Use of general circulation model output in the creation of climate change scenarios for impact analysis

Many scientific studies warn of a rapid global climate change during the next century. These changes are understood with much less certainty on a regional scale than on a global scale, but effects on ecosystems and society will occur at local and regional scales. Consequently, in order to study the true impacts of climate change, regional scenarios of future climate are needed. One of the most important sources of information for creating scenarios is the output from general circulation models (GCMs) of the climate system. However, current state-of-the-art GCMs are unable to simulate accurately even the current seasonal cycle of climate on a regional basis. Thus the simple technique of adding the difference between 2 × CO₂ and 1 × CO₂ GCM simulations to current climatic time series cannot produce scenarios with appropriate spatial and temporal details without corrections for model deficiencies. In this study a technique is developed to allow the information from GCM simulations to be used, while accommodating for the deficiencies. GCM output is combined with knowledge of the regional climate to produce scenarios of the equilibrium climate response to a doubling of the atmospheric CO₂ concentration for three case study regions, China, Sub-Saharan Africa and Venezuela, for use in biological effects models. By combining the general climate change calculated with several GCMs with the observed patterns of interannual climate variability, reasonable scenarios of temperature and precipitation variations can be created. Generalizations of this procedure to other regions of the world are discussed.     

Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP)

We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.     

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.     

Influence of seasonality on glacier mass balance, and implications for palaeoclimate reconstructions

Climates inferred from former glacier geometries in some areas exhibit discrepancies with regional palaeoclimates predicted by General Circulation Models (GCMs) and modelling of palaeoecological data, possibly as a consequence of their differing treatments of climatic seasonality. Since glacier-based climate reconstructions potentially offer an important tool in the calibration of GCMs, which themselves need validation if used to predict future climate scenarios, we attempt to resolve mismatches between these techniques by (1) investigating the influence of seasonality on glacier mass balance, and (2) refining the methodology used for the derivation of glacier-based palaeoclimates. Focussing on the Younger Dryas stadial glaciation of Scotland, northeast Atlantic, we show that sea-ice amplified seasonality led to a significantly drier climate than has been suggested by glacier-based interpretations. This was characterised by a relatively short ablation season and the survival of a more substantial winter snowpack. We suggest that if palaeoglaciological studies were to account for changes in seasonal temperature and precipitation variability, their results would agree more closely with the cold, arid, northeast Atlantic palaeoenvironment predicted by atmospheric modelling and northwest European pollen studies, and would therefore provide more accurate constraints for GCM calibration.     

Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models

The uncertainties and sources of variation in projected impacts of climate change on agriculture and terrestrial ecosystems depend not only on the emission scenarios and climate models used for projecting future climates, but also on the impact models used, and the local soil and climatic conditions of the managed or unmanaged ecosystems under study. We addressed these uncertainties by applying different impact models at site, regional and continental scales, and by separating the variation in simulated relative changes in ecosystem performance into the different sources of uncertainty and variation using analyses of variance. The crop and ecosystem models used output from a range of global and regional climate models (GCMs and RCMs) projecting climate change over Europe between 1961–1990 and 2071–2100 under the IPCC SRES scenarios. The projected impacts on productivity of crops and ecosystems included the direct effects of increased CO₂ concentration on photosynthesis. The variation in simulated results attributed to differences between the climate models were, in all cases, smaller than the variation attributed to either emission scenarios or local conditions. The methods used for applying the climate model outputs played a larger role than the choice of the GCM or RCM. The thermal suitability for grain maize cultivation in Europe was estimated to expand by 30–50% across all SRES emissions scenarios. Strong increases in net primary productivity (NPP) (35–54%) were projected in northern European ecosystems as a result of a longer growing season and higher CO₂ concentrations. Changing water balance dominated the projected responses of southern European ecosystems, with NPP declining or increasing only slightly relative to present-day conditions. Both site and continental scale models showed large increases in yield of rain-fed winter wheat for northern Europe, with smaller increases or even decreases in southern Europe. Site-based, regional and continental scale models showed large spatial variations in the response of nitrate leaching from winter wheat cultivation to projected climate change due to strong interactions with soils and climate. The variation in simulated impacts was smaller between scenarios based on RCMs nested within the same GCM than between scenarios based on different GCMs or between emission scenarios.     

