scholarly journals Changes in the European Precipitation Climatologies as Derived by an Ensemble of Regional Models

2008 ◽  
Vol 21 (11) ◽  
pp. 2540-2557 ◽  
Author(s):  
Francisco J. Tapiador ◽  
Enrique Sánchez
Keyword(s):  
Annual Cycle ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Climatic Research Unit ◽  
Validation Data ◽  
Climate Conditions ◽  
Current Climate ◽  
The Future

Abstract This paper analyzes the changes in the precipitation climatologies of Europe for the periods 1960–90 and 2070–2100 using a heterogeneous set of regional climate models (RCMs). The authors used the Climatic Research Unit (CRU) database to define a precipitation climatology for current climate conditions (1960–90), then compare the estimates with the RCMs’ simulations for the same period using spectral analysis. After the authors evaluated the performance of the models compared with validation data for current climate, they calculated the future climate spectra (2070–2100). Changes in the future climate have been evaluated in terms of differences in the phase and amplitude of the annual cycle with respect to present conditions. The results show that models provide consistent results and that under the A2 scenario (increased greenhouse gases conditions) precipitation climatologies in Europe are expected to suffer noticeable changes, the most important being a strengthening of the annual cycle in most of the Atlantic coastal areas of the continent. While total amounts of rainfall might undergo little change, the consequences of changes in the seasonal distribution of precipitation will strongly affect both ecosystems and human activities. Differences were also found in the probability distribution function (pdf) of precipitation, indicating an overall increase in the frequency of precipitation-related hazards in Europe.

scholarly journals Solar Irradiance and Temperature Variability and Projected Trends Analysis in Burundi

Climate ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 83 ◽  
Author(s):  
Agnidé Emmanuel Lawin ◽  
Marc Niyongendako ◽  
Célestin Manirakiza
Keyword(s):  
Solar Irradiance ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Historical Period ◽  
Projection Data ◽  
Data Set ◽  
Variability Analysis ◽  
The Future

This paper assessed the variability and projected trends of solar irradiance and temperature in the East of Burundi. Observed temperature from meteorological stations and the MERRA-2 data set provided by NASA/Goddard Space Flight Center are used over the historical period 1976–2005. In addition, solar irradiance data provided by SoDa database were considered. Furthermore, projection data from eight Regional Climate Models were used over the periods 2026–2045 and 2066–2085. The variability analysis was performed using a standardized index. Projected trends and changes in the future climate were respectively detected through Mann-Kendall and t-tests. The findings over the historical period revealed increase temperature and decrease in solar irradiance over the last decades of the 20th century. At a monthly scale, the variability analysis showed that excesses in solar irradiance coincide with the dry season, which led to the conclusion that it may be a period of high production for solar energy. In the future climate, upward trends in temperature are expected over the two future periods, while no significant trends are forecasted in solar irradiance over the entire studied region. However, slight decreases and significant changes in solar irradiance have been detected over all regions.

scholarly journals Impacts of climate change on the seasonality of low flows in 134 catchments in the River Rhine basin using an ensemble of bias-corrected regional climate simulations

2013 ◽  
Vol 10 (5) ◽  
pp. 6807-6845
Author(s):  
M. C. Demirel ◽  
M. J. Booij ◽  
A. Y. Hoekstra
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Future Climate ◽  
Low Flow ◽  
River Rhine ◽  
Emission Scenarios ◽  
Climate Scenarios ◽  
Low Flows ◽  
Current Climate ◽  
The Future

