scholarly journals Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed

2014 ◽  
Vol 18 (5) ◽  
pp. 1695-1704 ◽  
Author(s):  
P. Gagnon ◽  
A. N. Rousseau
Keyword(s):  
Climate Change ◽  
Spatial Resolution ◽  
Extreme Precipitation ◽  
Regional Climate ◽  
Regional Scale ◽  
Annual Maximum ◽  
Small Watershed ◽  
Area Of Interest ◽  
Extreme Precipitation Events ◽  
Spatial Disaggregation

Abstract. Regional climate models (RCMs) are valuable tools to evaluate impacts of climate change (CC) at regional scale. However, as the size of the area of interest decreases, the ability of a RCM to simulate extreme precipitation events decreases due to the spatial resolution. Thus, it is difficult to evaluate whether a RCM bias on localized extreme precipitation is caused by the spatial resolution or by a misrepresentation of the physical processes in the model. Thereby, it is difficult to trust the CC impact projections for localized extreme precipitation. Stochastic spatial disaggregation models can bring the RCM precipitation data at a finer scale and reduce the bias caused by spatial resolution. In addition, disaggregation models can generate an ensemble of outputs, producing an interval of possible values instead of a unique discrete value. The objective of this work is to evaluate whether a stochastic spatial disaggregation model applied on annual maximum daily precipitation (i) enables the validation of a RCM for a period of reference, and (ii) modifies the evaluation of CC impacts over a small area. Three simulations of the Canadian RCM (CRCM) covering the period 1961–2099 are used over a small watershed (130 km2) located in southern Québec, Canada. The disaggregation model applied is based on Gibbs sampling and accounts for physical properties of the event (wind speed, wind direction, and convective available potential energy – CAPE), leading to realistic spatial distributions of precipitation. The results indicate that disaggregation has a significant impact on the validation. However, it does not provide a precise estimate of the simulation bias because of the difference in resolution between disaggregated values (4 km) and observations, and because of the underestimation of the spatial variability by the disaggregation model for the most convective events. Nevertheless, disaggregation illustrates that the simulations used mostly overestimated annual maximum precipitation depth in the study area during the reference period. Also, disaggregation slightly increases the signal of CC compared to the RCM raw simulations, highlighting the importance of spatial resolution in CC impact evaluation of extreme events.

scholarly journals Stochastic spatial disaggregation of extreme precipitation to validate a Regional Climate Model and to evaluate climate change impacts over a small watershed

2013 ◽  
Vol 10 (6) ◽  
pp. 8167-8195 ◽  
Author(s):  
P. Gagnon ◽  
A. N. Rousseau
Keyword(s):  
Climate Change ◽  
Spatial Resolution ◽  
Extreme Precipitation ◽  
Regional Climate ◽  
Regional Scale ◽  
Annual Maximum ◽  
Small Watershed ◽  
Area Of Interest ◽  
Extreme Precipitation Events ◽  
Spatial Disaggregation

Abstract. Regional Climate Models (RCMs) are valuable tools to evaluate impacts of climate change (CC) at regional scale. However, as the size of the area of interest decreases, the ability of a RCM to simulate extreme precipitation events decreases due to the spatial resolution. Thus, it is difficult to evaluate whether a RCM bias on localized extreme precipitation is caused by the spatial resolution or by a misrepresentation of the physical processes in the model. Thereby, it is difficult to trust the CC impact projections for localized extreme precipitation. Stochastic spatial disaggregation models can bring the RCM precipitation data at a finer scale and reduce the bias caused by spatial resolution. In addition, disaggregation models can generate an ensemble of outputs, producing an interval of possible values instead of a unique discrete value. The objective of this work is to evaluate whether a stochastic spatial disaggregation model applied on annual maximum daily precipitation: (i) enables the validation of a RCM for a period of reference, and (ii) modifies the evaluation of CC impacts over a small area. Three simulations of the Canadian RCM (CRCM) covering the period 1961–2099 are used over a small watershed (130 km2) located in southern Québec, Canada. The disaggregation model applied is based on Gibbs sampling and accounts for physical properties of the event (wind speed, wind direction, and convective available potential energy (CAPE)), leading to realistic spatial distributions of precipitation. The results indicate that disaggregation has a significant impact on the validation. However it does not provide a precise estimate of the simulation bias because of the difference in resolution between disaggregated values (4 km) and observations, and because of the underestimation of the spatial variability by the disaggregation model for the most convective events. Nevertheless, disaggregation permits to determine that the simulations used mostly overestimated annual maximum precipitation depth in the study area during the reference period. Also, disaggregation slightly increases the signal of CC compared to the RCM raw simulations, highlighting the importance of spatial resolution in CC impact evaluation of extreme events.

