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Title: An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models

Abstract

Here, we present an improved procedure of generating initial conditions (ICs) for climate model hindcast experiments with specified sea surface temperature and sea ice. The motivation is to minimize errors in the ICs and lead to a better evaluation of atmospheric parameterizations' performance in the hindcast mode. We apply state variables (horizontal velocities, temperature and specific humidity) from the operational analysis/reanalysis for the atmospheric initial states. Without a data assimilation system, we apply a two-step process to obtain other necessary variables to initialize both the atmospheric (e.g., aerosols and clouds) and land models (e.g., soil moisture). First, we nudge only the model horizontal velocities towards operational analysis/reanalysis values, given a 6-hour relaxation time scale, to obtain all necessary variables. Compared to the original strategy in which horizontal velocities, temperature and specific humidity are nudged, the revised approach produces a better representation of initial aerosols and cloud fields which are more consistent and closer to observations and model's preferred climatology. Second, we obtain land ICs from an offline land model simulation forced with observed precipitation, winds, and surface fluxes. This approach produces more realistic soil moisture in the land ICs. With this refined procedure, the simulated precipitation, clouds, radiation, and surfacemore » air temperature over land are improved in the Day 2 mean hindcasts. Following this procedure, we propose a “Core” integration suite which provides an easily repeatable test allowing model developers to rapidly assess the impacts of various parameterization changes on the fidelity of modelled cloud-associated processes relative to observations.« less

Authors:
 [1];  [1];  [1];  [2];  [1];  [1];  [1];  [3];  [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. National Taiwan Univ., Taipei (Taiwan)
  3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE; Ministry of Science and Technology (MOST) Taiwan
OSTI Identifier:
1239491
Alternate Identifier(s):
OSTI ID: 1367986
Report Number(s):
PNNL-SA-110968; LLNL-JRNL-671916
Journal ID: ISSN 1942-2466; KP1703010
Grant/Contract Number:  
AC05-76RL01830; AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Advances in Modeling Earth Systems
Additional Journal Information:
Journal Volume: 7; Journal Issue: 4; Journal ID: ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; GCM; model evaluation; transpose-AMIP; initial conditions; hindcast

Citation Formats

Ma, H. -Y., Chuang, C. C., Klein, S. A., Lo, M. -H., Zhang, Y., Xie, S., Zheng, X., Ma, P. -L., Zhang, Y., and Phillips, T. J. An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models. United States: N. p., 2015. Web. doi:10.1002/2015MS000490.
Ma, H. -Y., Chuang, C. C., Klein, S. A., Lo, M. -H., Zhang, Y., Xie, S., Zheng, X., Ma, P. -L., Zhang, Y., & Phillips, T. J. An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models. United States. doi:10.1002/2015MS000490.
Ma, H. -Y., Chuang, C. C., Klein, S. A., Lo, M. -H., Zhang, Y., Xie, S., Zheng, X., Ma, P. -L., Zhang, Y., and Phillips, T. J. Fri . "An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models". United States. doi:10.1002/2015MS000490. https://www.osti.gov/servlets/purl/1239491.
@article{osti_1239491,
title = {An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models},
author = {Ma, H. -Y. and Chuang, C. C. and Klein, S. A. and Lo, M. -H. and Zhang, Y. and Xie, S. and Zheng, X. and Ma, P. -L. and Zhang, Y. and Phillips, T. J.},
abstractNote = {Here, we present an improved procedure of generating initial conditions (ICs) for climate model hindcast experiments with specified sea surface temperature and sea ice. The motivation is to minimize errors in the ICs and lead to a better evaluation of atmospheric parameterizations' performance in the hindcast mode. We apply state variables (horizontal velocities, temperature and specific humidity) from the operational analysis/reanalysis for the atmospheric initial states. Without a data assimilation system, we apply a two-step process to obtain other necessary variables to initialize both the atmospheric (e.g., aerosols and clouds) and land models (e.g., soil moisture). First, we nudge only the model horizontal velocities towards operational analysis/reanalysis values, given a 6-hour relaxation time scale, to obtain all necessary variables. Compared to the original strategy in which horizontal velocities, temperature and specific humidity are nudged, the revised approach produces a better representation of initial aerosols and cloud fields which are more consistent and closer to observations and model's preferred climatology. Second, we obtain land ICs from an offline land model simulation forced with observed precipitation, winds, and surface fluxes. This approach produces more realistic soil moisture in the land ICs. With this refined procedure, the simulated precipitation, clouds, radiation, and surface air temperature over land are improved in the Day 2 mean hindcasts. Following this procedure, we propose a “Core” integration suite which provides an easily repeatable test allowing model developers to rapidly assess the impacts of various parameterization changes on the fidelity of modelled cloud-associated processes relative to observations.},
doi = {10.1002/2015MS000490},
journal = {Journal of Advances in Modeling Earth Systems},
number = 4,
volume = 7,
place = {United States},
year = {2015},
month = {11}
}

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