Land‐atmosphere coupling and climate prediction over the U.S. Southern Great Plains. Issue 20 (28th October 2016)

• Record Type: Journal Article
• Authors: Williams, Ian N.; Lu, Yaqiong; Kueppers, Lara M.; Riley, William J.; Biraud, Sebastien C.; Bagley, Justin E.; Torn, Margaret S.
• Is Part Of: Journal of geophysical research. Volume 121:Issue 20(2016)
• Page Start: 12, 125
• Page End: 12, 144
• Publication Date: 2016-10-28
• Subjects: land‐atmosphere -- evapotranspiration -- soil moisture -- vegetation -- feedback; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2016JD025223
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Biases in land‐atmosphere coupling in climate models can contribute to climate prediction biases, but land models are rarely evaluated in the context of this coupling. We tested land‐atmosphere coupling and explored effects of land surface parameterizations on climate prediction in a single‐column version of the National Center for Atmospheric Research Community Earth System Model (CESM1.2.2) and an off‐line Community Land Model (CLM4.5). The correlation between leaf area index (LAI) and surface evaporative fraction (ratio of latent to total turbulent heat flux) was substantially underpredicted compared to observations in the U.S. Southern Great Plains, while the correlation between soil moisture and evaporative fraction was overpredicted by CLM4.5. To estimate the impacts of these errors on climate prediction, we modified CLM4.5 by prescribing observed LAI, increasing soil resistance to evaporation, increasing minimum stomatal conductance, and increasing leaf reflectance. The modifications improved the predicted soil moisture‐evaporative fraction (EF) and LAI‐EF correlations in off‐line CLM4.5 and reduced the root‐mean‐square error in summer 2 m air temperature and precipitation in the coupled model. The modifications had the largest effect on prediction during a drought in summer 2006, when a warm bias in daytime 2 m air temperature was reduced from +6°C to a smaller cold bias of −1.3°C, and a corresponding dry bias in precipitation was reduced from −111 mm to −23 mm. The role of vegetation in droughts and heat waves is underpredicted in CESM1.2.2, and improvements in land surface models can improve prediction of climate extremes. Key Points: Surface energy partitioning is too strongly correlated with soil moisture in CESM1.2.2 Surface energy partitioning is too weakly dependent on vegetation state compared to observations Improving the terrestrial segment of land‐atmosphere coupling improved summer climate prediction