气候变化领域集成服务门户(CCPortal): Climate field completion via markov random fields: Application to the HadCRUT4.6 temperature dataset

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DOI	10.1175/JCLI-D-19-0814.1
	Climate field completion via markov random fields: Application to the HadCRUT4.6 temperature dataset
	Vaccaro A.; Emile-Geay J.; Guillot D.; Verna R.; Morice C.; Kennedy J.; Rajaratnam B.
发表日期	2021
ISSN	08948755
起始页码	4169
结束页码	4188
卷号	34 期号:10
英文摘要	Surface temperature is a vital metric of Earth’s climate state but is incompletely observed in both space and time: over half of monthly values are missing from the widely used HadCRUT4.6 global surface temperature dataset. Here we apply the graphical expectation–maximization algorithm (GraphEM), a recently developed imputation method, to construct a spatially complete estimate of HadCRUT4.6 temperatures. GraphEM leverages Gaussian Markov random fields (also known as Gaussian graphical models) to better estimate covariance relationships within a climate field, detecting anisotropic features such as land–ocean contrasts, orography, ocean currents, and wave-propagation pathways. This detection leads to improved estimates of missing values compared to methods (such as kriging) that assume isotropic covariance relationships, as we show with real and synthetic data. This interpolated analysis of HadCRUT4.6 data is available as a 100-member ensemble, propagating information about sampling variability available from the original HadCRUT4.6 dataset. A comparison of Niño-3.4 and global mean monthly temperature series with published datasets reveals similarities and differences due in part to the spatial interpolation method. Notably, the GraphEM-completed HadCRUT4.6 global temperature displays a stronger early twenty-first-century warming trend than its uninterpolated counterpart, consistent with recent analyses using other datasets. Known events like the 1877/78 El Niño are recovered with greater fidelity than with kriging, and result in different assessments of changes in ENSO variability through time. Gaussian Markov random fields provide a more geophysically motivated way to impute missing values in climate fields, and the associated graph provides a powerful tool to analyze the structure of teleconnection patterns. We close with a discussion of wider applications of Markov random fields in climate science. © 2021 American Meteorological Society.
英文关键词	Climate change; ENSO; Interpolation schemes; Sea surface temperature; Statistical techniques; Surface temperature
语种	英语
scopus关键词	Atmospheric temperature; Climatology; Gaussian distribution; Gaussian noise (electronic); Interpolation; Markov processes; Ocean currents; Surface properties; Wave propagation; Gaussian graphical models; Gaussian Markov random field; Global surface temperature; Markov Random Fields; Maximization algorithm; Spatial interpolation method; Surface temperatures; Teleconnection patterns; Climate models
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178566
作者单位	Department of Earth Sciences, University of Southern California, Los Angeles, CA, United States; Department of Mathematical Sciences, University of Delaware, Newark, DE, United States; Met Office Hadley Centre, Exeter, United Kingdom; Department of Statistics, University of California, Davis, Davis, CA, United States
推荐引用方式 GB/T 7714	Vaccaro A.,Emile-Geay J.,Guillot D.,et al. Climate field completion via markov random fields: Application to the HadCRUT4.6 temperature dataset[J],2021,34(10).
APA	Vaccaro A..,Emile-Geay J..,Guillot D..,Verna R..,Morice C..,...&Rajaratnam B..(2021).Climate field completion via markov random fields: Application to the HadCRUT4.6 temperature dataset.Journal of Climate,34(10).
MLA	Vaccaro A.,et al."Climate field completion via markov random fields: Application to the HadCRUT4.6 temperature dataset".Journal of Climate 34.10(2021).