scholarly journals Evaluation of uncertainties in mean and extreme precipitation under climate change for northwestern Mediterranean watersheds from high-resolution Med and Euro-CORDEX ensembles

2018 ◽  
Vol 22 (1) ◽  
pp. 673-687 ◽  
Author(s):  
Antoine Colmet-Daage ◽  
Emilia Sanchez-Gomez ◽  
Sophie Ricci ◽  
Cécile Llovel ◽  
Valérie Borrell Estupina ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Historical Period ◽  
Mountainous Regions ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract. The climate change impact on mean and extreme precipitation events in the northern Mediterranean region is assessed using high-resolution EuroCORDEX and MedCORDEX simulations. The focus is made on three regions, Lez and Aude located in France, and Muga located in northeastern Spain, and eight pairs of global and regional climate models are analyzed with respect to the SAFRAN product. First the model skills are evaluated in terms of bias for the precipitation annual cycle over historical period. Then future changes in extreme precipitation, under two emission scenarios, are estimated through the computation of past/future change coefficients of quantile-ranked model precipitation outputs. Over the 1981–2010 period, the cumulative precipitation is overestimated for most models over the mountainous regions and underestimated over the coastal regions in autumn and higher-order quantile. The ensemble mean and the spread for future period remain unchanged under RCP4.5 scenario and decrease under RCP8.5 scenario. Extreme precipitation events are intensified over the three catchments with a smaller ensemble spread under RCP8.5 revealing more evident changes, especially in the later part of the 21st century.

The effect of climate change on urban drainage: an evaluation based on regional climate model simulations

2006 ◽  
Vol 54 (6-7) ◽  
pp. 9-15 ◽  
Author(s):  
M. Grum ◽  
A.T. Jørgensen ◽  
R.M. Johansen ◽  
J.J. Linde
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Regional Climate ◽  
Rain Gauge ◽  
Urban Drainage ◽  
Extreme Precipitation Events ◽  
Rainfall Extremes ◽  
The Future ◽  
Precipitation Events

That we are in a period of extraordinary rates of climate change is today evident. These climate changes are likely to impact local weather conditions with direct impacts on precipitation patterns and urban drainage. In recent years several studies have focused on revealing the nature, extent and consequences of climate change on urban drainage and urban runoff pollution issues. This study uses predictions from a regional climate model to look at the effects of climate change on extreme precipitation events. Results are presented in terms of point rainfall extremes. The analysis involves three steps: Firstly, hourly rainfall intensities from 16 point rain gauges are averaged to create a rain gauge equivalent intensity for a 25 × 25 km square corresponding to one grid cell in the climate model. Secondly, the differences between present and future in the climate model is used to project the hourly extreme statistics of the rain gauge surface into the future. Thirdly, the future extremes of the square surface area are downscaled to give point rainfall extremes of the future. The results and conclusions rely heavily on the regional model's suitability in describing extremes at time-scales relevant to urban drainage. However, in spite of these uncertainties, and others raised in the discussion, the tendency is clear: extreme precipitation events effecting urban drainage and causing flooding will become more frequent as a result of climate change.

scholarly journals Analysis and evaluation of the CORDEX simulations and derived extreme precipitation characteristics in European lowlands

2021 ◽  
Author(s):  
Alexandra Berényi ◽  
Judit Bartholy ◽  
Rita Pongrácz
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Western Europe ◽  
Climate Model ◽  
Regional Climate ◽  
Mediterranean Area ◽  
Point Of View ◽  
East European Plain ◽  
Climate Indices ◽  
Extreme Precipitation Events

<p>As the effects of climate change become more severe, the possible shifts in precipitation patterns can cause severe natural hazards, such as extended drought periods, floods and flash floods, therefore, appropriate risk management is essential. The future adaptation strategies and decisions should definitely consider the results of physically-based climate model simulations, that is why the validation and analysis of these results play a key role in climate change issues.</p><p>            The main goal of this study is to analyse the spatio-temporal changes in main and extreme precipitation indices, and validate the Euro-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) simulations from this specific point of view. For the evaluation and analysis, we use the current version of E-OBS database. Both the simulations and the database are available in a 0.11° grid with daily temporal resolution.</p><p>            Since plain regions play an important role in agricultural economy and are more exposed to floods due to their geographic features, our primary goals are (i) to examine temporal and spatial changes in extreme precipitation events, and (ii) to explore possible connections between the different lowlands across Europe. Altogether 14 plain regions were selected with an objective multi-step methodology where the selected plains have to fulfil several criteria.<em> </em>These target regions represent different climatic types within Europe and cover different geographical areas (e.g. near the sea, surrounded by mountains, etc.). More specifically, five plain regions are parts of the East European Plain, two regions are located in the Scandinavian basin, five regions are located in Western Europe, one in Southern Europe, and finally, the Pannonian Plain (including mostly Hungary) is also selected.</p><p>            To analyse and validate the simulations, we calculated 17 climate indices (most of them defined by the Expert Team of Climate Change Indices, ETCCD). These indices are capable to represent the differences and similarities between and within the plains, and measure the changes in the occurrence an intensity of main and extreme precipitation, the lack of precipitation, and dry spells. The validation results serve as a basis of selecting the most suitable simulations for subsequent analysis of extreme conditions predicted for lowlands within Europe under different future scenarios.</p>

