBIS) climate model and by imposing an exaggerated land surface forcing of complete tropical forest removal, the thermodynamic and dynamical atmospheric response is evaluated regionally within the tropics, globally as the climate signal propagates to the Northern Hemisphere, and then regionally in Eurasia where land–atmosphere feedbacks contribute to amplifying the climate signal and warming the surface and lower troposphere by 1–4 K. Model results indicate that removal of the tropical forests causes weakening of deep tropical convection that excites a Rossby wave train emanating northeastward away from the South American continent. Changes in European storm-track activity cause an intensification and northward shift in the Ferrel cell that leads to anomalous adiabatic warming over a broad region of Eurasia. Regional-scale land–atmosphere feedbacks are found to amplify the warming. While hypothetical, this approach illustrates the atmospheric mechanisms linking the tropics with Eurasia that may otherwise not be detectable with more realistic land-use change simulations.

Abstract Data from a dense urban meteorological network (UMN) are analyzed, revealing the spatial heterogeneity and temporal variability of the Twin Cities (Minneapolis–St. Paul, Minnesota) canopy-layer urban heat island (UHI). Data from individual sensors represent surface air temperature (SAT) across a variety of local climate zones within a 5000-km 2 area and span the 3-yr period from 1 August 2011 to 1 August 2014. Irregularly spaced data are interpolated to a uniform 1 km × 1 km grid using two statistical methods: 1) kriging and 2) cokriging with impervious surface area data. The cokriged SAT field exhibits lower bias and lower RMSE than does the kriged SAT field when evaluated against an independent set of observations. Maps, time series, and statistics that are based on the cokriged field are presented to describe the spatial structure and magnitude of the Twin Cities metropolitan area (TCMA) UHI on hourly, daily, and seasonal time scales. The average diurnal variation of the TCMA UHI exhibits distinct seasonal modulation wherein the daily maximum occurs by night during summer and by day during winter. Daily variations in the UHI magnitude are linked to changes in weather patterns. Seasonal variations in the UHI magnitude are discussed in terms of land–atmosphere interactions. To the extent that they more fully resolve the spatial structure of the UHI, dense UMNs are advantageous relative to limited collections of existing urban meteorological observations. Dense UMNs are thus capable of providing valuable information for UHI monitoring and for implementing and evaluating UHI mitigation efforts.

Abstract Afforestation is a viable and widely practiced method of sequestering carbon dioxide from the atmosphere. However, because of a change in surface albedo, placement of less reflective forests can cause an increase in net‐absorbed radiation and localized surface warming. This effect is enhanced in northern high latitudes where the presence of snow cover exacerbates the albedo difference. Regions where afforestation could provide a climate benefit are determined by comparing net ecosystem production and net radiation differences from afforestation in midlatitude and high latitude of North America. Using the dynamic vegetation model Integrated Biosphere Simulator, agricultural version (Agro‐IBIS), we find a boundary through North America where afforestation results in a positive equivalent carbon balance (cooling) to the south, and a negative equivalent carbon balance (warming) to the north. Including the effects of stand age and fraction cover affect whether a site contributes to mitigating global warming.

Abstract The rapid expansion of irrigation in the Great Plains since World War II has resulted in significant water table declines, threatening the long-term sustainability of the Ogallala Aquifer. As discussed in Part I of this paper, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation at subgrid scales. Simulations of nine April–October periods (three drought, three normal, and three pluvial) over the Great Plains were completed to assess the full impact of irrigation on the water budget. Averaged over all simulated years, irrigation over the Great Plains contributes to May–September evapotranspiration increases of approximately 4% and precipitation increases of 1%, with localized increases of up to 20%. Results from these WRF simulations are used along with a backward trajectory analysis to identify where evapotranspiration from irrigated fields falls as precipitation (i.e., irrigation-induced precipitation) and how irrigation impacts precipitation recycling. On average, only 15.8% of evapotranspiration from irrigated fields falls as precipitation over the Great Plains, resulting in 5.11 mm of May–September irrigation-induced precipitation and contributing to 6.71 mm of recycled precipitation. Reductions in nonrecycled precipitation suggest that irrigation reduces precipitation of moisture advected into the region. The heaviest irrigation-induced precipitation is coincident with simulated and observed precipitation increases, suggesting that observed precipitation increases in north-central Nebraska are strongly related to evapotranspiration of irrigated water. Water losses due to evapotranspiration are much larger than irrigation-induced precipitation and recycled precipitation increases, confirming that irrigation results in net water loss over the Great Plains.

