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Identifying errors in dust models from data assimilation
Airborne mineral dust is an important component of the Earth system and is increasingly predicted prognostically in weather and climate models. The recent development of data assimilation for remotely sensed aerosol optical depths (AODs) into models offers a new opportunity to better understand the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5082526/ https://www.ncbi.nlm.nih.gov/pubmed/27840459 http://dx.doi.org/10.1002/2016GL070621 |
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author | Pope, R. J. Marsham, J. H. Knippertz, P. Brooks, M. E. Roberts, A. J. |
author_facet | Pope, R. J. Marsham, J. H. Knippertz, P. Brooks, M. E. Roberts, A. J. |
author_sort | Pope, R. J. |
collection | PubMed |
description | Airborne mineral dust is an important component of the Earth system and is increasingly predicted prognostically in weather and climate models. The recent development of data assimilation for remotely sensed aerosol optical depths (AODs) into models offers a new opportunity to better understand the characteristics and sources of model error. Here we examine assimilation increments from Moderate Resolution Imaging Spectroradiometer AODs over northern Africa in the Met Office global forecast model. The model underpredicts (overpredicts) dust in light (strong) winds, consistent with (submesoscale) mesoscale processes lifting dust in reality but being missed by the model. Dust is overpredicted in the Sahara and underpredicted in the Sahel. Using observations of lighting and rain, we show that haboobs (cold pool outflows from moist convection) are an important dust source in reality but are badly handled by the model's convection scheme. The approach shows promise to serve as a useful framework for future model development. |
format | Online Article Text |
id | pubmed-5082526 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-50825262016-11-09 Identifying errors in dust models from data assimilation Pope, R. J. Marsham, J. H. Knippertz, P. Brooks, M. E. Roberts, A. J. Geophys Res Lett Research Letters Airborne mineral dust is an important component of the Earth system and is increasingly predicted prognostically in weather and climate models. The recent development of data assimilation for remotely sensed aerosol optical depths (AODs) into models offers a new opportunity to better understand the characteristics and sources of model error. Here we examine assimilation increments from Moderate Resolution Imaging Spectroradiometer AODs over northern Africa in the Met Office global forecast model. The model underpredicts (overpredicts) dust in light (strong) winds, consistent with (submesoscale) mesoscale processes lifting dust in reality but being missed by the model. Dust is overpredicted in the Sahara and underpredicted in the Sahel. Using observations of lighting and rain, we show that haboobs (cold pool outflows from moist convection) are an important dust source in reality but are badly handled by the model's convection scheme. The approach shows promise to serve as a useful framework for future model development. John Wiley and Sons Inc. 2016-09-03 2016-09-16 /pmc/articles/PMC5082526/ /pubmed/27840459 http://dx.doi.org/10.1002/2016GL070621 Text en ©2016. The Authors. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Letters Pope, R. J. Marsham, J. H. Knippertz, P. Brooks, M. E. Roberts, A. J. Identifying errors in dust models from data assimilation |
title | Identifying errors in dust models from data assimilation |
title_full | Identifying errors in dust models from data assimilation |
title_fullStr | Identifying errors in dust models from data assimilation |
title_full_unstemmed | Identifying errors in dust models from data assimilation |
title_short | Identifying errors in dust models from data assimilation |
title_sort | identifying errors in dust models from data assimilation |
topic | Research Letters |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5082526/ https://www.ncbi.nlm.nih.gov/pubmed/27840459 http://dx.doi.org/10.1002/2016GL070621 |
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