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Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model
Using uncertainty quantification techniques, we carry out a sensitivity analysis of a large number (17) of parameters used in the NCAR CAM5 cloud parameterization schemes. The LLNL PSUADE software is used to identify the most sensitive parameters by performing sensitivity analysis. Using Morris One-...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567854/ https://www.ncbi.nlm.nih.gov/pubmed/33060758 http://dx.doi.org/10.1038/s41598-020-74441-x |
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author | Pathak, Raju Sahany, Sandeep Mishra, Saroj K. |
author_facet | Pathak, Raju Sahany, Sandeep Mishra, Saroj K. |
author_sort | Pathak, Raju |
collection | PubMed |
description | Using uncertainty quantification techniques, we carry out a sensitivity analysis of a large number (17) of parameters used in the NCAR CAM5 cloud parameterization schemes. The LLNL PSUADE software is used to identify the most sensitive parameters by performing sensitivity analysis. Using Morris One-At-a-Time (MOAT) method, we find that the simulations of global annual mean total precipitation, convective, large-scale precipitation, cloud fractions (total, low, mid, and high), shortwave cloud forcing, longwave cloud forcing, sensible heat flux, and latent heat flux are very sensitive to the threshold-relative-humidity-for-stratiform-low-clouds ([Formula: see text] and the auto-conversion-size-threshold-for-ice-to-snow [Formula: see text] The seasonal and regime specific dependence of some parameters in the simulation of precipitation is also found for the global monsoons and storm track regions. Through sensitivity analysis, we find that the Somali jet strength and the tropical easterly jet associated with the south Asian summer monsoon (SASM) show a systematic dependence on [Formula: see text] and [Formula: see text] . The timing of the withdrawal of SASM over India shows a monotonic increase (delayed withdrawal) with an increase in [Formula: see text] . Overall, we find that [Formula: see text] , [Formula: see text] , [Formula: see text] and [Formula: see text] are the most sensitive cloud parameters and thus are of high priority in the model tuning process, in order to reduce uncertainty in the simulation of past, present, and future climate. |
format | Online Article Text |
id | pubmed-7567854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75678542020-10-19 Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model Pathak, Raju Sahany, Sandeep Mishra, Saroj K. Sci Rep Article Using uncertainty quantification techniques, we carry out a sensitivity analysis of a large number (17) of parameters used in the NCAR CAM5 cloud parameterization schemes. The LLNL PSUADE software is used to identify the most sensitive parameters by performing sensitivity analysis. Using Morris One-At-a-Time (MOAT) method, we find that the simulations of global annual mean total precipitation, convective, large-scale precipitation, cloud fractions (total, low, mid, and high), shortwave cloud forcing, longwave cloud forcing, sensible heat flux, and latent heat flux are very sensitive to the threshold-relative-humidity-for-stratiform-low-clouds ([Formula: see text] and the auto-conversion-size-threshold-for-ice-to-snow [Formula: see text] The seasonal and regime specific dependence of some parameters in the simulation of precipitation is also found for the global monsoons and storm track regions. Through sensitivity analysis, we find that the Somali jet strength and the tropical easterly jet associated with the south Asian summer monsoon (SASM) show a systematic dependence on [Formula: see text] and [Formula: see text] . The timing of the withdrawal of SASM over India shows a monotonic increase (delayed withdrawal) with an increase in [Formula: see text] . Overall, we find that [Formula: see text] , [Formula: see text] , [Formula: see text] and [Formula: see text] are the most sensitive cloud parameters and thus are of high priority in the model tuning process, in order to reduce uncertainty in the simulation of past, present, and future climate. Nature Publishing Group UK 2020-10-15 /pmc/articles/PMC7567854/ /pubmed/33060758 http://dx.doi.org/10.1038/s41598-020-74441-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Pathak, Raju Sahany, Sandeep Mishra, Saroj K. Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model |
title | Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model |
title_full | Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model |
title_fullStr | Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model |
title_full_unstemmed | Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model |
title_short | Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model |
title_sort | uncertainty quantification based cloud parameterization sensitivity analysis in the ncar community atmosphere model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567854/ https://www.ncbi.nlm.nih.gov/pubmed/33060758 http://dx.doi.org/10.1038/s41598-020-74441-x |
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