Abstract This study demonstrates the relationship between the Pacific–North American (PNA) teleconnection pattern and the Great Plains low-level jet (GPLLJ). The negative phase of the PNA, which is associated with lower heights over the Great Plains and ridging in the southeastern United States, enhances the GPLLJ by increasing the pressure gradient within the GPLLJ on 6-hourly to monthly time scales. Strong GPLLJ events predominantly occur when the PNA is negative. Warm-season strong GPLLJ events with a very negative PNA (<−1) are associated with more persistent, longer wavelength planetary waves that increase the duration of GPLLJ events and enhance precipitation over the north central United States. When one considers the greatest 5-day north central U.S. precipitation events, a large majority occur when the PNA is negative, with most exhibiting a very negative PNA. Stronger moisture transport during heavy rainfall events with a very negative PNA decreases the precipitation of locally derived moisture compared to events with a very positive PNA. The PNA becomes negative 2–12 days before heavy rainfall events and is very negative within two weeks of 78% of heavy rainfall events in the north central United States, a finding that could be used to improve medium-range forecasts of heavy rainfall events.

Abstract Despite supporting exceptionally productive agricultural lands, the Central U.S. is susceptible to severe droughts and floods. Such precipitation extremes are expected to worsen with climate change. However, future projections are highly uncertain as global climate models (GCMs) generally fail to resolve precipitation extremes. In this study, we assess how well models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulate summer means, variability, extremes, and the diurnal cycle of Central U.S. summer rainfall. Output from a subset of historical CMIP5 simulations are used to drive the Weather Research and Forecasting model to determine whether dynamical downscaling improves the representation of Central U.S. rainfall. We investigate which boundary conditions influence dynamically downscaled precipitation estimates and identify GCMs that can reasonably simulate precipitation when downscaled. The CMIP5 models simulate the seasonal mean and variability of summer rainfall reasonably well but fail to resolve extremes, the diurnal cycle, and the dynamic forcing of precipitation. Downscaling to 30 km improves these characteristics of precipitation, with the greatest improvement in the representation of extremes. Additionally, sizeable diurnal cycle improvements occur with higher (10 km) resolution and convective parameterization disabled, as the daily rainfall peak shifts 4 h closer to observations than 30 km resolution simulations. This lends greater confidence that the mechanisms responsible for producing rainfall are better simulated. Because dynamical downscaling can more accurately simulate these aspects of Central U.S. summer rainfall, policymakers can have added confidence in dynamically downscaled rainfall projections, allowing for more targeted adaptation and mitigation.

Abstract. Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems and human communities worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean–land–atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "big data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.

Abstract Climate change is expected to increase the frequency of hydrological extremes, producing more droughts and heavy rainfall events globally. How warm‐season precipitation extremes will change over the Central U.S. is unclear because most coarse spatial resolution global climate models inadequately simulate hydrological extremes resulting from convective precipitation. However, the higher spatial resolution from dynamical downscaling potentially enables improved projections of future changes in extreme rainfall events. In this study, we downscaled two models from the Coupled Model Intercomparison Project‐Phase 5 (CMIP5) using the Weather Research and Forecasting model for one historical period (1990–1999), two future periods (2040–2049, 2090–2099) in a midrange emissions scenario (Representative Concentration Pathway (RCP) 4.5), and one period (2090–2099) in a high emissions (RCP8.5) scenario. The diurnal cycle, extremes, and averages of precipitation in historical simulations compare well with observations. While the future change in the total amount of precipitation is unclear, model simulations suggest that summer rainfall will be less frequent, but more intense when precipitation does occur. Significant intensification of the heaviest rainfall events occurs in the models, with the greatest changes in the early warm season (April). Increases in total April–July rainfall and the enhancement of extreme rainfall events in the RCP8.5 2090s are related to a stronger Great Plains Low‐Level Jet (GPLLJ) during those months. Conversely, late warm‐season drying over the North Central U.S. is present in nearly all future simulations, with increased drought in August–September associated with a slight weakening of the GPLLJ. Simulated trends generally increase with stronger greenhouse gas forcing.

