2000 Progress Report: Sensitivity Analysis of the Effect of Changes in Mean and Variability of Climate on Crop Production and Regional Economics in the Southeastern U.S.

EPA Grant Number: R824997
Title: Sensitivity Analysis of the Effect of Changes in Mean and Variability of Climate on Crop Production and Regional Economics in the Southeastern U.S.
Investigators: Mearns, Linda , Adams, Richard , Carbone, Greg , Easterling, William Ewart , Katz, R. , McCarl, B.
Institution: National Center for Atmospheric Research , Oregon State University , Pennsylvania State University , Texas A & M University , University of South Carolina at Columbia
EPA Project Officer: Chung, Serena
Project Period: November 1, 1996 through October 31, 1999 (Extended to September 30, 2001)
Project Period Covered by this Report: November 1, 1999 through October 31, 2000
Project Amount: $1,200,901
RFA: Global Climate (1996) RFA Text |  Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Global Climate Change , Climate Change


This project is investigating the effects of: (1) the spatial scale of climate change scenarios; and (2) changes in interannual (and daily) climatic variability compared to the effects of changes in mean climate on crop production in the Southeastern United States. We will determine differences in crop responses to several types of possible future climate: two from a control and doubled CO2 experiment of a high resolution regional climate model, and two from the coarse resolution general circulation model (GCM) that provided boundary conditions for the regional model. Two subtypes of scenarios are being developed from these runs: one including only mean changes in the relevant climate variables, and another including both mean and variability changes. We are applying the scenarios to crop models (i.e., CERES, CROPGRO, and EPIC), which are being run for conditions of: (1) climate change only; (2) climate change and direct CO2 effects; and (3) climate change, direct CO2 effects, and management adaptations. The baseline simulated crop responses are being determined from a dense network of about 500 climate stations, organized in a GIS framework, as are other inputs to the crop models.

We also are analyzing the effect of large scale circulation indices on daily climatological characteristics of the region. Such phenomena as the El Nino Southern Oscillation (ENSO), the North Atlantic Oscillation, and the Bermuda High Index are being considered. We may develop sensitivity experiments derived from stochastically generated changes in these circulation features and use these as variability change scenarios.

Resulting changes in mean and variability of yield from the different scenarios will provide input to an agricultural sector economic model (ASM) for evaluation of economic sensitivity to the different sets of yield changes. The overarching goal of this research is to establish the sensitivity of the regional economics to changes in crop yields resulting from a range of changes in mean and variability of climate at different spatial resolutions.

Progress Summary:

I. Spatial Application of Crop Models

Background?Previous Accomplishments. In the prior year, CERES and EPIC crop models were applied to the two spatial scales of climate changes scenarios for climate change only and climate change plus CO2 fertilization effects. CERES models included: corn, rice, sorghum, soybean, and wheat. Also, one cotton model, COTTAM was used. EPIC models included corn, soybean, wheat, and clover. The level of CO2 used in the crop models was 330 ppm for current conditions, the same for the climate change only case, and 540 ppm for the climate change plus CO2 effect. Thirty-six years (1960-1995) of crop yields were produced at each grid point. Interesting contrasts in the crop model response to the spatial scale of scenarios were noted.

Changes in Simulated Crop Yields With Adaptations. Adaptation runs were completed this year. The adaptations used were changes in sowing dates and changes in variety, for both the coarse and fine scale climate change scenarios. The CO2 fertilization effect was assumed in the adaptation runs.

CERES Models. We examined the response of the CERES crop models with adaptations on three different spatial scales: at the level of the 50 km grid scale (the scale of the observations and regional model), the scale of the entire region (Southeast), and the scale at which the yield results were aggregated for input to the ASM (state level). In most cases, the adaptation results produced better yields than did the climate change plus CO2 fertilization effect. With adaptation domain wide percentage changes in yield for corn and rice are positive for both scenarios. Soybean yields now increase for the coarse scale scenario (CSIRO), but still decrease for the fine scale scenario (RegCM). Wheat, which showed the least change in yield with adaptation, remained negative, as did sorghum.

