scholarly journals High resolution climate change projections for the Pyrenees region

2020 ◽  
Vol 17 ◽  
pp. 191-208
Author(s):  
María P. Amblar-Francés ◽  
Petra Ramos-Calzado ◽  
Jorge Sanchis-Lladó ◽  
Alfonso Hernanz-Lázaro ◽  
María C. Peral-García ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
21St Century ◽  
Statistical Downscaling ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Climate Projections ◽  
Climate Change Projections ◽  
Climate Conditions ◽  
The Impact

Abstract. The Pyrenees, located in the transition zone of Atlantic and Mediterranean climates, constitute a paradigmatic example of mountains undergoing rapid changes in environmental conditions, with potential impact on the availability of water resources, mainly for downstream populations. High-resolution probabilistic climate change projections for precipitation and temperature are a crucial element for stakeholders to make well-informed decisions on adaptation to new climate conditions. In this line, we have generated high–resolution climate projections for 21st century by applying two statistical downscaling methods (regression for max and min temperatures, and analogue for precipitation) over the Pyrenees region in the frame of the CLIMPY project over a new high-resolution (5 km × 5 km) observational grid using 24 climate models from CMIP5. The application of statistical downscaling to such a high resolution observational grid instead of station data partially circumvent the problems associated to the non-uniform distribution of observational in situ data. This new high resolution projections database based on statistical algorithms complements the widely used EUROCORDEX data based on dynamical downscaling and allows to identify features that are dependent on the particular downscaling method. In our analysis, we not only focus on maximum and minimum temperatures and precipitation changes but also on changes in some relevant extreme indexes, being 1986–2005 the reference period. Although climate models predict a general increase in temperature extremes for the end of the 21st century, the exact spatial distribution of changes in temperature and much more in precipitation remains uncertain as they are strongly model dependent. Besides, for precipitation, the uncertainty associated to models can mask – depending on the zones- the signal of change. However, the large number of downscaled models and the high resolution of the used grid allow us to provide differential information at least at massif level. The impact of the RCP becomes significant for the second half of the 21st century, with changes – differentiated by massifs – of extreme temperatures and analysed associated extreme indexes for RCP8.5 at the end of the century.

Is Institutional Democracy a Good Proxy for Model Independence?

2016 ◽  
Vol 29 (23) ◽  
pp. 8301-8316 ◽  
Author(s):  
Martin Leduc ◽  
René Laprise ◽  
Ramón de Elía ◽  
Leo Šeparović
Keyword(s):  
Climate Change ◽  
Model Comparison ◽  
Climate Models ◽  
Regional Climate ◽  
Burden Of Proof ◽  
Climate Projections ◽  
Climate Change Projections ◽  
Cross Model ◽  
Structural Differences ◽  
The Impact

Abstract Climate models developed within a given research group or institution are prone to share structural similarities, which may induce resembling features in their simulations of the earth’s climate. This assertion, known as the “same-center hypothesis,” is investigated here using a subsample of CMIP3 climate projections constructed by retaining only the models originating from institutions that provided more than one model (or model version). The contributions of individual modeling centers to this ensemble are first presented in terms of climate change projections. A metric for climate change disagreement is then defined to analyze the impact of typical structural differences (such as resolution, parameterizations, or even entire atmosphere and ocean components) on regional climate projections. This metric is compared to a present climate performance metric (correlation of error patterns) within a cross-model comparison framework in terms of their abilities to identify the same-center models. Overall, structural differences between the pairs of same-center models have a stronger impact on climate change projections than on how models reproduce the observed climate. The same-center criterion is used to detect agreements that might be attributable to model similarities and thus that should not be interpreted as implying greater confidence in a given result. It is proposed that such noninformative agreements should be discarded from the ensemble, unless evidence shows that these models can be assumed to be independent. Since this burden of proof is not generally met by the centers participating in a multimodel ensemble, the authors propose an ensemble-weighting scheme based on the assumption of institutional democracy to prevent overconfidence in climate change projections.

Structural safety and design under climate change

2019 ◽  
Author(s):  
Pietro Croce ◽  
Paolo Formichi ◽  
Filippo Landi
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Structural Reliability ◽  
Greenhouse Gas Emission ◽  
Extreme Value ◽  
Structural Safety ◽  
Climate Projections ◽  
Climate Conditions ◽  
Impact Of Climate Change ◽  
The Impact