Agriculture has significantly transformed the face of the planet. In particular, croplands have replaced natural vegetation over large areas of the global land surface, covering around 18 million km2 of the land surface today. To grow crops, humans have taken advantage of the resource provided by climate — optimum temperature and precipitation. However, the clearing of land for establishing croplands might have resulted in an inadvertent change in the climate. This feedback might, in turn, have altered the suitability of land for growing crops. In this sensitivity study, we used a combination of land cover data sets, numerical models, and cropland suitability analysis, to estimate the degree to which the replacement of natural vegetation by croplands might have altered the land suitability for cultivation. We found that the global changes in cropland suitability are likely to have been fairly small, however large regional changes in cropland suitability might have occurred. Our theoretical study showed that major changes in suitability occurred in Canada, Eastern Europe, the Former Soviet Union, northern India, and China. Although the magnitude, sign, and spatial patterns of change indicated by this study may be an artifact of our particular model and experimental design, our study is illustrative of the potential inadvertent consequences of human activities on the land. Moreover, it offers a methodology for evaluating how climate changes due to human activities on the land may alter the multiple services offered by ecosystems to human beings.

Pressure dipoles are important long distance climate phenomena (teleconnection) characterized by pressure anomalies of the opposite polarity appearing at two different locations at the same time. Such dipoles have been proven important for understanding and explaining the variability in climate in many regions of the world, e.g. the El Niño Southern Oscillation (ENSO) climate phenomenon, which is described by opposite pressure anomalies between the west and east Pacific and is known to be responsible for precipitation and temperature anomalies worldwide. This paper presents a graph-based approach called shared reciprocal nearest neighbor approach that considers only reciprocal positive and negative edges in the shared nearest neighbor graph to find the dipoles. One crucial aspect of our approach to the analysis of such networks is a careful treatment of negative correlations, whose proper consideration is critical for finding the dipoles. Further, our work shows the importance of modeling the time-dependent patterns of the dipoles in a changing climate in order to better capture the impact of important climate phenomena on the globe. To show the utility of finding dipoles using our approach, we show that the data driven dynamic climate indices generated from our algorithm generally perform better than static indices formed from the fixed locations used by climate scientists in terms of capturing impact on global temperature and precipitation. Our approach can generate a single snapshot picture of all the dipole interconnections on the globe in a given dataset and thus makes it possible to study the changes in dipole interactions and movements. As teleconnections are crucial in the understanding of the global climate system, there is a pressing need to better understand the behavior and interactions of these atmospheric processes as well as to capture them precisely. Our systematic graph-based approach to find the teleconnections in climate data is an attempt in that direction. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 6: 158–179, 2013

Previous studies have indicated how tropical deforestation can have a significant influence on regional and global climate through altered biophysical exchanges of water, energy, and momentum at the land‐atmosphere boundary. However, the mechanisms for translating a surface forcing to changes in the atmospheric thermodynamics and circulation have not received as much attention. Here we present a new moist static energy budget method for examining the regional atmospheric response to removal of tropical forests and how land surface forcing is propagated into the atmosphere. A detailed three‐dimensional grid cell energy budget approach is used within a coupled atmosphere‐biosphere model (Community Climate Model, Version 3–Integrated Biosphere Simulator (CCM3‐IBIS)) to identify how land surface forcing affects the regional climate through the vertical and horizontal movement of moist static energy. This approach allows us to clearly identify where the moist static energy budget changes, which mechanisms are responsible for the changes, and how energy moves to adjacent areas and affects rainfall. Generally, replacement of the tropical forests with bare soil in the model leads to decreased rainfall in the tropics due to regional drying, while enhanced rainfall occurs in the subtropics associated with strengthened monsoon winds importing more moisture. Interesting regional complexities emerge, notably in tropical Africa. There, removal of the forests leads to lower rainfall near the coast but enhanced rainfall in central tropical Africa. This approach provides a useful diagnostic tool for examining the implications of land use and land cover change on the regional and global atmospheric thermodynamics and circulation.