When we considered the changes in yield aggregated at the state level (the level of aggregation needed for the economic model), contrasts were more complex. For example, with adaptation, corn yields increased for most southern states for both scenarios, but still decreased for North and South Carolina and Tennessee for the fine scale scenario. Also, contrasts in yield changes between the scenarios decreased under the adaptation case, indicating that adaptations decrease the unique effects of the climate change scenarios.

EPIC Models. A different approach was taken for EPIC adaptations. The effect of transient climate change was mimiced by dividing up the climate change into discrete periods. Agronomic adaptations were lagged behind evolving climate changes to simulate farmer dependence on historical climate information in making current management decisions. Results show clearly that adaptation is less effective when the assumption of farmer "clairvoyance" is relaxed.

Application of a New Cotton Model (GOSSYM). Initially, the cotton model COTTAM was applied to the climate change scenarios. This model resulted in very large yield increases with climate change. After consultation with other crop modelers and cotton experts, we determined that the response of COTTAM to the climate change scenarios was likely unrealistic. We decided to apply a different cotton model, GOSSYM, which we believe provides a more realistic response to the climate changes. This is a much more detailed model, which has been updated recently to be particularly suited for climate change research (R. Reddy, personal communication).

In comparing the results of COTTAM with those of GOSSYM, we note that the baseline yields are very similar for both models, except for the far northern states, Tennessee and North Carolina, where the GOSSYM yields are lower. The GOSSYM model is rather sensitive to temperature thresholds, and tends to produce rather low yields in areas that may be borderline for cotton production from the point of view of temperature. For most states, the percentage change in yield, regardless of scenario, is much lower for the GOSSYM model compared to the COTTAM model. Exceptions include Tennessee and North Carolina, where the baseline yields for GOSSYM are relatively low. Here, the improved temperature environment with climate change results in rather large increases in yield, and hence high percentage increases.

The adaptation runs for GOSSYM are now almost complete, and interestingly, indicate relatively high increases in yield (compared to the other crops), for example, around 50 increase domain wide for the RegCM scenario.

II. Economic Modelling?ASM

Crop Yield Changes for the Rest of the U.S. To usefully model the response of the ASM to the different sets of changes in yield, changes in yield in the rest of the United States were needed, as well as changes in yields of crops necessary for the economic model, but not explicitly modeled in our study. In the prior year, we developed a coarser resolution observed climate data set for the rest of the United States, and developed two different scales of climate change scenarios based on the CSIRO results over the rest of the United States and based on earlier runs of the RegCM for the western two-thirds of the United States (Giorgi, et al., 1998) and the Great Lakes region (G. Bates, personal communication), which also used the boundary conditions from the CSIRO.

This year we improved on that data set and made the station representation denser and thus produced a much better spatial representation of the other regions of the United States. We then applied these scenarios to the appropriate CERES crop models for the different units of the United States (usually states) used in the economic model. The changes in yields for crops we did not explicitly consider (e.g., citrus, tomatoes, potatoes) were determined via a proxy method based on other crop simulations for similar studies of agricultural crop yield change over the United States.

Final Crop Yield Input for ASM. Finally, percentage changes in crop yields for all 63 primary spatial units of the ASM were produced, based on the two different climate change scenario resolutions. So far, only the case of crop yield changes from the climate change plus direct CO2 effects cases have been considered for economic modeling. We will be submitting the crop yield changes for the adaptation results within the next month.

ASM Results

Effects of Mean Yield Changes. The results reported here are those using the COTTAM model. Newest results using the crop yield changes from the GOSSYM model are almost complete, and will be used for ASM input in the next few weeks.

On a country-wide basis, the coarse scale scenario results in increased total surplus for the agricultural sector, but the fine resolution produces a small decrease. The change in total surplus depends primarily on the relative change in welfare based on the changes in consumer surplus and producer surplus.

