Optimally choosing small ensemble members to produce robust climate simulations
Bibliographic Data
| ID | 15547220 |
|---|---|
| Authors | Jason P Evans (0000-0003-1776-3429, UNSW Sydney, corresponding author), Fei Ji (0000-0002-7985-1558, NSW Environment and Heritage), Gab Abramowitz (0000-0002-4205-001X, UNSW Sydney), Marie Ekström (0000-0001-9716-2337, CSIRO Land and Water) |
| Year | 2013 |
| Volume | 8 |
| Issue | 4 |
| Pages | 044050-044050 |
| Publication date | 2013-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/8/4/044050 |
| OpenAlex | W2166412315 |
| Language | EN |
| Citations received | 2 |
| References cited | 24 |
This study examines the subset climate model ensemble size required to reproduce certain statistical characteristics from a full ensemble. The ensemble characteristics examined are the root mean square error, the ensemble mean and standard deviation. Subset ensembles are created using measures that consider the simulation performance alone or include a measure of simulation independence relative to other ensemble members. It is found that the independence measure is able to identify smaller subset ensembles that retain the desired full ensemble characteristics than either of the performance based measures. It is suggested that model independence be considered when choosing ensemble subsets or creating new ensembles. © 2013 IOP Publishing Ltd
Canonical ensemble · Climatology · Data mining · Ensemble average · Ensemble forecasting · Ensemble learning · Independence (probability theory · Measure (data warehouse · Monte Carlo method · Physics · Standard deviation · Statistical ensemble · Statistical physics · Statistics · Atmospheric and Environmental Gas Dynamics · Climate variability and models · Computer Science · Mathematics · Meteorological Phenomena and Simulations · Artificial Intelligence
| Citations per year | 0.25 |
|---|---|
| Citation span | 2018 - 2019 (2) |
| Highly cited | No |