ALBERT

Notes: : Water from the Missouri River Basin is used for multiple purposes. The climatic change of doubling the atmospheric carbon dioxide may produce dramatic water yield changes across the basin. Estimated changes in basin water yield from doubled CO2 climate were simulated using a Regional Climate Model (RegCM) and a physically based rainfall-runoff model. RegCM output from a five-year, equilibrium climate simulation at twice present CO2 levels was compared to a similar present-day climate run to extract monthly changes in meteorologic variables needed by the hydrologic model. These changes, simulated on a 50-km grid, were matched at a commensurate scale to the 310 subbasin in the rainfall-runoff model climate change impact analysis. The Soil and Water Assessment Tool (SWAT) rainfall-runoff model was used in this study. The climate changes were applied to the 1965 to 1989 historic period. Overall water yield at the mouth of the Basin decreased by 10 to 20 percent during spring and summer months, but increased during fall and winter. Yields generally decreased in the southern portions of the basin but increased in the northern reaches. Northern subbasin yields increased up to 80 percent: equivalent to 1.3 cm of runoff on an annual basis.

Notes: Abstract A nested regional climate model is used to generate a scenario of climate change over the MINK region (Missouri, Iowa, Nebraska, Kansas) due to doubling of carbon dioxide concentration (2 × CO2) for use in agricultural impact assessment studies. Five-year long present day (control) and 2 × CO2 simulations are completed at a horizontal grid point spacing of 50 km. Monthly and seasonal precipitation and surface air temperature over the MINK region are reproduced well by the model in the control run, except for an underestimation of both variables during the spring months. The performance of the nested model in the control run is greatly improved compared to a similar experiment performed with a previous version of the nested modeling system by Giorgi et al. (1994). The nested model generally improves the simulation of spatial precipitation patterns compared to the driving general circulation model (GCM), especially during the summer. Seasonal surface warming of 4 to 6 K and seasonal precipitation increases of 6 to 24% are simulated in 2 × CO2 conditions. The control run temperature biases are smaller than the simulated changes in all seasons, while the precipitation biases are of the same order of magnitude as the simulated changes. Although the large scale patterns of change in the driving GCM and nested RegCM model are similar, significant differences between the models, and substantial spatial variability, occur within the MINK region.

Notes: Abstract 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.

Notes: Abstract We investigate the effect of changes in daily and interannual variability of temperature and precipitation on yields simulated by the CERES-Wheat model at two locations in the central Great Plains. Changes in variability were effected by adjusting parameters of the Richardson daily weather generator. Two types of changes in precipitation were created: one with both intensity and frequency changed; and another with change only in persistence. In both types mean total monthly precipitation is held constant. Changes in daily (and interannual) variability of temperature result in substantial changes in the mean and variability of simulated wheat yields. With a doubling of temperature variability, large reductions in mean yield and increases in variability of yield result primarily from crop failures due to winter kill at both locations. Reduced temperature variability has little effect. Changes in daily precipitation variability also resulted in substantial changes in mean and variability of yield. Interesting interactions of the precipitation variability changes with the contrasting base climates are found at the two locations. At one site where soil moisture is not limiting, mean yield decreased and variability of yield increased with increasing precipitation variability, whereas mean yields increased at the other location, where soil moisture is limiting. Yield changes were similar for the two different types of precipitation variability change investigated. Compared to an earlier study for the same locations wherein variability changes were effected by altering observed time series, and the focus was on interannual variability, the present results for yield changes are much more substantial. This study demonstrates the importance of taking into account change in daily (and interannual) variability of climate when analyzing the effect of climate change on crop yields.

Notes: Abstract 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.