Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1175/JCLI-D-18-0224.1 |
Calibrated Probabilistic Forecasts of Arctic Sea Ice Concentration | |
Dirkson, Arlan1; Merryfield, William J.2; Monahan, Adam H.3 | |
2019-02-01 | |
发表期刊 | JOURNAL OF CLIMATE |
ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2019 |
卷号 | 32期号:4页码:1251-1271 |
文章类型 | Article |
语种 | 英语 |
国家 | Canada |
英文摘要 | Seasonal forecasts of Arctic sea ice using dynamical models are inherently uncertain and so are best communicated in terms of probabilities. Here, we describe novel statistical postprocessing methodologies intended to improve ensemble-based probabilistic forecasts of local sea ice concentration (SIC). The first of these improvements is the application of the parametric zero-and one-inflated beta (BEINF) probability distribution, suitable for doubly bounded variables such as SIC, for obtaining a smoothed forecast probability distribution. The second improvement is the introduction of a novel calibration technique, termed trendadjusted quantile mapping (TAQM), that explicitly takes into account SIC trends and is applied using the BEINF distribution. We demonstrate these methods using a set of 10-member ensemble SIC hindcasts from the Third Generation Canadian Climate Coupled Global Climate Model (CanCM3) over the period 19812017. Though fitting ensemble SIC hindcasts to the BEINF distribution consistently improves probabilistic hindcast skill relative to a simpler `` count based'' probability approach in perfect model experiments, it does not itself correct model biases that may reduce this improvement when verifying against observations. The TAQM calibration technique is effective at removing SIC biases present in CanCM3 and improving forecast reliability. Over the recent 2000-17 period, TAQM-calibrated SIC hindcasts show improved skill relative to uncalibrated hindcasts. Compared against a climatological reference forecast adjusted for the trend, TAQMcalibrated hindcasts show widespread skill, particularly in September, even at 3-4-month lead times. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000473259100001 |
WOS关键词 | MULTIMODEL ENSEMBLE ; CLIMATE ; SKILL ; VARIABILITY ; MODELS ; TERM |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/181276 |
专题 | 气候变化 |
作者单位 | 1.Univ Quebec Montreal, Ctr Etud & Simulat Climat Echelle Reg, Montreal, PQ, Canada; 2.Environm & Climate Change Canada, Canadian Ctr Climate Modelling & Anal, Victoria, BC, Canada; 3.Univ Victoria, Sch Earth & Ocean Sci, Victoria, BC, Canada |
推荐引用方式 GB/T 7714 | Dirkson, Arlan,Merryfield, William J.,Monahan, Adam H.. Calibrated Probabilistic Forecasts of Arctic Sea Ice Concentration[J]. JOURNAL OF CLIMATE,2019,32(4):1251-1271. |
APA | Dirkson, Arlan,Merryfield, William J.,&Monahan, Adam H..(2019).Calibrated Probabilistic Forecasts of Arctic Sea Ice Concentration.JOURNAL OF CLIMATE,32(4),1251-1271. |
MLA | Dirkson, Arlan,et al."Calibrated Probabilistic Forecasts of Arctic Sea Ice Concentration".JOURNAL OF CLIMATE 32.4(2019):1251-1271. |
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