Show simple item record

dc.creatorMiller, Norman L.
dc.creatorDuffy, P. B.
dc.creatorCayan, Daniel R.
dc.creatorHidalgo León, Hugo G.
dc.creatorJin, J.
dc.creatorKanamaru, H.
dc.creatorKanamitsu, M.
dc.creatorO'Brien, T.
dc.creatorSchlegel, N. J.
dc.creatorSloan, L. C.
dc.creatorSnyder, M. A.
dc.creatorYoshimura, Kei
dc.date.accessioned2017-05-26T17:21:43Z
dc.date.available2017-05-26T17:21:43Z
dc.date.issued2008-06
dc.identifier.urihttp://hdl.handle.net/10669/29836
dc.descriptionPara citar: N.L. Miller, P.B. Duffy, D.R. Cayan, H. Hidalgo, J. Jin, H. Kanamaru, M. Kanamitsu, T. O’Brien, N.J. Schlegel, L.C. Sloan, M.A. Snyder, K. Yoshimura 2008. An Evaluation of Simulated California Climate Using Multiple Dynamical and Statistical Downscaling Techniques. The California Energy Commission, PIER Energy-Related Environmental Research Program. CEC-500-2008-XXX.es_ES
dc.description.abstractFour dynamic regional climate models (RCMs) and one statistical downscaling approach were used to downscale 10 years of historical climate in California. To isolate possible limitations of the downscaling methods, we used initial and lateral boundary conditions from the NCEP global reanalysis. Results of this downscaling were compared to observations and to an independent, fine-resolution reanalysis (NARR). This evaluation is preparation for simulations of future-climate scenarios, the second phase of this CEC scenarios project. Each model has its own strengths and weaknesses, which are reported here. In general, the dynamic models perform as well as other state-of-the-art dynamical regional climate models, and the statistical model has comparable or superior skill, although for a very limited set of meteorological variables. As is typical, the dynamical models have the most trouble simulating clouds, precipitation, and related processes, especially snow. This suggests that the weakest aspects of the models are parameterized subgrid scale processes, the hydrological cycle, and land surface processes. However, the resulting probabilistic ensemble simulations result in reduced model uncertainty and a better understanding of model spread.es_ES
dc.description.sponsorshipCalifornia Energy Commission PIER Program under the Climate Change Sub-program///Estados Unidoses_ES
dc.language.isoen_USes_ES
dc.sourceCalifornia Energy Commissiones_ES
dc.subjectCaliformia Climatees_ES
dc.subjectBaseline simulationes_ES
dc.subjectDynamic and statistic downscalinges_ES
dc.titleCalifornia Climate Change Center Report Series Number 2008-0XXes_ES
dc.typeinfo:eu-repo/semantics/reportes_ES
dc.typeInforme de investigaciónes_ES
dc.description.procedenceUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI)es_ES


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record