Progress towards better weather forecasts for city dwellers: from short range to climate change

Summary The current resolution of operational weather forecast model is not sufficient in general to explicitly resolve even the major cities of the World. As a consequence, urban areas have traditionally been neglected in such models. The introduction of tiled land surface models has enabled sub-gridscale landuse to be modelled, and hence has provided the opportunity to model cities within weather forecast models. However, to date there has been little effort made within the operational weather forecast community. At present there is only one operational centre that explicitly resolves urban areas. This centre includes a simple urban scheme within its mesoscale and global models, which has been shown to have a positive impact on the forecast. However, with the recent developments within urban meteorology there are now a variety of urban schemes, which vary in their complexity and parameter requirements, that would be suitable for operational weather forecast models. So it is likely that more operational models, and in particular mesoscale models, will include urban areas in the near future. With the majority of the World‘s population living in cities, the resilience of these cities to the impacts of climate change is also becoming of increasing interest. This means that urban areas will have to be included within climate change simulations, as well as weather forecast simulations, in the future. At present, only one climate change model has included a parametrisation for urban areas. However, this is likely to increase if work in this area grows rapidly.     

A sensitivity-based approach to evaluating future changes in Colorado River discharge

Projections of a drier, warmer climate in the U.S. Southwest would complicate management of the Colorado River system—yet these projections, often based on coarse resolution global climate models, are quite uncertain. We present an approach to understanding future Colorado River discharge based on land surface characterizations that map the Colorado River basin’s hydrologic sensitivities (e.g., changes in streamflow magnitude) to annual and seasonal temperature and precipitation changes. The approach uses a process-based macroscale land surface model (LSM; in this case, the Variable Infiltration Capacity hydrologic model, although methods are applicable to any LSM) to develop sensitivity maps (equivalent to a simple empirical model), and uses these maps to evaluate long-term annual streamflow responses to future precipitation and temperature change. We show that global climate model projections combined with estimates of hydrologic sensitivities, estimated for different seasons and at different change increments, can provide a basis for approximating cumulative distribution functions of streamflow changes similar to more common, computationally intensive full-simulation approaches that force the hydrologic model with downscaled future climate scenarios. For purposes of assessing risk, we argue that the sensitivity-based approach produces viable first-order estimates that can be easily applied to newly released climate information to assess underlying drivers of change and bound, at least approximately, the range of future streamflow uncertainties for water resource planners.     

Mitigating the Effects of Climate Change on the Water Resources of the Columbia River Basin

The potential effects of climate change on the hydrology and water resources of the Columbia River Basin (CRB) were evaluated using simulations from the U.S. Department of Energy and National Center for Atmospheric Research Parallel Climate Model (DOE/NCAR PCM). This study focuses on three climate projections for the 21st century based on a `business as usual' (BAU) global emissions scenario, evaluated with respect to a control climate scenario based on static 1995 emissions. Time-varying monthly PCM temperature and precipitation changes were statistically downscaled and temporally disaggregated to produce daily forcings that drove a macro-scale hydrologic simulation model of the Columbia River basin at 1/4-degree spatial resolution. For comparison with the direct statistical downscaling approach, a dynamical downscaling approach using a regional climate model (RCM) was also used to derive hydrologic model forcings for 20-year subsets from the PCM control climate (1995–2015) scenario and from the three BAU climate(2040–2060) projections. The statistically downscaled PCM scenario results were assessed for three analysis periods (denoted Periods 1–3: 2010–2039,2040–2069, 2070–2098) in which changes in annual average temperature were +0.5,+1.3 and +2.1 °C, respectively, while critical winter season precipitation changes were –3, +5 and +1 percent. For RCM, the predicted temperature change for the 2040–2060 period was +1.2 °C and the average winter precipitation change was –3 percent, relative to the RCM controlclimate. Due to the modest changes in winter precipitation, temperature changes dominated the simulated hydrologic effects by reducing winter snow accumulation, thus shifting summer streamflow to the winter. The hydrologic changes caused increased competition for reservoir storage between firm hydropower and instream flow targets developed pursuant to the Endangered Species Act listing of Columbia River salmonids. We examined several alternative reservoir operating policies designed to mitigate reservoir system performance losses. In general, the combination of earlier reservoir refill with greater storage allocations for instream flow targets mitigated some of the negative impacts to flow, but only with significant losses in firm hydropower production (ranging from –9 percent in Period1 to –35 percent for RCM). Simulated hydropower revenue changes were lessthan 5 percent for all scenarios, however, primarily due to small changes inannual runoff.     

Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel ClimateModel (PCM), and the implications of the comparison for a future(2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly(at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at 1/2-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.     

沿海地区复合洪水危险性研究进展

沿海地区频繁遭受洪水灾害,往往并非单一灾种驱动,而是多种致灾因子相互影响的综合结果.文中梳理了沿海地区复合洪水的主要驱动机理,归纳了复合洪水危险性研究中统计模型和动力数值模型两类主要研究方法,并分别阐述了近年来主要进展.复合洪水是极端高潮位(包括天文潮位、风暴潮和海浪)、河流洪水和强降水过程的两两组合或者是三者同时发生...     

Impact of reservoir operation and climate change on the hydrological regime of the Sesan and Srepok Rivers in the Lower Mekong Basin

The transboundary Sesan and Srepok sub-basins (2S) are the “hot-spot” areas for reservoir development in the Lower Mekong region, with 12 reservoirs built in the Vietnam territory. This study examines the impacts of reservoir operations in Vietnam and projected climate change on the downstream hydrologic regime of the 2S Rivers by jointly applying the Soil Water Assessment Tool (SWAT) and Water Evaluation and Planning (WEAP) models. Different scenarios of reservoir operation are considered and simulated to assess their impact on annual, seasonal, and monthly flow regimes under maximum hydropower capacity generation with and without taking into account the minimum flow requirement downstream near the Vietnam border with Cambodia. The precipitation and temperature projections from the high-resolution regional climate model HadGEM3-RA under two Representative Concentration Pathways, 4.5 and 8.5, of HadGEM2-AO are used as future climate change scenarios for the impact assessment. The study results show that reservoir operation leads to an increase in the dry season stream flows and a decrease in the wet season stream flows. The monthly flow regime exhibits considerable changes for both the Sesan and Srepok Rivers but with different magnitudes and patterns of increase and decrease. Climate change is likely to induce considerable changes in stream flows, though these changes are comparatively lower than those caused by reservoir operation. Climate change is likely to have both counterbalancing and reinforcing effects over the impact of reservoir operation, reducing changes during dry season but increasing changes in most of the other months.     

A study of impact of the geographic dependence of observing system on parameter estimation with an intermediate coupled model

Observational information has a strong geographic dependence that may directly influence the quality of parameter estimation in a coupled climate system. Using an intermediate atmosphere-ocean-land coupled model, the impact of geographic dependent observing system on parameter estimation is explored within a “twin” experiment framework. The “observations” produced by a “truth” model are assimilated into an assimilation model in which the most sensitive model parameter has a different geographic structure from the “truth”, for retrieving the “truth” geographic structure of the parameter. To examine the influence of data-sparse areas on parameter estimation, the twin experiment is also performed with an observing system in which the observations in some area are removed. Results show that traditional single-valued parameter estimation (SPE) attains a global mean of the “truth”, while geographic dependent parameter optimization (GPO) can retrieve the “truth” structure of the parameter and therefore significantly improves estimated states and model predictability. This is especially true when an observing system with data-void areas is applied, where the error of state estimate is reduced by 31 % and the corresponding forecast skill is doubled by GPO compared with SPE.     

Estimation of possible climate change impacts on water availability, extreme flow events and soil moisture in the Goulburn and Ovens Basins, Victoria

