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| 1.Poster Title | Tuning the NE30_L72 ACME V1 Atmosphere Model |
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| 2.Authors | Po-Lun Ma, Phil Rasch (pnl.gov), Shaocheng Xie, Hailong Wang, Balwinder Singh, Wuyin Lin, Kai Zhang, Hui Wan, Yun Qian, Chris Golaz, Julio Bacmeister (Unlicensed), Richard Easter (Unlicensed), Steve Ghan (Unlicensed), Richard Neale, Cecile Hannay, Yaga Richter, Susannah Burrows, Philip Cameron-Smith (Unlicensed), Peter Bogenschutz, Vince Larson, Peter Caldwell, Bryce Harrop |
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| 3.Group | Atmosphere |
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| 4.Experiment | |
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| 5.Poster Category | Early Results |
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| 6.Submission Type | |
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| 7.Poster Link | |
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| 8.Lightning Talk Slide | ACME_PMA_tuning_c20160602.pdf |
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| View file |
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| name | ACME_PMA_tuning_c160607.pdf |
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| height | 400 |
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Abstract
We have completed over 250 1-yr and multi-year simulations to deliver a reasonably tuned atmosphere model with the complete ACME V1 physics package at ne30 horizontal resolution with 72 levels in the vertical. In the calibration procedure, we keep all tunable parameters in their physically justifiable ranges and tune multiple parameters to achieve multiple objectives (the “cocktail” approach). We have calibrated cloud microphysics, shallow convection, turbulence, deep convection, aerosols, gravity wave drag, and turbulent mountain stress. Model features are calibrated against observations: CERES-EBAF for shortwave and longwave cloud forcing, GPCP, TRMM, and CMORPH for precipitation, MERRA and ERA-Interim for temperature and sea level pressure, CALIPSO and Satellite-AERONET composite for aerosols, Large-Yeager dataset for surface wind stress, etc. The model has a close to zero energy imbalance at TOA along with improved simulation of clouds, aerosols, precipitation, temperature, sea level pressure, etc., compared with the untuned ACME V1 atmosphere model. The model also produces many healthy features including a strong QBO and smaller aerosol indirect effects, which are significant advancement from its predecessor. Our ongoing effort includes merging various configurations and addressing issues associated with regional features such as precipitation over Amazon, southern ocean wind stress, etc., through parameter adjustment (short-term) and improvement of process representation (longer term).