Regional index numbers for the total value of production, which is a measure of economic activity within the regions, show interesting contrasts across the regions, based on the scenarios. The Southeast and Appalachia show the largest decreases in activity for both scenarios, but the decrease with the fine scale scenario is much larger. In the Northeast, the coarse scale scenario results in a gain in activity, while the fine scale scenario shows a slight decrease. Note also that considering all regions, there is a greater variability of regional response with the fine scale scenario compared to the coarse scale.

Our analysis so far indicates that the spatial scale of climate change scenario substantially affects the simulation of changes in crop yields on various levels of spatial aggregation. Moreover, we have demonstrated that these contrasts in changes in yield are substantial enough to affect the results of an agricultural economic model both on national and regional levels.

Additional Effect of Changes in Variability of Yield. The effect of yield variability was incorporated into the ASM. This version of the ASM is referred to as the Stochastic Programming with Recourse ASM or SPARM.

The inclusion of risk (variability) into the analysis does not change the basic direction of total welfare effects. Specifically, the results discussed above with respect to the non-stochastic model outcomes of the RegCM (negative change in economic welfare) and CSIRO (of positive change in economic welfare) are also observed in the stochastic case. However, the magnitudes of these effects are increased as a result of including yield variability in the economic model, with the CSIRO total welfare change now increasing to approximately $5.6 billion (from $2.547 billion) and the economic loss associated with the RegCM increases from $-0.156 billion to from $-0.420 billion.

III. Conditional Stochastic Models for Daily Weather in Southeast U.S

Work During Past Year. Claudia Tebaldi visited The National Research Center for Statistics and the Environment (NRCSE), an activity primarily funded by EPA. In collaboration with NRCSE researchers, she investigated ways in which the conditional stochastic model for daily precipitation amount given the state of a large-scale atmospheric circulation index could be improved. The only substantial improvement arises if the Bermuda High Index (BHI) of pressure is changed from a monthly to a daily time scale. This improvement, unfortunately, only comes at the price of no longer conditioning on low frequency information alone. A technical report summarizing this work was produced (Tebaldi, 2000).

Work in Progress. The only remaining work will involve writing up a journal article, summarizing the research performed over the past few years on the topic of conditional stochastic modeling of daily weather in the Southeast United States (Katz, et al., in preparation).

Future Activities:

In the remaining time of the project we plan to: (1) run the ASM and SPARM with the new CO2 fertilization effect results using the GOSSYM cotton model results instead of COTTAM; (2) run the ASM and SPARM with the adaptation crop runs (including the GOSSYM cotton model); (3) quantitatively examine, through detailed statistical techniques, the significance of mean changes in yield, and contrasts in the spatial variability of simulated yields, and also determine which aspects of the contrasts in the climate change scenarios are most responsible for the contrasts in the change in yield; (4) complete comparison between CERES and EPIC crop model results; and (5) complete the formation of variance change scenarios, apply to the crop models and then apply the resultant changes in yields to the ASM.

Journal Articles:

No journal articles submitted with this report: View all 38 publications for this project

Supplemental Keywords:

global climate, regionalization, general circulation model, climate model, southeast, agriculture., RFA, Scientific Discipline, Air, Geographic Area, climate change, Economics, Southeast, Atmospheric Sciences, Ecological Risk Assessment, Agronomy, environmental monitoring, sensitivity analysis, economic models, green house gas concentrations, regional ecosystems, socioeconomic indicators, carbon dioxide, circulation model, climate models, CO2 concentrations, GENESIS climate model, environmental stressors, agriculture, ecosystem sustainability, climate variability, crop production

Relevant Websites:

http://www.esig.ucar.edu/asr00 Exit EPA icon (click on Enhancing Productivity and Resilience of Natural Resources, then on Climate Variability and Agriculture in the Southeast). This site provides the project overview.

http://www.esig.ucar.edu/pi Exit EPA icon (this site about the Southeast Project is under construction)

Progress and Final Reports:

Original Abstract
  • 1997
  • 1998
  • 1999 Progress Report
  • Final Report