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Abstract
As is true of many tropical regions, northeastern Puerto Rico is an ecologically sensitive area with biological life that is highly elevation dependent on precipitation and temperature. Climate change has the potential to increase the risk of losing endemic species and habitats. Consequently, it is important to explore the pattern of trends in precipitation and temperature along an elevation gradient. Statistical derivatives of a frequently sampled dataset of precipitation and temperature at 20 sites along an elevation gradient of 1000 m in northeastern Puerto Rico were examined for trends from 2001 to 2013 with nonparametric methods accounting for annual periodic variations such as yearly weather cycles. Overall daily precipitation had an increasing trend of around 0.1 mm day−1 yr−1. The driest months of the annual dry, early, and late rainfall seasons showed a small increasing trend in the precipitation (around 0.1 mm day−1 yr−1). There was strong evidence that precipitation in the driest months of each rainfall season increased faster at higher elevations (0.02 mm day−1 more increase for 100-m elevation gain) and some evidence for the same pattern in precipitation in all months of the year but at half the rate. Temperature had a positive trend in the daily minimum (around 0.02°C yr−1) and a negative trend in the daily maximum whose size is likely an order of magnitude larger than the size of the daily minimum trend. Physical mechanisms behind the trends may be related to climate change; longer-term studies will need to be undertaken in order to assess the future climatic trajectory of tropical forests.
Abstract
As is true of many tropical regions, northeastern Puerto Rico is an ecologically sensitive area with biological life that is highly elevation dependent on precipitation and temperature. Climate change has the potential to increase the risk of losing endemic species and habitats. Consequently, it is important to explore the pattern of trends in precipitation and temperature along an elevation gradient. Statistical derivatives of a frequently sampled dataset of precipitation and temperature at 20 sites along an elevation gradient of 1000 m in northeastern Puerto Rico were examined for trends from 2001 to 2013 with nonparametric methods accounting for annual periodic variations such as yearly weather cycles. Overall daily precipitation had an increasing trend of around 0.1 mm day−1 yr−1. The driest months of the annual dry, early, and late rainfall seasons showed a small increasing trend in the precipitation (around 0.1 mm day−1 yr−1). There was strong evidence that precipitation in the driest months of each rainfall season increased faster at higher elevations (0.02 mm day−1 more increase for 100-m elevation gain) and some evidence for the same pattern in precipitation in all months of the year but at half the rate. Temperature had a positive trend in the daily minimum (around 0.02°C yr−1) and a negative trend in the daily maximum whose size is likely an order of magnitude larger than the size of the daily minimum trend. Physical mechanisms behind the trends may be related to climate change; longer-term studies will need to be undertaken in order to assess the future climatic trajectory of tropical forests.
Abstract
To investigate topographic–thermal circulations and the associated moisture variability over western Puerto Rico, field data were collected from 15 to 31 March 2011. Surface meteorological instruments and ground-based GPS receivers measured the circulation and precipitable water with high spatial and temporal resolution, and the Weather Research and Forecasting (WRF) Model was used to simulate the mesoscale flow at 1-km resolution. A westerly onshore flow of ~4 m s−1 over Mayaguez Bay was observed on many days, due to an interaction between thermally driven [3°C (10 km)−1] sea-breeze circulation and an island wake comprised of twin gyres. The thermally driven sea breeze occurred only when easterly synoptic winds favorably oriented the gyres with respect to the coast. Moisture associated with onshore flow was characterized by GPS measured precipitable water (PW). There is diurnal cycling of PW > 3 cm over the west coast during periods of onshore flow. The WRF Model tends to overestimate PW on the west side of the island, suggesting evapotranspiration as a process needing further attention. Fluctuations of PW affect local rainfall in times of convective instability.
Abstract
To investigate topographic–thermal circulations and the associated moisture variability over western Puerto Rico, field data were collected from 15 to 31 March 2011. Surface meteorological instruments and ground-based GPS receivers measured the circulation and precipitable water with high spatial and temporal resolution, and the Weather Research and Forecasting (WRF) Model was used to simulate the mesoscale flow at 1-km resolution. A westerly onshore flow of ~4 m s−1 over Mayaguez Bay was observed on many days, due to an interaction between thermally driven [3°C (10 km)−1] sea-breeze circulation and an island wake comprised of twin gyres. The thermally driven sea breeze occurred only when easterly synoptic winds favorably oriented the gyres with respect to the coast. Moisture associated with onshore flow was characterized by GPS measured precipitable water (PW). There is diurnal cycling of PW > 3 cm over the west coast during periods of onshore flow. The WRF Model tends to overestimate PW on the west side of the island, suggesting evapotranspiration as a process needing further attention. Fluctuations of PW affect local rainfall in times of convective instability.
