Model evaluation and uncertainties in projected changes of drought over northern China based on CMIP5 models

Author(s):  
Xi Lu ◽  
Xiaoqiang Rao ◽  
Wenjie Dong
2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.</p><p>Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.</p><p>To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.</p><p>The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.</p>


2015 ◽  
Vol 28 (2) ◽  
pp. 451-467 ◽  
Author(s):  
Kelly Lombardo ◽  
Brian A. Colle ◽  
Zhenhai Zhang

Abstract This study analyzed the contribution of cyclones to projected changes in cool season (1 November–31 March) precipitation over the eastern United States and western North Atlantic Ocean. First, global climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were compared to Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) precipitation analyses for the period 1979–2004. The CMIP5 ensemble mean realistically reproduced the historical distribution of regional precipitation with no discernable effect because of model spatial resolution. Subsequently, the projected changes in precipitation on cyclone and noncyclone days under the representative concentration pathway 8.5 (RCP8.5) scenario were quantified. While precipitation on both types of days was projected to increase, the increase on noncyclone days (23%) was greater than the increase on cyclone days (12%). The increase in precipitation on cyclone days occurred despite a decrease in the number of cyclone days. This increase can be attributed primarily to a shift toward more frequent extreme precipitation events coupled with a decline in light precipitation events.


2016 ◽  
Vol 43 (17) ◽  
pp. 9315-9323 ◽  
Author(s):  
Alexandre M. Ramos ◽  
Ricardo Tomé ◽  
Ricardo M. Trigo ◽  
Margarida L. R. Liberato ◽  
Joaquim G. Pinto

2020 ◽  
Vol 82 ◽  
pp. 75-95
Author(s):  
M Darand

Climate extremes have large impacts on human societies and natural ecosystems. Projection of changes in climate extremes is very important for long-term planning. The current study investigated future changes in extreme precipitation events over Iran based on 18 CMIP5 models for the period 2006-2100. National gridded data from the Asfazari database were used to evaluate climate model simulation. Results indicate that models with higher spatial resolution (CCSM4 and MRI-CGCM3) perform better than those with lower resolution in capturing the spatial features of extreme precipitation events. Bias correction was applied to the models and the projected changes were assessed with the nonparametric modified Mann-Kendal trend test and Sen slope estimator at a 95% confidence level. Annual total precipitation (PRPCTOT) and rainy days (RD) were projected to decrease but the intensity and frequency of precipitation extremes were predicted to increase significantly. The projected decreases were larger in northwestern parts than other regions, with PRPCTOT decreasing by 18 to 22 mm decade-1 and RD by 4 to 4.8 d decade-1. Although there were discrepancies in rates between the models, extreme precipitation events over Iran were generally projected to increase. An increase in consecutive dry days (CDD) was predicted for most regions by the end of the 21st century under RCP8.5, with the largest increase of 5 to 6.8 d decade-1 found for northwestern Iran. In eastern areas of Iran, where precipitation occurs extremely rarely, the number of days with daily precipitation exceeding 10 mm (R10) or even 20 mm (R20) were projected to increase significantly. In conclusion, these changes suggest an increased risk of flash floods in Iran from increased extreme precipitation under the RCP8.5 emission scenario.


2017 ◽  
Vol 30 (24) ◽  
pp. 9949-9964 ◽  
Author(s):  
Aleksandra Borodina ◽  
Erich M Fischer ◽  
Reto Knutti

Projected changes in temperature extremes, such as regional changes in the intensity and frequency of hot extremes, differ strongly across climate models. This study shows that this disagreement can be partly explained by discrepancies in the representation of the present-day temperature distribution, motivating the evaluation of models with observations. By evaluating climate models on carefully selected metrics, the models that are more likely to be reliable for long-term projections of temperature extremes are identified. The study found that frequencies of hot extremes are likely to increase at a higher rate than the multimodel mean estimate over large parts of the Northern Hemisphere and Australia. This implies that a higher degree of adaptation is required for a given global temperature target. It also found that projected changes in the intensity of hot extremes can be constrained in several regions, including Australia, central North America, and north Asia. In many other regions, large internal variability can often hamper model evaluation. For both aspects—the intensity and the frequency of hot extremes—the total area over which the constraints can be implemented is limited by the quality and completeness of observations. Thereby, this study highlights the importance of long-term, high-quality, and easily accessible observational records for model evaluation, which are vital to ultimately reduce uncertainties in projections of temperature extremes.


