Climate Predictions with Multimodel Ensembles

By V. Kharin, F. Zwiers. 2002

211 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/2b13d1c9260c6dc24e686787400abcffc6a64093

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Citations in the corpus

Forecast calibration and combination: A simple Bayesian approach for ENSO (2004, methodology): “The inverse regression model is the typical regression model used in previous climate forecasting studies (e.g., Kharin and Zwiers 2002; Pavan and Doblas-Reyes 2000).”
Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, background): “The need for postprocessing of ensemble output is also discussed in the climate literature (Kharin and Zwiers 2002; Tebaldi and Knutti 2007; Smith et al. 2009).”
The rationale behind the success of multi-model ensembles in seasonal forecasting — I. Basic concept (2005, background): “…motivated by the the following facts: (i) robust optimal weights are difficult to calculate given the short samples available to train the models (Kharin and Zwiers, 2002; Peng et al., 2002), i.e. often the use of the simple multi-model is the only practical way of utilizing the multi-model…”
Skill of Multimodel ENSO Probability Forecasts (2008, background): “However, unless the difference in skill is sufficiently large, removing the “worse” model may have the unintended consequence of degrading skill (Kharin and Zwiers 2002; Kug et al. 2007).”
Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation (2005, background): “It is well documented in the literature that multiple regression or superensemble techniques improve the deterministic-style forecast accuracy of ensembles systems (Krishnamurti et al. 1999, 2000; Kharin and Zwiers 2002).”
Detecting anthropogenic influence with a multi‐model ensemble (2002, background): “[1999] and Kharin and Zwiers [2002] argued that a weighted sum of multiple model predictions performs better than predictions using an individual model.”
Development of New Ensemble Methods Based on the Performance Skills of Regional Climate Models over South Korea (2012, background): “…(Peng et al. 2002; Palmer et al. 2004), the second is a version of theweighted ensemblemethod (Krishnamurti et al. 1999; Giorgi and Mearns 2002; Kharin and Zwiers 2002; Kug et al. 2007; Christensen et al. 2010; Coppola et al. 2010; Feng et al. 2011), and the third is a synthetic method…”
The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, methodology): “The combination of single-model predictions to create a multi-model forecast by fitting different weights has already been addressed in long-range forecasting (Fraedrich and Smith, 1989; Sarda et al., 1996; Krishnamurti et al. 1999, 2000; Pavan and Doblas-Reyes 2000; Derome et al., 2001; Kharin and ”
Medium‐range multimodel ensemble combination and calibration (2009, methodology): “One method is to use multiple-regression based weights, as used by Krishnamurti et al. (1999); Kharin and Zwiers (2002); Doblas-Reyes et al. (2005); Stephenson et al. (2005).”
Multimodel Simulation of Water Flow in a Field Soil Using Pedotransfer Functions (2009, background): “Various statistics have been proposed to select the “best” subset of input variables (Neter and Wasserman, 1974; Kharin and Zwiers, 2002; Regonda et al., 2006).”
On the bias correction of general circulation model output for Indian summer monsoon (2013, methodology): “Kharin and Zwiers (2002) described this method as bias-removed individual forecast.”
Using Bayesian Model Averaging to Calibrate Forecast Ensembles (2005, methodology): “It has also been proposed that forecasts be combined using multiple linear regression to produce a single deterministic forecast or “superensemble” (Van den Dool and Rukhovets 1994; Krishnamurti et al. 1999; Kharin and Zwiers 2002).”