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  • Source Publication: Climate Dynamics,doi:10.1007/s00382-015-2674-2 Authors: Kim, Y.H., S.K. Min , X. Zhang, F. Zwiers, L.V. Alexander, M.G. Donat and Y.S. Tung Publication Date: May 2015

    An attribution analysis of extreme temperature changes is conducted using updated observations (HadEX2) and multi-model climate simulation (CMIP5) datasets for an extended period of 1951–2010. Compared to previous HadEX/CMIP3-based results, which identified human contributions to the observed warming of extreme temperatures on global and regional scales, the current results provide better agreement with observations, particularly for the intensification of warm extremes. Removing the influence of two major modes of natural internal variability (the Arctic Oscillation and Pacific Decadal Oscillation) from observations further improves attribution results, reducing the model-observation discrepancy in cold extremes. An optimal fingerprinting technique is used to compare observed changes in annual extreme temperature indices of coldest night and day (TNn, TXn) and warmest night and day (TNx, TXx) with multi-model simulated changes that were simulated under natural-plus-anthropogenic and natural-only (NAT) forcings. Extreme indices are standardized for better intercomparisons between datasets and locations prior to analysis and averaged over spatial domains from global to continental regions following a previous study. Results confirm previous HadEX/CMIP3-based results in which anthropogenic (ANT) signals are robustly detected in the increase in global mean and northern continental regional means of the four indices of extreme temperatures. The detected ANT signals are also clearly separable from the response to NAT forcing, and results are generally insensitive to the use of different model samples as well as different data availability.

  • Authors: Faron Anslow Publication Date: May 2015

    This report outlines the research activities of the Climate Analysis and Monitoring (CAM) Theme for the period of 2015-2019. It also provides some background on CAM and discusses the progress made on the last CAM research plan.

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-015-2642-x. Authors: Seiler, C. and F.W. Zwiers Publication Date: May 2015

    Extratropical explosive cyclones are rapidly intensifying low pressure systems with severe wind speeds and heavy precipitation, affecting livelihoods and infrastructure primarily in coastal and marine environments. This study evaluates how well the most recent generation of climate models reproduces extratropical explosive cyclones in the Northern Hemisphere for the period 1980–2005. An objective-feature tracking algorithm is used to identify and track cyclones from 25 climate models and three reanalysis products. Model biases are compared to biases in the sea surface temperature (SST) gradient, the polar jet stream, the Eady growth rate, and model resolution. Most models accurately reproduce the spatial distribution of explosive cyclones when compared to reanalysis data (R = 0.94), with high frequencies along the Kuroshio Current and the Gulf Stream. Three quarters of the models however significantly underpredict explosive cyclone frequencies, by a third on average and by two thirds in the worst case. This frequency bias is significantly correlated with jet stream speed in the inter-model spread (R ≥ 0.51), which in the Atlantic is correlated with a negative meridional SST gradient (R = −0.56). The importance of the jet stream versus other variables considered in this study also applies to the interannual variability of explosive cyclone frequency. Furthermore, models with fewer explosive cyclones tend to underpredict the corresponding deepening rates (R ≥ 0.88). A follow-up study will assess the impacts of climate change on explosive cyclones, and evaluate how model biases presented in this study affect the projections.

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-015-2791-y Authors: Seiler, C. and, F.W. Zwiers Publication Date: May 2015

    Explosive cyclones are rapidly intensifying low pressure systems generating severe wind speeds and heavy precipitation primarily in coastal and marine environments. This study presents the first analysis on how explosive cyclones respond to climate change in the extratropics of the Northern Hemisphere. An objective-feature tracking algorithm is used to identify and track cyclones from 23 CMIP5 climate models for the recent past (1981–1999) and future (2081–2099). Explosive cyclones are projected to shift northwards by about 2.2∘ latitude on average in the northern Pacific, with fewer and weaker events south of 45∘N, and more frequent and stronger events north of this latitude. This shift is correlated with a poleward shift of the jet stream in the inter-model spread (R=0.56). In the Atlantic, the total number of explosive cyclones is projected to decrease by about 17 % when averaging across models, with the largest changes occurring along North America’s East Coast. This reduction is correlated with a decline in the lower-tropospheric Eady growth rate (R=0.51), and is stronger for models with smaller frequency biases (R=−0.65). The same region is also projected to experience a small intensification of explosive cyclones, with larger vorticity values for models that predict stronger increases in the speed of the jet stream (R=0.58). This strengthening of the jet stream is correlated with an enhanced sea surface temperature gradient in the North Atlantic (R=−0.63). The inverse relationship between model bias and projection, and the role of model resolution are discussed.

