Regional climate change scenarios over South Asia in the CMIP5 coupled climate model simulations

2015 ◽  
Vol 127 (5) ◽  
pp. 561-578 ◽  
Author(s):  
Venkatraman Prasanna
2017 ◽  
Vol 98 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Linda O. Mearns ◽  
Melissa S. Bukovsky ◽  
Vanessa J. Schweizer

Abstract In this brief article, we report the initial results of an expert elicitation with the co-PIs (regional climate modelers) of the North American Regional Climate Change Assessment Program regarding their evaluation of the relative quality of regional climate model simulations focusing on the subregion dominated by the North American monsoon (NAM). We assumed that an expert elicitation framework might reveal interesting beliefs and understanding that would be different from what would be obtained from calculating quantitative metrics associated with model quality. The simulations considered were of six regional climate models (RCMs) that used NCEP Reanalysis 2 as boundary conditions for the years 1980–2004. The domain covers most of North America and adjacent oceans. The seven participating regional modelers were asked to complete surveys on their background beliefs about model credibility and their judgments regarding the quality of the six models based on a series of plots of variables related to the NAM (e.g., temperature, winds, humidity, moisture flux, precipitation). The specific RCMs were not identified. We also compared the results of the expert elicitation with those obtained from using a series of metrics developed to evaluate a European collection of climate model simulations. The results proved to be quite different in the two cases. The results of this exercise proved very enlightening regarding regional modelers’ perceptions of model quality and their beliefs about how this information should or should not be used. Based on these pilot study results, we believe a more complete study is warranted.


2021 ◽  
Author(s):  
Clemens Schwingshackl ◽  
Anne Sophie Daloz ◽  
Carley Iles ◽  
Nina Schuhen ◽  
Jana Sillmann

<p>Cities are hotspots of human heat stress due to their large number of inhabitants and the urban heat island effect leading to amplified temperatures. Exposure to heat stress in urban areas is projected to further increase in the future, mainly due to climate change and expected increases in the number of people living in cities. The impacts of climate change in cities have been investigated in numerous studies, but rarely using climate models due to their coarse spatial resolution compared to the typical areal extent of cities. Recent advances in regional climate modelling now give access to an ensemble of high-resolution simulations for Europe, allowing for much more detailed analyses of small-scale features, such as city climate.</p><p>Focusing on Europe, we compare the evolution of several heat stress indicators for 36 major European cities, based on regional climate model simulations from EURO-CORDEX. The applied EURO-CORDEX ensemble (Vautard et al., 2020) has a spatial resolution of 0.11° (~11 km; comparable to the extent of large cities) and contains over 60 ensemble members, allowing thus for robust multi-model analyses of climate change on city levels. We analyze changes in heat stress both relative to the climatological heat stress variability in each city during 1981-2010 using the Heat Wave Magnitude Index daily (HWMId, Russo et al., 2015) and in absolute terms by counting the yearly number of exceedances of impact-relevant thresholds. Relative and absolute heat stress increase throughout Europe but with distinct patterns. Absolute heat stress increases predominantly in Southern Europe, primarily due to the hotter climate in the South. Relative changes are also highest in Southern Europe but exhibit a secondary maximum in Northern Europe, while being lowest in Central Europe. The main reason for this pattern is that day-to-day variability in heat stress indicators during present climate conditions is highest in Central Europe but lower in Southern and Northern Europe. Large Northern European cities, which are all located at the shore, are further influenced by different heat stress evolutions over land and sea surfaces.</p><p>As human vulnerability does not only depend on the absolute heat stress but also on what people are adapted to (i.e., the climatological range), the results of this study highlight that cities in all parts of Europe – including in Northern Europe – must prepare for higher heat stress in the future.</p><p> </p><p>References:</p><p>Russo, S., et al. (2015). Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environmental Research Letters, 10(12). doi:10.1088/1748-9326/10/12/124003</p><p>Vautard, R., et al. (2020). Evaluation of the large EURO‐CORDEX regional climate model ensemble. Journal of Geophysical Research: Atmospheres. doi:10.1029/2019jd032344</p>


2015 ◽  
Vol 12 (3) ◽  
pp. 3011-3028 ◽  
Author(s):  
D. Maraun ◽  
M. Widmann

Abstract. To assess potential impacts of climate change for a specific location, one typically employs climate model simulations at the grid box corresponding to the same geographical location. But based on regional climate model simulations, we show that simulated climate might be systematically displaced compared to observations. In particular in the rain shadow of moutain ranges, a local grid box is therefore often not representative of observed climate: the simulated windward weather does not flow far enough across the mountains; local grid boxes experience the wrong airmasses and atmospheric circulation. In some cases, also the local climate change signal is deteriorated. Classical bias correction methods fail to correct these location errors. Often, however, a distant simulated time series is representative of the considered observed precipitation, such that a non-local bias correction is possible. These findings also clarify limitations of bias correcting global model errors, and of bias correction against station data.


2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Hyung-Il Eum ◽  
Philippe Gachon ◽  
René Laprise

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affected by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. These results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.


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