Abstract
This study develops a new conceptual tool to explore the potential societal consequences of climate change. The conceptual tool delineates three quasi-independent factors that contribute to the societal consequences of climate change: how climate changes; the sensitivity of physical systems, biological resources, and social institutions to climate change; and the degree of human dependence on those systems, resources, and institutions. This conceptual tool, as currently developed, is not predictive, but it enables the exploration of the dependence of climate change risks on key contributing factors. In exploring a range of plausible behaviors for these factors and methods for their synthesis, the authors show that plausible assumptions lead to a wide range in potential societal consequences of climate change. This illustrates that the societal consequences of climate change are currently difficult to constrain and that high-consequence climate change outcomes are not necessarily low probability, as suggested by leading economic analyses. With careful implementation, this new conceptual tool has potential to increase public understanding of climate change risks, to support risk management decision making, or to facilitate communication of climate risks across disciplinary boundaries.
Abstract
This study develops a new conceptual tool to explore the potential societal consequences of climate change. The conceptual tool delineates three quasi-independent factors that contribute to the societal consequences of climate change: how climate changes; the sensitivity of physical systems, biological resources, and social institutions to climate change; and the degree of human dependence on those systems, resources, and institutions. This conceptual tool, as currently developed, is not predictive, but it enables the exploration of the dependence of climate change risks on key contributing factors. In exploring a range of plausible behaviors for these factors and methods for their synthesis, the authors show that plausible assumptions lead to a wide range in potential societal consequences of climate change. This illustrates that the societal consequences of climate change are currently difficult to constrain and that high-consequence climate change outcomes are not necessarily low probability, as suggested by leading economic analyses. With careful implementation, this new conceptual tool has potential to increase public understanding of climate change risks, to support risk management decision making, or to facilitate communication of climate risks across disciplinary boundaries.
Abstract
Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001–10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.
Abstract
Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001–10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.
Abstract
Riparian shallow groundwater and nutrient movement is important for aquatic and forest ecosystem health. Understanding stream water (SW)–shallow groundwater (GW) interactions is necessary for proper management of floodplain biodiversity, but it is particularly confounding in underrepresented semikarst hydrogeological systems. The Modular Three-Dimensional Finite-Difference Ground-Water Flow Model (MODFLOW) was used to simulate shallow groundwater flow and nutrient transport processes in a second-growth Ozark border forest for the 2011 water year. MODFLOW provided approximations of hydrologic head that were statistically comparable to observed data (Nash–Sutcliffe = 0.47, r 2 = 0.77, root-mean-square error = 0.61 cm, and mean difference = 0.46 cm). Average annual flow estimates indicated that 82% of the reach length was a losing stream, while the remaining 18% was gaining. The reach lost more water to the GW during summer (2405 m3 day−1) relative to fall (2184 m3 day−1), spring (2102 m3 day−1), and winter (1549 m3 day−1) seasons. Model results showed that the shallow aquifer had the highest nitrate loading during the winter season (707 kg day−1). A Particle-Tracking Model for MODFLOW (MODPATH) revealed significant spatial variations between piezometer sites (p = 0.089) in subsurface flow path and travel time, ranging from 213 m and 3.6 yr to 197 m and 11.6 yr. The current study approach is novel with regard to the use of transient flow conditions (as opposed to steady state conditions) in underrepresented semikarst geological systems of the U.S. Midwest. This study emphasizes the significance of semikarst geology in regulating SW–GW hydrologic and nutrient interactions and provides baseline information and modeling predictions that will facilitate future studies and management plans.
