scholarly journals A climate model projection weighting scheme accounting for performance and interdependence

2017 ◽  
Author(s):  
Reto Knutti ◽  
Jan Sedláček ◽  
Benjamin M. Sanderson ◽  
Ruth Lorenz ◽  
Erich M. Fischer ◽  
...  
Keyword(s):  
Climate Model ◽  
Weighting Scheme ◽  
Model Projection ◽  
Climate Model Projection

scholarly journals A novel method for assessing climate change impacts in ecotron experiments

2020 ◽  
Vol 64 (10) ◽  
pp. 1709-1727
Author(s):  
Inne Vanderkelen ◽  
Jakob Zscheischler ◽  
Lukas Gudmundsson ◽  
Klaus Keuler ◽  
Francois Rineau ◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Air Temperature ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Forcing ◽  
Future Climate Change ◽  
Ecosystem Responses ◽  
Model Projection ◽  
Global Mean

Abstract Ecotron facilities allow accurate control of many environmental variables coupled with extensive monitoring of ecosystem processes. They therefore require multivariate perturbation of climate variables, close to what is observed in the field and projections for the future. Here, we present a new method for creating realistic climate forcing for manipulation experiments and apply it to the UHasselt Ecotron experiment. The new methodology uses data derived from the best available regional climate model projection and consists of generating climate forcing along a gradient representative of increasingly high global mean air temperature anomalies. We first identified the best-performing regional climate model simulation for the ecotron site from the Coordinated Regional Downscaling Experiment in the European domain (EURO-CORDEX) ensemble based on two criteria: (i) highest skill compared to observations from a nearby weather station and (ii) representativeness of the multi-model mean in future projections. The time window is subsequently selected from the model projection for each ecotron unit based on the global mean air temperature of the driving global climate model. The ecotron units are forced with 3-hourly output from the projections of the 5-year period in which the global mean air temperature crosses the predefined values. With the new approach, Ecotron facilities become able to assess ecosystem responses on changing climatic conditions, while accounting for the co-variation between climatic variables and their projection in variability, well representing possible compound events. The presented methodology can also be applied to other manipulation experiments, aiming at investigating ecosystem responses to realistic future climate change.

scholarly journals A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 775 ◽  
Author(s):  
Yonggwan Shin ◽  
Youngsaeng Lee ◽  
Jeong-Soo Park
Keyword(s):  
Bayesian Model Averaging ◽  
Climate Model ◽  
Model Performance ◽  
Coupled Model ◽  
Model Averaging ◽  
Mapping Method ◽  
Weighting Scheme ◽  
Climate Simulation ◽  
Ensemble Prediction ◽  
Test Statistic

A model weighting scheme is important in multi-model climate ensembles for projecting future changes. The climate model output typically needs to be bias corrected before it can be used. When a bias-correction (BC) is applied, equal model weights are usually derived because some BC methods cause the observations and historical simulation to match perfectly. This equal weighting is sometimes criticized because it does not take into account the model performance. Unequal weights reflecting model performance may be obtained from raw data before BC is applied. However, we have observed that certain models produce excessively high weights, while the weights generated in all other models are extremely low. This phenomenon may be partly due to the fact that some models are more fit or calibrated to the observations for a given applications. To address these problems, we consider, in this study, a hybrid weighting scheme including both equal and unequal weights. The proposed approach applies an “imperfect” correction to the historical data in computing their weights, while it applies ordinary BC to the future data in computing the ensemble prediction. We employ a quantile mapping method for the BC and a Bayesian model averaging for performance-based weighting. Furthermore, techniques for selecting the optimal correction rate based on the chi-square test statistic and the continuous ranked probability score are examined. Comparisons with ordinary ensembles are provided using a perfect model test. The usefulness of the proposed method is illustrated using the annual maximum daily precipitation as observed in the Korean peninsula and simulated by 21 models from the Coupled Model Intercomparison Project Phase 6.

GISS Model E2.2: A Climate Model Optimized for the Middle Atmosphere—2. Validation of Large‐Scale Transport and Evaluation of Climate Response

2020 ◽  
Vol 125 (24) ◽  
Author(s):  
Clara Orbe ◽  
David Rind ◽  
Jeffrey Jonas ◽  
Larissa Nazarenko ◽  
Greg Faluvegi ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Middle Atmosphere ◽  
Climate Response

Legacy of a 100-year-old climate model

Nature ◽  
2020 ◽  
Vol 583 (7815) ◽  
pp. 202-202
Author(s):  
Marco Romano ◽  
Bruce Rubidge
Keyword(s):  
Climate Model

Qinghai-Xizang (Tibetan) Plateau climate simulation using the regional climate model RegCM3

2013 ◽  
Vol 57 (3) ◽  
pp. 173-186 ◽  
Author(s):  
X Wang ◽  
M Yang ◽  
G Wan ◽  
X Chen ◽  
G Pang
Keyword(s):  
Tibetan Plateau ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Simulation
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