Projected changes in temperature and precipitation extremes over the Silk Road Economic Belt regions by the Coupled Model Intercomparison Project Phase 5 multi-model ensembles

2018 ◽  
Vol 38 (11) ◽  
pp. 4077-4091 ◽  
Author(s):  
Tingting Han ◽  
Huopo Chen ◽  
Xin Hao ◽  
Huijun Wang
Keyword(s):  
Coupled Model ◽  
Silk Road ◽  
Precipitation Extremes ◽  
Model Intercomparison ◽  
Temperature And Precipitation ◽  
Silk Road Economic Belt ◽  
Intercomparison Project ◽  
Model Ensembles ◽  
Projected Changes ◽  
The Silk Road

scholarly journals Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6

2021 ◽  
Vol 18 (13) ◽  
pp. 6760
Author(s):  
Isaac Kwesi Nooni ◽  
Daniel Fiifi T. Hagan ◽  
Guojie Wang ◽  
Waheed Ullah ◽  
Jiao Lu ◽  
...  
Keyword(s):  
Extreme Events ◽  
Coupled Model ◽  
Baseline Period ◽  
Model Intercomparison ◽  
Additional Information ◽  
Intercomparison Project ◽  
Key Drivers ◽  
Model Ensembles ◽  
Projected Changes ◽  
Near Future

The main goal of this study was to assess the interannual variations and spatial patterns of projected changes in simulated evapotranspiration (ET) in the 21st century over continental Africa based on the latest Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) provided by the France Centre National de Recherches Météorologiques (CNRM-CM) model in the Sixth Phase of Coupled Model Intercomparison Project (CMIP6) framework. The projected spatial and temporal changes were computed for three time slices: 2020–2039 (near future), 2040–2069 (mid-century), and 2080–2099 (end-of-the-century), relative to the baseline period (1995–2014). The results show that the spatial pattern of the projected ET was not uniform and varied across the climate region and under the SSP-RCPs scenarios. Although the trends varied, they were statistically significant for all SSP-RCPs. The SSP5-8.5 and SSP3-7.0 projected higher ET seasonality than SSP1-2.6 and SSP2-4.5. In general, we suggest the need for modelers and forecasters to pay more attention to changes in the simulated ET and their impact on extreme events. The findings provide useful information for water resources managers to develop specific measures to mitigate extreme events in the regions most affected by possible changes in the region’s climate. However, readers are advised to treat the results with caution as they are based on a single GCM model. Further research on multi-model ensembles (as more models’ outputs become available) and possible key drivers may provide additional information on CMIP6 ET projections in the region.

scholarly journals Sensitivity of Crop Water Need to 2071–95 Projected Temperature and Precipitation Changes in Jamaica

2014 ◽  
Vol 18 (12) ◽  
pp. 1-17 ◽  
Author(s):  
Scott Curtis ◽  
Douglas W. Gamble ◽  
Jeff Popke
Keyword(s):  
Temperature Change ◽  
Coupled Model ◽  
Precipitation Change ◽  
Model Intercomparison ◽  
Crop Water ◽  
Temperature And Precipitation ◽  
Crop Suitability ◽  
Intercomparison Project ◽  
Potential Impact ◽  
Precipitation Changes

Abstract This study uses empirical models to examine the potential impact of climate change, based on a range of 100-yr phase 5 of the Coupled Model Intercomparison Project (CMIP5) projections, on crop water need in Jamaica. As expected, crop water need increases with rising temperature and decreasing precipitation, especially in May–July. Comparing the temperature and precipitation impacts on crop water need indicates that the 25th percentile of CMIP5 temperature change (moderate warming) yields a larger crop water deficit than the 75th percentile of CMIP5 precipitation change (wet winter and dry summer), but the 25th percentile of CMIP5 precipitation change (substantial drying) dominates the 75th percentile of CMIP5 temperature change (extreme warming). Over the annual cycle, the warming contributes to larger crop water deficits from November to April, while the drying has a greater influence from May to October. All experiments decrease crop suitability, with the largest impact from March to August.

scholarly journals Projections of global changes in precipitation extremes from Coupled Model Intercomparison Project Phase 5 models

2013 ◽  
Vol 40 (18) ◽  
pp. 4887-4892 ◽  
Author(s):  
Andrea Toreti ◽  
Philippe Naveau ◽  
Matteo Zampieri ◽  
Anne Schindler ◽  
Enrico Scoccimarro ◽  
...  
Keyword(s):  
Coupled Model ◽  
Precipitation Extremes ◽  
Global Changes ◽  
Model Intercomparison ◽  
Intercomparison Project

scholarly journals Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David C. Lafferty ◽  
Ryan L. Sriver ◽  
Iman Haqiqi ◽  
Thomas W. Hertel ◽  
Klaus Keller ◽  
...  
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Climate Information ◽  
Model Intercomparison ◽  
Temperature And Precipitation ◽  
Panel Model ◽  
Trade Offs ◽  
Intercomparison Project ◽  
Maize Yields

AbstractEfforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.

scholarly journals BCC-ESM1 Model Datasets for the CMIP6 Aerosol Chemistry Model Intercomparison Project (AerChemMIP)

