Dynamical seasonal predictions of tropical cyclone activity: Roles of sea surface temperature errors and atmosphere-land initialization
Climate models often show errors in simulating and predicting tropical cyclone (TC) activity, but the sources of these errors are not well understood. This study proposes an evaluation framework and analyzes three sets of experiments conducted using a seasonal prediction model developed at the Geoph...
Published in: | Journal of Climate |
---|---|
Other Authors: | , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://doi.org/10.1175/JCLI-D-20-0215.1 |
id |
ftncar:oai:drupal-site.org:articles_24136 |
---|---|
record_format |
openpolar |
spelling |
ftncar:oai:drupal-site.org:articles_24136 2024-04-28T08:31:02+00:00 Dynamical seasonal predictions of tropical cyclone activity: Roles of sea surface temperature errors and atmosphere-land initialization Zhang, Gan (author) Murakami, Hiroyuki (author) Yang, Xiaosong (author) Findell, Kirsten L. (author) Wittenberg, Andrew T. (author) Jia, Liwei (author) 2021-03 https://doi.org/10.1175/JCLI-D-20-0215.1 en eng Journal of Climate--0894-8755--1520-0442 articles:24136 ark:/85065/d7r214rj doi:10.1175/JCLI-D-20-0215.1 Copyright 2021 American Meteorological Society (AMS). article Text 2021 ftncar https://doi.org/10.1175/JCLI-D-20-0215.1 2024-04-04T17:35:13Z Climate models often show errors in simulating and predicting tropical cyclone (TC) activity, but the sources of these errors are not well understood. This study proposes an evaluation framework and analyzes three sets of experiments conducted using a seasonal prediction model developed at the Geophysical Fluid Dynamics Laboratory (GFDL). These experiments apply the nudging technique to the model integration and/or initialization to estimate possible improvements from nearly perfect model conditions. The results suggest that reducing sea surface temperature (SST) errors remains important for better predicting TC activity at long forecast leads-even in a flux-adjusted model with reduced climatological biases. Other error sources also contribute to biases in simulated TC activity, with notable manifestations on regional scales. A novel finding is that the coupling and initialization of the land and atmosphere components can affect seasonal TC prediction skill. Simulated year-to-year variations in June land conditions over North America show a significant lead correlation with the North Atlantic large-scale environment and TC activity. Improved land-atmosphere initialization appears to improve the Atlantic TC predictions initialized in some summer months. For short-lead predictions initialized in June, the potential skill improvements attributable to land-atmosphere initialization might be comparable to those achievable with perfect SST predictions. Overall, this study delineates the SST and non-oceanic error sources in predicting TC activity and highlights avenues for improving predictions. The nudging-based evaluation framework can be applied to other models and help improve predictions of other weather extremes. Article in Journal/Newspaper North Atlantic OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) Journal of Climate 34 5 1743 1766 |
institution |
Open Polar |
collection |
OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) |
op_collection_id |
ftncar |
language |
English |
description |
Climate models often show errors in simulating and predicting tropical cyclone (TC) activity, but the sources of these errors are not well understood. This study proposes an evaluation framework and analyzes three sets of experiments conducted using a seasonal prediction model developed at the Geophysical Fluid Dynamics Laboratory (GFDL). These experiments apply the nudging technique to the model integration and/or initialization to estimate possible improvements from nearly perfect model conditions. The results suggest that reducing sea surface temperature (SST) errors remains important for better predicting TC activity at long forecast leads-even in a flux-adjusted model with reduced climatological biases. Other error sources also contribute to biases in simulated TC activity, with notable manifestations on regional scales. A novel finding is that the coupling and initialization of the land and atmosphere components can affect seasonal TC prediction skill. Simulated year-to-year variations in June land conditions over North America show a significant lead correlation with the North Atlantic large-scale environment and TC activity. Improved land-atmosphere initialization appears to improve the Atlantic TC predictions initialized in some summer months. For short-lead predictions initialized in June, the potential skill improvements attributable to land-atmosphere initialization might be comparable to those achievable with perfect SST predictions. Overall, this study delineates the SST and non-oceanic error sources in predicting TC activity and highlights avenues for improving predictions. The nudging-based evaluation framework can be applied to other models and help improve predictions of other weather extremes. |
author2 |
Zhang, Gan (author) Murakami, Hiroyuki (author) Yang, Xiaosong (author) Findell, Kirsten L. (author) Wittenberg, Andrew T. (author) Jia, Liwei (author) |
format |
Article in Journal/Newspaper |
title |
Dynamical seasonal predictions of tropical cyclone activity: Roles of sea surface temperature errors and atmosphere-land initialization |
spellingShingle |
Dynamical seasonal predictions of tropical cyclone activity: Roles of sea surface temperature errors and atmosphere-land initialization |
title_short |
Dynamical seasonal predictions of tropical cyclone activity: Roles of sea surface temperature errors and atmosphere-land initialization |
title_full |
Dynamical seasonal predictions of tropical cyclone activity: Roles of sea surface temperature errors and atmosphere-land initialization |
title_fullStr |
Dynamical seasonal predictions of tropical cyclone activity: Roles of sea surface temperature errors and atmosphere-land initialization |
title_full_unstemmed |
Dynamical seasonal predictions of tropical cyclone activity: Roles of sea surface temperature errors and atmosphere-land initialization |
title_sort |
dynamical seasonal predictions of tropical cyclone activity: roles of sea surface temperature errors and atmosphere-land initialization |
publishDate |
2021 |
url |
https://doi.org/10.1175/JCLI-D-20-0215.1 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
Journal of Climate--0894-8755--1520-0442 articles:24136 ark:/85065/d7r214rj doi:10.1175/JCLI-D-20-0215.1 |
op_rights |
Copyright 2021 American Meteorological Society (AMS). |
op_doi |
https://doi.org/10.1175/JCLI-D-20-0215.1 |
container_title |
Journal of Climate |
container_volume |
34 |
container_issue |
5 |
container_start_page |
1743 |
op_container_end_page |
1766 |
_version_ |
1797588709379407872 |