Articles | Volume 15, issue 6
Geosci. Model Dev., 15, 2365–2393, 2022
https://doi.org/10.5194/gmd-15-2365-2022
Geosci. Model Dev., 15, 2365–2393, 2022
https://doi.org/10.5194/gmd-15-2365-2022
Development and technical paper
21 Mar 2022
Development and technical paper | 21 Mar 2022

Impacts of a revised surface roughness parameterization in the Community Land Model 5.1

Ronny Meier et al.

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Cited articles

Belušić, D., Fuentes-Franco, R., Strandberg, G., and Jukimenko, A.: Afforestation reduces cyclone intensity and precipitation extremes over Europe, Environ. Res. Lett., 14, 074009, https://doi.org/10.1088/1748-9326/ab23b2, 2019. a
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Bingöl, F.: A simplified method on estimation of forest roughness by use of aerial LIDAR data, Energy Sci. Eng., 7, 3274–3282, https://doi.org/10.1002/ese3.496, 2019. a
Bonan, G.: Turbulent Fluxes and Scalar Profiles in the Surface Layer, Cambridge University Press, 80–100, https://doi.org/10.1017/9781107339217.007, 2019. a
Bonan, G. B., Patton, E. G., Harman, I. N., Oleson, K. W., Finnigan, J. J., Lu, Y., and Burakowski, E. A.: Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0), Geosci. Model Dev., 11, 1467–1496, https://doi.org/10.5194/gmd-11-1467-2018, 2018. a
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We revise the roughness of the land surface in the CESM climate model. Guided by observational data, we increase the surface roughness of forests and decrease that of bare soil, snow, ice, and crops. These modifications alter simulated temperatures and wind speeds at and above the land surface considerably, in particular over desert regions. The revised model represents the diurnal variability of the land surface temperature better compared to satellite observations over most regions.