scholarly journals Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data

2015 ◽  
Vol 12 (3) ◽  
pp. 2657-2706 ◽  
Author(s):  
T. Olsson ◽  
J. Jakkila ◽  
N. Veijalainen ◽  
L. Backman ◽  
J. Kaurola ◽  
...  
Keyword(s):  
Climate Change ◽  
Seasonal Changes ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Catchment Scale ◽  
Temperature And Precipitation

Abstract. Assessment of climate change impacts on climate and hydrology on catchment scale requires reliable information about the average values and climate fluctuations of the past, present and future. Regional Climate Models (RCMs) used in impact studies often produce biased time series of meteorological variables. In this study bias correction of RCM temperature and precipitation for Finland is carried out using different versions of distribution based scaling (DBS) method. The DBS adjusted RCM data is used as input of a hydrological model to simulate changes in discharges in four study catchments in different parts of Finland. The annual mean discharges and seasonal variation simulated with the DBS adjusted temperature and precipitation data are sufficiently close to observed discharges in the control period (1961–2000) and produce more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data. Furthermore, with most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data during 1961–2100. However, if the biases in the mean or the SD of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections. The DBS method influences especially the projected seasonal changes in discharges and the use of uncorrected data can produce unrealistic seasonal discharges and changes. The projected changes in annual mean discharges are moderate or small, but seasonal distribution of discharges will change significantly.

scholarly journals Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data

2015 ◽  
Vol 19 (7) ◽  
pp. 3217-3238 ◽  
Author(s):  
T. Olsson ◽  
J. Jakkila ◽  
N. Veijalainen ◽  
L. Backman ◽  
J. Kaurola ◽  
...  
Keyword(s):  
Climate Change ◽  
Seasonal Changes ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Catchment Scale ◽  
Temperature And Precipitation

Abstract. Assessment of climate change impacts on climate and hydrology on catchment scale requires reliable information about the average values and climate fluctuations of the past, present and future. Regional climate models (RCMs) used in impact studies often produce biased time series of meteorological variables. In this study bias correction (BC) of RCM temperature and precipitation for Finland is carried out using different versions of the distribution based scaling (DBS) method. The DBS-adjusted RCM data are used as input of a hydrological model to simulate changes in discharges of four study catchments in different parts of Finland. The annual mean discharges and seasonal variation simulated with the DBS-adjusted temperature and precipitation data are sufficiently close to observed discharges in the control period 1961–2000 and produce more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data. Furthermore, with most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data during 1961–2100. However, if the biases in the mean or the standard deviation of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections. The DBS method influences especially the projected seasonal changes in discharges and the use of uncorrected data can produce unrealistic seasonal discharges and changes. The projected changes in annual mean discharges are moderate or small, but seasonal distribution of discharges will change significantly.

Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Data ◽  
Urban Rural

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>

scholarly journals Quantification of the Expected Changes in Annual Maximum Daily Precipitation Quantiles under Climate Change in the Iberian Peninsula

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 23 ◽  
Author(s):  
Carlos Garijo ◽  
Luis Mediero
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Regional Climate Models ◽  
The Future

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.

Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies

2010 ◽  
Vol 41 (3-4) ◽  
pp. 211-229 ◽  
Author(s):  
Wei Yang ◽  
Johan Andréasson ◽  
L. Phil Graham ◽  
Jonas Olsson ◽  
Jörgen Rosberg ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Impact Studies ◽  
Emissions Scenarios ◽  
Precipitation And Temperature

As climate change could have considerable influence on hydrology and corresponding water management, appropriate climate change inputs should be used for assessing future impacts. Although the performance of regional climate models (RCMs) has improved over time, systematic model biases still constrain the direct use of RCM output for hydrological impact studies. To address this, a distribution-based scaling (DBS) approach was developed that adjusts precipitation and temperature from RCMs to better reflect observations. Statistical properties, such as daily mean, standard deviation, distribution and frequency of precipitation days, were much improved for control periods compared to direct RCM output. DBS-adjusted precipitation and temperature from two IPCC Special Report on Emissions Scenarios (SRESA1B) transient climate projections were used as inputs to the HBV hydrological model for several river basins in Sweden for the period 1961–2100. Hydrological results using DBS were compared to results with the widely-used delta change (DC) approach for impact studies. The general signal of a warmer and wetter climate was obtained using both approaches, but use of DBS identified differences between the two projections that were not seen with DC. The DBS approach is thought to better preserve the future variability produced by the RCM, improving usability for climate change impact studies.

scholarly journals Impacts of climate change on extreme floods in Finland – studies using bias corrected Regional Climate Model data

2017 ◽  
Author(s):  
Noora Veijalainen ◽  
Juho Jakkila ◽  
Taru Olsson ◽  
Leif Backman ◽  
Bertel Vehviläinen ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Regional Climate Models ◽  
Heavy Rain ◽  
Climate Change Signal ◽  
Winter Temperatures ◽  
Delta Change