Abstract. The impacts of climate change on the seasonality of low flows are analysed for 134 sub-catchments covering the River Rhine basin upstream of the Dutch–German border. Three seasonality indices for low flows are estimated, namely seasonality ratio (SR), weighted mean occurrence day (WMOD) and weighted persistence (WP). These indices are related to the discharge regime, timing and variability in timing of low flow events respectively. The three indices are estimated from: (1) observed low flows; (2) simulated low flows by the semi distributed HBV model using observed climate; (3) simulated low flows using simulated inputs from seven climate scenarios for the current climate (1964–2007); (4) simulated low flows using simulated inputs from seven climate scenarios for the future climate (2063–2098) including different emission scenarios. These four cases are compared to assess the effects of the hydrological model, forcing by different climate models and different emission scenarios on the three indices. The seven climate scenarios are based on different combinations of four General Circulation Models (GCMs), four Regional Climate Models (RCMs) and three greenhouse gas emission scenarios. Significant differences are found between cases 1 and 2. For instance, the HBV model is prone to overestimate SR and to underestimate WP and simulates very late WMODs compared to the estimated WMODs using observed discharges. Comparing the results of cases 2 and 3, the smallest difference is found in the SR index, whereas large differences are found in the WMOD and WP indices for the current climate. Finally, comparing the results of cases 3 and 4, we found that SR has decreased substantially by 2063–2098 in all seven subbasins of the River Rhine. The lower values of SR for the future climate indicate a shift from winter low flows (SR > 1) to summer low flows (SR < 1) in the two Alpine subbasins. The WMODs of low flows tend to be earlier than for the current climate in all subbasins except for the Middle Rhine and Lower Rhine subbasins. The WP values are slightly larger, showing that the predictability of low flow events increases as the variability in timing decreases for the future climate. From comparison of the uncertainty sources evaluated in this study, it is obvious that the RCM/GCM uncertainty has the largest influence on the variability in timing of low flows for future climate.

scholarly journals Changes in the Baiu Frontal Activity in the Future Climate Simulated by Super-High-Resolution Global and Cloud-Resolving Regional Climate Models

2006 ◽  
Vol 84 (1) ◽  
pp. 199-220 ◽  
Author(s):  
Kazuaki YASUNAGA ◽  
Chiashi MUROI ◽  
Teruyuki KATO ◽  
Masanori YOSHIZAKI ◽  
Kazuo KURIHARA ◽  
...  
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
The Future ◽  
Frontal Activity

scholarly journals A Method to Assess the Wind and Solar Resource and to Quantify Interannual Variability over the United States under Current and Projected Future Climate

2016 ◽  
Vol 55 (2) ◽  
pp. 345-363 ◽  
Author(s):  
Sue Ellen Haupt ◽  
Jeffrey Copeland ◽  
William Y. Y. Cheng ◽  
Yongxin Zhang ◽  
Caspar Ammann ◽  
...  
Keyword(s):  
United States ◽  
Interannual Variability ◽  
Regional Climate ◽  
Climate Scenario ◽  
Future Climate ◽  
Frequency Of Occurrence ◽  
Future Climate Scenario ◽  
Climate Conditions ◽  
Current Climate ◽  
The Future

AbstractThe National Center for Atmospheric Research and the National Renewable Energy Laboratory (NREL) collaborated to develop a method to assess the interannual variability of wind and solar power over the contiguous United States under current and projected future climate conditions, for use with NREL’s Regional Energy Deployment System (ReEDS) model. The team leveraged a reanalysis-derived database to estimate the wind and solar power resources and their interannual variability under current climate conditions (1985–2005). Then, a projected future climate database for the time range of 2040–69 was derived on the basis of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) simulations driven by free-running atmosphere–ocean general circulation models. To compare current and future climate variability, the team developed a baseline by decomposing the current climate reanalysis database into self-organizing maps (SOMs) to determine the predominant modes of variability. The current climate patterns found were compared with those of an NARCCAP-based future climate scenario, and the CRCM–CCSM combination was chosen to describe the future climate scenario. The future climate scenarios’ data were projected onto the Climate Four Dimensional Data Assimilation reanalysis SOMs. The projected future climate database was then created by resampling the reanalysis on the basis of the frequency of occurrence of the future SOM patterns, adjusting for the differences in magnitude of the wind speed or solar irradiance between the current and future climate conditions. Comparison of the changes in the frequency of occurrence of the SOM modes between current and future climate conditions indicates that the annual mean wind speed and solar irradiance could be expected to change by up to 10% (increasing or decreasing regionally).