2016 ◽  
Author(s):  
Lucile Verrot ◽  
Georgia Destouni

Abstract. Soil moisture is a key variable in hydrology, ecology, and climate change science. It is also of primary importance for the agricultural and water resource sectors of society. This paper investigates how hydro-climatic changes, projected by 14 CMIP5 models and for different radiative forcing (RCP) scenarios to occur from 2006-2025 to 2080-2099, may affect different soil moisture aspects in 81 large catchments worldwide. Overall, for investigated changes in dry/wet event occurrence and in average value and inter-annual variability of seasonal water content, different RCP scenarios imply opposite directions of change in around half or more of the study catchments. Regardless of RCP scenario, the greatest projected changes are found for the inter-annual variability of seasonal soil water content. Especially for the dry-season water content, large increases in inter-annual variability emerge for several large catchments over the world; the considered RCP scenario determines precisely which these catchments are.


2013 ◽  
Vol 13 (11) ◽  
pp. 28395-28451 ◽  
Author(s):  
L. T. Wang ◽  
Z. Wei ◽  
J. Yang ◽  
Y. Zhang ◽  
F. F. Zhang ◽  
...  

Abstract. Extremely severe and persistent haze occurred in January 2013 over the eastern and northern China. The record-breaking high concentrations of fine particulate matter (PM2.5) of more than 700 μg m−3 on hourly average and the persistence of the episodes have raised widespread, considerable public concerns. During that period, seven of the top ten polluted cities in China were within Hebei Province. The three cities in southern Hebei, Shijiazhuang, Xingtai, and Handan, have been listed as the top three polluted cities according to the statistics for the first half year of 2013. In this study, the Mesoscale Modeling System Generation 5 (MM5) and the Models-3/Community Multiscale Air Quality (CMAQ) modeling system are applied to simulate the 2013 severe winter regional hazes in East Asia and the northern China at horizontal grid resolutions of 36 and 12 km, respectively, using the Multi-resolution Emission Inventory of China (MEIC). The source contributions of major source regions and sectors to PM2.5 concentrations in the three most-polluted cities in southern Hebei are quantified aiming at the understanding of the sources of the severe haze pollution in this region, and the results are compared with December 2007, the haziest month in 2001–2010. Model evaluation against meteorological and air quality observations indicates an overall acceptable performance and the model tends to underpredict PM2.5 and coarse particulate matter (PM10) concentrations during the extremely severe polluted episodes. The MEIC inventory is proved to be a good estimation in terms of total emissions of cities but uncertainties exist in the spatial allocations of emissions into fine grid resolutions within cities. The source apportionment shows that emissions from the northern Hebei and the Beijing–Tianjin city cluster are two major regional contributors to the pollution in January 2013 in Shijiazhuang, comparing with those from Shanxi and the northern Hebei for December 2007. For Xingtai and Handan, the emissions from the northern Hebei and Henan are important. The industrial and domestic sources are the most significant local contributors, and the domestic and agricultural emissions from Shandong and Henan are unnegligible regional sources, especially for Xingtai and Handan. Even in the top two haziest months (i.e., January 2013 and December 2007), a large fraction of PM2.5 in the three cities may originate from quite different regional sources. These results indicate the importance of establishing a regional joint framework of policymaking and action system to effectively mitigate air pollution in this area, not only over Beijing–Tianjin–Hebei area, but also surrounding provinces such as Henan, Shandong, and Shanxi.


2018 ◽  
Vol 38 (15) ◽  
pp. 5589-5604 ◽  
Author(s):  
Xiaojing Yu ◽  
Yong Zhao ◽  
Xiaojiao Ma ◽  
Junqiang Yao ◽  
Hongjun Li

2016 ◽  
Author(s):  
Steve J. Birkinshaw ◽  
Selma B. Guerreiro ◽  
Alex Nicholson ◽  
Qiuhua Liang ◽  
Paul Quinn ◽  
...  