  • Source Publication: Hydrology and Earth System Sciences Discussions, 12, 6, doi: 10.5194/hessd-12-6179-2015 Authors: Werner, A.T. and A.J. Cannon Publication Date: May 2015

    Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e., correlation tests) and distributional properties (i.e., tests for equality of probability distributions). Outputs from seven downscaling methods – bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), climate imprint delta method (CI), and bias corrected CI (BCCI) – are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3 day peak flow and 7 day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational datasets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational dataset. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7 day low flow events, regardless of reanalysis or observational dataset. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis datasets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical datasets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.

  • Authors: Markus A. Schnorbus Publication Date: May 2015

    This report outlines the research activities of the Hydrologic Impacts (HI) Theme for the period of 2015-2019. It also provides some background on HI and discusses the progress made on the last HI research plan.

  • Source Publication: Nature Geoscience 8, 372–377, doi:10.1038/ngeo2407 Authors: Clarke, G.K.C., A.H. Jarosch, F.S. Anslow, V. Radic and B. Menounos Publication Date: May 2015

    Retreat of mountain glaciers is a significant contributor to sea-level rise and a potential threat to human populations through impacts on water availability and regional hydrology. Like most of Earth’s mountain glaciers, those in western North America are experiencing rapid mass loss. Projections of future large-scale mass change are based on surface mass balance models that are open to criticism, because they ignore or greatly simplify glacier physics. Here we use a high-resolution regional glaciation model, developed by coupling physics-based ice dynamics with a surface mass balance model, to project the fate of glaciers in western Canada. We use twenty-first-century climate scenarios from an ensemble of global climate models in our simulations; the results indicate that by 2100, the volume of glacier ice in western Canada will shrink by 70 ± 10% relative to 2005. According to our simulations, few glaciers will remain in the Interior and Rockies regions, but maritime glaciers, in particular those in northwestern British Columbia, will survive in a diminished state. We project the maximum rate of ice volume loss, corresponding to peak input of deglacial meltwater to streams and rivers, to occur around 2020–2040. Potential implications include impacts on aquatic ecosystems, agriculture, forestry, alpine tourism and water quality.

  • Authors: Trevor Q. Murdoch, Francis W. Zwiers Publication Date: May 2015

    This report outlines the research activities of the Regional Climate Impacts (RCI) Theme for the period of 2015-2019. It also provides some background on RCI and discusses the progress made on the last RCI research plan.

  • Source Publication: International Journal of Climatology, doi: 0.1002/joc.4361 Authors: Salimun, E., F. Tangang, L. Juneng, F.W. Zwiers and W..J. Merryfield Publication Date: May 2015

    This study evaluates the forecast skill of the fourth version of the Canadian coupled ocean–atmosphere general circulation model (CanCM4) and its model output statistics (MOS) to forecast the seasonal rainfall in Malaysia, particularly during early (October–November–December) and late (January–February–March) winter monsoon periods. CanCM4 is the latest component of the Canadian Seasonal to Inter-annual Prediction System (CanSIPS), which is a multi-seasonal climate prediction system developed particularly for Canada but applicable globally. Generally, CanCM4's skill in reproducing the climatology during winter is not as good as in other seasons because of the model's inability to simulate the regional synoptic circulations over the western Maritime Continent. In particular, the model fails to forecast the cold surges and Borneo vortex circulations that play critical roles in moisture horizontal advection. Moreover, its forecast skill during the early winter monsoon period is poorer than during the late period. Interestingly, forecast skill is enhanced when MOS models are applied as the MOS utilizes the predictive signals in the quasi-global predictors from the CanCM4 forecast system. The predictability can be traced to the conventional El Niño–Southern Oscillation (ENSO) and ENSO Modoki signals that are present in the CanCM4 forecast MOS predictor fields. The quasi-global sea-surface temperature and quasi-global sea-level pressure fields are found to be the most useful predictors. Interestingly, CanCM4 forecast signals associated with the Indian Ocean Dipole also contribute to the skill. Skill enhancement is particularly significant for northern Borneo during early monsoon periods in medium- and long-lead forecasts when the CanCM4 has minimal direct skill in the region.