Quantifying the added value of downscaling in extreme precipitation attribution

2020 ◽  
Author(s):  
Jonathan Eden ◽  
Bastien Dieppois
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
General Circulation ◽  
Regional Climate ◽  
Precipitation Event ◽  
Extreme Precipitation Events ◽  
Statistical Correction ◽  
Precipitation Events

<p>While there is a discernible global warming fingerprint in the increase observed daily temperature extremes, there is far greater uncertainty of the role played by anthropogenic climate change with regard to extreme precipitation. A logical progression of thought is that an increase in extreme precipitation results from the 7% increase in atmospheric moisture per 1°C global temperature increase predicted by the Clausius-Clapeyron (CC) relation.  While this is supported by observations on the global scale, rates of extreme precipitation at smaller spatial and temporal scales are influenced to a far greater extent by atmospheric circulation and vertical stability in addition to local moisture availability. Many of these processes and other features of extreme precipitation events are not sufficiently represented in general circulation model (GCM) simulations. Meanwhile, limited observational networks mean that many short-term convective events are not accurately represented in the observational data.  </p><p>Errors and biases are common to all global and regional climate models, and many users of climate information require some form of statistical correction to improve the usefulness of model output. As so-called bias correction has become commonplace in climate impact research, its development has been hastened by a sustained debate regarding model correction in general leading to techniques that merge statistical correction and downscaling, represent random variability using stochasticity and are explicitly applicable to extremes. To date, attribution of extreme precipitation has not fully utilised the tools available from recent advances in bias correction, stochastic postprocessing and statistical downscaling. In the same way that GCMs are the most important tool in making climate change projections, understanding the degree to which the nature of a particular weather event has changed due to global warming requires long-term simulations of global climate from the pre-industrial era to the present day.  The lack of a correction and/or downscaling step in almost all precipitation event attribution methodologies is therefore surprising. </p><p>Here, we present a multi-scale attribution analysis of a sample of extreme precipitation events across Europe using a blend of observation- and model-based data. Attribution information generated using the raw output of global and regional climate model ensembles will be compared to that generated using the same set of models following a statistical postprocessing and downscaling step. Our conclusions will make recommendations for the value and wider application of downscaling methodologies in attribution science.</p>

Trend in precipitation and drought extremes in southern lowland regions of Europe

2021 ◽  
Author(s):  
Alexandra Berényi ◽  
Rita Pongrácz ◽  
Judit Bartholy
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Regional Scale ◽  
Climate Indices ◽  
Subtropical Climate ◽  
Precipitation Patterns ◽  
Extreme Precipitation Events ◽  
Dry Spells ◽  
Precipitation Events

<p>The effects of climate change on precipitation patterns can be observed on global scale, however, global climate change affects different regions more or less severely. Because of the high variability of precipitation in particular, future changes related to precipitation can be very different, even opposite on continental/regional scale. Even within Europe, the detected trends in precipitation patterns and extremes differ across the continent. According to climate model simulations for the future, Northern Europe is projected to become wetter, while the southern parts of the continent will tend to become drier by the end of the 21st century. The frequency and intensity of extreme precipitation will also increase in the whole continent. The possible shifts in precipitation patterns from wetter to drier conditions with fewer but increased extreme precipitation events can cause severe natural hazards, such as extended drought periods, water scarcity, floods and flash floods, therefore appropriate risk management is essential. For this purpose the analysis of possible hazards associated to specific precipitation-related weather phenomena is necessary and serves as key input.</p><p>Since plain regions play an important role in agricultural economy and are more exposed to floods because of their geographic features and the gravitational movement of surface water, our primary goal was to examine temporal and spatial changes in extreme precipitation events and dry spells in three European lowlands, located in the southern part of the continent. We selected the following regions: the Po-Valley located in Italy with humid subtropical climate; the Romanian Plain in Romania, and the Pannonian Plain covering different parts of Hungary, Serbia, Slovakia, Croatia, Romania and Ukraine with humid continental climatic conditions.</p><p>Precipitation time series were used from the E-OBS v.22 dataset on a 0.1° regular grid. The dataset is based on station measurements from Europe and are available from 1950 onward with daily temporal resolution. For the analysis of main precipitation patterns, dry spells and extreme events, we use 17 climate indices (most of them are defined by the Expert Team on Climate Change Detection and Indices, ECCDI). The analysis focuses on annual and seasonal changes in the three regions. The selected indices are capable to represent the differences and similarities between and within the plains. Our preliminary results show that the occurrence and intensity of extreme precipitation events increased in all regions, while the trends of duration and frequency of dry spells show both intra- and inter regional variability across the plains.</p>

