Cloud and water vapor Influences on ERA5, AMPS, and ModelE3 Surface Downwelling Longwave Radiation Biases in West Antarctica
Authors
Johannes Verlinde — The Pennsylvania State University
Israel Silber — Pennsylvania State University
Sheng-Hung Wang — The Ohio State University
David Bromwich — Ohio State University
Ann M. Fridlind — NASA - Goddard Institute for Space Studies
Maria Paola Cadeddu — Argonne National Laboratory
Edwin W. Eloranta — University of Wisconsin
Connor J. Flynn — University of Oklahoma School of Meteorology
Category
High-latitude clouds and aerosols
Description
The surface downwelling longwave radiation component (LW↓) is crucial for the determination of the surface energy budget and has significant implications for the resilience of ice surfaces in the polar regions. Accurate model evaluation of this radiation component requires knowledge about the phase, vertical distribution, and associated temperature of water in the atmosphere, all of which control the LW↓ signal measured at the surface. In this study, we examine the LW↓ model errors found in the Antarctic Mesoscale Prediction System (AMPS) operational forecast model, the ERA5 reanalysis model, and the ModelE3 climate model (with nudged horizontal winds) relative to observations from the AWARE campaign at McMurdo Station and the West Antarctic Ice Sheet (WAIS) Divide. The errors are calculated separately for observed clear sky conditions, ice cloud occurrences, and liquid-bearing cloud layer (LBCL) occurrences. The analysis results show large biases and variability in all of the observed regimes. The magnitude of the LW↓ errors is examined relative to the vapor, ice, and liquid water in the atmospheric column. We suggest that a generally dry and liquid water-deficient atmosphere is responsible for the identified LW↓ biases in the models and is the result of excessive ice formation and growth, which could stem from model initial and lateral boundary conditions, microphysics scheme, aerosol representation, and/or limited vertical resolution. In ModelE3, low-biased cloud fraction when liquid water is predicted also contributes to negative LW↓ biases.