Abstract. The Yangtze River Basin is home to more than 400 million people, contributes to nearly half of China’s food production, and is susceptible to major floods. Therefore planning for climate change impacts on river discharges is essential. We used a physically-based distributed hydrological model, Shetran, to simulate discharge in the Yangtze River just below the Three Gorges Dam at Yichang (1,007,200 km2), obtaining an excellent match between simulated and measured daily discharge, with Nash-Sutcliffe efficiencies of 0.95 for the calibration period (1996–2000) and 0.92 for the validation period (2001–2005). We then used a simple monthly delta change approach for 78 climate model projections (35 different GCMs) from the Coupled Model Intercomparison Project-5 (CMIP5) to examine the effect of climate change on river discharge for 2041–2070 for Representative Concentration Pathway 8.5. Projected changes to the basin’s annual precipitation varied between −3.6 % and +14.8 % but increases in temperature and consequently evapotranspiration (calculated using the Thornthwaite equation) were projected by all CMIP5 models, resulting in projected changes in the basin’s annual discharge from −29.8 % to +16.0 %. These large differences were mainly due to the predicted expansion of the summer monsoon north and west into the Yangtze basin in some CMIP5 models, e.g. CanESM2, but not in others, e.g. CSIRO-Mk3-6-0. This was despite both models being able to simulate current climate well. Until projections of the strength and location of the monsoon under a future climate improve there will remain large uncertainties in the direction and magnitude of future change in discharge for the Yangtze.


2021 ◽  
Vol 18 (6) ◽  
pp. 2221-2240
Author(s):  
Jens Terhaar ◽  
Olivier Torres ◽  
Timothée Bourgeois ◽  
Lester Kwiatkowski

Abstract. The uptake of anthropogenic carbon (Cant) by the ocean leads to ocean acidification, causing the reduction of pH and the saturation states of aragonite (Ωarag) and calcite (Ωcalc). The Arctic Ocean is particularly vulnerable to ocean acidification due to its naturally low pH and saturation states and due to ongoing freshening and the concurrent reduction in total alkalinity in this region. Here, we analyse ocean acidification in the Arctic Ocean over the 21st century across 14 Earth system models (ESMs) from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared to the previous model generation (CMIP5), models generally better simulate maximum sea surface densities in the Arctic Ocean and consequently the transport of Cant into the Arctic Ocean interior, with simulated historical increases in Cant in improved agreement with observational products. Moreover, in CMIP6 the inter-model uncertainty of projected changes over the 21st century in Arctic Ocean Ωarag and Ωcalc averaged over the upper 1000 m is reduced by 44–64 %. The strong reduction in projection uncertainties of Ωarag and Ωcalc can be attributed to compensation between Cant uptake and total alkalinity reduction in the latest models. Specifically, ESMs with a large increase in Arctic Ocean Cant over the 21st century tend to simulate a relatively weak concurrent freshening and alkalinity reduction, while ESMs with a small increase in Cant simulate a relatively strong freshening and concurrent total alkalinity reduction. Although both mechanisms contribute to Arctic Ocean acidification over the 21st century, the increase in Cant remains the dominant driver. Even under the low-emissions Shared Socioeconomic Pathway 1-2.6 (SSP1-2.6), basin-wide averaged Ωarag undersaturation in the upper 1000 m occurs before the end of the century. While under the high-emissions pathway SSP5-8.5, the Arctic Ocean mesopelagic is projected to even become undersaturated with respect to calcite. An emergent constraint identified in CMIP5 which relates present-day maximum sea surface densities in the Arctic Ocean to the projected end-of-century Arctic Ocean Cant inventory is found to generally hold in CMIP6. However, a coincident constraint on Arctic declines in Ωarag and Ωcalc is not apparent in the new generation of models. This is due to both the reduction in Ωarag and Ωcalc projection uncertainty and the weaker direct relationship between projected changes in Arctic Ocean Cant and changes in Ωarag and Ωcalc.


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