Fine-resolution regional climate model simulations of the impact of climate change on tropical cyclones near Australia

2004 ◽  
Vol 22 (1) ◽  
pp. 47-56 ◽  
Author(s):  
J. L. McGregor ◽  
K. J. E. Walsh ◽  
K.-C. Nguyen
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Tropical Cyclones ◽  
Climate Model ◽  
Regional Climate ◽  
Model Simulations ◽  
Impact Of Climate Change ◽  
Climate Model Simulations ◽  
Fine Resolution ◽  
The Impact

Projected climate change over Kuwait simulated using a WRF high resolution regional climate model

2020 ◽  
Author(s):  
Hussain Alsarraf
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Regional Climate Model ◽  
Arabian Peninsula ◽  
Climate Model ◽  
Regional Climate ◽  
Dynamic Downscaling ◽  
Model Simulations ◽  
Average Maximum ◽  
The Impact

<p>The purpose of this study is to examine the impact of climate change on the changes on summer surface temperatures between present (2000-2010) and future (2050-2060) over the Arabian Peninsula and Kuwait. In this study, the influence of climate change in the Arabian Peninsula and especially in Kuwait was investigated by high resolution (36, 12, and 4 km grid spacing) dynamic downscaling from the Community Climate System Model CCSM4 using the WRF Weather Research and Forecasting model. The downscaling results were first validated by comparing National Centers for Environmental Prediction NCEP model outputs with the observational data. The global climate change dynamic downscaling model was run using WRF regional climate model simulations (2000-2010) and future projections (2050-2060). The influence of climate change in the Arabian Peninsula can be projected from the differences between the two period’s model simulations. The regional model simulations of the average maximum surface temperature in summertime predicted an increase from 1◦C to 3 ◦C over the summertime in Kuwait by midcentury.</p><p><strong> </strong></p>

Heat stress projections for major European cities from high-resolution regional climate model simulations

2021 ◽  
Author(s):  
Clemens Schwingshackl ◽  
Anne Sophie Daloz ◽  
Carley Iles ◽  
Nina Schuhen ◽  
Jana Sillmann
Keyword(s):  
Climate Change ◽  
Heat Stress ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Northern Europe ◽  
Stress Indicators ◽  
Model Simulations ◽  
European Cities ◽  
Climate Model Simulations

<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>

scholarly journals Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Hyung-Il Eum ◽  
Philippe Gachon ◽  
René Laprise
Keyword(s):  
Climate Change ◽  
Spatial Variability ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Model Bias ◽  
Model Simulations ◽  
Seasonal Climate ◽  
Ensemble Mean ◽  
Climate Model Simulations

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.

scholarly journals Development of probability distributions for regional climate change from uncertain global mean warming and an uncertain scaling relationship

2007 ◽  
Vol 11 (3) ◽  
pp. 1097-1114 ◽  
Author(s):  
B. Hingray ◽  
A. Mezghani ◽  
T. A. Buishand
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Probability Distributions ◽  
Regional Climate ◽  
Natural Variability ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Climate Model Simulations ◽  
Global Mean

Abstract. To produce probability distributions for regional climate change in surface temperature and precipitation, a probability distribution for global mean temperature increase has been combined with the probability distributions for the appropriate scaling variables, i.e. the changes in regional temperature/precipitation per degree global mean warming. Each scaling variable is assumed to be normally distributed. The uncertainty of the scaling relationship arises from systematic differences between the regional changes from global and regional climate model simulations and from natural variability. The contributions of these sources of uncertainty to the total variance of the scaling variable are estimated from simulated temperature and precipitation data in a suite of regional climate model experiments conducted within the framework of the EU-funded project PRUDENCE, using an Analysis Of Variance (ANOVA). For the area covered in the 2001–2004 EU-funded project SWURVE, five case study regions (CSRs) are considered: NW England, the Rhine basin, Iberia, Jura lakes (Switzerland) and Mauvoisin dam (Switzerland). The resulting regional climate changes for 2070–2099 vary quite significantly between CSRs, between seasons and between meteorological variables. For all CSRs, the expected warming in summer is higher than that expected for the other seasons. This summer warming is accompanied by a large decrease in precipitation. The uncertainty of the scaling ratios for temperature and precipitation is relatively large in summer because of the differences between regional climate models. Differences between the spatial climate-change patterns of global climate model simulations make significant contributions to the uncertainty of the scaling ratio for temperature. However, no meaningful contribution could be found for the scaling ratio for precipitation due to the small number of global climate models in the PRUDENCE project and natural variability, which is often the largest source of uncertainty. In contrast, for temperature, the contribution of natural variability to the total variance of the scaling ratio is small, in particular for the annual mean values. Simulation from the probability distributions of global mean warming and the scaling ratio results in a wider range of regional temperature change than that in the regional climate model experiments. For the regional change in precipitation, however, a large proportion of the simulations (about 90%) is within the range of the regional climate model simulations.

Estimating the impact of climate change on wind and solar energy in Brazil using a South American regional climate model

2019 ◽  
Vol 141 ◽  
pp. 390-401 ◽  
Author(s):  
Pieter de Jong ◽  
Tarssio B. Barreto ◽  
Clemente A.S. Tanajura ◽  
Daniel Kouloukoui ◽  
Karla P. Oliveira-Esquerre ◽  
...  
Keyword(s):  
Climate Change ◽  
Solar Energy ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
South American ◽  
Impact Of Climate Change ◽  
The Impact
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