Evaluating the Met Office Unified Model land surface temperature in Global Atmosphere/Land 3.1 (GA/L3.1), Global Atmosphere/Land 6.1 (GA/L6.1) and limited area 2.2km configurations

Authors: BROOKE, J.K. - Met Office; HARLOW, R.C. - Met Office; Scott, Russell - Russ; BEST, M.J. - Met Office; EDWARDS, J.M. - Met Office; THELEN, J.C. - Met Office; WEEKS, M. - Met Office

Submitted to: Geoscientific Model Development
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/7/2019
Publication Date: 4/20/2019
Citation: Brooke, J., Harlow, R., Scott, R.L., Best, M., Edwards, J., Thelen, J., Weeks, M. 2019. Evaluating the Met Office Unified Model land surface temperature in Global Atmosphere/Land 3.1 (GA/L3.1), Global Atmosphere/Land 6.1 (GA/L6.1) and limited area 2.2km configurations. Geoscientific Model Development. 12:1703-1724. https://doi.org/10.5194/gmd-12-1703-2019.
DOI: https://doi.org/10.5194/gmd-12-1703-2019

Interpretive Summary: The United Kingdom’s Met Office makes routine regional and global weather forecasts. The computer models that they use to do this have substantial errors in surface temperature, which consequentially can have big impacts on weather forecasts, particularly over arid regions, To investigate these errors and improve the weather model forecasts, an experimental campaign using ground, airborne and satellite measurements was conducted over the Walnut Gulch Experimental Watershed, operated by the USDA-ARS, in May 2013. The model temperatures were confirmed to be too cold with respect to the ground-based temperatures, and this bias was related to the model having too low bare soil fractions and not adequately simulating the patchy shrub-covered landscapes found in this region. Improving the representation of vegetation, and the fractional coverage of bare soil cover, demonstrated improved simulation of the land surface to atmosphere exchanges of heat and moisture, and consequently reduced the model errors in simulating surface temperatures.

Technical Abstract: A limitation of the Met Office operational data assimilation scheme is that surface-sensitive infrared satellite sounding channels cannot be used during daytime periods where biases in the Numerical Weather Prediction (NWP) model background land surface temperature (LST) are greater than 2 K. The Met Office Unified Model (UM) has a significant cold bias in LST in semi-arid regions when compared with satellite observations. The UM LST biases were evaluated at global resolution and in Limited Area Models (LAMs) at 4.4 km and 2.2 km resolution over the SALSTICE (Semi-Arid Land Surface Temperature and IASI Calibration Experiment) experimental domain in southeastern Arizona USA. This validation is in conjunction with eddy-covariance flux tower measurements in southeastern Arizona. LST biases in the Global Atmosphere/Land 3.1 (GA/L3.1) configuration were largest in the mid-morning with respect to Terra (-13.6+2.8 K at the Kendall Grassland site). The diurnal cycle of LST in Global Atmosphere/Land 6.1 (GA/L6.1) showed a significant improvement relative to GA/L3.1; biases were reduced to -5.9+4.2 K. The higher resolution LAMs showed added value over the global configurations. The spatial distribution of the LST biases relative to Moderate Resolution Imaging Spectroradiometer (MODIS) and the modelled bare soil cover fraction were found to be moderately correlated (0.61+0.08) during the daytime, which suggests that regions of cold LST bias are associated with low bare soil cover fraction. Coefficients of correlation with the shrub surface fractions followed the same trend as the bare soil cover fraction although with a less significant correlation (0.36+0.09), and indicate that the sparse vegetation canopies in southeastern Arizona are not well represented. The x-component of the orographic slope was positively correlated with the LST bias (0.35+0.06) and identified that regions of cold model LST bias are found on easterly slopes and regions of warm model LST bias are found on westerly slopes. The UM and the standalone Joint UK Land Environment Simulator (JULES) land surface model overestimate the modelled turbulent heat and moisture fluxes and are deficient in representing ground heat fluxes. Improving the representation of vegetation (e.g. canopy height and leaf area index), and the fractional coverage of each surface type, in particular the bare soil cover fractions, have demonstrated improved partitioning of available energy into dissipative fluxes, which minimize errors in the surface energy balance, and consequently improve the simulated land surface temperature.