scholarly journals Estimating heat stress from climate-based indicators: present-day biases and future spreads in the CMIP5 global climate model ensemble

2015 ◽  
Vol 10 (8) ◽  
pp. 084013 ◽  
Author(s):  
Y Zhao ◽  
A Ducharne ◽  
B Sultan ◽  
P Braconnot ◽  
R Vautard
Keyword(s):  
Heat Stress ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Model Ensemble

scholarly journals Comparison of CMIP5 and CMIP6 Multi-Model Ensemble for Precipitation Downscaling Results and Observational Data: The Case of Hanjiang River Basin

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 867
Author(s):  
Dong Wang ◽  
Jiahong Liu ◽  
Weiwei Shao ◽  
Chao Mei ◽  
Xin Su ◽  
...  
Keyword(s):  
River Basin ◽  
Seasonal Variations ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Poor Performance ◽  
Model Ensemble ◽  
Downscaling Method ◽  
Simulated Precipitation ◽  
Hanjiang River

Evaluating global climate model (GCM) outputs is essential for accurately simulating future hydrological cycles using hydrological models. The GCM multi-model ensemble (MME) precipitation simulations of the Climate Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6, respectively) were spatially and temporally downscaled according to a multi-site statistical downscaling method for the Hanjiang River Basin (HRB), China. Downscaled precipitation accuracy was assessed using data collected from 14 meteorological stations in the HRB. The spatial performances, temporal performances, and seasonal variations of the downscaled CMIP5-MME and CMIP6-MME were evaluated and compared with observed data from 1970–2005. We found that the multi-site downscaling method accurately downscaled the CMIP5-MME and CMIP6-MME precipitation simulations. The downscaled precipitation of CMIP5-MME and CMIP6-MME captured the spatial pattern, temporal pattern, and seasonal variations; however, precipitation was slightly overestimated in the western and central HRB and precipitation was underestimated in the eastern HRB. The precipitation simulation ability of the downscaled CMIP6-MME relative to the downscaled CMIP5-MME improved because of reduced biases. The downscaled CMIP6-MME better simulated precipitation for most stations compared to the downscaled CMIP5-MME in all seasons except for summer. Both the downscaled CMIP5-MME and CMIP6-MME exhibit poor performance in simulating rainy days in the HRB.

scholarly journals Global Climate Model Ensemble Approaches for Future Projections of Atmospheric Rivers

2019 ◽  
Vol 7 (10) ◽  
pp. 1136-1151 ◽  
Author(s):  
E.C. Massoud ◽  
V. Espinoza ◽  
B. Guan ◽  
D.E. Waliser
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Model Ensemble ◽  
Future Projections ◽  
Atmospheric Rivers

scholarly journals Climate change hotspots in the CMIP5 global climate model ensemble

2012 ◽  
Vol 114 (3-4) ◽  
pp. 813-822 ◽  
Author(s):  
Noah S. Diffenbaugh ◽  
Filippo Giorgi
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Model Ensemble

scholarly journals Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics

2016 ◽  
Author(s):  
Sebastian Sippel ◽  
Jakob Zscheischler ◽  
Miguel D. Mahecha ◽  
Rene Orth ◽  
Markus Reichstein ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Heat Waves ◽  
Global Climate ◽  
Global Climate Model ◽  
Large Fraction ◽  
Cmip5 Models ◽  
Temperature Extremes ◽  
Model Ensemble

Abstract. The Earth's land surface and the atmosphere are strongly interlinked through the exchange of energy and matter (e.g. water and carbon). This coupled behaviour causes various land–atmosphere feedbacks and an insufficient understanding of these feedbacks contributes to uncertain global climate model projections. For example, a crucial role of the land surface in exacerbating summer heat waves in mid-latitude regions has been identified empirically for high-impact heatwaves, but individual climate models differ widely in their respective representation of land-atmosphere coupling. Here, we combine an ensemble of observations-based and simulated temperature (T) and evapotranspiration (ET) datasets and investigate coincidences of T anomalies with ET anomalies as a proxy for land-atmosphere interactions during periods of anomalously warm temperatures. We demonstrate that a relatively large fraction of state-of-the-art climate models from the Coupled Model Intercomparison Project (CMIP5) archive produces systematically too frequent coincidences of high T anomalies with negative ET anomalies in mid-latitude regions during the warm season and in several tropical regions year-round. Further, we show that these coincidences (high T, low ET), as diagnosed by the land-coupling coincidence metrics, are closely related to the variability and extremes of simulated temperatures across a multi-model ensemble. Thus, our approach offers a physically consistent, diagnostic-based avenue to evaluate these ensembles, and subsequently reduce model biases in simulated and predicted extreme temperatures. Following this idea, we derive a land-coupling constraint based on the spread of 54 combinations of T-ET benchmarking datasets and consequently retain only a subset of CMIP5 models that produce a land-coupling behaviour that is compatible with these observations-based benchmark estimates. The constrained multi-model projections exhibit lower temperature extremes in regions where models show substantial spread in T-ET coupling, and in addition, biases in the climate model ensemble are consistently reduced.

High bit rate ACTS experiments for performing global science - Keck Telescope and global climate model

1996 ◽  
Author(s):  
Larry Bergman ◽  
J. Gary ◽  
Burt Edelson ◽  
Neil Helm ◽  
Judith Cohen ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Bit Rate ◽  
Global Science ◽  
High Bit Rate

scholarly journals A comparison of ship and satellite measurements of cloud properties with global climate model simulations in the southeast Pacific stratus deck

2010 ◽  
Vol 10 (14) ◽  
pp. 6527-6536 ◽  
Author(s):  
M. A. Brunke ◽  
S. P. de Szoeke ◽  
P. Zuidema ◽  
X. Zeng
Keyword(s):  
Diurnal Cycle ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Cloud Fraction ◽  
Cloud Base ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Southeast Pacific ◽  
Cloud Thickness

Abstract. Here, liquid water path (LWP), cloud fraction, cloud top height, and cloud base height retrieved by a suite of A-train satellite instruments (the CPR aboard CloudSat, CALIOP aboard CALIPSO, and MODIS aboard Aqua) are compared to ship observations from research cruises made in 2001 and 2003–2007 into the stratus/stratocumulus deck over the southeast Pacific Ocean. It is found that CloudSat radar-only LWP is generally too high over this region and the CloudSat/CALIPSO cloud bases are too low. This results in a relationship (LWP~h9) between CloudSat LWP and CALIPSO cloud thickness (h) that is very different from the adiabatic relationship (LWP~h2) from in situ observations. Such biases can be reduced if LWPs suspected to be contaminated by precipitation are eliminated, as determined by the maximum radar reflectivity Zmax>−15 dBZ in the apparent lower half of the cloud, and if cloud bases are determined based upon the adiabatically-determined cloud thickness (h~LWP1/2). Furthermore, comparing results from a global model (CAM3.1) to ship observations reveals that, while the simulated LWP is quite reasonable, the model cloud is too thick and too low, allowing the model to have LWPs that are almost independent of h. This model can also obtain a reasonable diurnal cycle in LWP and cloud fraction at a location roughly in the centre of this region (20° S, 85° W) but has an opposite diurnal cycle to those observed aboard ship at a location closer to the coast (20° S, 75° W). The diurnal cycle at the latter location is slightly improved in the newest version of the model (CAM4). However, the simulated clouds remain too thick and too low, as cloud bases are usually at or near the surface.

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