Spatial Scale Effects of Climate Scenarios on Simulated Cotton Production in the Southeastern U.S.A.

We examine the effect of climate scenarios generated using results from climate models of different spatial resolution on yields simulated by the deterministic cotton model GOSSYM for the southeastern U.S.A. Two related climate change scenarios were used: a coarse-scale scenario produced from results of a general circulation model (GCM) which also provided the boundary conditions to a regional climate model (RCM), from which a fine-scale scenario was constructed. Cotton model simulations were performed for three cases: climate change alone; climate change and elevatedCO₂; climate change, elevated CO₂ and adaptations to climate change. In general, significant differences in state-average projected yield changes between the coarse and fine-scale scenarios are found for these three cases. In the first two cases, different directions of change are found in some sub-regions. With adaptation, yields substantially increase for both climate scenarios, but more so for the coarse-scale scenario (30%domain-average increase). Under irrigation, yield change differences between the two climate scenarios are small in all three cases, and yields are higher under irrigation ( 35% domain-average increase with adaptation case) compared to dryland conditions. For the climate change alone case, differences in summer water-stress levels explain the contrasts in dryland yield patterns between the coarse and fine-scale climate scenarios.     

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.     

Adaptation: Sensitivity to Natural Variability, Agent Assumptions and Dynamic Climate Changes

The role of adaptation in impact assessment and integrated assessment of climate policy is briefly reviewed. Agriculture in the US is taken as exemplary of this issue. Historic studies in which no adaptation is assumed (so-called "dumb farmer") versus farmer-agents blessed with perfect foresight (so-called "clairvoyant farmer") are contrasted, and considered limiting cases as compared to "realistic farmers." What kinds of decision rules such realistic farmer-agents would adopt to deal with climate change involves a range of issues. These include degrees of belief the climate is actually changing, knowledge about how it will change, foresight on how technology is changing, estimation of what will happen in competitive granaries and assumptions about what governmental policies will be in various regions and over time. Clearly, a transparent specification of such agent-based decision rules is essential to model adaptation explicitly in any impact assessment. Moreover, open recognition of the limited set of assumptions contained in any one study of adaptation demands that authors clearly note that each individual study can represent only a fraction of plausible outcomes. A set of calculations using the Erosion Productivity Impact Calculator (EPIC) crop model is offered here as an example of explicit decision rules on adaptive behavior on climate impacts. The model is driven by a 2xCO₂ regional climate model scenario (from which a "mock" transient scenario was devised) to calculate yield changes for farmer-agents that practice no adaptation, perfect adaptation and 20-year-lagged adaptation, the latter designed to mimic the masking effects of natural variability on farmers' capacity to see how climate is changing. The results reinforce the expectation that the likely effects of natural variability, which would mask a farmer's capacity to detect climate change, is to place the calculated impacts of climate changes in two regions of the US in between that of perfect and no adaptation. Finally, the use of so-called "hedonic" methods (in which land prices in different regions with different current average climates are used to derive implicitly farmers' adaptive responses to hypothesized future climate changes) is briefly reviewed. It is noted that this procedure in which space and time are substituted, amounts to "ergodic economics." Such cross-sectional analyses are static, and thus neglect the dynamics of both climate and societal evolution. Furthermore, such static methods usually consider only a single measure of change (local mean annual temperature), rather than higher moments like climatic variability, diurnal temperature range, etc. These implicit assumptions in ergodic economics make use of such cross-sectional studies limited for applications to integrated assessments of the actual dynamics of adaptive capacity. While all such methods are appropriate for sensitivity analyses and help to define a plausible range of outcomes, none is by itself likely to define the range of plausible adaptive capacities that might emerge in response to climate change scenarios.     

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.     

The impact of climate change on continuous corn production in the Southern U.S.A.

