Kwinten Van Weverberg — Met Office - UK
Cyril Julien Morcrette — Met Office - UK
Jon Petch — UK Meteorological Office
Chengzhu Zhang — Lawrence Livermore National Laboratory
Hsi-Yen Ma — Lawrence Livermore National Laboratory
Stephen Klein — Lawrence Livermore National Laboratory
William I. Gustafson — Pacific Northwest National Laboratory
Maike Ahlgrimm — Deutscher Wetterdienst
Jason N. S. Cole — Canadian Centre for Climate Modelling and Analysis
Yi-Chi Wang — Research Center for Environmental Change Academia Sinica T
Shaocheng Xie — Lawrence Livermore National Laboratory
Qi Tang — Lawrence Livermore National Laboratory
Karen Lee Johnson — Brookhaven National Laboratory
Category
Radiation
Description
Net surface shortwave bias attribution in all nine simulations under investigation. The average contributions from albedo (Alb), clouds (Cld), positive CWV biases (IWV+), negative CWV biases (IWV-), and residual processes (Res; e.g., aerosols) are provided by the thick horizontal lines (for the full simulation period from April til August, 2011). Boxes range from the 15th to the 85th percentile and whiskers from the 5th to the 95th percentile. Those contributions that are significantly different from zero, based on a Wilcoxon Rank-Sum test (with 5 % confidence interval) are highlighted by bold edges of the boxes. Observational data used in this analysis are obtained using the continuous forcing analysis by Zhang and Li (2001).
Many numerical weather prediction (NWP) and climate models exhibit too-warm lower tropospheres near the mid-latitude continents. This warm bias (WB) has been extensively studied before, but evidence about its origins remains inconclusive. Some studies point to deficiencies in the deep convective or low clouds. Other studies found an important contribution from errors in the land surface properties. The WB has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the WB. Documenting these radiation errors is hence an important step towards understanding and alleviating the WB.
This poster presents an attribution study to quantify the origin of the net radiation biases in nine model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, column water vapor (CWV), and aerosols are quantified, using an array of radiation measurement stations near the ARM SGP site. Furthermore, an in-depth analysis is shown to attribute the radiation errors to specific cloud regimes,
The net surface SW (LW) radiation is over/under-estimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, except for the CAM5, which has a dominant albedo issue. The contribution from the albedo to radiation errors in most other models is also positive, but mostly only of secondary importance. CWV and aerosol contributions tend to be small, but still significant.
The analysis has been able to highlight the specific cloud and radiation biases in all nine models under investigation. For instance, the CAM5 captures the cloud radiatve properties well on average, but this is a compensation between triggering broken deep cloud too often, while systematically underestimating its cloud fraction. The TAIESM, largely similar to the CAM5 apart from a convection scheme that slows down convective triggering, has a much-improved frequency of the deep cloud regime, but given the underestimated cloud fraction, the overall cloud radiative errors are degraded compared to the CAM5. Follow-up studies need to further explore why the models exhibit the deficiencies described and study the surface energy balance. The ultimate goal of the CAUSES project will be to bring these pieces of the puzzle together into a coherent explanation of the WB.