Given a spatiotemporal (ST) dataset and a path in its embedding spatiotemporal framework, the goal is to to identify all interesting sub-paths defined by an interest measure. Sub-path discovery is of fundamental importance for understanding climate changes, agriculture, and many other application. However, this problem is computationally challenging due to the massive volume of data, the varying length of sub-paths and non-monotonicity of interestingness throughout a sub-path. Previous approaches find interesting unit sub-paths (e.g., unit time interval) or interesting points. By contrast, we propose a Sub-path Enumeration and Pruning (SEP) approach that finds collections of long interesting sub-paths. Two case studies using climate change datasets show that SEP can find long interesting sub-paths which represent abrupt climate change. We provide theoretical analyses of correctness, completeness and computational complexity of the proposed approach. We also provide experimental evaluation of two traversal strategies for enumerating and pruning candidate sub-paths.

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2012 SIAM International Conference on Data Mining (SDM)Drought Detection of the Last Century: An MRF-based ApproachQiang Fu, Arindam Banerjee, Stefan Liess, and Peter K. SnyderQiang Fu, Arindam Banerjee, Stefan Liess, and Peter K. Snyderpp.24 - 34Chapter DOI:https://doi.org/10.1137/1.9781611972825.3PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Droughts are one of the most damaging climate-related hazards. The late 1960s Sahel drought in Africa and the North American Dust Bowl of the 1930s are two examples of severe droughts that have an impact on society and the environment. Due to the importance of understanding droughts, we consider the problem of their detection based on gridded datasets of precipitation. We formulate the problem as the one of finding the most likely configuration of a Markov Random Field and propose an efficient inference algorithm. We apply this algorithm to the Climate Research Unit precipitation dataset spanning 106 years. The empirical results show that the algorithm successfully identifies the major droughts of the twentieth century in different regions of the world. Previous chapter Next chapter RelatedDetails Published:2012ISBN:978-1-61197-232-0eISBN:978-1-61197-282-5 https://doi.org/10.1137/1.9781611972825Book Series Name:ProceedingsBook Code:PRDT12Book Pages:1-1150

Abstract A new approach is used to detect atmospheric teleconnections without being bound by orthogonality (such as empirical orthogonal functions). This method employs negative correlations in a global dataset to detect potential teleconnections. One teleconnection occurs between the Tasman Sea and the Southern Ocean. It is related to El Niño–Southern Oscillation (ENSO), the Indian Ocean dipole (IOD), and the southern annular mode (SAM). This teleconnection is significantly correlated with SAM during austral summer, fall, and winter, with IOD during spring, and with ENSO in summer. It can thus be described as a hybrid between these modes. Given previously found relationships between IOD and ENSO, and IOD’s proximity to the teleconnection centers, correlations to IOD are generally stronger than to ENSO. Increasing pressure over the Tasman Sea leads to higher (lower) surface temperature over eastern Australia (the southwestern Pacific) in all seasons and is related to reduced surface temperature over Wilkes Land and Adélie Land in Antarctica during fall and winter. Precipitation responses are generally negative over New Zealand. For one standard deviation of the teleconnection index, precipitation anomalies are positive over Australia in fall, negative over southern Australia in winter and spring, and negative over eastern Australia in summer. When doubling the threshold, the size of the anomalous high-pressure center increases and annual precipitation anomalies are negative over southeastern Australia and northern New Zealand. Eliassen–Palm fluxes quantify the seasonal dependence of SAM, ENSO, and IOD influences. Analysis of the dynamical interactions between these teleconnection patterns can improve prediction of seasonal temperature and precipitation patterns in Australia and New Zealand.