Comparison of uncertainty sources for climate change impacts: flood frequency in England

This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions; Global Climate Model (GCM) structure; downscaling from GCMs (including Regional Climate Model structure); hydrological model structure; hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology.     

Evaluation of HadCM2 and Direct Use of Daily GCM Data in Impact Assessment Studies

This paper investigates two important aspects of methods used to explore possible effects of climatic changes on agricultural productivity on regional spatial scales. First, an evaluation of precipitation and near surface air temperature in two successive versions of the Hadley Centre General Circulation Model (GCM) has been performed to consider to what extent GCMs are capable of simulating the mean and variability of local climates. This is explored by comparing the output of an individual GCM grid box with three station observations. Several ancillary issues associated with the comparisons of observations of daily precipitation and model output that affect the statistical results are also discussed. Finally, daily data from the control and sulphate runs of the latest Hadley Centre GCM (HadCM2) have been used directly as input to the CERES-Wheat model, and the modelled yield distribution is compared to that produced with the historical data series. Our results imply that for this particular grid box covering the study region in central France, the daily raw data from HadCM2 experiment can be used directly to assess the potential impact of the greenhouse gas and sulphate aerosol radiative induced forcings and the associated climatic change on average regional winter wheat production. On the other hand, less confidence should be placed on their use regarding the estimation of future agricultural risk and variability assessment. Furthermore, a possibly more severe methodological problem that has arisen from our study is the inability of CERES-Wheat to simulate the waterlogging effects of excessive soil water on crop growth and development. Finally, we assess the potential impact of changing climate on regional winter wheat production by using the daily data from the sulphate integration up to the end of the 21st century.     

Projections of uncertainties in climate change scenarios into expected winter wheat yields

Summary The crop model CERES-Wheat in combination with the stochastic weather generator were used to quantify the effect of uncertainties in selected climate change scenarios on the yields of winter wheat, which is the most important European cereal crop. Seven experimental sites with the high quality experimental data were selected in order to evaluate the crop model and to carry out the climate change impact analysis. The analysis was based on the multi-year crop model simulations run with the daily weather series prepared by the stochastic weather generator. Seven global circulation models (GCMs) were used to derive the climate change scenarios. In addition, seven GCM-based scenarios were averaged in order to derive the average scenario (AVG). The scenarios were constructed for three time periods (2025, 2050 and 2100) and two SRES emission scenarios (A2 and B1). The simulated results showed that: (1) Wheat yields tend to increase (40 out of 42 applied scenarios) in most locations in the range of 7.5–25.3% in all three time periods. In case of the CCSR scenario that predicts the most severe increase of air temperature, the yields would be reduced by 9.6% in 2050 and by 25.8% in 2100 if the A2 emission scenario would become reality. Differences between individual scenarios are large and statistically significant. Particularly for the time periods 2050 and 2100 there are doubts about the trend of the yield shifts. (2) The site effect was caused by the site-specific soil and climatic conditions. Importance of the site influence increases with increasing severity of imposed climatic changes and culminates for the emission scenario A2 and the time period 2100. The sustained tendency benefiting two warmest sites has been found as well as more positive response to the changed climatic conditions of the sites with deeper soil profiles. (3) Temperature variability proved to be an important factor and influenced both mean and standard deviation of the yields. Change of temperature variability by more than 25% leads to statistically significant changes in yield distribution. The effect of temperature variability decreases with increased values of mean temperature. (4) The study proved that the application of the AVG scenarios – despite possible objections of physical inconsistency – might be justifiable and convenient in some cases. It might bring results comparable to those derived from averaging outputs based on number of scenarios and provide more robust estimate than the application of only one selected GCM scenario.     

