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Title: Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”

Journal Article · · Journal of Agricultural, Biological and Environmental Statistics
 [1];  [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States). Computational Science Center

We thank the authors for this interesting paper that highlights important ideas and concepts for the future of climate model ensembles and their storage, as well as future uses of stochastic emulators. Stochastic emulators are particularly relevant because of the statistical nature of climate model ensembles, as discussed in previous work of the authors (Castruccio et al. in J Clim 32:8511–8522, 2019; Hu and Castruccio in J Clim 34:8409–8418, 2021). We thank the authors for sharing of some of their data with us in order to illustrate this discussion. In the following, in Sect. 1 we discuss alternative techniques currently used and studied, namely lossy compression and ideas emerging from the climate modeling community, that could feed the discussion on ensemble and storage. In that section, we also present numerical results of compression performed on the data shared by the authors. In Sect.  2, we discuss the current statistical model proposed by the authors and its context. We discuss other potential uses of stochastic emulators in climate and Earth modeling.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE National Renewable Energy Laboratory (NREL); USDOE
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1973272
Alternate ID(s):
OSTI ID: 1984222
Report Number(s):
NREL/JA-2C00-86477; MainId:87250; UUID:a3dfbf63-1444-46d5-b4cc-ffa4d04df88d; MainAdminID:69707
Journal Information:
Journal of Agricultural, Biological and Environmental Statistics, Vol. 28, Issue 2; ISSN 1085-7117
Publisher:
International Biometric SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (1)

Rejoinder on ‘Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach’ journal May 2023