A new vegetation trend is emerging in northeastern forests of the United States, characterized by an expansion of red maple at the expense of oak. This has changed emissions of biogenic volatile organic compounds (BVOCs), primarily isoprene and monoterpenes. Oaks strongly emit isoprene while red maple emits a negligible amount. This species shift may impact nearby urban centers because the interaction of isoprene with anthropogenic nitrogen oxides can lead to tropospheric ozone formation and monoterpenes can lead to the formation of particulate matter. In this study the Global Biosphere Emissions and Interactions System was used to estimate the spatial changes in BVOC emission fluxes resulting from a shift in forest composition between oak and maple. A 70% reduction in isoprene emissions occurred when oak was replaced with maple. Ozone simulations with a chemical box model at two rural and two urban sites showed modest reductions in ozone concentrations of up to 5–6 ppb resulting from a transition from oak to red maple, thus suggesting that the observed change in forest composition may benefit urban air quality. This study illustrates the importance of monitoring and representing changes in forest composition and the impacts to human health indirectly through changes in BVOCs.

Humans have profoundly influenced their environment. It has been estimated that nearly one-third of the global land cover has been modified while approximately 40% of the photosynthesis has been appropriated. As the interface between the subsurface and the atmosphere is altered, it is imperative that we understand the influence this alteration has in terms of changing regional and global climates. Land surface heterogeneity is sometimes a principal modulator of local and regional climates and, as such, there are potential aggregation and teleconnection effects ranging in scales from soil pores to the general atmospheric circulation when the land surface is altered across a range of scales. The human fingerprint on land surface processes is critical and must also be accounted for in the discourse on land-atmosphere coupling as it pertains to climate and global change as well as local processes such as evapotranspiration and streamflow. It is at this pivotal interface where hydrologists, atmospheric scientists and ecologists must understand how their disciplines interact and influence each other.Fluxes across the land-surface directly influence predictions of ecological processes, atmospheric dynamics, and terrestrial hydrology. However, many simplifications are made in numerical models when considering terrestrial hydrology from the view point of the atmosphere and visa-versa. While this may be a necessity in the current generation of operational models used for forecasting, it can create obstacles to the advancement of process understanding. These simplifications can limit the numerical prediction capabilities on how water partitions itself throughout all phases of the water cycle. The feedbacks between terrestrial and atmospheric water dynamics are not well understood or represented by the current generation of operational land-surface and atmospheric models. This can lead to erroneous spatial patterns and anomalous temporal persistence in land-atmosphere exchanges and atmospheric water cycle predictions. Cross-disciplinary efforts are needed not only to identify but also to quantify feedbacks between terrestrial and atmospheric water at appropriate spatiotemporal scales. This is especially true as today’s young scientists set their sights on improving process understanding and prediction skill from both research and operational models used to describe such linked systems.In recognition of these challenges, a junior faculty and early career scientist forum was recently held at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado with the intent of identifying and characterizing feedback interactions, and their attendant spatial and temporal scales, important for coupling terrestrial and atmospheric water dynamics. The primary focus of this forum is on improved process understanding, rather than operational products, as the possibility of incorporating more realistic physics into operational models is computationally prohibitive. We approached the subject of improved predictability through better process understanding by focusing on the following three framework questions described and discussed below.