  • Source Publication: International Journal of Climatology, doi: 10.1002/joc.4361 Authors: Salimun, E., F. Tangang, L. Juneng, F. W. Zwiers and W. J. Merryfield Publication Date: May 2015

    This study evaluates the forecast skill of the fourth version of the Canadian coupled ocean–atmosphere general circulation model (CanCM4) and its model output statistics (MOS) to forecast the seasonal rainfall in Malaysia, particularly during early (October–November–December) and late (January–February–March) winter monsoon periods. CanCM4 is the latest component of the Canadian Seasonal to Inter-annual Prediction System (CanSIPS), which is a multi-seasonal climate prediction system developed particularly for Canada but applicable globally. Generally, CanCM4's skill in reproducing the climatology during winter is not as good as in other seasons because of the model's inability to simulate the regional synoptic circulations over the western Maritime Continent. In particular, the model fails to forecast the cold surges and Borneo vortex circulations that play critical roles in moisture horizontal advection. Moreover, its forecast skill during the early winter monsoon period is poorer than during the late period. Interestingly, forecast skill is enhanced when MOS models are applied as the MOS utilizes the predictive signals in the quasi-global predictors from the CanCM4 forecast system. The predictability can be traced to the conventional El Niño–Southern Oscillation (ENSO) and ENSO Modoki signals that are present in the CanCM4 forecast MOS predictor fields. The quasi-global sea-surface temperature and quasi-global sea-level pressure fields are found to be the most useful predictors. Interestingly, CanCM4 forecast signals associated with the Indian Ocean Dipole also contribute to the skill. Skill enhancement is particularly significant for northern Borneo during early monsoon periods in medium- and long-lead forecasts when the CanCM4 has minimal direct skill in the region.

  • Source Publication: Geophysical Research Letters, 41, 10, 3586‐3593, doi:10.1002/2014GL059586 Authors: Kumar, S., P. Dirmeyer and J. Kinter III Publication Date: May 2015

    Typically, sub-seasonal to intra-annual climate forecasts are based on ensemble mean (EM) predictions. The EM prediction provides only a part of the information available from the ensemble forecast. Here we test the null hypothesis that the observations are randomly distributed about the EM predictions using a new metric that quantifies the distance between the EM predictions from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) and the observations represented by CFSv2 Reanalysis. The null hypothesis cannot be rejected in this study. Hence, we argue that the higher order statistics such as ensemble standard deviation are also needed to describe the forecast. We also show that removal of systematic errors that are a function of the forecast initialization month and lead time is a necessary pre-processing step. Finally, we show that CFSv2 provides useful ensemble climate forecasts from 0 to 9 month lead time in several regions.

  • Source Publication: Theoretical and Applied Climatology, 120, 1, 377-390, doi:10.1007/s00704‐014‐1157‐4 Authors: Farajzadeh, M., R. Oji, A.J. Cannon, Y. Ghavidel, and A.R. Massah Publication Date: Apr 2015

    Seven single-site statistical downscaling methods for daily temperature and precipitation, including four deterministic algorithms [analog model (ANM), quantile mapping with delta method extrapolation (QMD), cumulative distribution function transform (CDFt), and model-based recursive partitioning (MOB)] and three stochastic algorithms [generalized linear model (GLM), Conditional Density Estimation Network Creation and Evaluation (CaDENCE), and Statistical Downscaling Model–Decision Centric (SDSM–DC] are evaluated at nine stations located in the mountainous region of Iran’s Midwest. The methods are of widely varying complexity, with input requirements that range from single-point predictors of temperature and precipitation to multivariate synoptic-scale fields. The period 1981–2000 is used for model calibration and 2001–2010 for validation, with performance assessed in terms of 27 Climate Extremes Indices (CLIMDEX). The sensitivity of the methods to large-scale anomalies and their ability to replicate the observed data distribution in the validation period are separately tested for each index by Pearson correlation and Kolmogorov–Smirnov (KS) tests, respectively. Combined tests are used to assess overall model performances. MOB performed best, passing 14.5 % (49.6 %) of the combined (single) tests, respectively, followed by SDSM, CaDENCE, and GLM [14.5 % (46.5 %), 13.2 % (47.1 %), and 12.8 % (43.2 %), respectively], and then by QMD, CDFt, and ANM [7 % (45.7 %), 4.9 % (45.3 %), and 1.6 % (37.9 %), respectively]. Correlation tests were passed less frequently than KS tests. All methods downscaled temperature indices better than precipitation indices. Some indices, notably R20, R25, SDII, CWD, and TNx, were not successfully simulated by any of the methods. Model performance varied widely across the study region.

