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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
Journal Name:
Journal of Geophysical Research: Atmospheres
Additional Journal Information:
Journal Volume: 123; Journal Issue: 7; Journal ID: ISSN 2169-897X
Publisher:
American Geophysical Union
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},
issn = {2169-897X},
number = 7,
volume = 123,
place = {United States},
year = {2018},
month = {4}
}

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    Works referencing / citing this record:

    Evaluation of two cloud parametrization schemes using ARM and Cloud-Net observations
    journal, November 2011

    • Morcrette, Cyril J.; O'Connor, Ewan J.; Petch, Jon C.
    • Quarterly Journal of the Royal Meteorological Society, Vol. 138, Issue 665
    • DOI: 10.1002/qj.969

    Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model
    journal, April 1996

    • Edwards, J. M.; Slingo, A.
    • Quarterly Journal of the Royal Meteorological Society, Vol. 122, Issue 531
    • DOI: 10.1002/qj.49712253107

    The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis
    journal, January 2015

    • Jensen, M. P.; Toto, T.; Troyan, D.
    • Atmospheric Measurement Techniques, Vol. 8, Issue 1
    • DOI: 10.5194/amt-8-421-2015

    Using regime analysis to identify the contribution of clouds to surface temperature errors in weather and climate models: Cloud-Regime Analysis and Surface Temperature Errors
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    • Van Weverberg, Kwinten; Morcrette, Cyril J.; Ma, Hsi-Yen
    • Quarterly Journal of the Royal Meteorological Society, Vol. 141, Issue 693
    • DOI: 10.1002/qj.2603

    Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations
    journal, January 2012


    The CNRM-CM5.1 global climate model: description and basic evaluation
    journal, January 2012


    ARM: Radiative Flux Analysis: QCRAD data, Long algorithm
    dataset, January 1994

    • Gaustad, Krista; Riihimaki, Laura; Long, Chuck
    • Atmospheric Radiation Measurement (ARM) Archive, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US);
    • DOI: 10.5439/1179822

    The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4). Part I: Representation of Physical Processes
    journal, February 2013


    Evaluation of the warm season diurnal cycle of precipitation over Sweden simulated by the Rossby Centre regional climate model RCA3
    journal, January 2013


    The Met Office Unified Model Global Atmosphere 4.0 and JULES Global Land 4.0 configurations
    journal, January 2014

    • Walters, D. N.; Williams, K. D.; Boutle, I. A.
    • Geoscientific Model Development, Vol. 7, Issue 1
    • DOI: 10.5194/gmd-7-361-2014

    How well can a convection-permitting climate model reproduce decadal statistics of precipitation, temperature and cloud characteristics?
    journal, February 2016

    • Brisson, Erwan; Van Weverberg, Kwinten; Demuzere, Matthias
    • Climate Dynamics, Vol. 47, Issue 9-10
    • DOI: 10.1007/s00382-016-3012-z

    Assessing the CAM5 physics suite in the WRF-Chem model: implementation, resolution sensitivity, and a first evaluation for a regional case study
    journal, January 2014

    • Ma, P. -L.; Rasch, P. J.; Fast, J. D.
    • Geoscientific Model Development, Vol. 7, Issue 3
    • DOI: 10.5194/gmd-7-755-2014

    Regions of Strong Coupling Between Soil Moisture and Precipitation
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    Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization
    journal, January 2016

    • Eyring, Veronika; Bony, Sandrine; Meehl, Gerald A.
    • Geoscientific Model Development, Vol. 9, Issue 5
    • DOI: 10.5194/gmd-9-1937-2016

    The ERA-Interim reanalysis: configuration and performance of the data assimilation system
    journal, April 2011

    • Dee, D. P.; Uppala, S. M.; Simmons, A. J.
    • Quarterly Journal of the Royal Meteorological Society, Vol. 137, Issue 656
    • DOI: 10.1002/qj.828

    LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection
    journal, April 2012

    • Hourdin, Frédéric; Grandpeix, Jean-Yves; Rio, Catherine
    • Climate Dynamics, Vol. 40, Issue 9-10
    • DOI: 10.1007/s00382-012-1343-y

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    • Tang, Qi; Klein, Stephen A.; Xie, Shaocheng
    • Geoscientific Model Development, Vol. 12, Issue 7
    • DOI: 10.5194/gmd-12-2679-2019

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    • DOI: 10.5439/1178331

    Regionally refined test bed in E3SM atmosphere model version 1 (EAMv1) and applications for high-resolution modeling
    journal, January 2019

    • Tang, Qi; Klein, Stephen A.; Xie, Shaocheng
    • Geoscientific Model Development, Vol. 12, Issue 7
    • DOI: 10.5194/gmd-12-2679-2019