DOI: 10.1002/joc.5848
论文题名: 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
作者: Bayasgalan G. ; Ahn J.-B.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2018
卷: 38, 期: 14 起始页码: 5418
结束页码: 5429
语种: 英语
英文关键词: artificial neural network
; coupled general circulation model
; Mongolian temperature
; seasonal prediction
Scopus关键词: Atmospheric temperature
; Climatology
; Forecasting
; Neural networks
; Sea ice
; Sea level
; Surface waters
; Coupled general circulation models
; Mongolians
; Multiple linear regressions
; Probabilistic forecasts
; Sea surface temperature (SST)
; Seasonal prediction
; Statistical downscaling
; Surface air temperatures
; Climate models
; air temperature
; artificial neural network
; climate prediction
; general circulation model
; hindcasting
; sea ice
; sea surface temperature
; winter
; Arctic Ocean
; Barents Sea
; Chukchi Sea
; Kara Sea
; Mongolia
英文摘要: 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, TImdl and SHImdl obtained from PNU CGCM, and TIMLR and SHIMLR 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. © 2018 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116717
Appears in Collections: 气候减缓与适应
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作者单位: Division of Earth Environmental System, Pusan National University, Busan, South Korea
Recommended Citation:
Bayasgalan G.,Ahn J.-B.. 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[J]. International Journal of Climatology,2018-01-01,38(14)