A rainfall-runoff model (IHACRES) is applied on a daily timestep to a large area of the state of Victoria, Australia. Successful calibrations of this dynamic lumped parameter model were performed for 5 rivers contributing streamflow to the Ovens Basin, and for 9 rivers of the Goulburn Basin. This is the first application of the model on such a scale, involving two basins where the total drainage area of the catchments modelled is about 6,500 km². The models were tested by simulation over the entire common period of observation for the 14 catchments under consideration. The results show that the models closely simulate the observed streamflow.The effect of historical climate variability on streamflow was investigated. The models were used for estimation of the potential impact of climatic change on water availability for irrigation for different climate scenarios developed in the Division of Atmospheric Research, CSIRO. This allows conditional estimates to be made of water supply in these basins for the periods 2030 and 2070 under current vegetation conditions. Projecting the future hydrologic regime in this region is extremely important, in particular for supporting irrigation management of the Basin.The problem of estimating the impact of climate change on the probability of extreme events of the hydrological regime was analysed. Flood frequency was found to increase for the scenarios providing the maximum amount of water; to 50% at 2030 and 100% at 2070. The probability of flood events for the dry scenarios rapidly decreases for these dates. Drought frequency, as defined by a soil wetness index, increased 35% for the dry scenario at 2030 and 80% for this scenario at 2070.     

Modelling the impact of climate change on Pacific skipjack tuna population and fisheries

IPCC-type climate models have produced simulations of the oceanic environment that can be used to drive models of upper trophic levels to explore the impact of climate change on marine resources. We use the Spatial Ecosystem And Population Dynamics Model (SEAPODYM) to investigate the potential impact of Climate change under IPCC A2 scenario on Pacific skipjack tuna (Katsuwonus pelamis). IPCC-type models are still coarse in resolution and can produce significant anomalies, e.g., in water temperature. These limitations have direct and strong effects when modeling the dynamics of marine species. Therefore, parameter estimation experiments based on assimilation of historical fishing data are necessary to calibrate the model to these conditions before exploring the future scenarios. A new simulation based on corrected temperature fields of the A2 simulation from one climate model (IPSL-CM4) is presented. The corrected fields led to a new parameterization close to the one achieved with more realistic environment from an ocean reanalysis and satellite-derived primary production. Projected changes in skipjack population under simple fishing effort scenarios are presented. The skipjack catch and biomass is predicted to slightly increase in the Western Central Pacific Ocean until 2050 then the biomass stabilizes and starts to decrease after 2060 while the catch reaches a plateau. Both feeding and spawning habitat become progressively more favourable in the eastern Pacific Ocean and also extend to higher latitudes, while the western equatorial warm pool is predicted to become less favorable for skipjack spawning.     

Simulating future Caspian sea level changes using regional climate model outputs

We report on simulations of present-day climate (1961–1990) and future climate conditions (2071–2100, Special Report on Emissions Scenario A2) over the Caspian sea basin with a regional climate model (RCM) nested in time-slice general circulation model (GCM) simulations. We also calculate changes (A2 scenario minus present-day) in Caspian sea level (CSL) in response to changes in the simulated hydrologic budget of the basin. For the present-day run, both the GCM and RCM show a good performance in reproducing the water budget of the basin and the magnitude of multi-decadal changes in CSL. Compared to present-day climate, in the A2 scenario experiment we find an increase in cold season precipitation and an increase in temperature and evaporation, both over land and over the Caspian sea. We also find a large decrease of CSL in the A2 scenario run compared to the present-day run. This is due to increased evaporation loss from the basin (particularly over the sea) exceeding increased cold season precipitation over the basin. Our results suggest that the CSL might undergo large changes under future climate change, leading to potentially devastating consequences for the economy and environment of the region.     

Multi-factor impact analysis of agricultural production in Bangladesh with climate change

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040–2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins’ hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.     

Estimating hydrological characteristics in the Amu Darya River basin under climate change conditions

Under consideration are results of solving the problem of the river water content estimation under conditions of uncertainties of climate change forecasts and the catchment state with a reference to the Amu Darya River basin. When constructing regional climate models, one selected a multimodel approach using the results of several global models and a statistical downscaling method that made the climate scenarios more detailed. The estimates demonstrated that in the medium- and long-term perspective, the Amu Darya River runoff is expected to decrease. As a result of the Bayesian ideology application, using the calculations got with a total probability formula, a prognostic probability curve of an annual river runoff supply of the basin rivers was derived based on different weights given to the estimates of a mean value for different climate scenarios. Prognostic characteristics of the annual runoff for the Amu Darya basin rivers are estimated in a form acceptable for hydrologic and hydroeconomic application.     