Abstract. Bias correction of precipitation and temperature of five Regional Climate Models (RCMs) was carried out using Distribution Based Scaling (DBS) method with two versions for precipitation adjustment: single gamma and double gamma. This data were then used as input for a hydrological model to simulate changes in floods by the end of this century, and the results were compared to corresponding changes simulated using delta change approach. The results show that while the DBS adjustment significantly improves the RCM precipitations and temperatures compared to observations, especially the double gamma distribution does not always preserve trends of the uncorrected RCM data. The simulation of floods in the control period is improved by the DBS adjustment with no significant differences between single and double gamma. However, some scenarios are still unable to match the observed hydrology adequately due to remaining biases especially in near zero winter temperatures. These scenarios may produce an unrealistic climate change signal and should therefore be discarded from further use. A simple criterion for evaluating the adequate performance of the RCMs and hydrological models compared to observed floods is presented. The results of climate change simulations show that extreme summer precipitations increase more than average values in Finland. The changes in floods by 2070–2099 vary in different regions depending on season and the main flood producing mechanism (snowmelt or heavy rain). The changes in floods simulated with the DBS adjusted RCM data are mostly similar as with delta change approach, but the DBS method produces larger range of changes.

scholarly journals Assessment of Changes in Annual Maximum Precipitations in the Iberian Peninsula under Climate Change

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2375 ◽  
Author(s):  
Carlos Garijo ◽  
Luis Mediero
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Control Period ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
The Future

Climate model projections can be used to assess the future expected behavior of extreme precipitation due to climate change. In Europe, the EURO-CORDEX project provides precipitation projections in the future under various representative concentration pathways (RCP), through regionalized outputs of Global Climate Models (GCM) by a set of Regional Climate Models (RCM). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX project are analyzed in the Iberian Peninsula and the Balearic Islands. Precipitation quantiles for a set of exceedance probabilities are estimated by using the Generalized Extreme Value (GEV) distribution function fitted by the L-moment method. Precipitation quantiles expected in the future period are compared with the precipitation quantiles in the control period, for each climate model. An approach based on Monte Carlo simulations is developed to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period to identify statistically significant changes. The higher the significance threshold, the fewer cells with changes are identified. Consequently, a set of maps are obtained for various thresholds to assist the decision making process in subsequent climate change studies.

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.

A statistical framework for evaluating EURO-CORDEX simulations and derived drought characteristics

2021 ◽  
Author(s):  
David J. Peres ◽  
Alfonso Senatore ◽  
Paola Nanni ◽  
Antonino Cancelliere ◽  
Giuseppe Mendicino ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Circulation Model ◽  
Regional Climate Models ◽  
Monitoring Networks ◽  
Global Circulation Model ◽  
Drought Characteristics ◽  
Precipitation And Temperature

<p>Regional climate models (RCMs) are commonly used for assessing, at proper spatial resolutions, future impacts of climate change on hydrological events. In this study, we propose a statistical methodological framework to assess the quality of the EURO-CORDEX RCMs concerning their ability to simulate historic observed climate (temperature and precipitation). We specifically focus on the models’ performance in reproducing drought characteristics (duration, accumulated deficit, intensity, and return period) determined by the theory of runs at seasonal and annual timescales, by comparison with high-density and high-quality ground-based observational datasets. In particular, the proposed methodology is applied to the Sicily and Calabria regions (Southern Italy), where long historical precipitation and temperature series were recorded by the ground-based monitoring networks operated by the former Regional Hydrographic Offices. The density of the measurements is considerably greater than observational gridded datasets available at the European level, such as E-OBS or CRU-TS. Results show that among the models based on the combination of the HadGEM2 global circulation model (GCM) with the CLM-Community RCMs are the most skillful in reproducing precipitation and temperature variability as well as drought characteristics. Nevertheless, the ranking of the models may slightly change depending on the specific variable analysed, as well as the temporal and spatial scale of interest. From this point of view, the proposed methodology highlights the skills and weaknesses of the different configurations, aiding on the selection of the most suitable climate model for assessing climate change impacts on drought processes and the underlying variables.</p>

scholarly journals The Impact of Climate Change on Material Degradation Criteria in Heritage over Iran: Regional Climate Model Evaluation

2020 ◽  
Vol 172 ◽  
pp. 02006
Author(s):  
Hamed Hedayatnia ◽  
Marijke Steeman ◽  
Nathan Van Den Bossche
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Salt Crystallization ◽  
Climate Modelling ◽  
Impact Of Climate Change ◽  
Climate Change Effects ◽  
The Impact

Understanding how climate change accelerates or slows down the process of material deterioration is the first step towards assessing adaptive approaches for the preservation of historical heritage. Analysis of the climate change effects on the degradation risk assessment parameters like salt crystallization cycles is of crucial importance when considering mitigating actions. Due to the vulnerability of cultural heritage in Iran to climate change, the impact of this phenomenon on basic parameters plus variables more critical to building damage like salt crystallization index needs to be analyzed. Regional climate modelling projections can be used to asses the impact of climate change effects on heritage. The output of two different regional climate models, the ALARO-0 model (Ghent University-RMI, Belgium) and the REMO model (HZG-GERICS, Germany), is analyzed to find out which model is more adapted to the region. So the focus of this research is mainly on the evaluation to determine the reliability of both models over the region. For model validation, a comparison between model data and observations was performed in 4 different climate zones for 30 years to find out how reliable these models are in the field of building pathology.

Sign in / Sign up

Export Citation Format

Share Document