scholarly journals Future Crop Yield Projections Using a Multi-model Set of Regional Climate Models and a Plausible Adaptation Practice in the Southeast United States

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1300
Author(s):  
D. W. Shin ◽  
Steven Cocke ◽  
Guillermo A. Baigorria ◽  
Consuelo C. Romero ◽  
Baek-Min Kim ◽  
...  
Keyword(s):  
United States ◽  
Crop Yield ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Large Temperature ◽  
Climate Projections ◽  
Southeast United States ◽  
The Future

Since maize, peanut, and cotton are economically valuable crops in the southeast United States, their yield amount changes in a future climate are attention-grabbing statistics demanded by associated stakeholders and policymakers. The Crop System Modeling—Decision Support System for Agrotechnology Transfer (CSM-DSSAT) models of maize, peanut, and cotton are, respectively, driven by the North American Regional Climate Change Assessment Program (NARCCAP) Phase II regional climate models to estimate current (1971–2000) and future (2041–2070) crop yield amounts. In particular, the future weather/climate data are based on the Special Report on Emission Scenarios (SRES) A2 emissions scenario. The NARCCAP realizations show on average that there will be large temperature increases (~2.7 °C) and minor rainfall decreases (~−0.10 mm/day) with pattern shifts in the southeast United States. With these future climate projections, the overall future crop yield amounts appear to be reduced under rainfed conditions. A better estimate of future crop yield amounts might be achievable by utilizing the so-called weighted ensemble method. It is proposed that the reduced crop yield amounts in the future could be mitigated by altering the currently adopted local planting dates without any irrigation support.

scholarly journals Impacts of climate change on the seasonality of low flows in 134 catchments in the River Rhine basin using an ensemble of bias-corrected regional climate simulations

2013 ◽  
Vol 17 (10) ◽  
pp. 4241-4257 ◽  
Author(s):  
M. C. Demirel ◽  
M. J. Booij ◽  
A. Y. Hoekstra
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Future Climate ◽  
Low Flow ◽  
River Rhine ◽  
Emission Scenarios ◽  
Low Flows ◽  
Current Climate ◽  
The Future

Abstract. The impacts of climate change on the seasonality of low flows were analysed for 134 sub-catchments covering the River Rhine basin upstream of the Dutch-German border. Three seasonality indices for low flows were estimated, namely the seasonality ratio (SR), weighted mean occurrence day (WMOD) and weighted persistence (WP). These indices are related to the discharge regime, timing and variability in timing of low flow events respectively. The three indices were estimated from: (1) observed low flows; (2) simulated low flows by the semi-distributed HBV model using observed climate as input; (3) simulated low flows using simulated inputs from seven combinations of General Circulation Models (GCMs) and Regional Climate Models (RCMs) for the current climate (1964–2007); (4) simulated low flows using simulated inputs from seven combinations of GCMs and RCMs for the future climate (2063–2098) including three different greenhouse gas emission scenarios. These four cases were compared to assess the effects of the hydrological model, forcing by different climate models and different emission scenarios on the three indices. Significant differences were found between cases 1 and 2. For instance, the HBV model is prone to overestimate SR and to underestimate WP and simulates very late WMODs compared to the estimated WMODs using observed discharges. Comparing the results of cases 2 and 3, the smallest difference was found for the SR index, whereas large differences were found for the WMOD and WP indices for the current climate. Finally, comparing the results of cases 3 and 4, we found that SR decreases substantially by 2063–2098 in all seven sub-basins of the River Rhine. The lower values of SR for the future climate indicate a shift from winter low flows (SR > 1) to summer low flows (SR < 1) in the two Alpine sub-basins. The WMODs of low flows tend to be earlier than for the current climate in all sub-basins except for the Middle Rhine and Lower Rhine sub-basins. The WP values are slightly larger, showing that the predictability of low flow events increases as the variability in timing decreases for the future climate. From comparison of the error sources evaluated in this study, it is obvious that different RCMs/GCMs have a larger influence on the timing of low flows than different emission scenarios. Finally, this study complements recent analyses of an international project (Rhineblick) by analysing the seasonality aspects of low flows and extends the scope further to understand the effects of hydrological model errors and climate change on three important low flow seasonality properties: regime, timing and persistence.