A simple hydrologic framework for simulating wetlands in climate and earth system models

Wetlands are ecosystems of important functions in the earth??s climate system. Through relatively high evapotranspiration, they affect surface water and energy exchange with the atmosphere directly influencing the physical climate. Through CH₄, CO₂ and N₂O fluxes, they regulate the biogeochemical cycles, indirectly influencing the physical climate. However, current models do not explicitly include the water table, present under all large and stable wetlands; model wetlands are identified as flat land with wet soil resulting from precipitation events. That is, the wetlands are only ??wetted?? from above but not from below by the high water table. Furthermore, without the knowledge of the water table position, estimates of CH₄ and other gases (e.g., CO₂ and N₂O) are poorly constrained. We present a simple hydrologic framework for simulating wetlands based on water table depth. A synthesis of hydrologic controls on wetlands highlights the key role that groundwater plays. It directly feeds wetlands, supports surface-water fed wetlands by maintaining a saturated substrate, and links land drainage to sea level by impeding drainage in lowlands. Forced by routine climate model output (precipitation?Cevapotranspiration-surface runoff), land topography, and sea level, we simulate the present-day water table in North America at the 1?km scale. We validate the simulation with water table observations and compare regions of shallow water table to mapped wetlands. Our results show that the framework captures the salient features of wetland distribution and extent at regional and continental scales, a direct result of large-scale groundwater convergence that nourishes the lowlands even in arid climates. The low requirement of forcing and computation make the framework easy to adopt in climate and earth system models for simulating wetland responses to climate and sea level change for the present, paleo reconstructions, and future projections.     

An integrated approach to assessing 21st century climate change over the contiguous U.S. using the NARCCAP RCM output

We utilize a revised Thornthwaite climate classification system for model intercomparisons and to visualize future climate change. This classification system uses an improved moisture factor that accounts for both evapotranspiration and precipitation, a thermal index based on potential evapotranspiration, and even intervals between categories for ease of interpretation. The use of climate types is a robust way to assess a model’s ability to reproduce mutlivariate conditions. We compare output from multiple regional climate models (RCMs) participating in NARCCAP (North American Regional Climate Change Assessment Program) as well as their coarser driving general circulation models (GCMs). Overall, the RCM ensemble does a good job in reproducing the main features of U.S. climate types. The “added-value” gained by downscaling with RCMs is significant, particularly in topographic regions such as the west coast and Appalachian Mountains. Ensemble model output from the scenario simulations indicates a recession of cold climate zones across the eastern U.S. and northern tier of the country as well as in mountainous areas. Projections also indicate the development of a novel climate zone, the torrid climate, across southern portions of the country. In addition, the U.S. will become drier, particularly across the Midwest as the moisture boundary shifts eastward, and in the the Appalachian region. Climate types in the Pacific Northwest, however, will not change greatly. Finally, we demonstrate possible applications for the forecast climate types and associated output variables.     

Diagnostic analysis of future climate scenarios applied to urban flooding in the Chicago metropolitan area

Past heavy precipitation events in the Chicago metropolitan area have caused significant flood-related economic and environmental damages. A key component in flood management policies and actions is determining flood magnitudes for specified return periods. This is a particularly difficult task in areas with a complex and changing climate and land-use, such as the Chicago metropolitan area. The standard design storm methodology based on the NOAA Atlas 14 and ISWS Bulletin 70 has been used in the past to estimate flood hydrographs with variable return periods in this region. In a changing climate, however, these publications may not be accurate. This study presents and illustrates a methodology for diagnostic analysis of future climate scenarios in the framework of urban flooding, and assesses the corresponding uncertainties. First, the design storms are calculated using data downscaled by a regional climate model (RCM) at 30-km spacing for the present and 2050s under the IPCC A1Fi (high) and B1 (low) emissions scenarios. Next, the corresponding flood discharges at six watersheds in suburban Chicago are estimated using a hydrologic event model. The resulting scenarios in flood frequency were first assessed through a set of diagnostic tests for precipitation timing and frequency. The study did not reveal any significant changes in the 2050s in the average timing of heavy storms, but their regularity decreased. The average timing did not exhibit any significant spatial variability throughout the region. The precipitation frequency analysis revealed distinct differences between the northern and southeastern subregions of the Chicago metropolitan area. The quantiles in the northern subregion averaged for 2-year, 5-year, and 10-year return periods exhibited a 20% and 16% increase in daily precipitation for scenarios B1 and A1Fi, respectively. The southeastern subregion, however, exhibited a decrease of 12% for scenario B1 and a minor increase of 3% for scenario A1Fi. The hydrologic effects of changing precipitation on the flood quantiles were illustrated using six small watersheds in the region. The relative increases or decreases in precipitation translated into even larger relative increases or decreases in flood peaks, due to the nonlinear nature of the rainfall-runoff process. Simulations using multiple climate models, for longer periods, finer spatio-temporal resolution, and larger areal coverage could be used to more accurately account for numerous uncertainties in the precipitation and flood projections.     

