scholarly journals Evaluation of historical and future simulations of precipitation and temperature in central Africa from CMIP5 climate models

2016 ◽  
Vol 121 (1) ◽  
pp. 130-152 ◽  
Author(s):  
Noel R. Aloysius ◽  
Justin Sheffield ◽  
James E. Saiers ◽  
Haibin Li ◽  
Eric F. Wood
Keyword(s):  
Climate Models ◽  
Central Africa ◽  
Cmip5 Climate Models ◽  
Precipitation And Temperature

scholarly journals Diagnosing atmosphere–land feedbacks in CMIP5 climate models

2012 ◽  
Vol 7 (4) ◽  
pp. 044003 ◽  
Author(s):  
C J R Williams ◽  
R P Allan ◽  
D R Kniveton
Keyword(s):  
Climate Models ◽  
Cmip5 Climate Models

Evaluating synoptic systems in the CMIP5 climate models over the Australian region

2016 ◽  
Vol 47 (7-8) ◽  
pp. 2235-2251 ◽  
Author(s):  
Peter B. Gibson ◽  
Petteri Uotila ◽  
Sarah E. Perkins-Kirkpatrick ◽  
Lisa V. Alexander ◽  
Andrew J. Pitman
Keyword(s):  
Climate Models ◽  
Australian Region ◽  
Cmip5 Climate Models

scholarly journals Arctic sea ice area changes in CMIP3 and CMIP5 climate models’ ensembles

2017 ◽  
Vol 57 (1) ◽  
pp. 77-107 ◽  
Author(s):  
V. A. Semenov ◽  
T. Martin ◽  
L. K. Behrens ◽  
M. Latif ◽  
E. S. Astafieva
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
Cmip5 Climate Models ◽  
Arctic Sea

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>

Fractal-multifractal ensembles of downscaled precipitation and temperature sets as implied by climate models

2021 ◽  
Author(s):  
Mahesh Lal Maskey ◽  
David Joseph Serrano Suarez ◽  
Joshua H. Viers ◽  
Josue Medellin-Azuara ◽  
Bellie Sivakumar ◽  
...  
Keyword(s):  
Climate Models ◽  
Geometric Approach ◽  
Holistic Approach ◽  
Sensitivity Study ◽  
Climatic Data ◽  
Fractal Function ◽  
The Past ◽  
Global Circulation Models ◽  
The Future ◽  
Precipitation And Temperature

<p>Describing the specific details and textures implicit in real-world hydro-climatic data sets is paramount for the proper description and simulation of variables such as precipitation, streamflow, and temperature time series. To this aim, a couple of decades ago, a deterministic geometric approach, the so-called fractal-multifractal (FM) method,<sup>1,2</sup> was introduced. Such is a holistic approach capable of faithfully encoding (describing)<sup>3</sup>, simulating<sup>4</sup>, and downscaling<sup>5</sup> hydrologic records in time, as the outcome of a fractal function illuminated by a multifractal measure. This study employs the FM method to generate ensembles of daily precipitation and temperature sets obtained from global circulation models (GCMs). Specifically, this study uses data obtained via ten GCM models, two sets of daily records, as implied from the past, over a year, and three sets projected for the future, as downscaled via localized constructed analogs (LOCA) for a couple of sites in California. The study demonstrates that faithful representations of all sets may be achieved via the FM approach, using encodings relying on 10 and 8 geometric (FM) parameters for rainfall and temperature, respectively. They result in close approximations of the data's histogram, entropy, and autocorrelation functions. By presenting a sensitivity study of FM parameters' for historical and projected data, this work concludes that the FM representations are useful for tracking and foreseeing the records' complexity<sup>6</sup> in the past and the future and other applications in hydrology such as bias correction.</p><p> </p><p> </p><p><strong>References</strong></p>

scholarly journals Effects of climate model radiation, humidity and wind estimates on hydrological simulations

2012 ◽  
Vol 16 (2) ◽  
pp. 305-318 ◽  
Author(s):  
I. Haddeland ◽  
J. Heinke ◽  
F. Voß ◽  
S. Eisner ◽  
C. Chen ◽  
...  
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Correction Method ◽  
Hydrological Models ◽  
Temperature And Precipitation ◽  
Effects Of Radiation ◽  
Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

scholarly journals Influence of Bias Correction Methods on Simulated Köppen−Geiger Climate Zones in Europe

Climate ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 18 ◽  
Author(s):  
Beáta Szabó-Takács ◽  
Aleš Farda ◽  
Petr Skalák ◽  
Jan Meitner
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Generalized Pareto Distribution ◽  
Regional Climate Models ◽  
Climate Classification ◽  
Quantile Mapping ◽  
Temperature Bias ◽  
Simulated Precipitation ◽  
Precipitation And Temperature

Our goal was to investigate the influence of bias correction methods on climate simulations over the European domain. We calculated the Köppen−Geiger climate classification using five individual regional climate models (RCM) of the ENSEMBLES project in the European domain during the period 1961−1990. The simulated precipitation and temperature data were corrected using the European daily high-resolution gridded dataset (E-OBS) observed data by five methods: (i) the empirical quantile mapping of precipitation and temperature, (ii) the quantile mapping of precipitation and temperature based on gamma and Generalized Pareto Distribution of precipitation, (iii) local intensity scaling, (iv) the power transformation of precipitation and (v) the variance scaling of temperature bias corrections. The individual bias correction methods had a significant effect on the climate classification, but the degree of this effect varied among the RCMs. Our results on the performance of bias correction differ from previous results described in the literature where these corrections were implemented over river catchments. We conclude that the effect of bias correction may depend on the region of model domain. These results suggest that distribution free bias correction approaches are the most suitable for large domain sizes such as the pan-European domain.

Variability of Indian summer monsoon droughts in CMIP5 climate models

2019 ◽  
Vol 53 (3-4) ◽  
pp. 1937-1962 ◽  
Author(s):  
B. Preethi ◽  
R. Ramya ◽  
S. K. Patwardhan ◽  
M. Mujumdar ◽  
R. H. Kripalani
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Climate Models ◽  
Cmip5 Climate Models
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