An Improved Hindcast Approach for Evaluation and Diagnosis of Physical Processes in Global Climate Models

TitleAn Improved Hindcast Approach for Evaluation and Diagnosis of Physical Processes in Global Climate Models
Publication TypeJournal Article
Year of Publication2015
JournalJournal of Advances in Modeling Earth Systems
Date Published10/2015
Abstract / Summary

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 hind- cast 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 toward operational analysis/reanalysis values, given a 6 h relaxation time scale, to obtain all necessary varia- bles. 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 off-line 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 simu- lated 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 repeat- able test allowing model developers to rapidly assess the impacts of various parameterization changes on the fidelity of modeled cloud-associated processes relative to observations.

URLhttp://onlinelibrary.wiley.com/doi/10.1002/2015MS000490/full?campaign=wlytk-41855.5282060185
DOI10.1002/2015MS000490
Journal: Journal of Advances in Modeling Earth Systems
Year of Publication: 2015
Date Published: 10/2015

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 hind- cast 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 toward operational analysis/reanalysis values, given a 6 h relaxation time scale, to obtain all necessary varia- bles. 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 off-line 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 simu- lated 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 repeat- able test allowing model developers to rapidly assess the impacts of various parameterization changes on the fidelity of modeled cloud-associated processes relative to observations.

DOI: 10.1002/2015MS000490
Citation:
2015.  "An Improved Hindcast Approach for Evaluation and Diagnosis of Physical Processes in Global Climate Models."  Journal of Advances in Modeling Earth Systems.  https://doi.org/10.1002/2015MS000490.