  • Source Publication: Journal of Climate, 28, 3435‐3438, doi:10.1175/JCLI‐D‐14‐00691.1 Authors: Ribes, A., N.P. Gillett and F.W. Zwiers Publication Date: Apr 2015

    Climate change detection and attribution studies rely on historical simulations using specified combinations of forcings to quantify the contributions from greenhouse gases and other forcings to observed climate change. In the last CMIP5 exercise, in addition to the so-called all-forcings simulations, which are driven with a combination of anthropogenic and natural forcings, natural forcings–only and greenhouse gas–only simulations were prioritized among other possible experiments. This study addresses the question of optimally designing this set of experiments to estimate the recent greenhouse gas–induced warming, which is highly relevant to the problem of constraining estimates of the transient climate response. Based on Monte Carlo simulations and considering experimental designs with a fixed budget for the number of simulations that modeling centers can perform, the most accurate estimate of historical greenhouse gas–induced warming is obtained with a design using a combination of all-forcings, natural forcings–only, and aerosol forcing–only simulations. An investigation of optimal ensemble sizes, given the constraint on the total number of simulations, indicates that allocating larger ensemble sizes to weaker forcings, such as natural-only, is optimal

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Apr 2015

    Two articles recently published in the peer-review literature seek to answer two related questions: What role could utilizing vegetation burning for energy, with methods to capture the carbon dioxide emitted, have in aggressive short-term climate mitigation in western North America? And, how might North American vegetation and its interactions with the climate change in the future?

    Addressing the first question in Nature Climate Change, Sanchez et al. (2015) find that western North America could attain a carbon-negative power system by 2050 through strong deployment of renewable energy sources, including BioEnergy with Carbon Capture and Storage (BECCS), and fossil fuel reductions. Their results indicate that reductions of up to 145% from 1990s emissions are possible. They also find that the primary value of BECCS is not electricity production, but carbon sequestration, and note that BECCS can also be used to reduce emissions in the transportation and industrial sectors.

    Publishing in the Journal of Geophysical Research: Atmospheres, Garnaud and Sushama (2015) examine the second question. In order to do this they downscale output from a global climate model using a regional climate model that can simulate vegetation dynamics. They find that the projected future increases to growing season length result in greater vegetation productivity and biomass, though this plateaus at the end of of the 21st century. Their projections also indicate an increase in the water-use efficiency of plants, but decreased plant productivity in the southeastern US over the 2071-2100 period. In addition, they find that accounting for vegetation feedbacks leads to increased warming in summer at higher latitudes and a reduction in summer warming at lower latitudes.

  • Source Publication: Nature Geoscience, 8, 372–377, doi:10.1038/ngeo2407 Authors: Clarke, G.K.C., A.H. Jarosch, F.S. Anslow, V. Radić and B. Menounos Publication Date: Apr 2015

    Retreat of mountain glaciers is a significant contributor to sea-level rise and a potential threat to human populations through impacts on water availability and regional hydrology. Like most of Earth’s mountain glaciers, those in western North America are experiencing rapid mass loss. Projections of future large-scale mass change are based on surface mass balance models that are open to criticism, because they ignore or greatly simplify glacier physics. Here we use a high-resolution regional glaciation model, developed by coupling physics-based ice dynamics with a surface mass balance model, to project the fate of glaciers in western Canada. We use twenty-first-century climate scenarios from an ensemble of global climate models in our simulations; the results indicate that by 2100, the volume of glacier ice in western Canada will shrink by 70 ± 10% relative to 2005. According to our simulations, few glaciers will remain in the Interior and Rockies regions, but maritime glaciers, in particular those in northwestern British Columbia, will survive in a diminished state. We project the maximum rate of ice volume loss, corresponding to peak input of deglacial meltwater to streams and rivers, to occur around 2020–2040. Potential implications include impacts on aquatic ecosystems, agriculture, forestry, alpine tourism and water quality.

  • Source Publication: Journal of Hydrometeorology, doi:10.1175/JHM-D-14-0167.1. Authors: Rajesh R. Shrestha, Markus A. Schnorbus and Alex J. Cannon Publication Date: Mar 2015

    Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model-driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically-downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical/future climate traces-driven Ensemble Streamflow Prediction (ESP) was employed. The Variable Infiltration Capacity (VIC) hydrologic model set-up for the Fraser River basin, British Columbia, Canada was used as a test-bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño Southern Oscillation (ENSO) and its teleconnections. The root mean square error, bias and categorical skills for the two methods are mostly similar. Hydrologic modelling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skilful hydrologic predictions.