The Goddard Institute for Space Studies (GISS) General Circulation Model (GCM) has been used in conjunction with a field level plant process model (CERES-Maize) and a field level pesticide transport model (PRZM) to study the impacts of doubled levels of atmospheric CO₂ on various aspects of corn production in the Southern U.S.A. Grid-box scale GCM output has been applied to a 38-year time series of historical weather data at 28 different locations for several typical soil profiles throughout the South. Limitations on the use of the climate scenario in conjunction with the process models are discussed. Major shortcomings include: 1) no direct impacts of atmospheric CO₂ on plant growth and development in the plant process model; 2) neither macro-pore solute transport nor chemical decay rate response to temperature are included in the pesticide transport model; and 3) the climate change scenario output does not provide information concerning changes in temperature extremes and variability or precipitation frequency, intensity or duration. The latter are particularly critical parameters for the detailed simulation of hydrological processes. In spite of these omissions, the combination of the three models facilitates the study of the impacts of GCM modeled climate change on several inter-related agro-climatic issues of interest to agricultural policy makers. These issues include: changes in dryland and irrigated corn yields; changes in sowing and harvest dates; modification of crop water demand; and estimates of effects on pesticide losses from the soil surface and through leaching from the bottom of the active corn root zone. Model generated results which address these issues are presented but must be used with caution in light of the GCM and process model limitations. The results of this study suggest that substantial changes in agricultural production and management practices may be needed to respond to the climate changes expected to take place throughout the Southern U.S.A.     

Climatic change and grain corn yields in the North American Great Plains

A basic parametric crop yield model (YIELD) that uses climatic and environmental data to calculate yield and associated parameters for grain corn (maize) was applied to a transect through the North American Great Plains. This paper continues our examination of the impact of probable climatic change scenarios on crop evapotranspiration and irrigation requirements (Terjung et al., 1984), This study of grain corn yields showed highest yield for the first (or primary) harvest under full irrigation occurring under a sunny and cold scenario in Austin, TX, sunny and cool in Kansas City, KS, and sunny and warm in Bismarck, ND. Lowest irrigated yield was found with cloudy and hot and very dry climate change scenarios. Under rainfed-only conditions, minima were obtained under the sunny-hot and -warm scenarios and very dry conditions.J. T. Hayes is a professor at the Department of Geography and Regional Planning, State University of New York, Albany, NY, U.S.A.L. O. Mearns is also currently associated with the National Center for Atmospheric Research, funded by the National Science Foundation.Dr. Liverman is a professor at the Department of Geography, University of Wisconsin, Madison, Wis., U.S.A.     

Impacts of a Ghg-Induced Climate Change on Crop Yields: Effects of Acceleration in Maturation, Moisture Stress and Optimal Temperature

The present study involves using the Canadian Climate Centre (CCC) climate change scenario to evaluate the impacts of a CO₂-induced climate change on agriculture in Québec and vicinity. Climate change using the CCC General Circulation Model (GCM) data are fed into a crop model (FAO) so as to gauge the changes in agroclimatic factors such as growing season length and growing degree days, and subsequently potential yield changes for a variety of cereal (C₃ and C₄), leguminous, oleaginous, vegetable and special crops, for twelve major agricultural regions in southern Québec. Our results show that depending upon the agricultural zone and crop type, yields may increase (ex. corn and sorghum by 20%) or decrease (ex. wheat and soybean by 20 to 30%). Also, these crop yield changes appear to be related to acceleration in maturation rates, mainly to change in moisture stress and to shifts in optimal thermal growth conditions. These possible shifts in agricultural production potentials would solicit the formulation of appropriate adaptation strategies.     

Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa

Multiple cropping systems provide more harvest security for farmers, allow for crop intensification and furthermore influence ground cover, soil erosion, albedo, soil chemical properties, pest infestation and the carbon sequestration potential. We identify the traditional sequential cropping systems in ten sub-Saharan African countries from a survey dataset of more than 8600 households. We find that at least one sequential cropping system is traditionally used in 35% of all administrative units in the dataset, mainly including maize or groundnuts. We compare six different management scenarios and test their susceptibility as adaptation measure to climate change using the dynamic global vegetation model for managed land LPJmL. Aggregated mean crop yields in sub-Saharan Africa decrease by 6–24% due to climate change depending on the climate scenario and the management strategy. As an exception, some traditional sequential cropping systems in Kenya and South Africa gain by at least 25%. The crop yield decrease is typically weakest in sequential cropping systems and if farmers adapt the sowing date to changing climatic conditions. Crop calorific yields in single cropping systems only reach 40–55% of crop calorific yields obtained in sequential cropping systems at the end of the 21st century. The farmers’ choice of adequate crops, cropping systems and sowing dates can be an important adaptation strategy to climate change and these management options should be considered in climate change impact studies on agriculture.     