Estimating the consequences of CO2-induced climatic change on north american grain agriculture using general circulation model information

A procedure to estimate the potential climatic effects of a doubling of atmospheric carbon dioxide concentration on agricultural production is illustrated. The method combines use of atmospheric general circulation models (GCMs) and process-oriented crop models. Wheat and corn (maize) yields in three important North American grain cropping regions are treated. Combined use of these two types of models can provide insights into the impacts of climate changes at the level of plant physiology, and potential means by which agricultural production practices may adapt to these changes.Specific agronomic predictions are found to depend critically on the details of the projected climate change. Uncertainties in the specification of the doubled-CO₂ climate by the GCM, particularly with respect to precipitation, dictate that agricultural predictions derived from them at this time must be regarded only as illustrative of the impact assessment method.     

Evaluating GCM Output with Impact Models

This study uses empirical agricultural impact models to compare the U.S. climate change predictions of 16 General Circulation Models (GCMs). The impact analysis provides a policy-relevant index by which to judge complex climate predictions. National aggregate impacts vary widely across the 16 GCMs because of varying regional and seasonal patterns of predicted climate change. Examining the predicted impacts from the full set of GCMs reveals that the seasonal detail in the GCM predictions is so noisy that it is not significantly different from a constant annual change. However, a consistent regional pattern does emerge across the set of models. Nonetheless, aggregating climate change across seasons and regions within the United States, using a national-annual climate change provides a reasonable and efficient approximation to the expected impact predicted by the 16 GCM models.     

CO2 climate sensitivity and snow-sea-ice albedo parameterization in an atmospheric GCM coupled to a mixed-layer ocean model

The snow-sea-ice albedo parameterization in an atmospheric general circulation model (GCM), coupled to a simple mixed-layer ocean and run with an annual cycle of solar forcing, is altered from a version of the same model described by Washington and Meehl (1984). The model with the revised formulation is run to equilibrium for 1 × CO₂ and 2 × CO₂ experiments. The 1 ×CO₂ (control) simulation produces a global mean climate about 1° warmer than the original version, and sea-ice extent is reduced. The model with the altered parameterization displays heightened sensitivity in the global means, but the geographical patterns of climate change due to increased carbon dioxide (CO₂) are qualitatively similar. The magnitude of the climate change is affected, not only in areas directly influenced by snow and ice changes but also in other regions of the globe, including the tropics where sea-surface temperature, evaporation, and precipitation over the oceans are greater. With the less-sensitive formulation, the global mean surface air temperature increase is 3.5 °C, and the increase of global mean precipitation is 7.12%. The revised formulation produces a globally averaged surface air temperature increase of 4.04 °C and a precipitation increase of 7.25%, as well as greater warming of the upper tropical troposphere. Sensitivity of surface hydrology is qualitatively similar between the two cases with the larger-magnitude changes in the revised snow and ice-albedo scheme experiment. Variability of surface air temperature in the model is comparable to observations in most areas except at high latitudes during winter. In those regions, temporal variation of the sea-ice margin and fluctuations of snow cover dependent on the snow-ice-albedo formulation contribute to larger-than-observed temperature variability. This study highlights an uncertainty associated with results from current climate GCMs that use highly parameterized snow-sea-ice albedo schemes with simple mixed-layer ocean models.The National Center for Atmospheric Research is sponsored by the National Science Foundation.     

