skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

Many numerical weather prediction (NWP) and climate models exhibit too warm lower tropospheres near the mid-latitude continents. This warm bias has been extensively studied before, but evidence about its origin 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 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. Documenting these radiation errors is hence an important step towards understanding and alleviating the warm bias. This paper presents an attribution study to quantify the net radiation biases in 9 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, integrated water vapor (IWV) 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 radiation is overestimated (LW underestimated) in all models throughout most of the simulation period. Cloud errors aremore » shown to contribute most to this overestimation in all but one model, which has a dominant albedo issue. Using a cloud regime analysis, it was shown that missing deep cloud events and/or simulating deep clouds with too weak cloud-radiative effects account for most of these 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 however, 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, the deep cloud problem in many models could be related to too weak convective cloud detrainment and too large precipitation efficiencies. This does not rule out that previously documented issues with the evaporative fraction contribute to the warm bias as well, since the majority of the models underestimate the surface rain rates overall, as they miss the observed large nocturnal precipitation peak.« less

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [3]; ORCiD logo [3]; ORCiD logo [3]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [4]; ORCiD logo [5]; ORCiD logo [5]; ORCiD logo [6]; ORCiD logo [6]; ORCiD logo [6] more »; ORCiD logo [7];  [7]; ORCiD logo [8]; ORCiD logo [9];  [10] « less
  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
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1439023
Report Number(s):
PNNL-SA-126059
Journal ID: ISSN 2169-897X; KP1701000
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Geophysical Research: Atmospheres; Journal Volume: 123; Journal Issue: 7
Country of Publication:
United States
Language:
English

Citation Formats

Van Weverberg, K., Morcrette, C. J., Petch, J., Klein, S. A., Ma, H. -Y., Zhang, C., Xie, S., Tang, Q., Gustafson, W. I., Qian, Y., Berg, L. K., Liu, Y., Huang, M., Ahlgrimm, M., Forbes, R., Bazile, E., Roehrig, R., Cole, J., Merryfield, W., Lee, W. -S., Cheruy, F., Mellul, L., Wang, Y. -C., Johnson, K., and Thieman, M. M.. CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains. United States: N. p., 2018. Web. doi:10.1002/2017JD027188.
Van Weverberg, K., Morcrette, C. J., Petch, J., Klein, S. A., Ma, H. -Y., Zhang, C., Xie, S., Tang, Q., Gustafson, W. I., Qian, Y., Berg, L. K., Liu, Y., Huang, M., Ahlgrimm, M., Forbes, R., Bazile, E., Roehrig, R., Cole, J., Merryfield, W., Lee, W. -S., Cheruy, F., Mellul, L., Wang, Y. -C., Johnson, K., & Thieman, M. M.. CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains. United States. doi:10.1002/2017JD027188.
Van Weverberg, K., Morcrette, C. J., Petch, J., Klein, S. A., Ma, H. -Y., Zhang, C., Xie, S., Tang, Q., Gustafson, W. I., Qian, Y., Berg, L. K., Liu, Y., Huang, M., Ahlgrimm, M., Forbes, R., Bazile, E., Roehrig, R., Cole, J., Merryfield, W., Lee, W. -S., Cheruy, F., Mellul, L., Wang, Y. -C., Johnson, K., and Thieman, M. M.. Fri . "CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains". United States. doi:10.1002/2017JD027188.
@article{osti_1439023,
title = {CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains},
author = {Van Weverberg, K. and Morcrette, C. J. and Petch, J. and Klein, S. A. and Ma, H. -Y. and Zhang, C. and Xie, S. and Tang, Q. and Gustafson, W. I. and Qian, Y. and Berg, L. K. and Liu, Y. and Huang, M. and Ahlgrimm, M. and Forbes, R. and Bazile, E. and Roehrig, R. and Cole, J. and Merryfield, W. and Lee, W. -S. and Cheruy, F. and Mellul, L. and Wang, Y. -C. and Johnson, K. and Thieman, M. M.},
abstractNote = {Many numerical weather prediction (NWP) and climate models exhibit too warm lower tropospheres near the mid-latitude continents. This warm bias has been extensively studied before, but evidence about its origin 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 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. Documenting these radiation errors is hence an important step towards understanding and alleviating the warm bias. This paper presents an attribution study to quantify the net radiation biases in 9 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, integrated water vapor (IWV) 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 radiation is overestimated (LW underestimated) in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation in all but one model, which has a dominant albedo issue. Using a cloud regime analysis, it was shown that missing deep cloud events and/or simulating deep clouds with too weak cloud-radiative effects account for most of these 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 however, 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, the deep cloud problem in many models could be related to too weak convective cloud detrainment and too large precipitation efficiencies. This does not rule out that previously documented issues with the evaporative fraction contribute to the warm bias as well, since the majority of the models underestimate the surface rain rates overall, as they miss the observed large nocturnal precipitation peak.},
doi = {10.1002/2017JD027188},
journal = {Journal of Geophysical Research: Atmospheres},
number = 7,
volume = 123,
place = {United States},
year = {Fri Apr 06 00:00:00 EDT 2018},
month = {Fri Apr 06 00:00:00 EDT 2018}
}