Abstract. Solar radiation management (SRM) and carbon dioxide removal (CDR) are geoengineering methods that have been proposed to mitigate global warming in the event of insufficient greenhouse gas emission reductions. Here, we have studied temperature and precipitation responses to CDR and SRM with the Representative Concentration Pathway 4.5 (RCP4.5) scenario using the MPI-ESM and CESM Earth system models (ESMs). The SRM scenarios were designed to meet one of the two different long-term climate targets: to keep either global mean (1) surface temperature or (2) precipitation at the 2010–2020 level via stratospheric sulfur injections. Stratospheric sulfur fields were simulated beforehand with an aerosol–climate model, with the same aerosol radiative properties used in both ESMs. In the CDR scenario, atmospheric CO2 concentrations were reduced to keep the global mean temperature at approximately the 2010–2020 level. Results show that applying SRM to offset 21st century climate warming in the RCP4.5 scenario leads to a 1.42 % (MPI-ESM) or 0.73 % (CESM) reduction in global mean precipitation, whereas CDR increases global precipitation by 0.5 % in both ESMs for 2080–2100 relative to 2010–2020. In all cases, the simulated global mean precipitation change can be represented as the sum of a slow temperature-dependent component and a fast temperature-independent component, which are quantified by a regression method. Based on this component analysis, the fast temperature-independent component of the changed atmospheric CO2 concentration explains the global mean precipitation change in both SRM and CDR scenarios. Based on the SRM simulations, a total of 163–199 Tg S (CESM) or 292–318 Tg S (MPI-ESM) of injected sulfur from 2020 to 2100 was required to offset global mean warming based on the RCP4.5 scenario. To prevent a global mean precipitation increase, only 95–114 Tg S was needed, and this was also enough to prevent global mean climate warming from exceeding 2∘ above preindustrial temperatures. The distinct effects of SRM in the two ESM simulations mainly reflected differing shortwave absorption responses to water vapour. Results also showed relatively large differences in the individual (fast versus slow) precipitation components between ESMs.

Abstract The frequency and intensity of heavy rainfall events have increased in the Central U.S. over the last several decades, and model projections from dynamical downscaling suggest a continuation with climate change. In this study, we examine how climate change might affect mechanisms related to the development of heavy rainfall events that occur on the scale of mesoscale convective systems over the Central U.S. To accomplish these goals, we incorporate dynamical downscaled simulations of two Coupled Model Intercomparison Project phase 5 models in the Weather Research and Forecasting model that accurately simulate heavy rainfall events. For each model, a set of heavy rainfall events that match the frequency, timing, and intensity of observed events are objectively identified in historical and future simulations. We then examine multimodel composites of select atmospheric fields during these events in simulations of historical and future scenarios, enabling an identification of possible physical mechanisms that could contribute to the intensification of heavy rainfall events with climate change. Simulations show that additional moisture is transported into convective updrafts during heavy rain events in future simulations, driving stronger evaporative cooling from the entrainment of drier midtropospheric air. This results in the formation of a stronger low‐level cold pool, which enhances moisture convergence above the cold pool and increases rainfall rates during future heavy precipitation events. In addition, a warmer profile in future simulations might allow for heavier rainfall rates as a deeper atmospheric column can support additional collision‐coalescence of liquid hydrometeors.

Pressure dipoles in global climate data capture recurring and persistent, large-scale patterns of pressure and circulation anomalies that span distant geographical areas (teleconnections). In this paper, we present a novel graph based approach called shared reciprocal nearest neighbors that considers only reciprocal positive and negative edges in the shared nearest neighbor graph to find dipoles in pressure data. To show the utility of finding dipoles using our approach, we show that the data driven dynamic climate indices generated from our algorithm always perform better than static indices formed from the fixed locations used by climate scientists in terms of capturing impact on land temperature and precipitation. Another salient point of this approach is that it can generate a single snapshot picture of all the dipole interconnections on the globe in a given dataset making it possible to differentiate between various climate model simulations via data driven dipole analysis. Given the importance of teleconnections in climate and the importance of model simulations in understanding the impact of climate change, this methodology has the potential to provide significant insights.

