气候变化领域集成服务门户(CCPortal): An assessment of scale-dependent variability and bias in global prediction models

CCPortal

DOI	10.1007/s00382-019-05001-x
	An assessment of scale-dependent variability and bias in global prediction models
	Žagar N.; Kosovelj K.; Manzini E.; Horvat M.; Castanheira J.
发表日期	2020
ISSN	0930-7575
起始页码	287
结束页码	306
卷号	54 期号:2020-01-02
英文摘要	The paper presents a method for the scale-dependent validation of the spatio-temporal variability in global weather or climate models and for their bias quantification in relation to dynamics. The method provides a relationship between the bias and simulated spatial and temporal variance by a model in comparison with verifying reanalysis data. For the low resolution (T30L8) subset of ERA-20C data, it was found that 80–90% (depending on season) of the global interannual variance is at planetary scales (zonal wavenumbers k = 0−3), and only about 1% of the variance is at scales with k> 7. The reanalysis is used to validate a T30L8 GCM in two configurations, one with the prescribed sea-surface temperature (SST) and another using a slab ocean model. Although the model with the prescribed SST represents the average properties of surface fields well, the interannual variability is underestimated at all scales. Similar to variability, model bias is strongly scale dependent. Biases found in the experiment with the prescribed SST are largely increased in the experiment using a slab ocean, especially in k= 0 , in scales with missing variability and in seasons with poorly simulated energy distribution. The perfect model scenario (a comparison between the GCM coupled to a slab ocean vs. the same model with prescribed SSTs) shows that the representation of the ocean is not critical for synoptic to subsynoptic variability, but essential for capturing the planetary scales. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Bias spectra; Climate models; Model validation; Spatio-temporal variability; Variability quantification
语种	英语
scopus关键词	annual variation; climate modeling; climate prediction; model validation; numerical model; sea surface temperature; spatial resolution; spatiotemporal analysis
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/145763
作者单位	Meteorologisches Institut, CEN, Universität Hamburg, Grindelberg 7, Hamburg, 20144, Germany; Faculty of Mathematics and Physics, University of Ljubljana, Jadranska 19, Ljubljana, 1000, Slovenia; University of Ljubljana, Jadranska 19, Ljubljana, 1000, Slovenia; Max-Planck-Institut fur Meteorologie, Bundesstraße 53, Hamburg, 20146, Germany; CESAM and Department of Physics, University of Aveiro, Campus de Santiago, Aveiro, 3810-193, Portugal
推荐引用方式 GB/T 7714	Žagar N.,Kosovelj K.,Manzini E.,et al. An assessment of scale-dependent variability and bias in global prediction models[J],2020,54(2020-01-02).
APA	Žagar N.,Kosovelj K.,Manzini E.,Horvat M.,&Castanheira J..(2020).An assessment of scale-dependent variability and bias in global prediction models.Climate Dynamics,54(2020-01-02).
MLA	Žagar N.,et al."An assessment of scale-dependent variability and bias in global prediction models".Climate Dynamics 54.2020-01-02(2020).