Potential climate change impact on wind energy resources in northern Europe: analyses using a regional climate model

2005 ◽  
Vol 25 (7-8) ◽  
pp. 815-835 ◽  
Author(s):  
S. C. Pryor ◽  
R. J. Barthelmie ◽  
E. Kjellström
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
Energy Resources ◽  
Northern Europe ◽  
Change Impact ◽  
Wind Energy Resources ◽  
Potential Climate Change

scholarly journals Assessment of climate change impact on the malaria vector Anopheles hyrcanus, West Nile disease, and incidence of melanoma in the Vojvodina Province (Serbia) using data from a regional climate model

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0227679
Author(s):  
Dragutin T. Mihailović ◽  
Dušan Petrić ◽  
Tamaš Petrović ◽  
Ivana Hrnjaković-Cvjetković ◽  
Vladimir Djurdjevic ◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Malaria Vector ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
West Nile ◽  
Change Impact ◽  
Using Data ◽  
Vojvodina Province

scholarly journals Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?

2012 ◽  
Vol 9 (11) ◽  
pp. 12765-12795 ◽  
Author(s):  
C. Teutschbein ◽  
J. Seibert
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Conditions ◽  
Impact Studies ◽  
Split Sample ◽  
Sample Test ◽  
Change Impact

Abstract. In hydrological climate-change impact studies, Regional Climate Models (RCMs) are commonly used to transfer large-scale Global Climate Model (GCM) data to smaller scales and to provide more detailed regional information. However, there are often considerable biases in RCM simulations, which have led to the development of a number of bias correction approaches to provide more realistic climate simulations for impact studies. Bias correction procedures rely on the assumption that RCM biases do not change over time, because correction algorithms and their parameterizations are derived for current climate conditions and assumed to apply also for future climate conditions. This underlying assumption of bias stationarity is the main concern when using bias correction procedures. It is in principle not possible to test whether this assumption is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well bias correction methods perform for conditions different from those used for calibration. For five Swedish catchments, several time series of RCM simulated precipitation and temperature were obtained from the ENSEMBLES data base and different commonly-used bias correction methods were applied. We then performed a differential split-sample test by dividing the data series into cold and warm respective dry and wet years. This enabled us to evaluate the performance of different bias correction procedures under systematically varying climate conditions. The differential split-sample test resulted in a large spread and a clear bias for some of the correction methods during validation years. More advanced correction methods such as distribution mapping performed relatively well even in the validation period, whereas simpler approaches resulted in the largest deviations and least reliable corrections for changed conditions. Therefore, we question the use of simple bias correction methods such as the widely used delta-change approach and linear scaling for RCM-based climate-change impact studies and recommend using higher-skill bias correction methods.

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 Variations in the Simulation of Climate Change Impact Indices due to Different Land Surface Schemes over the Mediterranean, Middle East and Northern Africa

Atmosphere ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 26 ◽  
Author(s):  
Katiana Constantinidou ◽  
George Zittis ◽  
Panos Hadjinicolaou
Keyword(s):  
Climate Change ◽  
Middle East ◽  
Land Surface ◽  
Climate Change Impact ◽  
Climate Model ◽  
Eastern Mediterranean ◽  
Regional Climate ◽  
Climatic Conditions ◽  
Relative Dispersion ◽  
Change Impact

The Eastern Mediterranean (EM) and the Middle East and North Africa (MENA) are projected to be exposed to extreme climatic conditions in the 21st century, which will likely induce adverse impacts in various sectors. Relevant climate change impact assessments utilise data from climate model projections and process-based impact models or simpler, index-based approaches. In this study, we explore the implied uncertainty from variations of climate change impact-related indices as induced by the modelled climate (WRF regional climate model) from different land surface schemes (Noah, NoahMP, CLM and RUC). The three climate change impact-related indicators examined here are the Radiative Index of Dryness (RID), the Fuel Dryness Index (Fd) and the Water-limited Yield (Yw). Our findings indicate that Noah simulates the highest values for both RID and Fd, while CLM gives the highest estimations for winter wheat Yw. The relative dispersion in the three indices derived by the different land schemes is not negligible, amounting, for the overall geographical domain of 25% for RID and Fd, and 10% for Yw. The dispersion is even larger for specific sub-regions.

scholarly journals Climate change impact on Northwestern African offshore wind energy resources

2019 ◽  
Vol 14 (12) ◽  
pp. 124065 ◽  
Author(s):  
Pedro M M Soares ◽  
Daniela C A Lima ◽  
Alvaro Semedo ◽  
William Cabos ◽  
Dmitry V Sein
Keyword(s):  
Climate Change ◽  
Wind Energy ◽  
Climate Change Impact ◽  
Energy Resources ◽  
Offshore Wind ◽  
Offshore Wind Energy ◽  
Change Impact ◽  
Wind Energy Resources
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