The impact of doubled CO2 on the energetics and hydrologic processes of mid-latitude transient eddies

The Northern Hemisphere winter (DJF) stationary eddy response of a general circulation model (GCM) to a doubling of atmospheric CO₂ is simulated with a linear steady state model as a response to anomalies in diabatic heating (latent, sensible and radiative), mountain and transient eddy effects. For this analysis the doubled CO₂ experiment performed by Wilson and Mitchell (1987) is used. The linear simulations of the control and perturbation climate capture most of the important features of the GCMs stationary eddies. The simulation of the anomalous stationary eddy pattern in the Northern Hemisphere captures only some of the important features of the GCMs anomalies. The climate anomalies in the Southern Hemisphere are poorly simulated. In the Northern Hemisphere the climate anomalies are dominated by the effect of transient eddies and mountains. In low latitudes also the contribution of latent heating is important. The contributions of sensible and radiative heating are small.     

Joint variable spatial downscaling

Joint Variable Spatial Downscaling (JVSD), a new statistical technique for downscaling gridded climatic variables, is developed to generate high resolution gridded datasets for regional watershed modeling and assessments. The proposed approach differs from previous statistical downscaling methods in that multiple climatic variables are downscaled simultaneously and consistently to produce realistic climate projections. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. Analysis and comparisons are performed for 20th Century Climate in Coupled Models (20C3M), broadly available for most GCMs. The results show that the proposed downscaling method is able to reproduce the sub-grid climatic features as well as their temporal/spatial variability in the historical periods. Comparisons are also performed for precipitation and temperature with other statistical and dynamic downscaling methods over the southeastern US and show that JVSD performs favorably. The downscaled sequences are used to assess the implications of GCM scenarios for the Apalachicola-Chattahoochee-Flint river basin as part of a comprehensive climate change impact assessment.     

Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province,China

Assessing the regional impact of climate change on agriculture, hydrology, and forests is vital for sustainable management. Trustworthy projections of climate change are needed to support these assessments. In this paper, 18 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to simulate regional climate change in Zhejiang Province, Southeast China. Simple graphical approaches and three indices are used to evaluate the performance of six key climatic variables during simulations from 1971 to 2000. These variables include maximum and minimum air temperature, precipitation, wind speed, solar radiation, and relative humidity. These variables are of great importance to researchers and decision makers in climate change impact studies and developing adaptation strategies. This study found that most GCMs failed to reproduce the observed spatial patterns, due to insufficient resolution. However, the seasonal variations of the six variables are simulated well by most GCMs. Maximum and minimum air temperatures are simulated well on monthly, seasonal, and yearly scales. Solar radiation is reasonably simulated on monthly, seasonal, and yearly scales. Compared to air temperature and solar radiation, it was found that precipitation, wind speed, and relative humidity can only be simulated well at seasonal and yearly scales. Wind speed was the variable with the poorest simulation results across all GCMs.     

Analyse régionale des simulations climatiques du modèle canadien de circulation générale de l'atmosphère pour le territoire québécois

Abstract

Current understanding of the possible nature of climatic change at the regional scale is limited by the spatial resolution of General Circulation Models (GCM). The use of GCM outputs without correction linked to the spatial variability of the variables can bring significant errors in their utilization at the regional scale. The potential of the Canadian GCM for regional applications in Quebec has been analysed by comparison to the climatic normals of temperature and precipitation, measured over the Quebec climatological network, on an annual and seasonal basis. This analysis has been undertaken with the support of a geographical information system (GIS) (PAMAP). In summary, a difference between the climatic normal and the GCM output has been estimated at 20% for temperature and 30% for precipitation. We present an analysis of a corrected regionalized scenario for the province of Quebec of the possible climatic change simulated by the Canadian GCM under the hypothesis of a doubling of atmospheric CO₂. Results show an increase of the annual average temperature of 4° C for summer and 6°C for winter, associated with an average increase of 80 mm (10%) in annual precipitation, reaching 25% in some regions.     

