Publication Abstracts

Mearns et al. 1997

Mearns, L.O., C. Rosenzweig, and R. Goldberg, 1997: Mean and variance change in climate scenarios: Methods, agricultural applications, and measures of uncertainty. Climatic Change, 35, 367-396, doi:10.1023/A:1005358130291.

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 variances 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 reginal 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 coterminous 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.

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BibTeX Citation

  author={Mearns, L. O. and Rosenzweig, C. and Goldberg, R.},
  title={Mean and variance change in climate scenarios: Methods, agricultural applications, and measures of uncertainty},
  journal={Climatic Change},

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RIS Citation

ID  - me08000d
AU  - Mearns, L. O.
AU  - Rosenzweig, C.
AU  - Goldberg, R.
PY  - 1997
TI  - Mean and variance change in climate scenarios: Methods, agricultural applications, and measures of uncertainty
JA  - Climatic Change
JO  - Climatic Change
VL  - 35
SP  - 367
EP  - 396
DO  - 10.1023/A%3A1005358130291
ER  -

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