DOI: 10.5194/tc-11-1625-2017
Scopus记录号: 2-s2.0-85023175583
论文题名: Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models
作者: Terzago S ; , Von Hardenberg J ; , Palazzi E ; , Provenzale A
刊名: Cryosphere
ISSN: 19940416
出版年: 2017
卷: 11, 期: 4 起始页码: 1625
结束页码: 1645
语种: 英语
英文关键词: air temperature
; alpine environment
; annual cycle
; climate modeling
; CMIP
; data set
; general circulation model
; mountain region
; regional climate
; remote sensing
; satellite altimetry
; snow water equivalent
; Alps
英文摘要: The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow-climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized. This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes - near-surface air temperature and precipitation - against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections. The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations. © 2017 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75530
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Corso Fiume 4, Turin, Italy; Institute of Geosciences and Earth Resources, National Research Council of Italy, Via Moruzzi 1, Pisa, Italy
Recommended Citation:
Terzago S,, Von Hardenberg J,, Palazzi E,et al. Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models[J]. Cryosphere,2017-01-01,11(4)