Possible Impacts of Global Warming on the Hydrology of the Ogallala Aquifer Region

The Ogallala or High Plains aquifer provides water for about 20% of the irrigated land in the United States. About 20 km³ (16.6 million acre-feet) of water are withdrawn annually from this aquifer. In general, recharge has not compensated for withdrawals since major irrigation development began in this region in the 1940s. The mining of the Ogallala has been pictured as an analogue to climate change in that many GCMs predict a warmer and drier future for this region. In this paper we attempt to anticipate the possible impacts of climate change on the sustainability of the aquifer as a source of water for irrigation and other purposes in the region. We have applied HUMUS, the Hydrologic Unit Model of the U.S. to the Missouri and Arkansas-White-Red water resource regions that overlie the Ogallala. We have imposed three general circulation model (GISS, UKTR and BMRC) projections of future climate change on this region and simulated the changes that may be induced in water yields (runoff plus lateral flow) and ground water recharge. Each GCM was applied to HUMUS at three levels of global mean temperature (GMT) to represent increasing severity of climate change (a surrogate for time). HUMUS was also run at three levels of atmospheric CO₂ concentration (hereafter denoted by [CO₂]) in order to estimate the impacts of direct CO₂ effects on photosynthesis and evapotranspiration. Since the UKTR and GISS GCMs project increased precipitation in the Missouri basin, water yields increase there. The BMRC GCM predicts sharply decreased precipitation and, hence, reduced water yields. Precipitation reductions are even greater in the Arkansas basin under BMRC as are the consequent water yield losses. GISS and UKTR climates lead to only moderate yield losses in the Arkansas. CO₂-fertilization reverses these losses and yields increase slightly. CO₂ fertilization increases recharge in the base (no climate change) case in both basins. Recharge is reduced under all three GCMs and severities of climate change.     

Intercomparsion of regional biases and doubled CO2-sensitivity of coupled atmosphere-ocean general circulation model experiments

 We compared regional biases and transient doubled CO₂ sensitivities of nine coupled atmosphere-ocean general circulation models (GCMs) from six international climate modeling groups. We evaluated biases and responses in winter and summer surface air temperatures and precipitation for seven subcontinental regions, including those in the 1990 Intergovernmental Panel on Climate Change (IPCC) Scientific Assessment. Regional biases were large and exceeded the variance among four climatological datasets, indicating that model biases were not primarily due to uncertainty in observations. Model responses to altered greenhouse forcing were substantial (average temperature change=2.7±0.9 ^°C, range of precipitation change =−35 to +120% of control). While coupled models include more climate system feedbacks than earlier GCMs implemented with mixed-layer ocean models, inclusion of a dynamic ocean alone did not improve simulation of long-term mean climatology nor increase convergence among model responses to altered greenhouse gas forcing. On the other hand, features of some of the coupled models including flux adjustment (which may have simply masked simulation errors), high horizontal resolution, and estimation of screen height temperature contributed to improved simulation of long-term surface climate. The large range of model responses was partly accounted for by inconsistencies in forcing scenarios and transient-simulation averaging periods. Nonetheless, the models generally had greater agreement in their sensitivities than their controls did with observations. This suggests that consistent, large-scale response features from an ensemble of model sensitivity experiments may not depend on details of their representation of present-day climate. Received: 9 September 1996 / Revised: 31 July 1997     

Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region

One of the main sources of uncertainty in estimating climate projections affected by global warming is the choice of the global climate model (GCM). The aim of this study is to evaluate the skill of GCMs from CMIP3 and CMIP5 databases in the north-east Atlantic Ocean region. It is well known that the seasonal and interannual variability of surface inland variables (e.g. precipitation and snow) and ocean variables (e.g. wave height and storm surge) are linked to the atmospheric circulation patterns. Thus, an automatic synoptic classification, based on weather types, has been used to assess whether GCMs are able to reproduce spatial patterns and climate variability. Three important factors have been analyzed: the skill of GCMs to reproduce the synoptic situations, the skill of GCMs to reproduce the historical inter-annual variability and the consistency of GCMs experiments during twenty-first century projections. The results of this analysis indicate that the most skilled GCMs in the study region are UKMO-HadGEM2, ECHAM5/MPI-OM and MIROC3.2(hires) for CMIP3 scenarios and ACCESS1.0, EC-EARTH, HadGEM2-CC, HadGEM2-ES and CMCC-CM for CMIP5 scenarios. These models are therefore recommended for the estimation of future regional multi-model projections of surface variables driven by the atmospheric circulation in the north-east Atlantic Ocean region.     