MEAN AND VARIANCE CHANGE IN CLIMATE SCENARIOS: METHODS, AGRICULTURAL APPLICATIONS, AND MEASURES OF UNCERTAINTY

Our central goal is to determine the importance of including both mean and variability changes in climate change scenarios in an agricultural context. By adapting and applying a stochastic weather generator, we first tested the sensitivity of the CERES-Wheat model to combinations of mean and variability changes of temperature and precipitation for two locations in Kansas. With a 2°C increase in temperature with daily (and interannual) variance doubled, yields were further reduced compared to the mean only change. In contrast, the negative effects of the mean temperature increase were greatly ameliorated by variance decreased by one-half. Changes for precipitation are more complex, since change in variability naturally attends change in mean, and constraining the stochastic generator to mean change only is highly artificial. The crop model is sensitive to precipitation variance increases with increased mean and variance decreases with decreased mean. With increased mean precipitation and a further increase in variability Topeka (where wheat cropping is not very moisture limited) experiences decrease in yield after an initial increase from the 'mean change only case. At Goodland Kansas, a moisture-limited site where summer fallowing is practiced, yields are decreased with decreased precipitation, but are further decreased when variability is further reduced. The range of mean and variability changes to which the crop model is sensitive are within the range of changes found in regional climate modeling (RegCM) experiments for a CO₂ doubling (compared to a control run experiment). We then formed two types of climate change scenarios based on the changes in climate found in the control and doubled CO₂ experiments over the conterminous U. S. of RegCM: (1) one using only mean monthly changes in temperature, precipitation, and solar radiation; and (2) another that included these mean changes plus changes in daily (and interannual) variability. The scenarios were then applied to the CERES-Wheat model at four locations (Goodland, Topeka, Des Moines, Spokane) in the United States. Contrasting model responses to the two scenarios were found at three of the four sites. At Goodland, and Des Moines mean climate change increased mean yields and decreased yield variability, but the mean plus variance climate change reduced yields to levels closer to their base (unchanged) condition. At Spokane mean climate change increased yields, which were somewhat further increased with climate variability change. Three key aspects that contribute to crop response are identified: the marginality of the current climate for crop growth, the relative size of the mean and variance changes, and timing of these changes. Indices for quantifying uncertainty in the impact assessment were developed based on the nature of the climate scenario formed, and the magnitude of difference between model and observed values of relevant climate variables.     

A Summary of Climate Change Impact Assessments from the U.S. Country Studies Program

Forty-nine countries participating in the U.S. Country Studies Program (USCSP) assessed climate change impacts in one or more of eight sectors: coastal resources, agriculture, grasslands/livestock, water resources, forests, fisheries, wildlife, and health. The studies were generally limited to analysis of first order biophysical effects, e.g., coastal inundation, crop yield, and runoff changes. There were some limited studies of adaptation. We review and synthesize the results of the impact assessments conducted under the USCSP. The studies found that sea level rise could cause substantial inundation and erosion of valuable lands, but, protecting developed areas would be economically sound. The studies showed mixed results for changes in crop yields, with a tendency toward decreased yields in African and Asian countries, particularly southern Asian countries, and mixed results in European and Latin American countries. Adaptation could significantly affect yields, but it is not clear whether the adaptations are affordable or feasible. The studies tend to show a high sensitivity of runoff to climate change, which could result in increases in droughts or floods. The impacts on grasslands and livestock are mixed, but there appears to be a large capacity for adaptation. Human health problems could increase, particularly for populations in low-latitude countries with inadequate access to health care. The USCSP assessments found that the composition of forests is likely to change, while biomass could be reduced. Some wildlife species were estimated to have reduced populations. The major contribution of the USCSP was in building capacity in developing countries to assess potential climate impacts. However, many of the studies did not analyze the implications of biophysical impacts of climate change on socioeconomic conditions, cross-sectoral integration of impacts, autonomous adaptation, or proactive adaptation. Follow-on work should attempt to develop capacity in developing and transition countries to conduct more integrated studies of climate change impacts.     

