A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses
Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed t...
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ftosti:oai:osti.gov:1975734 2023-07-30T04:01:21+02:00 A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses Zampieri, Lorenzo Arduini, Gabriele Holland, Marika Keeley, Sarah P. E. Mogensen, Kristian Shupe, Matthew D. Tietsche, Steffen 2023-06-05 application/pdf http://www.osti.gov/servlets/purl/1975734 https://www.osti.gov/biblio/1975734 https://doi.org/10.1175/mwr-d-22-0130.1 unknown http://www.osti.gov/servlets/purl/1975734 https://www.osti.gov/biblio/1975734 https://doi.org/10.1175/mwr-d-22-0130.1 doi:10.1175/mwr-d-22-0130.1 54 ENVIRONMENTAL SCIENCES 2023 ftosti https://doi.org/10.1175/mwr-d-22-0130.1 2023-07-11T10:27:22Z Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions. Other/Unknown Material Arctic Climate change Sea ice SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic Monthly Weather Review 151 6 1443 1458 |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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54 ENVIRONMENTAL SCIENCES |
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54 ENVIRONMENTAL SCIENCES Zampieri, Lorenzo Arduini, Gabriele Holland, Marika Keeley, Sarah P. E. Mogensen, Kristian Shupe, Matthew D. Tietsche, Steffen A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions. |
author |
Zampieri, Lorenzo Arduini, Gabriele Holland, Marika Keeley, Sarah P. E. Mogensen, Kristian Shupe, Matthew D. Tietsche, Steffen |
author_facet |
Zampieri, Lorenzo Arduini, Gabriele Holland, Marika Keeley, Sarah P. E. Mogensen, Kristian Shupe, Matthew D. Tietsche, Steffen |
author_sort |
Zampieri, Lorenzo |
title |
A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses |
title_short |
A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses |
title_full |
A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses |
title_fullStr |
A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses |
title_full_unstemmed |
A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses |
title_sort |
machine learning correction model of the winter clear-sky temperature bias over the arctic sea ice in atmospheric reanalyses |
publishDate |
2023 |
url |
http://www.osti.gov/servlets/purl/1975734 https://www.osti.gov/biblio/1975734 https://doi.org/10.1175/mwr-d-22-0130.1 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Sea ice |
genre_facet |
Arctic Climate change Sea ice |
op_relation |
http://www.osti.gov/servlets/purl/1975734 https://www.osti.gov/biblio/1975734 https://doi.org/10.1175/mwr-d-22-0130.1 doi:10.1175/mwr-d-22-0130.1 |
op_doi |
https://doi.org/10.1175/mwr-d-22-0130.1 |
container_title |
Monthly Weather Review |
container_volume |
151 |
container_issue |
6 |
container_start_page |
1443 |
op_container_end_page |
1458 |
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1772812097130856448 |