Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1007/s00382-017-3903-7 |
Assessing the fidelity of predictability estimates | |
Pegion, Kathy1,2; DelSole, Timothy1,2; Becker, Emily3,4; Cicerone, Teresa1,2 | |
2019-12-01 | |
发表期刊 | CLIMATE DYNAMICS
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ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2019 |
卷号 | 53期号:12页码:7251-7265 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Predictability is an intrinsic limit of the climate system due to uncertainty in initial conditions and the chaotic nature of the atmosphere. Estimates of predictability together with calculations of current prediction skill are used to define the gaps in our prediction capabilities, inform future model developments, and indicate to stakeholders the potential for making forecasts that can inform their decisions. The true predictability of the climate system is not known and must be estimated, typically using a perfect model estimate from an ensemble prediction system. However, different prediction systems can give different estimates of predictability. Can we determine which estimate of predictability is most representative of the true predictability of the climate system? We test three metrics as potential indicators of the fidelity of predictability estimates in an idealized framework-the spread-error relationship, autocorrelation and skill. Using the North American multi-model ensemble re-forecast database, we quantify whether these metrics accurately indicate a model's ability to properly estimate predictability. It is found that none of these metrics is a robust measure for determining whether a predictability estimate is realistic for El Nino-Southern oscillation events. For temperature and precipitation over land, errors in the spread-error ratio are related to errors in estimating predictability at the shortest lead-times, while skill is not related to predictability errors. The relationship between errors in the autocorrelation and errors in estimating predictability varies by lead-time and region. |
英文关键词 | Predictability NMME |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000495247200009 |
WOS关键词 | FORECAST SKILL ; STOCHASTIC PHYSICS ; ENSO PREDICTION ; SPREAD ; ERROR ; MODEL ; WEATHER ; IMPACT |
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/224291 |
专题 | 环境与发展全球科技态势 |
作者单位 | 1.George Mason Univ, Dept Atmospher Ocean & Earth Sci, Fairfax, VA 22030 USA; 2.George Mason Univ, Ctr Ocean Land Atmosphere Studies, Fairfax, VA 22030 USA; 3.NOAA, Climate Predict Ctr, College Pk, MD USA; 4.Innovim LLC, College Pk, MD USA |
推荐引用方式 GB/T 7714 | Pegion, Kathy,DelSole, Timothy,Becker, Emily,et al. Assessing the fidelity of predictability estimates[J]. CLIMATE DYNAMICS,2019,53(12):7251-7265. |
APA | Pegion, Kathy,DelSole, Timothy,Becker, Emily,&Cicerone, Teresa.(2019).Assessing the fidelity of predictability estimates.CLIMATE DYNAMICS,53(12),7251-7265. |
MLA | Pegion, Kathy,et al."Assessing the fidelity of predictability estimates".CLIMATE DYNAMICS 53.12(2019):7251-7265. |
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