Yield Variability as Influenced by Climate: A Statistical Investigation

One of the issues with respect to climate change involves its influence on the distribution of future crop yields. Many studies have been done regarding the effect on the mean of such distributions but few have addressed the effect on variance. Furthermore, those that have been done generally report the variance from crop simulators, not from observations. This paper examines the potential effects of climate change on crop yield variance in the context of current observed yields and then extrapolates to the effects under projected climate change. In particular, maximum likelihood panel data estimates of the impacts of climate on year-to-year yield variability are constructed for the major U.S. agricultural crops. The panel data technique used embodies a variance estimate developed along the lines of the stochastic production function approach suggested by Just and Pope. The estimation results indicate that changes in climate modify crop yield levels and variances in a crop-specific fashion. For sorghum, rainfall and temperature increases are found to increase yield level and variability. On the other hand, precipitation and temperature are individually found to have opposite effects on corn yield levels and variability.     

Effects of climate change on grain maize yield potential in the european community

Grain maize yield in the main arable areas of the European Community (E.C.) was calculated with a simulation model, WOFOST, using historical weather data and average soil characteristics. The sensitivity of the model to individual weather variables was determined. Subsequent analyses were made using climate change scenarios with and without the direct effects of increased atmospheric CO₂. The impact of crop management (sowing date, irrigation and cultivar type) in a changed climate was also assessed. Scenario climate change generally results in larger grain yields for the northern E.C., similar or slightly smaller yields for the central E.C. and considerably smaller yields for the southern E.C. The various climate change scenarios used appear to give considerably different changes in grain yield, both for each location and for the E.C. as a whole. Management analyses show that for both current and scenario climates the largest grain yield will be attained by varieties with an early start of grain filling, that average irrigation requirements to attain potential grain yield in the E.C. will increase with climate change but will decrease with both increased CO₂ and climate change, and that sowing at both current and scenarios climate should occur as early as possible.The U.S. Government right to retain a nonexclusive, royalty-free licence in and to any copyright is acknowledged.     

Response of Soybean and Sorghum to Varying Spatial Scales of Climate Change Scenarios in the Southeastern United States

This study examines how uncertainty associated with the spatial scale of climate change scenarios influences estimates of soybean and sorghum yield response in the southeastern United States. We investigated response using coarse (300-km, CSIRO) and fine (50-km, RCM) scale climate change scenarios and considering climate changes alone, climate changes with CO₂ fertilization, and climate changes with CO₂ fertilization and adaptation. Relative to yields simulatedunder a current, control climate scenario, domain-wide soybean yield decreased by 49% with the coarse-scale climate change scenario alone, and by26% with consideration for CO₂ fertilization. By contrast, thefine-scale climate change scenario generally exhibited higher temperatures and lower precipitation in the summer months resulting in greater yield decreases (69% for climate change alone and 54% with CO₂fertilization). Changing planting date and shifting cultivars mitigated impacts, but yield still decreased by 8% and 18% respectively for the coarse andfine climate change scenarios. The results were similar for sorghum. Yield decreased by 51%, 42%, and 15% in response to fine-scaleclimate change alone, CO₂ fertilization, and adaptation cases, respectively– significantly worse than with the coarse-scale (CSIRO) scenarios. Adaptation strategies tempered the impacts of moisture and temperature stress during pod-fill and grain-fill periods and also differed with respect to the scale of the climate change scenario.     

