气候变化领域集成服务门户(CCPortal): Linear Inverse Modeling for Coupled Atmosphere-Ocean Ensemble Climate Prediction

CCPortal

DOI	10.1029/2019MS001778
	Linear Inverse Modeling for Coupled Atmosphere-Ocean Ensemble Climate Prediction
	Perkins W.A.; Hakim G.
发表日期	2020
ISSN	19422466
卷号	12 期号:1
英文摘要	Paleoclimate data assimilation (PDA) experiments reconstruct climate fields by objectively blending information from climate models and proxy observations. Due to high computational cost and relatively low forecast skill, most reconstruction experiments omit the prediction step, where a forecast is made from the previously reconstructed state to the next time proxy data is available. In order to enable this critical aspect of PDA, we propose an efficient method of generating forecast ensembles of coupled climate fields using a linear inverse model (LIM). We describe the general calibration of a LIM on multiple fields using a two-step empirical orthogonal function field compression to efficiently represent the state. We find that a LIM calibrated on global climate model (GCM) data yields skillful forecasts, including for out-of-sample tests on data from a different GCM. The deterministic forecast skill tests for scalar indices show that the LIM outperforms damped persistence at leads up to 3 years and has skill up to 10 years for global average sea surface temperature. Analysis of 1-year forecasts reveals that the LIM captures dynamic climate features with local and remote predictability related to teleconnections. The forecast ensemble characteristics of the LIM, which in part determine the weighting of information for PDA experiments, show that the LIM generally produces ensemble forecast errors that are 10% to 70% larger than ensemble variance for 1-year forecasts on data representative of the last millennium. These results show that the LIM produces ensembles with reasonable calibration but also that LIMs for PDA may require some variance tuning to work optimally for data assimilation experiments. ©2019. The Authors.
英文关键词	climate reanalysis; coupled climate model; empirical climate model; ensemble forecasting; linear inverse model
语种	英语
scopus关键词	Blending; Calibration; Forecasting; Inverse problems; Oceanography; Orthogonal functions; Surface waters; Coupled climate model; Deterministic forecasts; Empirical Orthogonal Function; Ensemble forecasting; Global climate model; Linear inverse models; Reanalysis; Sea surface temperature (SST); Climate models; atmosphere-ocean coupling; climate modeling; climate prediction; ensemble forecasting; global climate; modeling; proxy climate record; reconstruction; teleconnection
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156773
作者单位	Department of Atmospheric Sciences, University of Washington, Seattle, WA, United States
推荐引用方式 GB/T 7714	Perkins W.A.,Hakim G.. Linear Inverse Modeling for Coupled Atmosphere-Ocean Ensemble Climate Prediction[J],2020,12(1).
APA	Perkins W.A.,&Hakim G..(2020).Linear Inverse Modeling for Coupled Atmosphere-Ocean Ensemble Climate Prediction.Journal of Advances in Modeling Earth Systems,12(1).
MLA	Perkins W.A.,et al."Linear Inverse Modeling for Coupled Atmosphere-Ocean Ensemble Climate Prediction".Journal of Advances in Modeling Earth Systems 12.1(2020).