Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network
The hindcast data of Pusan National University coupled general circulation model (PNU CGCM), a participant model of the Asia‐Pacific Economic Cooperation Climate Center (APCC) Multi‐Model Ensemble Climate Prediction System, and August–October sea‐surface temperature (SST) in the northern Barents–Kar...
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crwiley:10.1002/joc.5848 2024-06-02T08:05:21+00:00 Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network Bayasgalan, Gerelchuluun Ahn, Joong‐Bae Rural Development Administration 2018 http://dx.doi.org/10.1002/joc.5848 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjoc.5848 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.5848 en eng Wiley http://creativecommons.org/licenses/by-nc-nd/4.0/ International Journal of Climatology volume 38, issue 14, page 5418-5429 ISSN 0899-8418 1097-0088 journal-article 2018 crwiley https://doi.org/10.1002/joc.5848 2024-05-03T12:07:05Z The hindcast data of Pusan National University coupled general circulation model (PNU CGCM), a participant model of the Asia‐Pacific Economic Cooperation Climate Center (APCC) Multi‐Model Ensemble Climate Prediction System, and August–October sea‐surface temperature (SST) in the northern Barents–Kara Sea (BKI) and the sea‐ice extent (SIE) in the Chukchi Sea (East Siberian Sea index [ESI]) are used for predicting 20 × 20‐km‐resolution anomalous surface air temperature at 2‐m height (aT2m) over Mongolia for boreal winter. For this purpose, area‐averaged surface air temperature (TI) and sea‐level pressure (SLP) over Mongolia are defined. Then four large‐scale indices, TI mdl and SHI mdl obtained from PNU CGCM, and TI MLR and SHI MLR obtained from multiple linear regressions on BKI and ESI, are incorporated using the artificial neural network (ANN) method for the prediction and statistical downscaling to obtain the monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter. An additional statistical method, which uses BKI and ESI as predictors of TI and SHI together with dynamic prediction by the CGCM, is used because of the relatively low skill of seasonal predictions by most of the state‐of‐the‐art models and the multi‐model ensemble systems over high‐latitude landlocked Eurasian regions such as Mongolia. The results show that the predictabilities of monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter are improved by applying ANN to both statistical and dynamical predictions compared to utilizing only dynamic prediction. The predictability gained by the proposed method is also demonstrated by the probabilistic forecast implying that the method forecasts aT2m over Mongolia in winter reasonably well. Article in Journal/Newspaper Chukchi Chukchi Sea East Siberian Sea Kara Sea Sea ice Wiley Online Library Chukchi Sea East Siberian Sea ENVELOPE(166.000,166.000,74.000,74.000) Kara Sea Pacific International Journal of Climatology 38 14 5418 5429 |
institution |
Open Polar |
collection |
Wiley Online Library |
op_collection_id |
crwiley |
language |
English |
description |
The hindcast data of Pusan National University coupled general circulation model (PNU CGCM), a participant model of the Asia‐Pacific Economic Cooperation Climate Center (APCC) Multi‐Model Ensemble Climate Prediction System, and August–October sea‐surface temperature (SST) in the northern Barents–Kara Sea (BKI) and the sea‐ice extent (SIE) in the Chukchi Sea (East Siberian Sea index [ESI]) are used for predicting 20 × 20‐km‐resolution anomalous surface air temperature at 2‐m height (aT2m) over Mongolia for boreal winter. For this purpose, area‐averaged surface air temperature (TI) and sea‐level pressure (SLP) over Mongolia are defined. Then four large‐scale indices, TI mdl and SHI mdl obtained from PNU CGCM, and TI MLR and SHI MLR obtained from multiple linear regressions on BKI and ESI, are incorporated using the artificial neural network (ANN) method for the prediction and statistical downscaling to obtain the monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter. An additional statistical method, which uses BKI and ESI as predictors of TI and SHI together with dynamic prediction by the CGCM, is used because of the relatively low skill of seasonal predictions by most of the state‐of‐the‐art models and the multi‐model ensemble systems over high‐latitude landlocked Eurasian regions such as Mongolia. The results show that the predictabilities of monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter are improved by applying ANN to both statistical and dynamical predictions compared to utilizing only dynamic prediction. The predictability gained by the proposed method is also demonstrated by the probabilistic forecast implying that the method forecasts aT2m over Mongolia in winter reasonably well. |
author2 |
Rural Development Administration |
format |
Article in Journal/Newspaper |
author |
Bayasgalan, Gerelchuluun Ahn, Joong‐Bae |
spellingShingle |
Bayasgalan, Gerelchuluun Ahn, Joong‐Bae Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network |
author_facet |
Bayasgalan, Gerelchuluun Ahn, Joong‐Bae |
author_sort |
Bayasgalan, Gerelchuluun |
title |
Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network |
title_short |
Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network |
title_full |
Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network |
title_fullStr |
Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network |
title_full_unstemmed |
Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network |
title_sort |
seasonal prediction of high‐resolution temperature at 2‐m height over mongolia during boreal winter using both coupled general circulation model and artificial neural network |
publisher |
Wiley |
publishDate |
2018 |
url |
http://dx.doi.org/10.1002/joc.5848 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjoc.5848 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.5848 |
long_lat |
ENVELOPE(166.000,166.000,74.000,74.000) |
geographic |
Chukchi Sea East Siberian Sea Kara Sea Pacific |
geographic_facet |
Chukchi Sea East Siberian Sea Kara Sea Pacific |
genre |
Chukchi Chukchi Sea East Siberian Sea Kara Sea Sea ice |
genre_facet |
Chukchi Chukchi Sea East Siberian Sea Kara Sea Sea ice |
op_source |
International Journal of Climatology volume 38, issue 14, page 5418-5429 ISSN 0899-8418 1097-0088 |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
op_doi |
https://doi.org/10.1002/joc.5848 |
container_title |
International Journal of Climatology |
container_volume |
38 |
container_issue |
14 |
container_start_page |
5418 |
op_container_end_page |
5429 |
_version_ |
1800750147717562368 |