An assessment of regional vulnerability of rice to climate change in India

A simulation analysis was carried out using the InfoCrop-rice model to quantify impacts and adaptation gains, as well as to identify vulnerable regions for irrigated and rain fed rice cultivation in future climates in India. Climates in A1b, A2, B1 and B2 emission scenarios as per a global climate model (MIROC3.2.HI) and a regional climate model (PRECIS) were considered for the study. On an aggregated scale, the mean of all emission scenarios indicate that climate change is likely to reduce irrigated rice yields by ~4 % in 2020 (2010–2039), ~7 % in 2050 (2040–2069), and by ~10 % in 2080 (2070–2099) climate scenarios. On the other hand, rainfed rice yields in India are likely to be reduced by ~6 % in the 2020 scenario, but in the 2050 and 2080 scenarios they are projected to decrease only marginally (<2.5 %). However, spatial variations exist for the magnitude of the impact, with some regions likely to be affected more than others. Adaptation strategies comprising agronomical management can offset negative impacts in the near future—particularly in rainfed conditions—but in the longer run, developing suitable varieties coupled with improved and efficient crop husbandry will become essential. For irrigated rice crop, genotypic and agronomic improvements will become crucial; while for rainfed conditions, improved management and additional fertilizers will be needed. Basically climate change is likely to exhibit three types of impacts on rice crop: i) regions that are adversely affected by climate change can gain in net productivity with adaptation; ii) regions that are adversely affected will still remain vulnerable despite adaptation gains; and iii) rainfed regions (with currently low rainfall) that are likely to gain due to increase in rainfall can further benefit by adaptation. Regions falling in the vulnerable category even after suggested adaptation to climate change will require more intensive, specific and innovative adaptation options. The present analysis indicates the possibility of substantial improvement in yields with efficient utilization of inputs and adoption of improved varieties.     

Climate Change and Rice Yields in Diverse Agro-Environments of India. II. Effect of Uncertainties in Scenarios and Crop Models on Impact Assessment

Estimates of impact of climate change on crop production could be biased depending upon the uncertainties in climate change scenarios, region of study, crop models used for impact assessment and the level of management. This study reports the results of a study where the impact of various climate change scenarios has been assessed on grain yields of irrigated rice with two popular crop simulation models- Ceres-Rice and ORYZA1N at different levels of N management. The results showed that the direct effect of climate change on rice crops in different agroclimatic regions in India would always be positive irrespective of the various uncertainties. Rice yields increased between 1.0 and 16.8% in pessimistic scenarios of climate change depending upon the level of management and model used. These increases were between 3.5 and 33.8% in optimistic scenarios. At current as well as improved level of management, southern and western parts of India which currently have relatively lower temperatures compared to northern and eastern regions, are likely to show greater sensitivity in rice yields under climate change. The response to climate change is small at low N management compared to optimal management. The magnitude of this impact can be biased upto 32% depending on the uncertainty in climate change scenario, level of management and crop model used. These conclusions are highly dependent on the specific thresholds of phenology and photosynthesis to change in temperature used in the models. Caution is needed in using the impact assessment results made with the average simulated grain yields and mean changes in climatic parameters.     

Climate change impacts on dryland cropping systems in the Central Great Plains,USA

Agricultural systems models are essential tools to assess potential climate change (CC) impacts on crop production and help guide policy decisions. In this study, impacts of projected CC on dryland crop rotations of wheat-fallow (WF), wheat-corn-fallow (WCF), and wheat-corn-millet (WCM) in the U.S. Central Great Plains (Akron, Colorado) were simulated using the CERES V4.0 crop modules in RZWQM2. The CC scenarios for CO₂, temperature and precipitation were based on a synthesis of Intergovernmental Panel on Climate Change (IPCC 2007) projections for Colorado. The CC for years 2025, 2050, 2075, and 2100 (CC projection years) were super-imposed on measured baseline climate data for 15–17 years collected during the long-term WF and WCF (1992–2008), and WCM (1994–2008) experiments at the location to provide inter-annual variability. For all the CC projection years, a decline in simulated wheat yield and an increase in actual transpiration were observed, but compared to the baseline these changes were not significant (p > 0.05) in all cases but one. However, corn and proso millet yields in all rotations and projection years declined significantly (p < 0.05), which resulted in decreased transpiration. Overall, the projected negative effects of rising temperatures on crop production dominated over any positive impacts of atmospheric CO₂ increases in these dryland cropping systems. Simulated adaptation via changes in planting dates did not mitigate the yield losses of the crops significantly. However, the no-tillage maintained higher wheat yields than the conventional tillage in the WF rotation to year 2075. Possible effects of historical CO₂ increases during the past century (from 300 to 380 ppm) on crop yields were also simulated using 96 years of measured climate data (1912–2008) at the location. On average the CO₂ increase enhanced wheat yields by about 30%, and millet yields by about 17%, with no significant changes in corn yields.     