The Effect of Spatial Scale of Climatic Change Scenarios on Simulated Maize, Winter Wheat, and Rice Production in the Southeastern United States

We use the CERES family of crop models to assess the effect of different spatial scales of climate change scenarios on the simulated yield changes of maize (Zea mays L.), winter wheat (Triticum aestivum L.),and rice (Oryza sativa L.) in the Southeastern United States. The climate change scenarios were produced with the control and doubled CO₂ runs of a high resolution regional climate model anda coarse resolution general circulation model, which provided the initial and lateral boundary conditions for the regional model. Three different cases were considered for each scenario: climate change alone, climate change plus elevated CO₂, and the latter with adaptations. On the state level,for most cases, significant differences in the climate changed yields for corn were found, the coarse scale scenario usually producing larger modeled yield decreases or smaller increases. For wheat, however, which suffered large decreases in yields for all cases, very little contrast in yield based on scale of scenario was found. Scenario scale resulted in significantly different rice yields, but mainly because of low variability in yields. For maize the primary climate variable that explained the contrast in the yields calculated from the two scenarios is the precipitation during grain fill leading to different water stress levels. Temperature during vernalization explains some contrasts in winter wheat yields. With adaptation, the contrasts in the yields of all crops produced by the scenarios were reduced but not entirely removed. Our results indicate that spatial resolution of climate change scenarios can be an important uncertainty in climate change impact assessments, depending on the crop and management conditions.     

Evaluation of the impact of climate changes on water storage and groundwater recharge at the watershed scale

 The increase of concentration of carbon dioxide and other greenhouse gases in the atmosphere will certainly affect hydrological regimes. Global warning is thus expected to have major implications on water resources management. Our objective is to present a general approach to evaluate the effect of potential climate changes on groundwater resources. In the current stage of knowledge, large-scale global climate models are probably the best available tools to provide estimates of the effects of raising greenhouse gases on rainfall and evaporation patterns through a continuous, three dimensional simulation of atmospheric, oceanic and cryospheric processes. However their spatial resolution (generally some thousands of square kilometers) is not compatible with that of watershed hydrologic models. The main purpose of this study is to evaluate the impact of potential climate changes upon groundwater resources. A general methodology is proposed in order to disaggregate outputs of large-scale models and thus to make information directly usable by hydrologic models. As an illustration, this method is applied to a CO₂-doubling scenario through the development of a local weather generator, although many uncertainties are not yet assessed about the results of climate models. Two important hydrological variables: rainfall and potential evapotranspiration are thus generated. They are then used by coupling with a physically based hydrological model to estimate the effects of climate changes on groundwater recharge and soil moisture in the root zone. Received: 17 April 1998 / Accepted: 29 September 1998     

Projections of climate change impacts on river flood conditions in Germany by combining three different RCMs with a regional eco-hydrological model

