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Indian Summer Monsoon Simulations: Usefulness of Increasing Horizontal Resolution, Manual Tuning, and Semi-Automatic Tuning in Reducing Present-Day Model Biases

Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) are unable to resolve the spatial and temporal characteristics of the South Asian Monsoon satisfactorily. A CGCM with the capability to reliably project the global as well as the regional climatic feat...

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Autores principales: Anand, Abhishek, Mishra, Saroj K., Sahany, Sandeep, Bhowmick, Mansi, Rawat, Janmejai Singh, Dash, S. K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5824783/
https://www.ncbi.nlm.nih.gov/pubmed/29476176
http://dx.doi.org/10.1038/s41598-018-21865-1
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author Anand, Abhishek
Mishra, Saroj K.
Sahany, Sandeep
Bhowmick, Mansi
Rawat, Janmejai Singh
Dash, S. K.
author_facet Anand, Abhishek
Mishra, Saroj K.
Sahany, Sandeep
Bhowmick, Mansi
Rawat, Janmejai Singh
Dash, S. K.
author_sort Anand, Abhishek
collection PubMed
description Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) are unable to resolve the spatial and temporal characteristics of the South Asian Monsoon satisfactorily. A CGCM with the capability to reliably project the global as well as the regional climatic features would be a valuable tool for scientists and policymakers. Analysis of 28 CMIP5 models highlights varying degree of biases in precipitation and 2 m surface air temperature (T2m) over south Asia, and the Community Earth System Model (CESM) developed at the National Center for Atmospheric Research is found to be one of the best performing models. However, like all other CMIP5 models, CESM also has some inherent model biases. Using CESM, it is found that the precipitation and T2M biases reduce with increase in the model horizontal resolution from 2° to 0.5°. Further, a few deep convective parameters in the Zhang-McFarlane convection scheme are tuned for 2° and 1° model resolutions using both manual and semi-automatic model tuning methods. Comparing results from the two tuning methods we find that the performance of the manually tuned model is better than that of the semi-automated one.
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spelling pubmed-58247832018-03-01 Indian Summer Monsoon Simulations: Usefulness of Increasing Horizontal Resolution, Manual Tuning, and Semi-Automatic Tuning in Reducing Present-Day Model Biases Anand, Abhishek Mishra, Saroj K. Sahany, Sandeep Bhowmick, Mansi Rawat, Janmejai Singh Dash, S. K. Sci Rep Article Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) are unable to resolve the spatial and temporal characteristics of the South Asian Monsoon satisfactorily. A CGCM with the capability to reliably project the global as well as the regional climatic features would be a valuable tool for scientists and policymakers. Analysis of 28 CMIP5 models highlights varying degree of biases in precipitation and 2 m surface air temperature (T2m) over south Asia, and the Community Earth System Model (CESM) developed at the National Center for Atmospheric Research is found to be one of the best performing models. However, like all other CMIP5 models, CESM also has some inherent model biases. Using CESM, it is found that the precipitation and T2M biases reduce with increase in the model horizontal resolution from 2° to 0.5°. Further, a few deep convective parameters in the Zhang-McFarlane convection scheme are tuned for 2° and 1° model resolutions using both manual and semi-automatic model tuning methods. Comparing results from the two tuning methods we find that the performance of the manually tuned model is better than that of the semi-automated one. Nature Publishing Group UK 2018-02-23 /pmc/articles/PMC5824783/ /pubmed/29476176 http://dx.doi.org/10.1038/s41598-018-21865-1 Text en © The Author(s) 2018 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Anand, Abhishek
Mishra, Saroj K.
Sahany, Sandeep
Bhowmick, Mansi
Rawat, Janmejai Singh
Dash, S. K.
Indian Summer Monsoon Simulations: Usefulness of Increasing Horizontal Resolution, Manual Tuning, and Semi-Automatic Tuning in Reducing Present-Day Model Biases
title Indian Summer Monsoon Simulations: Usefulness of Increasing Horizontal Resolution, Manual Tuning, and Semi-Automatic Tuning in Reducing Present-Day Model Biases
title_full Indian Summer Monsoon Simulations: Usefulness of Increasing Horizontal Resolution, Manual Tuning, and Semi-Automatic Tuning in Reducing Present-Day Model Biases
title_fullStr Indian Summer Monsoon Simulations: Usefulness of Increasing Horizontal Resolution, Manual Tuning, and Semi-Automatic Tuning in Reducing Present-Day Model Biases
title_full_unstemmed Indian Summer Monsoon Simulations: Usefulness of Increasing Horizontal Resolution, Manual Tuning, and Semi-Automatic Tuning in Reducing Present-Day Model Biases
title_short Indian Summer Monsoon Simulations: Usefulness of Increasing Horizontal Resolution, Manual Tuning, and Semi-Automatic Tuning in Reducing Present-Day Model Biases
title_sort indian summer monsoon simulations: usefulness of increasing horizontal resolution, manual tuning, and semi-automatic tuning in reducing present-day model biases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5824783/
https://www.ncbi.nlm.nih.gov/pubmed/29476176
http://dx.doi.org/10.1038/s41598-018-21865-1
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