A comparison of sea level pressure (SLP) trends in a subset of seven Coupled Model Intercomparison Project (CMIP) phase 5 general circulation models (GCM), namely decadal simulations with CCSM4, CanCM4, MPI-ESM-LR, FGOALS-g2, MIROC4h, MIROC5, and MRI-CGCM3, to their CMIP3 counterparts reveals an unrealistically strong forecast skill in CMIP3 models for trend predictions for 2001–2011 when using the 1979–2000 period to train the forecast. Boreal-winter SLP trends over five high-, mid-, and low-latitude zones were calculated over the 1979–2000 initialization period for each ensemble member and then ranked based on their performance relative to HadSLP2 observations. The same method is used to rank the ensemble members during the 2001–2011 period. In CMIP3, 17 out of 38 ensemble members retain their rank in the 2001–2011 hindcast period and 3 retain the neighboring rank. However, numbers are much lower in more recent CMIP5 decadal predictions over the similar 2001–2010 period when using the 1981–2000 period as initialization with the same number of ensembles. Different periods were used for CMIP3 and CMIP5 because although the 1979–2000 initialization is widely used for CMIP3, CMIP5 decadal predictions are only available for 30-year periods. The conclusion to consider the forecast skill in CMIP3 predictions during 2001–2011 as unrealistic is corroborated by comparisons to earlier periods from the 1960s to the 1980s in both CMIP3 and CMIP5 simulations. Thus, although the 2001–2011 CMIP3 predictions show statistically significant forecast skill, this skill should be treated as a spurious result that is unlikely to be reproduced by newer more accurate GCMs.

Abstract. Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems, and human communities, worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean-land-atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "Big Data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically-based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.

Minnesota is the U.S. state with the strongest winter warming in the contiguous United States.We performed regional climate projections at 10 km horizontal resolution using the WRF model forced by an ensemble of eight CMIP5 GCMs.The selected GCMs have previously been found to be in relatively good agreement with observations compared to other members of the CMIP5 model ensemble.Our projections suggest ongoing warming in all seasons, especially in winter, as well as shallower snow cover and fewer days with snow cover.On the other hand, we expect significant increases in spring and early summer heavy precipitation events.Our comparisons between different time slices and two different emission scenarios indicate a climate for the state of Minnesota at the end of the 21st century that is significantly different from what has been observed by the end of the 20th century.Winters and summers are expected to be up to 6 o C and 4 o C warmer, respectively, over northern and central Minnesota and spring precipitation may increase by more than 1 mm d -1 over northern Minnesota.Especially over the central part of the state, winter snow height is suggested to decrease by more than 0.5 meters and the number of days per year with snow height of more than 0.0254 meters (one inch) is expected to decrease by up to 60.

Abstract. Solar Radiation Management (SRM) and Carbon Dioxide Removal (CDR) are geoengineering methods that have been proposed to prevent climate warming in the event of insufficient greenhouse gas emission reductions. Here, we have studied temperature and precipitation responses to CDR and SRM with the RCP4.5 scenario using the MPI-ESM and CESM Earth System Models (ESMs). The SRM scenarios were designed to meet one of the two different climate targets: to keep either global mean 1) surface temperature or 2) precipitation at the 2010–2020 level via stratospheric sulfur injections. Stratospheric sulfur fields were simulated beforehand with an aerosol-climate model, with the same aerosol radiative properties used in both ESMs. In the CDR scenario, atmospheric CO2 concentrations were reduced to keep the global mean temperature at approximately the 2010–2020 level. Results show that applying SRM to offset 21st century climate warming in the RCP4.5 scenario leads to a 1.42 % (MPI-ESM) or 0.73 % (CESM) reduction in global mean precipitation, whereas CDR increases global precipitation by 0.5 % in both ESMs for 2080–2100 relative to 2010–2020. In all cases, the simulated global mean precipitation change can be represented as the sum of a slow temperature-dependent component and a fast temperature-independent component, which are quantified by regression method. Based on this component analysis, the fast temperature-independent component of CO2 explains the global mean precipitation change in both SRM and CDR scenarios. Based on the SRM simulations, a total of 163–199 Tg(S) (CESM) or 292–318 Tg(S) (MPI-ESM) of injected sulfur from 2020 to 2100 was required to offset global mean warming based on the RCP4.5 scenario. To prevent a global mean precipitation increase, only 95–114 Tg(S) was needed and this was also enough to prevent global mean climate warming from exceeding 2 degrees above preindustrial temperatures. The distinct effects of SRM in the two ESM simulations mainly reflected differing shortwave absorption responses to water vapor. Results also showed relatively large differences in the individual (fast versus slow) precipitation components between ESMs.