How is CO2 affecting yields and technological progress? A statistical analysis

This paper analyzes the impact of climate, crop production technology, and atmospheric carbon dioxide (CO₂) on current and future crop yields. The analysis of crop yields endeavors to advance the literature by estimating the effect of atmospheric CO₂ on observed crop yields. This is done using an econometric model estimated over pooled historical data for 1950–2009 and data from the free air CO₂ enrichment experiments. The main econometric findings are: 1) Yields of C3 crops (soybeans, cotton, and wheat) directly respond to the elevated CO₂, while yields of C4 crops (corn and sorghum) do not, but they are found to indirectly benefit from elevated CO₂ in times and places of drought stress; 2) The effect of technological progress on mean yields is non-linear; 3) Ignoring atmospheric CO₂ in an econometric model of crop yield likely leads to overestimates of the pure effects of technological progress on crop yields of about 51, 15, 17, 9, and 1 % of observed yield gain for cotton, soybeans, wheat, corn and sorghum, respectively; 4) Average climate conditions and climate variability contribute in a statistically significant way to average crop yields and their variability; and 5) The effect of CO₂ fertilization generally outweighs the effect of climate change on mean crop yields in many regions resulting in an increase of 7–22, 4–47, 5–26, 65–96, and 3–35 % for yields of corn, sorghum, soybeans, cotton, and wheat, respectively.     

Climate Change Impacts on the Potential Productivity of Corn and Winter Wheat in Their Primary United States Growing Regions

We calculate the impacts of climate effects inferred from three atmospheric general circulation models (GCMs) at three levels of climate change severity associated with change in global mean temperature (GMT) of 1.0, 2.5 and 5.0 °C and three levels of atmospheric CO2 concentration ([CO2]) – 365 (no CO2 fertilization effect), 560 and 750 ppm – on the potential production of dryland winter wheat (Triticum aestivum L.) and corn (Zea mays L.) for the primary (current) U.S. growing regions of each crop. This analysis is a subset of the Global Change Assessment Model (GCAM) which has the goal of integrating the linkages and feedbacks among human activities and resulting greenhouse gas emissions, changes in atmospheric composition and resulting climate change, and impacts on terrestrial systems. A set of representative farms was designed for each of the primary production regions studied and the Erosion Productivity Impact Calculator (EPIC) was used to simulate crop response to climate change. The GCMs applied were the Goddard Institute of Space Studies (GISS), the United Kingdom Meteorological Transient (UKTR) and the Australian Bureau of Meteorological Research Center (BMRC), each regionalized by means of a scenario generator (SCENGEN). The GISS scenarios have the least impact on corn and wheat production, reducing national potential production for corn by 6% and wheat by 7% at a GMT of 2.5 °C and no CO2 fertilization effect; the UKTR scenario had the most severe impact on wheat, reducing production by 18% under the same conditions; BMRC had the greatest negative impact on corn, reducing production by 20%. A GMT increase of 1.0°C marginally decreased corn and wheat production. Increasing GMT had a detrimental impact on both corn and wheat production, with wheat production suffering the greatest losses. Decreases for wheat production at GMT 5.0 and [CO2] = 365 ppm range from 36% for the GISS to 76% for the UKTR scenario. Increases in atmospheric [CO2] had a positive impact on both corn and wheat production. AT GMT 1.0, an increase in [CO2] to 560 ppm resulted in a net increase in corn and wheat production above baseline levels (from 18 to 29% for wheat and 2 to 5% for corn). Increases in [CO2] help to offset yield reductions at higher GMT levels; in most cases, however, these increases are not sufficient to return crop production to baseline levels.     

A new scenario framework for climate change research: the concept of shared climate policy assumptions

The new scenario framework facilitates the coupling of multiple socioeconomic reference pathways with climate model products using the representative concentration pathways. This will allow for improved assessment of climate impacts, adaptation and mitigation. Assumptions about climate policy play a major role in linking socioeconomic futures with forcing and climate outcomes. The paper presents the concept of shared climate policy assumptions as an important element of the new scenario framework. Shared climate policy assumptions capture key policy attributes such as the goals, instruments and obstacles of mitigation and adaptation measures, and introduce an important additional dimension to the scenario matrix architecture. They can be used to improve the comparability of scenarios in the scenario matrix. Shared climate policy assumptions should be designed to be policy relevant, and as a set to be broad enough to allow a comprehensive exploration of the climate change scenario space.