<p>The impact of climate change on climatic actions could significantly affect, in the mid-term future, the design of new structures as well as the reliability of existing ones designed in accordance to the provisions of present and past codes. Indeed, current climatic loads are defined under the assumption of stationary climate conditions but climate is not stationary and the current accelerated rate of changes imposes to consider its effects.</p><p>Increase of greenhouse gas emissions generally induces a global increase of the average temperature, but at local scale, the consequences of this phenomenon could be much more complex and even apparently not coherent with the global trend of main climatic parameters, like for example, temperature, rainfalls, snowfalls and wind velocity.</p><p>In the paper, a general methodology is presented, aiming to evaluate the impact of climate change on structural design, as the result of variations of characteristic values of the most relevant climatic actions over time. The proposed procedure is based on the analysis of an ensemble of climate projections provided according a medium and a high greenhouse gas emission scenario. Factor of change for extreme value distribution’s parameters and return values are thus estimated in subsequent time windows providing guidance for adaptation of the current definition of structural loads.</p><p>The methodology is illustrated together with the outcomes obtained for snow, wind and thermal actions in Italy. Finally, starting from the estimated changes in extreme value parameters, the influence on the long-term structural reliability can be investigated comparing the resulting time dependent reliability with the reference reliability levels adopted in modern Structural codes.</p>

scholarly journals A high-resolution downscaled CMIP6 projections dataset of essential surface climate variables over the globe coherent with the ERA5-Land reanalysis for climate change impact assessments

2021 ◽  
Author(s):  
Thomas Noël ◽  
Harilaos Loukos ◽  
Dimitri Defrance
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Climate Models ◽  
Mapping Method ◽  
Cumulative Distribution ◽  
Climate Projections ◽  
Climate Modelling ◽  
Temperature And Precipitation ◽  
Surface Climate ◽  
Emissions Scenarios

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP6 experiment using the ERA5-Land reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.1°x 0.1°, comprises 5 climate models and includes two surface daily variables at monthly resolution: air temperature and precipitation. Two greenhouse gas emissions scenarios are available: one with mitigation policy (SSP126) and one without mitigation (SSP585). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modelling community standards and value checking for outlier detection.

scholarly journals Uncertainty Impacts of Climate Change and Downscaling Methods on Future Runoff Projections in the Biliu River Basin

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2130 ◽  
Author(s):  
Zhu ◽  
Zhang ◽  
Wu ◽  
Qi ◽  
Fu ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Water Resource Management ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Liaoning Province ◽  
Lower Envelope ◽  
The Impact

This paper assesses the uncertainties in the projected future runoff resulting from climate change and downscaling methods in the Biliu River basin (Liaoning province, Northeast China). One widely used hydrological model SWAT, 11 Global Climate Models (GCMs), two statistical downscaling methods, four dynamical downscaling datasets, and two Representative Concentration Pathways (RCP4.5 and RCP8.5) are applied to construct 22 scenarios to project runoff. Hydrology variables in historical and future periods are compared to investigate their variations, and the uncertainties associated with climate change and downscaling methods are also analyzed. The results show that future temperatures will increase under all scenarios and will increase more under RCP8.5 than RCP4.5, while future precipitation will increase under 16 scenarios. Future runoff tends to decrease under 13 out of the 22 scenarios. We also found that the mean runoff changes ranging from −38.38% to 33.98%. Future monthly runoff increases in May, June, September, and October and decreases in all the other months. Different downscaling methods have little impact on the lower envelope of runoff, and they mainly impact the upper envelope of the runoff. The impact of climate change can be regarded as the main source of the runoff uncertainty during the flood period (from May to September), while the impact of downscaling methods can be regarded as the main source during the non-flood season (from October to April). This study separated the uncertainty impact of different factors, and the results could provide very important information for water resource management.

scholarly journals Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2266 ◽  
Author(s):  
Enrique Soriano ◽  
Luis Mediero ◽  
Carlos Garijo
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Control Period ◽  
Flood Frequency ◽  
Climate Projections ◽  
The Future ◽  
Flood Quantiles ◽  
The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

scholarly journals Assessment of the impacts of climate change on maize production in the Wami Ruvu basin of Tanzania

2016 ◽  
Vol 8 (1) ◽  
pp. 142-164 ◽  
Author(s):  
Philbert Luhunga ◽  
Ladslaus Chang'a ◽  
George Djolov
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Climate Projections ◽  
Intergovernmental Panel ◽  
Lower Altitude ◽  
Rcp 8.5 ◽  
Maize Yields ◽  
The Impact ◽  
Impacts Of Climate Change

The IPCC (Intergovernmental Panel on Climate Change) assessment reports confirm that climate change will hit developing countries the hardest. Adaption is on the agenda of many countries around the world. However, before devising adaption strategies, it is crucial to assess and understand the impacts of climate change at regional and local scales. In this study, the impact of climate change on rain-fed maize (Zea mays) production in the Wami-Ruvu basin of Tanzania was evaluated using the Decision Support System for Agro-technological Transfer. The model was fed with daily minimum and maximum temperatures, rainfall and solar radiation for current climate conditions (1971–2000) as well as future climate projections (2010–2099) for two Representative Concentration Pathways: RCP 4.5 and RCP 8.5. These data were derived from three high-resolution regional climate models, used in the Coordinated Regional Climate Downscaling Experiment program. Results showed that due to climate change future maize yields over the Wami-Ruvu basin will slightly increase relative to the baseline during the current century under RCP 4.5 and RCP 8.5. However, maize yields will decline in the mid and end centuries. The spatial distribution showed that high decline in maize yields are projected over lower altitude regions due to projected increase in temperatures in those areas.