scholarly journals Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe

2011 ◽  
Vol 9 (67) ◽  
pp. 339-350 ◽  
Author(s):  
Helene Guis ◽  
Cyril Caminade ◽  
Carlos Calvete ◽  
Andrew P. Morse ◽  
Annelise Tran ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Climate Models ◽  
Mechanistic Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Transmission Risk ◽  
The North ◽  
Recent Emergence ◽  
The Future

Vector-borne diseases are among those most sensitive to climate because the ecology of vectors and the development rate of pathogens within them are highly dependent on environmental conditions. Bluetongue (BT), a recently emerged arboviral disease of ruminants in Europe, is often cited as an illustration of climate's impact on disease emergence, although no study has yet tested this association. Here, we develop a framework to quantitatively evaluate the effects of climate on BT's emergence in Europe by integrating high-resolution climate observations and model simulations within a mechanistic model of BT transmission risk. We demonstrate that a climate-driven model explains, in both space and time, many aspects of BT's recent emergence and spread, including the 2006 BT outbreak in northwest Europe which occurred in the year of highest projected risk since at least 1960. Furthermore, the model provides mechanistic insight into BT's emergence, suggesting that the drivers of emergence across Europe differ between the South and the North. Driven by simulated future climate from an ensemble of 11 regional climate models, the model projects increase in the future risk of BT emergence across most of Europe with uncertainty in rate but not in trend. The framework described here is adaptable and applicable to other diseases, where the link between climate and disease transmission risk can be quantified, permitting the evaluation of scale and uncertainty in climate change's impact on the future of such diseases.

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

Impact of aerosols on the future Euro-Mediterranean climate: results from the CORDEX FPS-Aerosol

2021 ◽  
Author(s):  
Pierre Nabat ◽  
Samuel Somot ◽  
Lola Corre ◽  
Eleni Katragkou ◽  
Shuping Li ◽  
...  
Keyword(s):  
Mediterranean Climate ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Surface Radiation ◽  
Climate Change Signal ◽  
Historical Period ◽  
Future Evolution ◽  
Aerosol Forcing ◽  
The Future

&lt;p&gt;The Euro-Mediterranean region is subject to numerous and various aerosol loads, which interact with radiation, clouds and atmospheric dynamics, with ensuing impact on regional climate. However up to now, aerosol variations are hardly taken into account in most regional climate simulations, although anthropogenic emissions have been dramatically reduced in Europe since the 1980s. Moreover, inconsistencies between regional climate models (RCMs) and their driving global model (GCM) have recently been identified in terms of future radiation and temperature evolution, which could be related to the differences in aerosol forcing.&amp;#160;&lt;br&gt;The present study aims at assessing the role of aerosols in the future evolution of the Euro-Mediterranean climate, using a specific multi-model protocol carried out in the Flagship Pilot Study &quot;Aerosol&quot; of the CORDEX program. This protocol relies on three simulations for each RCM: a historical run (1971-2000) and two future RCP8.5 simulations (2021-2050), a first one with evolving aerosols, and a second one with the same aerosols as in the historical period. Six modeling groups have taken part in this protocol, providing nine triplets of simulations. The analysis of these simulations will be presented here. First results show that the future evolution of aerosols has a significant impact on the evolution of surface radiation and surface temperature. In addition RCM runs taking into account the evolution of aerosols are simulating climate change signal closer to the one of their driving GCM than those with constant aerosols.&lt;/p&gt;

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