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Title: CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

Journal Article · · Journal of Geophysical Research: Atmospheres
DOI:https://doi.org/10.1002/2017JD027188· OSTI ID:1547796
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  1. Met Office Exeter UK
  2. Lawrence Livermore National Laboratory Livermore CA USA
  3. Pacific Northwest National Laboratory Richland WA USA
  4. European Centre for Medium‐Range Weather Forecasts Reading UK
  5. CNRM, Meteo‐France/CNRS Toulouse France
  6. Environment and Climate Change Canada Victoria British Columbia Canada
  7. Laboratoire de Meteorologie Dynamique Paris France
  8. Academia Sinica Taipei Taiwan
  9. Brookhaven National Laboratory Upton NY USA
  10. Science Systems and Applications, Inc Norfolk VA USA
+ Show Author Affiliations

Many Numerical Weather Prediction (NWP) and climate models exhibit too warm lower tropospheres near the midlatitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on 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, water vapor, and aerosols are quantified, using an array of radiation measurement stations near the Atmospheric Radiation Measurement Southern Great Plains site. Furthermore, an in-depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although nonnegligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud radiative effects dominate in the cloud-related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
SC0014122; SC0005259; AC52-07NA27344; AC05-76RL01830; AC02-05CH11231; SC0012704
OSTI ID:
1547796
Alternate ID(s):
OSTI ID: 1438759; OSTI ID: 1454812; OSTI ID: 1547797
Report Number(s):
LLNL-JRNL-732219; BNL-205767-2018-JAAM
Journal Information:
Journal of Geophysical Research: Atmospheres, Journal Name: Journal of Geophysical Research: Atmospheres Vol. 123 Journal Issue: 7; ISSN 2169-897X
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 58 works
Citation information provided by
Web of Science

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Related Subjects

58 GEOSCIENCES
54 ENVIRONMENTAL SCIENCES
warm bias
CAUSES
radiation
attribution
clouds