1.3 Land-atmosphere coupling and climate prediction over the U.S. Southern Great Plains

Monday, 20 June 2016: 9:00 AM
Arches (Sheraton Salt Lake City Hotel)
Ian N. Williams, Lawrence Berkeley National Laboratory, Berkeley, CA; and Y. Lu, J. E. Bagley, L. M. Kueppers, S. C. Biraud, and M. S. Torn

It has been shown that many climate models do not correctly represent land-atmosphere coupling, and we hypothesize that this contributes to climate prediction biases. We tested land-atmosphere coupling and explored effects of land surface parameterizations on climate prediction in a single-column version of the NCAR Community Earth System Model (CESM) and a corresponding offline version of the Community Land Model (CLM). The correlation between leaf area index (LAI) and evaporative fraction (ratio of latent to total turbulent heat flux) was substantially underpredicted compared to 10 years of observations in the U.S. Southern Great Plains, while the correlation between soil moisture and evaporative fraction was overpredicted by CLM. This coupling was improved by prescribing observed LAI, increasing soil and decreasing vegetation resistances to evapotranspiration, and increasing leaf reflectance. The primary control on evaporative fraction shifted from soil to vegetation between the default and modified CESM, and this change reduced the problem of excessively warm daytime temperatures in dry periods having depleted near-surface soil moisture. The modifications reduced the root mean squared error (RMSE) in daytime 2 m air temperature from 3.6 to 2°C in summer (JJA), and reduced RMSE in total JJA precipitation from 133 to 84 mm. The parameters had the largest effect on summer drought in 2006, when a warm bias in daytime 2 m air temperature was reduced from +6°C to -1.3°C, and a corresponding dry bias in total JJA precipitation was reduced from -111 mm to -23 mm. These results indicate that the role of vegetation in droughts and heat waves may be underpredicted in CESM, and that improvements in land surface models can improve prediction of climate extremes.
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