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The CNRM Global Atmosphere Model ARPEGE‐Climat 6.3: Description and Evaluation.
- Source :
- Journal of Advances in Modeling Earth Systems; Jul2020, Vol. 12 Issue 7, p1-53, 53p
- Publication Year :
- 2020
-
Abstract
- The present study describes the atmospheric component of the sixth‐generation climate models of the Centre National de Recherches Météorologiques (CNRM), namely, ARPEGE‐Climat 6.3. It builds up on more than a decade of model development and tuning efforts, which led to major updates of its moist physics. The vertical resolution has also been significantly increased, both in the boundary layer and in the stratosphere. ARPEGE‐Climat 6.3 is now coupled to the new version (8.0) of the SURFace EXternalisée (SURFEX) surface model, in which several new features (e.g., floodplains, aquifers, and snow processes) improve the water cycle realism. The model calibration is discussed in depth. An amip‐type experiment, in which the sea surface temperatures and sea ice concentrations are prescribed, and following the CMIP6 protocol, is extensively evaluated, in terms of climate mean state and variability. ARPEGE‐Climat 6.3 is shown to improve over its previous version (5.1) by many climate features. Major improvements include the top‐of‐atmosphere and surface energy budgets in their various components (shortwave and longwave, total and clear sky), cloud cover, near‐surface temperature, precipitation climatology and daily‐mean distribution, and water discharges at the outlet of major rivers. In contrast, clouds over subtropical stratocumulus decks, several dynamical variables (sea level pressure, 500‐hPa geopotential height), are still significantly biased. The tropical intraseasonal variability and diurnal cycle of precipitation, though improved, remained area of concerns for further model improvement. New biases also emerge, such as a lack of precipitation over several tropical continental areas. Within the CMIP6 context, ARPEGE‐Climat 6.3 is the atmospheric component of CNRM‐CM6‐1 and CNRM‐ESM2‐1. Plain Language Summary: Since the early 1990s, the Centre National de Recherches Météorologiques (CNRM) has been developing a global atmosphere model for climate applications. The present work presents its latest version, ARPEGE‐Climat 6.3, as prepared for the sixth phase of the Coupled Model Intercomparison Project (CMIP6). It builds up on more than a decade of model development and tuning efforts. A CMIP6 amip‐type numerical experiment, in which the sea surface temperatures and sea ice concentrations are prescribed, is evaluated, in terms of climate mean state and variability. ARPEGE‐Climat 6.3 is shown to have better or similar skills compared to its previous version and to rank rather high among CMIP5 state‐of‐the‐art models by many mean‐state metrics. Major improvements include the top‐of‐atmosphere and surface energy budgets, cloud cover, near‐surface temperature, precipitation climatology and daily‐mean distribution, and water discharges at the outlet of major rivers. In contrast, clouds over the eastern part of ocean basins, and a few dynamical variables, such as sea level pressure, are still significantly biased. New biases also emerge, such as a lack of precipitation over several tropical continental areas. The remaining and new biases call for further understanding, especially whether they arise from calibration issues or model structural limits. Key Points: Version 6.3 of the ARPEGE‐Climat atmospheric model includes an increased vertical resolution and a major update of the moist physicsImprovements include radiation, cloud and precipitation climatology, daily rainfall distribution, and water discharge at major river outletsWeaknesses still include biases in low clouds and some dynamical fields, while the West African monsoon is a new model deficiency [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19422466
- Volume :
- 12
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- Journal of Advances in Modeling Earth Systems
- Publication Type :
- Academic Journal
- Accession number :
- 144803789
- Full Text :
- https://doi.org/10.1029/2020MS002075