The issue of spatial scale in integrated assessments: an example of agriculture in the Southeastern U.S

The climate change impacts community has long bemoaned the coarse (100s of kilometers) resolution of climate change scenarios made available for impacts assessments and integrated assessments. Higher resolution scenarios (10s of kilometers) long have been demanded. There are now techniques available (regional climate modeling and statistical downscaling) for generating high resolution climate change scenarios. However, there has been little research indicating whether these scenarios result in important differences in the calculations of climate change impacts or in integrated assessments. As part of an integrated assessment of agriculture in the Southeastern United States, we are examining the effect of the spatial scale of climate change scenarios at specific levels of aggregation necessary for inputing the changes in crop yields into an agricultural economic model.

We formed two different resolutions of climate change scenarios from the control and doubled CO2 equilibrium experiments of a general circulation model (the Australian CSIRO model) and from experiments with a regional climate model (RegCM2) that was driven by the boundary conditions from the CSIRO model. We then used these scenarios to drive a number of crop models that represented the major crops produced in the southeastern US (wheat, corn, rice, sorghum, soybean, cotton). We ran three different cases for each climate change scenario: 1) climate change only; 2) climate change plus direct CO2 effect; 3) case 2 plus management adaptations (e.g., changed cultivars and planting dates).

We found that significantly different changes in yield resulted from the two different scenarios, when calculated on the common 50 km grid of the regional climate model, for all three cases. In the climate change only case for most crops, yield decreased for the two scenarios, but decreases were greater when determined from the regional climate scenario. Simulated cotton yields, however, increased for both scenarios. We then aggregated the yield results to the economic units (usually states) required for use in the Agricultural Sector Model (ASM), and found that for some states the significantly different results persisted. We will also report on the results from the application of these yield changes to the ASM.