<p>Solar Radiation Management (SRM) and Carbon Dioxide Removal (CDR) have been proposed to mitigate global warming in the event of insufficient greenhouse gas emission reductions. We have studied temperature and precipitation responses to CDR and SRM with the RCP4.5 scenario using the MPI-ESM and CESM Earth System Models (ESMs). The two SRM scenarios were designed to meet different climate targets to keep either global mean 1) surface temperature or 2) precipitation at the 2010-2020 level via stratospheric sulfur injections. This was done in two-fold method, where global aerosol fields were first simulated with aerosol-climate model ECHAM-HAMMOZ, which were then used as prescribed fields in ESM simulations. In the CDR scenario the annual CO<sub>2</sub> increase based on RCP4.5 was counteracted by a 1% annual removal of the atmospheric CO<sub>2</sub> concentration which decreased the global mean temperature back to the 2010-2020 level at the end of this century. </p><p>Results showed that applying SRM to offset 21st century climate warming in the RCP4.5 scenario led to a 1.42%  (MPI-ESM) or 0.73% (CESM) reduction in global mean precipitation, whereas CDR increased global precipitation by 0.5% in both ESMs for 2080-2100 relative to 2010-2020. To study this further we separated global precipitation responses to a temperature-dependent and a fast temperature-independent components. These were quantified by a regression method. In this method the climate variable (e.g. precipitation) is regressed against the temperature change due to the instantaneous forcing. Temperature-dependent slow response and temperature independent fast response are given by the fitted regression line. We showed that in all simulated geoengineering scenarios, the simulated global mean precipitation change can be represented as the sum of these response components. This component analysis shows that the fast temperature-independent component of atmospheric CO<sub>2</sub> concentration explains the global mean precipitation change in both SRM and CDR scenarios. Results showed relatively large differences in the individual precipitation components between two ESMs. This component analysis method can be generalized to evaluate and analyze precipitation, or other climate responses, basically in any emission scenario and in any ESM in a conceptually easy way. </p><p>Based on the SRM simulations, a total of or 292-318 Tg(S) (MPI-ESM) or 163-199 Tg(S) (CESM) of injected sulfur from 2020 to 2100 was required to offset global mean warming based on the RCP4.5 scenario. The distinct effects of SRM in the two ESM simulations mainly reflected differing shortwave absorption responses to water vapor. To prevent a global mean precipitation increase, only 95-114 Tg(S) was needed. Simultaneously this prevent the global mean climate warming from exceeding 2 degrees above preindustrial temperatures in both models. </p>

Minnesota is the U.S. state with the strongest winter warming in the contiguous United States. We performed regional climate projections at 10 km horizontal resolution using the WRF model forced by an ensemble of eight CMIP5 GCMs. The selected GCMs have previously been found to be in relatively good agreement with observations compared to other members of the CMIP5 model ensemble. Our projections suggest ongoing warming in all seasons, especially in winter, as well as shallower snow cover and fewer days with snow cover. On the other hand, we expect significant increases in spring and early summer heavy precipitation events. Our comparisons between different time slices and two different emission scenarios indicate a climate for the state of Minnesota at the end of the 21st century that is significantly different from what has been observed by the end of the 20th century. Winters and summers are expected to be up to 6oC and 4oC warmer, respectively, over northern and central Minnesota and spring precipitation may increase by more than 1 mm d-1 over northern Minnesota. Especially over the central part of the state, winter snow height is suggested to decrease by more than 0.5 meters and the number of days per year with snow height of more than 0.0254 meters (one inch) is expected to decrease by up to 60.