气候变化领域集成服务门户(CCPortal): Predicting the global temperature with the Stochastic Seasonal to Interannual Prediction System (StocSIPS)

CCPortal

DOI	10.1007/s00382-019-04791-4
	Predicting the global temperature with the Stochastic Seasonal to Interannual Prediction System (StocSIPS)
	Del Rio Amador L.; Lovejoy S.
发表日期	2019
ISSN	0930-7575
起始页码	4373
结束页码	4411
卷号	53 期号:2020-07-08
英文摘要	Many atmospheric fields—in particular the temperature—respect statistical symmetries that characterize the macroweather regime, i.e. time-scales between the ≈ 10 day lifetime of planetary sized structures and the (currently) 10–20 year scale at which the anthropogenic forcings begin to dominate the natural variability. The scale-invariance and the low intermittency of the fluctuations implies the existence of a huge memory in the system that can be exploited for macroweather forecasts using well-established (Gaussian) techniques. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. StocSIPS includes the previous ScaLIng Macroweather Model (SLIMM) as a core model for the prediction of the natural variability component of the temperature field. Here we present the theory for improving SLIMM using discrete-in-time fractional Gaussian noise processes to obtain an optimal predictor as a linear combination of past data. We apply StocSIPS to the prediction of globally-averaged temperature and confirm the applicability of the model with statistical testing of the hypothesis and a good agreement between the hindcast skill scores and the theoretical predictions. Finally, we compare StocSIPS with the Canadian Seasonal to Interannual Prediction System. From a forecast point of view, GCMs can be seen as an initial value problem for generating many “stochastic” realizations of the state of the atmosphere, while StocSIPS is effectively a past value problem that estimates the most probable future state from long series of past data. The results validate StocSIPS as a good alternative and a complementary approach to conventional numerical models. Temperature forecasts using StocSIPS are published on a regular basis in the website: http://www.physics.mcgill.ca/StocSIPS/. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
语种	英语
scopus关键词	air temperature; annual variation; climate modeling; Gaussian method; prediction; seasonal variation; stochasticity; weather forecasting
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/145948
作者单位	Physics, McGill University, 3600 University St., Montreal, QC H3A 2T8, Canada
推荐引用方式 GB/T 7714	Del Rio Amador L.,Lovejoy S.. Predicting the global temperature with the Stochastic Seasonal to Interannual Prediction System (StocSIPS)[J],2019,53(2020-07-08).
APA	Del Rio Amador L.,&Lovejoy S..(2019).Predicting the global temperature with the Stochastic Seasonal to Interannual Prediction System (StocSIPS).Climate Dynamics,53(2020-07-08).
MLA	Del Rio Amador L.,et al."Predicting the global temperature with the Stochastic Seasonal to Interannual Prediction System (StocSIPS)".Climate Dynamics 53.2020-07-08(2019).