  • Source Publication: Nature Climate Change, Advance Online Publication, doi:10.1038/NCLIMATE2524. Authors: Mohammad Reza Najafi, Francis Zwiers, Nathan Gillett Publication Date: Mar 2015

    The Arctic has warmed significantly more than global mean surface air temperature over recent decades, as expected from amplification mechanisms. Previous studies have attributed the observed Arctic warming to the combined effect of greenhouse gases and other anthropogenic influences. However, given the sensitivity of the Arctic to external forcing and the intense interest in the effects of aerosols on its climate, it is important to examine and quantify the effects of individual groups of anthropogenic forcing agents. Here we quantify the separate contributions to observed Arctic land temperature change from greenhouse gases, other anthropogenic forcing agents (which are dominated by aerosols) and natural forcing agents. We show that although increases in greenhouse-gas concentrations have driven the observed warming over the past century, approximately 60% of the greenhouse-gas-induced warming has been offset by the combined response to other anthropogenic forcings, which is substantially greater than the fraction of global greenhouse-gas-induced warming that has been offset by these forcings. The climate models considered on average simulate the amplitude of response to anthropogenic forcings well, increasing confidence in their projections of profound future Arctic climate change.

  • Source Publication: Climate, 3, 1, 241‐263, doi: 10.3390/cli3010241 Authors: Werner, A.T., T.D. Prowse and B.R. Bonsal Publication Date: Mar 2015

    Infrastructure such as dams and reservoirs are critical water-supply features in several regions of the world. However, ongoing population growth, increased demand and climate variability/change necessitate the better understanding of these systems, particularly in terms of their long-term trends. The Sooke Reservoir (SR) of British Columbia, Canada is one such reservoir that currently supplies water to ~300,000 people, and is subject to considerable inter and intra-annual climatic variations. The main objectives of this study are to better understand the characteristics of the SR through an in-depth assessment of the contemporary water balance when the basin was intensively monitored (1996–2005), to use standardized runoff to select the best timescale to compute the Standard Precipitation (SPI) and Standard Precipitation Evaporation Indices (SPEI) to estimate trends in water availability over 1919 to 2005. Estimates of runoff and evaporation were validated by comparing simulated change in storage, computed by adding inputs and subtracting outputs from the known water levels by month, to observed change in storage. Water balance closure was within ±11% of the monthly change in storage on average when excluding months with spill pre-2002. The highest evaporation, dry season (1998) and lowest precipitation, wet season (2000/2001) from the intensively monitored period were used to construct a worst-case scenario to determine the resilience of the SR to drought. Under such conditions, the SR could support Greater Victoria until the start of the third wet season. The SPEI and SPI computed on a three-month timescale had the highest correlation with the standardized runoff, R2 equaled 0.93 and 0.90, respectively. A trend toward drier conditions was shown by SPEI over 1919 to 2005, while moistening over the same period was shown by SPI, although trends were small in magnitude. This study contributes a validated application of SPI and SPEI, giving more credit to their trends and estimated changes in drought.

  • Source Publication: Journal of Climate, doi:10.1175/JCLI-D-14-00691.1. Authors: Aurélien Ribes, Nathan P. Gillett and Francis W. Zwiers Publication Date: Mar 2015

    Climate change detection and attribution studies rely on historical simulations using specified combinations of forcings to quantify the contributions from greenhouse gases and other forcings to observed climate change. In the last CMIP5 exercise, in addition to the so-called ALL-forcings simulations which are driven with a combination of anthropogenic and natural forcings, natural forcings-only and greenhouse-gas-only simulations were prioritized among other possible experiments. This study addresses the question of optimally designing this set of experiments to estimate the recent greenhouse-gas-induced warming, which is highly relevant to the problem of constraining estimates of the transient climate response. Based on Monte Carlo simulations and considering experimental designs with a fixed budget for the number of simulations that modelling centres can perform, we find that the most accurate estimate of historical greenhouse-gas-induced warming is obtained with a design using a combination of ALL-forcings, natural forcings-only and aerosol forcing-only simulations. An investigation of optimal ensemble sizes, given the constraint on the total number of simulations, indicates that allocating larger ensemble sizes to weaker forcings, such as natural-only, is optimal.

  • Source Publication: Acta Horticulturae (ISHS), 1068, 211-218. Authors: Neilsen, D., S. Smith, T. Van Der Gulik, B. Taylor and A.J. Cannon Publication Date: Mar 2015

    To identify agricultural water requirements and the potential for water conservation, we have developed a GIS based model to estimate water demand based on crop type, seasonal crop development, and spatial distribution. Multiple data layers characterize topography, surface and subsurface hydrology, crop distribution, irrigation management, soils, and a range of geographic and political boundaries. The model is driven 500×500 m gridded climate data for the years 1960-2006 and future climate data sets downscaled from GCM output (12 scenarios) from 1961-2100. These climate surfaces have been used to provide calculations of Penman Monteith reference evapotranspiration and a range of agroclimatic indices for each climate grid cell, in addition to crop and terrain based irrigation water demand. Sample scenarios of changing climate, expanded agriculture, and urbanization demonstrate the usefulness of this type of modeling for regional water planning.

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