Climate Change and Forest Fire Potential in Russian and Canadian Boreal Forests

In this study outputs from four current General Circulation Models (GCMs) were used to project forest fire danger levels in Canada and Russia under a warmer climate. Temperature and precipitation anomalies between 1 × CO₂ and 2 × CO₂ runs were combined with baseline observed weather data for both countries for the 1980–1989 period. Forecast seasonal fire weather severity was similar for the four GCMs, indicating large increases in the areal extent of extreme fire danger in both countries under a 2 × CO₂ climate scenario. A monthly analysis, using the Canadian GCM, showed an earlier start to the fire season, and significant increases in the area experiencing high to extreme fire danger in both Canada and Russia, particularly during June and July. Climate change as forecast has serious implications for forest fire management in both countries. More severe fire weather, coupled with continued economic constraints and downsizing, mean more fire activity in the future is a virtual certainty. The likely response will be a restructuring of protection priorities to support more intensive protection of smaller, high-value areas, and a return to natural fire regimes over larger areas of both Canada and Russia, with resultant significant impacts on the carbon budget.     

Water Resources Implications of Global Warming: A U.S. Regional Perspective

The implications of global warming for the performance of six U.S. water resource systems are evaluated. The six case study sites represent a range of geographic and hydrologic, as well as institutional and social settings. Large, multi-reservoir systems (Columbia River, Missouri River, Apalachicola-Chatahoochee-Flint (ACF) Rivers), small, one or two reservoir systems (Tacoma and Boston) and medium size systems (Savannah River) are represented. The river basins range from mountainous to low relief and semi-humid to semi-arid, and the system operational purposes range from predominantly municipal to broadly multi-purpose. The studies inferred, using a chain of climate downscaling, hydrologic and water resources systems models, the sensitivity of six water resources systems to changes in precipitation, temperature and solar radiation. The climate change scenarios used in this study are based on results from transient climate change experiments performed with coupled ocean-atmosphere General Circulation Models (GCMs) for the 1995 Intergovernmental Panel on Climate Change (IPCC) assessment. An earlier doubled-CO₂ scenario from one of the GCMs was also used in the evaluation. The GCM scenarios were transferred to the local level using a simple downscaling approach that scales local weather variables by fixed monthly ratios (for precipitation) and fixed monthly shifts (for temperature). For those river basins where snow plays an important role in the current climate hydrology (Tacoma, Columbia, Missouri and, to a lesser extent, Boston) changes in temperature result in important changes in seasonal streamflow hydrographs. In these systems, spring snowmelt peaks are reduced and winter flows increase, on average. Changes in precipitation are generally reflected in the annual total runoff volumes more than in the seasonal shape of the hydrographs. In the Savannah and ACF systems, where snow plays a minor hydrological role, changes in hydrological response are linked more directly to temperature and precipitation changes. Effects on system performance varied from system to system, from GCM to GCM, and for each system operating objective (such as hydropower production, municipal and industrial supply, flood control, recreation, navigation and instream flow protection). Effects were generally smaller for the transient scenarios than for the doubled CO₂ scenario. In terms of streamflow, one of the transient scenarios tended to have increases at most sites, while another tended to have decreases at most sites. The third showed no general consistency over the six sites. Generally, the water resource system performance effects were determined by the hydrologic changes and the amount of buffering provided by the system's storage capacity. The effects of demand growth and other plausible future operational considerations were evaluated as well. For most sites, the effects of these non-climatic effects on future system performance would about equal or exceed the effects of climate change over system planning horizons.