2021 ◽  
Vol 38 (2) ◽  
pp. 317-328
Author(s):  
Jie Zhang ◽  
Tongwen Wu ◽  
Fang Zhang ◽  
Kalli Furtado ◽  
Xiaoge Xin ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Earth System ◽  
Model Intercomparison ◽  
Aerosol Chemistry ◽  
Temperature And Precipitation ◽  
Intercomparison Project ◽  
The 1960S

AbstractBCC-ESM1 is the first version of the Beijing Climate Center’s Earth System Model, and is participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is the only CMIP6-endorsed MIP in which BCC-ESM1 is involved. All AerChemMIP experiments in priority 1 and seven experiments in priorities 2 and 3 have been conducted. The DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations have also been run as the entry card of CMIP6. The AerChemMIP outputs from BCC-ESM1 have been widely used in recent atmospheric chemistry studies. To facilitate the use of the BCC-ESM1 datasets, this study describes the experiment settings and summarizes the model outputs in detail. Preliminary evaluations of BCC-ESM1 are also presented, revealing that: the climate sensitivities of BCC-ESM1 are well within the likely ranges suggested by IPCC AR5; the spatial structures of annual mean surface air temperature and precipitation can be reasonably captured, despite some common precipitation biases as in CMIP5 and CMIP6 models; a spurious cooling bias from the 1960s to 1990s is evident in BCC-ESM1, as in most other ESMs; and the mean states of surface sulfate concentrations can also be reasonably reproduced, as well as their temporal evolution at regional scales. These datasets have been archived on the Earth System Grid Federation (ESGF) node for atmospheric chemistry studies.

scholarly journals Projected Changes in Late-Twenty-First-Century Tropical Cyclone Frequency in 13 Coupled Climate Models from Phase 5 of the Coupled Model Intercomparison Project

2013 ◽  
Vol 26 (24) ◽  
pp. 9946-9959 ◽  
Author(s):  
K. J. Tory ◽  
S. S. Chand ◽  
J. L. McBride ◽  
H. Ye ◽  
R. A. Dare
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Climate Models ◽  
Coupled Model ◽  
Reanalysis Data ◽  
First Century ◽  
Model Intercomparison ◽  
Intercomparison Project ◽  
Twenty First Century ◽  
Projected Changes

Abstract Changes in tropical cyclone (TC) frequency under anthropogenic climate change are examined for 13 global models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), using the Okubo–Weiss–Zeta parameter (OWZP) TC-detection method developed by the authors in earlier papers. The method detects large-scale conditions within which TCs form. It was developed and tuned in atmospheric reanalysis data and then applied without change to the climate models to ensure model and detector independence. Changes in TC frequency are determined by comparing TC detections in the CMIP5 historical runs (1970–2000) with high emission scenario (representative concentration pathway 8.5) future runs (2070–2100). A number of the models project increases in frequency of higher-latitude tropical cyclones in the late twenty-first century. Inspection reveals that these high-latitude systems were subtropical in origin and are thus eliminated from the analysis using an objective classification technique. TC detections in 8 of the 13 models reproduce observed TC formation numbers and geographic distributions reasonably well, with annual numbers within ±50% of observations. TC detections in the remaining five models are particularly low in number (10%–28% of observed). The eight models with a reasonable TC climatology all project decreases in global TC frequency varying between 7% and 28%. Large intermodel and interbasin variations in magnitude and sign are present, with the greatest variations in the Northern Hemisphere basins. These results are consistent with results from earlier-generation climate models and thus confirm the robustness of coupled model projections of globally reduced TC frequency.

scholarly journals Projected Changes in Temperature and Precipitation Extremes in China by the CMIP5 Multimodel Ensembles

2014 ◽  
Vol 27 (17) ◽  
pp. 6591-6611 ◽  
Author(s):  
Botao Zhou ◽  
Qiuzi Han Wen ◽  
Ying Xu ◽  
Lianchun Song ◽  
Xuebin Zhang
Keyword(s):  
Regional Scale ◽  
Coupled Model ◽  
First Century ◽  
Precipitation Extremes ◽  
Dominant Factor ◽  
Emission Scenarios ◽  
Temperature And Precipitation ◽  
Twenty First Century ◽  
The Difference ◽  
Projected Changes

Abstract This paper presents projected changes in temperature and precipitation extremes in China by the end of the twenty-first century based on the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations. The temporal changes and their spatial patterns in the Expert Team on Climate Change Detection and Indices (ETCCDI) indices under the RCP4.5 and RCP8.5 emission scenarios are analyzed. Compared to the reference period 1986–2005, substantial changes are projected in temperature and precipitation extremes under both emission scenarios. These changes include a decrease in cold extremes, an increase in warm extremes, and an intensification of precipitation extremes. The intermodel spread in the projection increases with time, with wider spread under RCP8.5 than RCP4.5 for most indices, especially at the subregional scale. The difference in the projected changes under the two RCPs begins to emerge in the 2040s. Analyses based on the mixed-effects analysis of variance (ANOVA) model indicate that by the end of the twenty-first century, at the national scale, the dominant contributor to the projection uncertainty of most temperature-based indices, and some precipitation extremes [including maximum 1-day precipitation (RX1day) and maximum 5-day precipitation (RX5day), and total extremely wet day total amount (R95p)], is the difference in emission scenarios. By the end of the twenty-first century, model uncertainty is the dominant factor at the regional scale and for the other indices. Natural variability can also play very important role.

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