Potential of remote sensing surface temperature- and evapotranspiration-based land-atmosphere coupling metrics for land surface model calibration

Imperfect land physics introduce significant levels of uncertainty into current land surface models (LSMs) and can cause bias in their representation of land-atmosphere coupling strength (rho). When LSMs are coupled with atmospheric prediction models, such errors will eventually degrade the accuracy of lower atmosphere forecasts. Here, we investigate the potential of two remote sensing (RS)-based rho references for addressing LSM rho bias. To minimize meteorological uncertainty and maximally attribute LSM rho bias to land sources, we focus specifically on off-line LSM calibration forced using high-quality, observation-based meteorological data. Both rho references are based on a newly proposed two-system approach for eliminating the impact of random errors in RS retrievals and quantified using the temporal correlations of soil moisture (SM) versus both evapotranspiration (ET) and the diurnal amplitude of surface temperature (dT). Experiments are conducted to calibrate an off-line LSM individually against each resulting rho reference and using a combination of both dT- and ET-represented rho references. The resulting calibrated LSM is further evaluated using independent ground-based ET observations and RS dT retrievals. Results show that although dT- and ET-represented rho references are physically consistent across space, model calibration results based on them are quite different. Specifically, the calibration experiment targeting ET-represented rho outperforms that targeting dT-represented rho in ET and dT modeling. Diagnostic results indicate that the failure of dT-based calibration experiments is due to the confounding impacts of transpiration/evapotranspiration partitioning error and large dT uncertainties in LSM. However, results also confirm the potential of both dT- and ET-represented rho references for jointly diagnosing and understanding LSM rho bias. As a result, we suggest diagnosing LSM rho bias using both ET- and dT-represented rho references - but calibrating LSM using only ET-represented rho reference data.

Automated summary

Imperfect land physics in land surface models (LSMs) introduce uncertainty and bias in the representation of land-atmosphere coupling (rho), which degrades the accuracy of lower atmosphere forecasts. This study investigates the potential of two remote sensing (RS)-based references for addressing LSM rho bias. Results show that calibrating LSM using ET-represented rho reference data outperforms using dT-represented rho reference data in ET and dT modeling, due to confounding impacts and uncertainties. Both ET and dT-represented rho references have the potential for diagnosing and understanding LSM rho bias.