scholarly journals A Bayesian hierarchical approach for spatial analysis of climate model bias in multi-model ensembles

2017 ◽  
Vol 31 (10) ◽  
pp. 2645-2657 ◽  
Author(s):  
Maeregu Woldeyes Arisido ◽  
Carlo Gaetan ◽  
Davide Zanchettin ◽  
Angelo Rubino
Keyword(s):  
Spatial Analysis ◽  
Climate Model ◽  
Hierarchical Approach ◽  
Model Bias ◽  
Bayesian Hierarchical ◽  
Climate Model Bias ◽  
Model Ensembles

Climate model bias correction for nonstationary conditions

2020 ◽  
Vol 47 (3) ◽  
pp. 326-336
Author(s):  
Mohammad Madani ◽  
Vinod Chilkoti ◽  
Tirupati Bolisetti ◽  
Rajesh Seth
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Bias Reduction ◽  
Historical Period ◽  
Monthly Precipitation ◽  
Model Bias ◽  
New Methods ◽  
Climate Model Bias

In most of the climate change impact assessment studies, climate model bias is considered to be stationary between the control and scenario periods. Few methods are found in the literature that addresses the issue of nonstationarity in correcting the bias. To overcome the shortcomings reported in these approaches, three new methods of bias correction (NBC_μ, NBC_σ, and NBC_bs) are presented. The methods are improvised versions of previous techniques relying on distribution mapping. The methods are tested using split sample approach over 50-year historical period for nine climate stations in Ontario, using six regional climate models. The average bias reduction improvement by new methods, in mean daily and monthly precipitation, was found to be 73.9%, 74.3%, and 77.4%, respectively, higher than that obtained by the previous methods (eQM 67.7% and CNCDFm_NP 64.1%). Thus, the methods are found to be more effective in accounting for nonstationarity in the model bias.

Plausible effect of climate model bias on abrupt climate change simulations in Atlantic sector

2011 ◽  
Vol 58 (17-18) ◽  
pp. 1904-1913 ◽  
Author(s):  
Xiuquan Wan ◽  
Ping Chang ◽  
Charles S. Jackson ◽  
Link Ji ◽  
Mingkui Li
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Abrupt Climate Change ◽  
Model Bias ◽  
Atlantic Sector ◽  
Climate Model Bias

scholarly journals Adjusting climate model bias for agricultural impact assessment: How to cut the mustard

2019 ◽  
Vol 13 ◽  
pp. 65-69 ◽  
Author(s):  
S. Galmarini ◽  
A.J. Cannon ◽  
A. Ceglar ◽  
O.B. Christensen ◽  
N. de Noblet-Ducoudré ◽  
...  
Keyword(s):  
Impact Assessment ◽  
Climate Model ◽  
Model Bias ◽  
Climate Model Bias ◽  
Agricultural Impact

scholarly journals Striking stationarity of large-scale climate model bias patterns under strong climate change

2018 ◽  
Vol 115 (38) ◽  
pp. 9462-9466 ◽  
Author(s):  
Gerhard Krinner ◽  
Mark G. Flanner
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Future Climate Change ◽  
Model Bias ◽  
Climate Model Bias ◽  
Regional Downscaling ◽  
Almost All ◽  
Historical Periods

Because all climate models exhibit biases, their use for assessing future climate change requires implicitly assuming or explicitly postulating that the biases are stationary or vary predictably. This hypothesis, however, has not been, and cannot be, tested directly. This work shows that under very large climate change the bias patterns of key climate variables exhibit a striking degree of stationarity. Using only correlation with a model’s preindustrial bias pattern, a model’s 4xCO2bias pattern is objectively and correctly identified among a large model ensemble in almost all cases. This outcome would be exceedingly improbable if bias patterns were independent of climate state. A similar result is also found for bias patterns in two historical periods. This provides compelling and heretofore missing justification for using such models to quantify climate perturbation patterns and for selecting well-performing models for regional downscaling. Furthermore, it opens the way to extending bias corrections to perturbed states, substantially broadening the range of justified applications of climate models.

scholarly journals Temperature seasonality in the North American continental interior during the early Eocene climatic optimum

2018 ◽  
Author(s):  
Ethan G. Hyland ◽  
Katharine W. Huntington ◽  
Nathan D. Sheldon ◽  
Tammo Reichgelt
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Early Eocene ◽  
Early Eocene Climatic Optimum ◽  
The North ◽  
Annual Range ◽  
Climatic Optimum ◽  
Temperature Records ◽  
Model Ensembles ◽  
Continental Interior

Abstract. Paleogene greenhouse climate equability has long been a paradox in paleoclimate research. However, recent developments in proxy and modeling methods have suggested that strong seasonality may be a feature of at least some greenhouse periods. Here we present the first multi-proxy record of seasonal temperatures during the Paleogene from paleofloras, paleosol geochemistry, and carbonate clumped isotope thermometry in the Green River Basin (Wyoming, USA). These combined temperature records allow for the reconstruction of past seasonality in the continental interior, which shows that temperatures were warmer in all seasons during the peak early Eocene climatic optimum and that the mean annual range of temperature was high, similar to the modern value (~ 26 °C). Proxy data and downscaled Eocene regional climate model results suggest amplified seasonality during greenhouse events. Increased seasonality reconstructed for the early Eocene is similar in scope to the higher seasonal range predicted by downscaled climate model ensembles for future high-CO2 emissions scenarios. Overall, these data and model comparisons have substantial implications for understanding greenhouse climates in general, and may be important for predicting future seasonal climate regimes and their impacts in continental regions.

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