Abstract
Riparian shallow groundwater and nutrient movement is important for aquatic and forest ecosystem health. Understanding stream water (SW)–shallow groundwater (GW) interactions is necessary for proper management of floodplain biodiversity, but it is particularly confounding in underrepresented semikarst hydrogeological systems. The Modular Three-Dimensional Finite-Difference Ground-Water Flow Model (MODFLOW) was used to simulate shallow groundwater flow and nutrient transport processes in a second-growth Ozark border forest for the 2011 water year. MODFLOW provided approximations of hydrologic head that were statistically comparable to observed data (Nash–Sutcliffe = 0.47, r 2 = 0.77, root-mean-square error = 0.61 cm, and mean difference = 0.46 cm). Average annual flow estimates indicated that 82% of the reach length was a losing stream, while the remaining 18% was gaining. The reach lost more water to the GW during summer (2405 m3 day−1) relative to fall (2184 m3 day−1), spring (2102 m3 day−1), and winter (1549 m3 day−1) seasons. Model results showed that the shallow aquifer had the highest nitrate loading during the winter season (707 kg day−1). A Particle-Tracking Model for MODFLOW (MODPATH) revealed significant spatial variations between piezometer sites (p = 0.089) in subsurface flow path and travel time, ranging from 213 m and 3.6 yr to 197 m and 11.6 yr. The current study approach is novel with regard to the use of transient flow conditions (as opposed to steady state conditions) in underrepresented semikarst geological systems of the U.S. Midwest. This study emphasizes the significance of semikarst geology in regulating SW–GW hydrologic and nutrient interactions and provides baseline information and modeling predictions that will facilitate future studies and management plans.
Abstract
The Ganges–Brahmaputra–Meghna (GBM) river basins exhibit extremes in surface water availability at seasonal to annual time scales. However, because of a lack of basinwide hydrological data from in situ platforms, whether they are real time or historical, water management has been quite challenging for the 630 million inhabitants. Under such circumstances, a large-scale and spatially distributed hydrological model, forced with more widely available satellite meteorological data, can be useful for generating high resolution basinwide hydrological state variable data [streamflow, runoff, and evapotranspiration (ET)] and for decision making on water management. The Variable Infiltration Capacity (VIC) hydrological model was therefore set up for the entire GBM basin at spatial scales ranging from 12.5 to 25 km to generate daily fluxes of surface water availability (runoff and streamflow). Results indicate that, with the selection of representative gridcell size and application of correction factors to evapotranspiration calculation, it is possible to significantly improve streamflow simulation and overcome some of the insufficient sampling and data quality issues in the ungauged basins. Assessment of skill of satellite precipitation forcing datasets revealed that the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product of 3B42RT fared comparatively better than the Climate Prediction Center (CPC) morphing technique (CMORPH) product for simulation of streamflow. The general conclusion that emerges from this study is that spatially distributed hydrologic modeling for water management is feasible for the GBM basins under the scenario of inadequate in situ data availability. Satellite precipitation forcing datasets provide the necessary skill for water balance studies at interannual and interseasonal scales. However, further improvement in skill may be required if these datasets are to be used for flood management at daily to weekly time scales and within a data assimilation framework.
Abstract
The Ganges–Brahmaputra–Meghna (GBM) river basins exhibit extremes in surface water availability at seasonal to annual time scales. However, because of a lack of basinwide hydrological data from in situ platforms, whether they are real time or historical, water management has been quite challenging for the 630 million inhabitants. Under such circumstances, a large-scale and spatially distributed hydrological model, forced with more widely available satellite meteorological data, can be useful for generating high resolution basinwide hydrological state variable data [streamflow, runoff, and evapotranspiration (ET)] and for decision making on water management. The Variable Infiltration Capacity (VIC) hydrological model was therefore set up for the entire GBM basin at spatial scales ranging from 12.5 to 25 km to generate daily fluxes of surface water availability (runoff and streamflow). Results indicate that, with the selection of representative gridcell size and application of correction factors to evapotranspiration calculation, it is possible to significantly improve streamflow simulation and overcome some of the insufficient sampling and data quality issues in the ungauged basins. Assessment of skill of satellite precipitation forcing datasets revealed that the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product of 3B42RT fared comparatively better than the Climate Prediction Center (CPC) morphing technique (CMORPH) product for simulation of streamflow. The general conclusion that emerges from this study is that spatially distributed hydrologic modeling for water management is feasible for the GBM basins under the scenario of inadequate in situ data availability. Satellite precipitation forcing datasets provide the necessary skill for water balance studies at interannual and interseasonal scales. However, further improvement in skill may be required if these datasets are to be used for flood management at daily to weekly time scales and within a data assimilation framework.
