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Title: Using the Atmospheric Radiation Measurement (ARM) Datasets to Evaluate Climate Models in Simulating Diurnal and Seasonal Variations of Tropical Clouds

Abstract

We use the long-term Atmospheric Radiation Measurement (ARM) datasets collected at the three Tropical Western Pacific (TWP) sites as a tropical testbed to evaluate the ability of the Community Atmosphere Model (CAM5) to simulate the various types of clouds, their seasonal and diurnal variations, and their impact on surface radiation. We conducted a series of CAM5 simulations at various horizontal grid spacing (around 2°, 1°, 0.5°, and 0.25°) with meteorological constraints from reanalysis. Model biases in the seasonal cycle of cloudiness are found to be weakly dependent on model resolution. Positive biases (up to 20%) in the annual mean total cloud fraction appear mostly in stratiform ice clouds. Higher-resolution simulations do reduce the positive bias in the frequency of ice clouds, but they inadvertently increase the negative biases in convective clouds and low-level liquid clouds, leading to a positive bias in annual mean shortwave fluxes at the sites, as high as 65 W m-2 in the 0.25° simulation. Such resolution-dependent biases in clouds can adversely lead to biases in ambient thermodynamic properties and, in turn, feedback on clouds. Both the CAM5 model and ARM observations show distinct diurnal cycles in total, stratiform and convective cloud fractions; however, they are out-of-phasemore » by 12 hours and the biases vary by site. Our results suggest that biases in deep convection affect the vertical distribution and diurnal cycle of stratiform clouds through the transport of vapor and/or the detrainment of liquid and ice. We also found that the modelled gridmean surface longwave fluxes are systematically larger than site measurements when the grid that the ARM sites reside in is partially covered by ocean. The modeled longwave fluxes at such sites also lack a discernable diurnal cycle because the ocean part of the grid is warmer and less sensitive to radiative heating/cooling compared to land. Higher spatial resolution is more helpful is this regard. Our testbed approach can be easily adapted for the evaluation of new parameterizations being developed for CAM5 or other global or regional model simulations at high spatial resolutions.« less

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1]
  1. Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1434867
Report Number(s):
PNNL-SA-125329
Journal ID: ISSN 0894-8755; KP1701000
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Climate; Journal Volume: 31; Journal Issue: 8
Country of Publication:
United States
Language:
English

Citation Formats

Wang, Hailong, Burleyson, Casey D., Ma, Po-Lun, Fast, Jerome D., and Rasch, Philip J. Using the Atmospheric Radiation Measurement (ARM) Datasets to Evaluate Climate Models in Simulating Diurnal and Seasonal Variations of Tropical Clouds. United States: N. p., 2018. Web. doi:10.1175/JCLI-D-17-0362.1.
Wang, Hailong, Burleyson, Casey D., Ma, Po-Lun, Fast, Jerome D., & Rasch, Philip J. Using the Atmospheric Radiation Measurement (ARM) Datasets to Evaluate Climate Models in Simulating Diurnal and Seasonal Variations of Tropical Clouds. United States. doi:10.1175/JCLI-D-17-0362.1.
Wang, Hailong, Burleyson, Casey D., Ma, Po-Lun, Fast, Jerome D., and Rasch, Philip J. Sun . "Using the Atmospheric Radiation Measurement (ARM) Datasets to Evaluate Climate Models in Simulating Diurnal and Seasonal Variations of Tropical Clouds". United States. doi:10.1175/JCLI-D-17-0362.1.
@article{osti_1434867,
title = {Using the Atmospheric Radiation Measurement (ARM) Datasets to Evaluate Climate Models in Simulating Diurnal and Seasonal Variations of Tropical Clouds},
author = {Wang, Hailong and Burleyson, Casey D. and Ma, Po-Lun and Fast, Jerome D. and Rasch, Philip J.},
abstractNote = {We use the long-term Atmospheric Radiation Measurement (ARM) datasets collected at the three Tropical Western Pacific (TWP) sites as a tropical testbed to evaluate the ability of the Community Atmosphere Model (CAM5) to simulate the various types of clouds, their seasonal and diurnal variations, and their impact on surface radiation. We conducted a series of CAM5 simulations at various horizontal grid spacing (around 2°, 1°, 0.5°, and 0.25°) with meteorological constraints from reanalysis. Model biases in the seasonal cycle of cloudiness are found to be weakly dependent on model resolution. Positive biases (up to 20%) in the annual mean total cloud fraction appear mostly in stratiform ice clouds. Higher-resolution simulations do reduce the positive bias in the frequency of ice clouds, but they inadvertently increase the negative biases in convective clouds and low-level liquid clouds, leading to a positive bias in annual mean shortwave fluxes at the sites, as high as 65 W m-2 in the 0.25° simulation. Such resolution-dependent biases in clouds can adversely lead to biases in ambient thermodynamic properties and, in turn, feedback on clouds. Both the CAM5 model and ARM observations show distinct diurnal cycles in total, stratiform and convective cloud fractions; however, they are out-of-phase by 12 hours and the biases vary by site. Our results suggest that biases in deep convection affect the vertical distribution and diurnal cycle of stratiform clouds through the transport of vapor and/or the detrainment of liquid and ice. We also found that the modelled gridmean surface longwave fluxes are systematically larger than site measurements when the grid that the ARM sites reside in is partially covered by ocean. The modeled longwave fluxes at such sites also lack a discernable diurnal cycle because the ocean part of the grid is warmer and less sensitive to radiative heating/cooling compared to land. Higher spatial resolution is more helpful is this regard. Our testbed approach can be easily adapted for the evaluation of new parameterizations being developed for CAM5 or other global or regional model simulations at high spatial resolutions.},
doi = {10.1175/JCLI-D-17-0362.1},
journal = {Journal of Climate},
number = 8,
volume = 31,
place = {United States},
year = {Sun Apr 01 00:00:00 EDT 2018},
month = {Sun Apr 01 00:00:00 EDT 2018}
}