scholarly journals A Statistical–Dynamical Methodology to Downscale Regional Climate Projections to Urban Scale

2020 ◽  
Vol 59 (6) ◽  
pp. 1109-1123 ◽  
Author(s):  
François DuchÊne ◽  
Bert Van Schaeybroeck ◽  
Steven Caluwaerts ◽  
Rozemien De Troch ◽  
Rafiq Hamdi ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Heat Waves ◽  
Regional Climate ◽  
Wave Number ◽  
Surface Model ◽  
Climate Projections ◽  
Short Period ◽  
The Impact

AbstractThe demand of city planners for quantitative information on the impact of climate change on the urban environment is increasing. However, such information is usually extracted from decadelong climate projections generated with global or regional climate models (RCMs). Because of their coarse resolution and unsuitable physical parameterization, however, their model output is not adequate to be used at city scale. A full dynamical downscaling to city level, on the other hand, is computationally too expensive for climatological time scales. A statistical–dynamical computationally inexpensive method is therefore proposed that approximates well the behavior of the full dynamical downscaling approach. The approach downscales RCM simulations using the combination of an RCM at high resolution (H-RES) and a land surface model (LSM). The method involves the setup of a database of urban signatures by running an H-RES RCM with and without urban parameterization for a relatively short period. Using an analog approach, these signatures are first selectively added to the long-term RCM data, which are then used as forcing for an LSM using an urban parameterization in a stand-alone mode. A comparison with a full dynamical downscaling approach is presented for the city of Brussels, Belgium, for 30 summers with the combined ALADIN–AROME model (ALARO-0) coupled to the Surface Externalisée model (SURFEX) as H-RES RCM and SURFEX as LSM. The average bias of the nocturnal urban heat island during heat waves is vanishingly small, and the RMSE is strongly reduced. Not only is the statistical–dynamical approach able to correct the heat-wave number and intensities, it can also improve intervariable correlations and multivariate and temporally correlated indices, such as Humidex.

scholarly journals Utilization of Climate Files Predicting Future Weather in Dynamic Building Performance Simulation – A review

2021 ◽  
Vol 2069 (1) ◽  
pp. 012070
Author(s):  
C N Nielsen ◽  
J Kolarik
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Weather Data ◽  
Building Performance ◽  
Comprehensive Overview ◽  
Local Climate ◽  
The Impact

Abstract As the climate is changing and buildings are designed with a life expectancy of 50+ years, it is sensible to take climate change into account during the design phase. Data representing future weather are needed so that building performance simulations can predict the impact of climate change. Currently, this usually requires one year of weather data with a temporal resolution of one hour, which represents local climate conditions. However, both the temporal and spatial resolution of global climate models is generally too coarse. Two general approaches to increase the resolution of climate models - statistical and dynamical downscaling have been developed. They exist in many variants and modifications. The present paper aims to provide a comprehensive overview of future weather application as well as critical insights in the model and method selection. The results indicate a general trend to select the simplest methods, which often involves a compromise on selecting climate models.

scholarly journals High-Resolution Climate Change Impact Analysis on Medium-Sized River Catchments in Germany: An Ensemble Assessment

2013 ◽  
Vol 14 (4) ◽  
pp. 1175-1193 ◽  
Author(s):  
Irena Ott ◽  
Doris Duethmann ◽  
Joachim Liebert ◽  
Peter Berg ◽  
Hendrik Feldmann ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Impact Analysis ◽  
Climate Models ◽  
Global Climate Models ◽  
Climate Change Signal ◽  
Climate Modelling ◽  
Community Land Model ◽  
The Impact ◽  
River Catchments

Abstract The impact of climate change on three small- to medium-sized river catchments (Ammer, Mulde, and Ruhr) in Germany is investigated for the near future (2021–50) following the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. A 10-member ensemble of hydrological model (HM) simulations, based on two high-resolution regional climate models (RCMs) driven by two global climate models (GCMs), with three realizations of ECHAM5 (E5) and one realization of the Canadian Centre for Climate Modelling and Analysis version 3 (CCCma3; C3) is established. All GCM simulations are downscaled by the RCM Community Land Model (CLM), and one realization of E5 is downscaled also with the RCM Weather Research and Forecasting Model (WRF). This concerted 7-km, high-resolution RCM ensemble provides a sound basis for runoff simulations of small catchments and is currently unique for Germany. The hydrology for each catchment is simulated in an overlapping scheme, with two of the three HMs used in the project. The resulting ensemble hence contains for each chain link (GCM–realization–RCM–HM) at least two members and allows the investigation of qualitative and limited quantitative indications of the existence and uncertainty range of the change signal. The ensemble spread in the climate change signal is large and varies with catchment and season, and the results show that most of the uncertainty of the change signal arises from the natural variability in winter and from the RCMs in summer.

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