Abstract
This study considers eastern Antilles (11°–18°N, 64°–57°W) weather and climate interactions in the context of the 2013 Christmas storm. This unseasonal event caused flash flooding in Grenada, St. Vincent, St. Lucia, Martinique, and Dominica from 24 to 25 December 2013, despite having winds <15 m s−1. The meteorological scenario and short-term forecasts are analyzed. At the low level, a convective wave propagated westward while near-equatorial upper westerly winds surged with eastward passage of a trough. The combination of tropical moisture, cyclonic vorticity, and uplift resulted in rain rates greater than 30 mm h−1 and many stations reporting 200 mm. Although forecast rainfall was low and a few hours late, weather services posted flood warnings in advance. At the climate scale, the fresh Orinoco River plume brought into the region by the North Brazil Current together with solar radiation greater than 200 W m−2, enabled sea temperatures to reach 28°C, and supplied convective available potential energy greater than 1800 J kg−1. Climate change model simulations are compared with reference fields and trends are analyzed in the eastern Antilles. While temperatures are set to increase, the frequency of flood events appears to decline in the future.
Abstract
This study considers eastern Antilles (11°–18°N, 64°–57°W) weather and climate interactions in the context of the 2013 Christmas storm. This unseasonal event caused flash flooding in Grenada, St. Vincent, St. Lucia, Martinique, and Dominica from 24 to 25 December 2013, despite having winds <15 m s−1. The meteorological scenario and short-term forecasts are analyzed. At the low level, a convective wave propagated westward while near-equatorial upper westerly winds surged with eastward passage of a trough. The combination of tropical moisture, cyclonic vorticity, and uplift resulted in rain rates greater than 30 mm h−1 and many stations reporting 200 mm. Although forecast rainfall was low and a few hours late, weather services posted flood warnings in advance. At the climate scale, the fresh Orinoco River plume brought into the region by the North Brazil Current together with solar radiation greater than 200 W m−2, enabled sea temperatures to reach 28°C, and supplied convective available potential energy greater than 1800 J kg−1. Climate change model simulations are compared with reference fields and trends are analyzed in the eastern Antilles. While temperatures are set to increase, the frequency of flood events appears to decline in the future.
Abstract
As carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent approach to estimating emissions at landscape, regional, and continental scales. The system taps into data and tools developed by the U.S. Forest Service to describe fuels, fuel loadings, and fuel consumption and merges information from the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration on fire location and timing. Currently, WFEIS provides web access to Moderate Resolution Imaging Spectroradiometer (MODIS) burned area for North America and U.S. fire-perimeter maps from the Monitoring Trends in Burn Severity products from the USGS, overlays them on 1-km fuel maps for the United States, and calculates fuel consumption and emissions with an open-source version of the Consume model. Mapped fuel moisture is derived from daily meteorological data from remote automated weather stations. In addition to tabular output results, WFEIS produces multiple vector and raster formats. This paper provides an overview of the WFEIS system, including the web-based system functionality and datasets used for emissions estimates. WFEIS operates on the web and is built using open-source software components that work with open international standards such as keyhole markup language (KML). Examples of emissions outputs from WFEIS are presented showing that the system provides results that vary widely across the many ecosystems of North America and are consistent with previous emissions modeling estimates and products.
Abstract
As carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent approach to estimating emissions at landscape, regional, and continental scales. The system taps into data and tools developed by the U.S. Forest Service to describe fuels, fuel loadings, and fuel consumption and merges information from the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration on fire location and timing. Currently, WFEIS provides web access to Moderate Resolution Imaging Spectroradiometer (MODIS) burned area for North America and U.S. fire-perimeter maps from the Monitoring Trends in Burn Severity products from the USGS, overlays them on 1-km fuel maps for the United States, and calculates fuel consumption and emissions with an open-source version of the Consume model. Mapped fuel moisture is derived from daily meteorological data from remote automated weather stations. In addition to tabular output results, WFEIS produces multiple vector and raster formats. This paper provides an overview of the WFEIS system, including the web-based system functionality and datasets used for emissions estimates. WFEIS operates on the web and is built using open-source software components that work with open international standards such as keyhole markup language (KML). Examples of emissions outputs from WFEIS are presented showing that the system provides results that vary widely across the many ecosystems of North America and are consistent with previous emissions modeling estimates and products.