A general increase in precipitation has been observed in Germany in the last century, and potential changes in flood generation and intensity are now at the focus of interest. The aim of the paper is twofold: a) to project the future flood conditions in Germany accounting for various river regimes (from pluvial to nival-pluvial regimes) and under different climate scenarios (the high, A2, low, B1, and medium, A1B, emission scenarios) and b) to investigate sources of uncertainty generated by climate input data and regional climate models. Data of two dynamical Regional Climate Models (RCMs), REMO (REgional Model) and CCLM (Cosmo-Climate Local Model), and one statistical-empirical RCM, Wettreg (Wetterlagenbasierte Regionalisierungsmethode: weather-type based regionalization method), were applied to drive the eco-hydrological model SWIM (Soil and Water Integrated Model), which was previously validated for 15 gauges in Germany. At most of the gauges, the 95 and 99 percentiles of the simulated discharge using SWIM with observed climate data had a good agreement with the observed discharge for 1961–2000 (deviation within ±10 %). However, the simulated discharge had a bias when using RCM climate as input for the same period. Generalized Extreme Value (GEV) distributions were fitted to the annual maximum series of river runoff for each realization for the control and scenario periods, and the changes in flood generation over the whole simulation time were analyzed. The 50-year flood values estimated for two scenario periods (2021–2060, 2061–2100) were compared to the ones derived from the control period using the same climate models. The results driven by the statistical-empirical model show a declining trend in the flood level for most rivers, and under all climate scenarios. The simulations driven by dynamical models give various change directions depending on region, scenario and time period. The uncertainty in estimating high flows and, in particular, extreme floods remains high, due to differences in regional climate models, emission scenarios and multi-realizations generated by RCMs.     

Comparison of Agricultural Impacts of Climate Change Calculated from High and Low Resolution Climate Change Scenarios: Part I. The Uncertainty Due to Spatial Scale

We investigated the effect of two different spatial scales of climate change scenarios on crop yields simulated by the EPIC crop model for corn, soybean, and wheat, in the central Great Plains of the United States. The effect of climate change alone was investigated in Part I. In Part II (Easterling et al., 2001) we considered the effects ofCO₂ fertilization effects and adaptation in addition to climate change. The scenarios were formed from five years of control and 2 ×CO₂ runs of a high resolution regional climate model (RegCM) and the same from an Australian coarse resolution general circulation model (GCM), which provided the initial and lateral boundary conditions for the regional model runs. We also investigated the effect of two different spatial resolutions of soil input parameters to the crop models. We found that for corn and soybean in the eastern part of the study area, significantly different mean yield changes were calculated depending on the scenario used. Changes in simulated dryland wheat yields in the western areas were very similar, regardless of the scale of the scenario. The spatial scale of soils had a strong effect on the spatial variance and pattern of yields across the study area, but less effect on the mean aggregated yields. We investigated what aspects of the differences in the scenarios were most important for explaining the different simulated yield responses. For instance, precipitation changes in June were most important for corn and soybean in the eastern CSIRO grid boxes. We establish the spatial scale of climate changescenarios as an important uncertainty for climate change impacts analysis.     

Aridity and the Potential Physiological Response of C 3 Crops to Doubled Atmospheric Co 2 : A Simple Demonstration of the Sensitivity of the Canadian Prairies

Since cultivated annual C₃ field crops cover about50% of the land surface of the Canadian Prairie grassland eco-climatic zone, this vegetationinfluences the aridity of the climate during the growing season. The physiological response of these cropsto a doubling of the atmospheric concentration of CO₂ may be a doubling of canopyresistance. If this physiological effect is not counteracted by interactive feedbacks, such as increasedleaf area, evapotranspiration rates could be reduced. To demonstrate the sensitivity of thearidity of the Prairie climate to this potential physiological effect, representative spring wheatgrowing-season soil moisture and Bowen ratio curves for a doubled canopy resistance(2 × CO₂) scenario were compared with a control (1 × CO₂) scenario.Lower evapotranspiration in the 2 × CO₂ scenario: (1) Increased root-zone soilmoisture levels, and (2) weakened the atmospheric component of the hydrologic cycle by raisingBowen ratios, which reduces the convective available energy, and reduces the regionalcontribution to the atmospheric water vapour over the Prairies. A weakened hydrologic cycleimplies less rainfall, and possibly, lower soil moisture levels. Thus, the net impact of a doublingof the atmospheric concentration of CO₂ on the aridity of the Canadian Prairies is uncertain.This simple sensitivity demonstration did not consider most of the potential feedback mechanisms,nor interactions of other processes. Nevertheless, the result illustrates that the physiologicaleffect should be explicitly included in climate change models for the Canadian Prairies.