Comparative Analysis or Precipitation Variation Characteristics between Subtropical Monsoon Climate and Temperate Monsoon Climate-Take Changsha and Chengde for Example

2021 ◽  
Vol 7 (5) ◽  
pp. 1113-1122
Author(s):  
Bo Chen ◽  
Shi-jun Xu ◽  
Xin-ping Zhang ◽  
Yi Xie
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Precipitation Intensity ◽  
Annual Precipitation ◽  
Wet Season ◽  
Precipitation Frequency ◽  
Extreme Precipitation Events ◽  
Average Precipitation ◽  
Autumn And Winter ◽  
Average Annual Precipitation

Using the methods of literature review, regression analysis and moving average, this paper selects the daily precipitation of Changsha and Chengde from 1951 to 1986 as samples, and analyzes the average precipitation, precipitation frequency, precipitation intensity, extreme precipitation time and other indicators of Changsha and Chengde from the perspective of interannual and seasonal changes Trends. The researches show that: the average precipitation of Changsha in the 36 years is 1151.2mm, spring is the wet season, autumn and winter are the dry seasons, and the maximum average precipitation is in spring; the average annual precipitation, precipitation frequency in spring, summer and winter, annual precipitation frequency, annual precipitation intensity and extreme precipitation events show a decreasing trend. The average annual precipitation of Chengde city is 454.1 mm, wet season in summer and dry season in spring, autumn and winter; the average annual precipitation, precipitation in four seasons, annual precipitation frequency, precipitation frequency in spring, autumn and winter, annual precipitation intensity and extreme precipitation events show a decreasing trend, while the precipitation frequency in summer shows an increasing trend. The study of regional climate change based on the time series data of this stage is of great significance to comprehensively understand the law of regional climate change and predict the future trend of climate change.

scholarly journals Ensemble Projections of Regional Climatic Changes over Ontario, Canada

2015 ◽  
Vol 28 (18) ◽  
pp. 7327-7346 ◽  
Author(s):  
Xiuquan Wang ◽  
Guohe Huang ◽  
Jinliang Liu ◽  
Zhong Li ◽  
Shan Zhao
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Regional Climate ◽  
Climatic Changes ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Local Climate ◽  
Ensemble Simulations ◽  
Extreme Precipitation Events ◽  
Data Portal

Abstract In this study, high-resolution climate projections over Ontario, Canada, are developed through an ensemble modeling approach to provide reliable and ready-to-use climate scenarios for assessing plausible effects of future climatic changes at local scales. The Providing Regional Climates for Impacts Studies (PRECIS) regional modeling system is adopted to conduct ensemble simulations in a continuous run from 1950 to 2099, driven by the boundary conditions from a HadCM3-based perturbed physics ensemble. Simulations of temperature and precipitation for the baseline period are first compared to the observed values to validate the performance of the ensemble in capturing the current climatology over Ontario. Future projections for the 2030s, 2050s, and 2080s are then analyzed to help understand plausible changes in its local climate in response to global warming. The analysis indicates that there is likely to be an obvious warming trend with time over the entire province. The increase in average temperature is likely to be varying within [2.6, 2.7]°C in the 2030s, [4.0, 4.7]°C in the 2050s, and [5.9, 7.4]°C in the 2080s. Likewise, the annual total precipitation is projected to increase by [4.5, 7.1]% in the 2030s, [4.6, 10.2]% in the 2050s, and [3.2, 17.5]% in the 2080s. Furthermore, projections of rainfall intensity–duration–frequency (IDF) curves are developed to help understand the effects of global warming on extreme precipitation events. The results suggest that there is likely to be an overall increase in the intensity of rainfall storms. Finally, a data portal named Ontario Climate Change Data Portal (CCDP) is developed to ensure decision-makers and impact researchers have easy and intuitive access to the refined regional climate change scenarios.

Sign in / Sign up

Export Citation Format

Share Document