Abstract
As a conveyor belt transferring inland ice to ocean, ice shelves shed mass through large, systematic tabular calving, which also plays a major role in the fluctuation of the buttressing forces. Tabular iceberg calving involves two stages: first is systematic cracking, which develops after the forward-slanting front reaches a limiting extension length determined by gravity–buoyancy imbalance; second is fatigue separation. The latter has greater variability, producing calving irregularity. Whereas ice flow vertical shear determines the timing of the systematic cracking, wave actions are decisive for ensuing viscoplastic fatigue. Because the frontal section has its own resonance frequency, it reverberates only to waves of similar frequency. With a flow-dependent, nonlocal attrition scheme, the present ice model [Scalable Extensible Geoflow Model for Environmental Research-Ice flow submodel (SEGMENT-Ice)] describes an entire ice-shelf life cycle. It is found that most East Antarctic ice shelves have higher resonance frequencies, and the fatigue of viscoplastic ice is significantly enhanced by shoaling waves from both storm surges and infragravity waves (~5 × 10−3 Hz). The two largest embayed ice shelves have resonance frequencies within the range of tsunami waves. When approaching critical extension lengths, perturbations from about four consecutive tsunami events can cause complete separation of tabular icebergs from shelves. For shelves with resonance frequencies matching storm surge waves, future reduction of sea ice may impose much larger deflections from shoaling, storm-generated ocean waves. Although the Ross Ice Shelf (RIS) total mass varies little in the twenty-first century, the mass turnover quickens and the ice conveyor belt is ~40% more efficient by the late twenty-first century, reaching 70 km3 yr−1. The mass distribution shifts oceanward, favoring future tabular calving.
Abstract
As a conveyor belt transferring inland ice to ocean, ice shelves shed mass through large, systematic tabular calving, which also plays a major role in the fluctuation of the buttressing forces. Tabular iceberg calving involves two stages: first is systematic cracking, which develops after the forward-slanting front reaches a limiting extension length determined by gravity–buoyancy imbalance; second is fatigue separation. The latter has greater variability, producing calving irregularity. Whereas ice flow vertical shear determines the timing of the systematic cracking, wave actions are decisive for ensuing viscoplastic fatigue. Because the frontal section has its own resonance frequency, it reverberates only to waves of similar frequency. With a flow-dependent, nonlocal attrition scheme, the present ice model [Scalable Extensible Geoflow Model for Environmental Research-Ice flow submodel (SEGMENT-Ice)] describes an entire ice-shelf life cycle. It is found that most East Antarctic ice shelves have higher resonance frequencies, and the fatigue of viscoplastic ice is significantly enhanced by shoaling waves from both storm surges and infragravity waves (~5 × 10−3 Hz). The two largest embayed ice shelves have resonance frequencies within the range of tsunami waves. When approaching critical extension lengths, perturbations from about four consecutive tsunami events can cause complete separation of tabular icebergs from shelves. For shelves with resonance frequencies matching storm surge waves, future reduction of sea ice may impose much larger deflections from shoaling, storm-generated ocean waves. Although the Ross Ice Shelf (RIS) total mass varies little in the twenty-first century, the mass turnover quickens and the ice conveyor belt is ~40% more efficient by the late twenty-first century, reaching 70 km3 yr−1. The mass distribution shifts oceanward, favoring future tabular calving.
Abstract
This study uses empirical models to examine the potential impact of climate change, based on a range of 100-yr phase 5 of the Coupled Model Intercomparison Project (CMIP5) projections, on crop water need in Jamaica. As expected, crop water need increases with rising temperature and decreasing precipitation, especially in May–July. Comparing the temperature and precipitation impacts on crop water need indicates that the 25th percentile of CMIP5 temperature change (moderate warming) yields a larger crop water deficit than the 75th percentile of CMIP5 precipitation change (wet winter and dry summer), but the 25th percentile of CMIP5 precipitation change (substantial drying) dominates the 75th percentile of CMIP5 temperature change (extreme warming). Over the annual cycle, the warming contributes to larger crop water deficits from November to April, while the drying has a greater influence from May to October. All experiments decrease crop suitability, with the largest impact from March to August.
Abstract
This study uses empirical models to examine the potential impact of climate change, based on a range of 100-yr phase 5 of the Coupled Model Intercomparison Project (CMIP5) projections, on crop water need in Jamaica. As expected, crop water need increases with rising temperature and decreasing precipitation, especially in May–July. Comparing the temperature and precipitation impacts on crop water need indicates that the 25th percentile of CMIP5 temperature change (moderate warming) yields a larger crop water deficit than the 75th percentile of CMIP5 precipitation change (wet winter and dry summer), but the 25th percentile of CMIP5 precipitation change (substantial drying) dominates the 75th percentile of CMIP5 temperature change (extreme warming). Over the annual cycle, the warming contributes to larger crop water deficits from November to April, while the drying has a greater influence from May to October. All experiments decrease crop suitability, with the largest impact from March to August.
