scholarly journals Projected climate changes in four different regions in Colombia

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Oscar D. Molina ◽  
Christian Bernhofer
Keyword(s):  
Relative Humidity ◽  
Statistical Downscaling ◽  
Minimum Temperature ◽  
Climate Models ◽  
Global Climate ◽  
Meteorological Data ◽  
Baseline Period ◽  
Global Climate Models ◽  
Reanalysis Dataset ◽  
Representative Concentration Pathways

Abstract Background Considering the lack of research over this region the Statistical Downscaling Model (SDSM) was used as a tool for downscaling meteorological data statistically over four representative regions in the eastern side of Colombia. Data from the two Global Climate Models CanESM2 and IPSL-CM5A-MR, which are part of the CMIP5-project have been used to project future maximum and minimum temperature, precipitation and relative humidity for the periods 2021–2050 and 2071–2100. For both models, the Representative Concentration Pathways RCP2.6 and RCP8.5 were considered, representing two different possible future emission trajectories and radiative forcings. Predictor variables from the National Centre for Environmental Prediction (NCEP-DOE 2) reanalysis dataset, together with analyzed correlation coefficient (R) and root mean square error (RMSE) were used as performance indicators during the calibration and validation process. Results Results indicate that Maximum and minimum temperature is projected to increase for both Global Climate Models and both Representative Concentration Pathways; relative humidity shows a decreasing trend for all scenarios and all regions; and precipitation shows a slight decrease over three regions and an increase over the warmest region. As expected, the results of the simulation for the period 2071–2100 show a more drastic change when compared to the baseline period of observations. Conclusions The SDSM model proves to be efficient in the downscaling of maximum/minimum temperature as well as relative humidity over the studied regions; while showing a lower performance for precipitation, agreeing with the results for other statistical downscaling studies. The results of the projections offer good information for the evaluation of possible future-case scenarios and decision-making management.

scholarly journals Comparing dynamical, stochastic and combined downscaling approaches – lessons from a case study in the Mediterranean region

2012 ◽  
Vol 9 (8) ◽  
pp. 9847-9884
Author(s):  
N. Guyennon ◽  
E. Romano ◽  
I. Portoghese ◽  
F. Salerno ◽  
S. Calmanti ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Max Planck ◽  
Basin Scale ◽  
Quantile Transform

Abstract. Various downscaling techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Such techniques may be grouped into two downscaling approaches: the deterministic dynamical downscaling (DD) and the stochastic statistical downscaling (SD). Although SD has been traditionally seen as an alternative to DD, recent works on statistical downscaling have aimed to combine the benefits of these two approaches. The overall objective of this study is to examine the relative benefits of each downscaling approach and their combination in making the GCM scenarios suitable for basin scale hydrological applications. The case study presented here focuses on the Apulia region (South East of Italy, surface area about 20 000 km2), characterized by a typical Mediterranean climate; the monthly cumulated precipitation and monthly mean of daily minimum and maximum temperature distribution were examined for the period 1953–2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile transform. The SD resulted efficient in reducing the mean bias in the spatial distribution at both annual and seasonal scales, but it was not able to correct the miss-modeled non-stationary components of the GCM dynamics. The DD provided a partial correction by enhancing the trend spatial heterogeneity and time evolution predicted by the GCM, although the comparison with observations resulted still underperforming. The best results were obtained through the combination of both DD and SD approaches.

scholarly journals Brief Communication: Drought Likelihood for East Africa

2017 ◽  
Author(s):  
Hui Yang ◽  
Chris Huntingford
Keyword(s):  
East Africa ◽  
Climate Models ◽  
Global Climate ◽  
Extreme Rainfall ◽  
Global Climate Models ◽  
Severe Drought ◽  
East African ◽  
Reanalysis Dataset ◽  
Weather Forecasts ◽  
Medium Range

Abstract. The on-going effects of severe drought in East Africa are causing high levels of malnutrition, hunger, illness and death. Close to 16 million people across Somalia, Ethiopia and Kenya need food, water and medical assistance (DEC, 2017). Many factors influence drought stress and ability to respond. However, inevitably it is asked: are elevated atmospheric greenhouse gas (GHG) concentrations altering the likelihood of extreme rainfall deficits? We find small increases in probability of this for East African, based on merging the observation-based reanalysis dataset by the European Centre for Medium-Range Weather Forecasts (ECMWF) (Dee et al., 2011) with Global Climate Models (GCMs) in the CMIP5 database (Taylor et al., 2012).

scholarly journals Performance Evaluation of CMIP6 Global Climate Models for Selecting Models for Climate Projection over Nigeria

2021 ◽  
Author(s):  
Mohamed Sanusi Shiru ◽  
Eun-Sung Chung
Keyword(s):  
Minimum Temperature ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Climate Projection ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Climatic Research Unit ◽  
Spatial Assessment ◽  
Taylor Diagram

Abstract This study assessed the performances of 13 GCMs of the CMIP6 in replicating precipitation and maximum and minimum temperatures over Nigeria during 1984–2014 in order to identify the best GCMs for multi model ensemble aggregation for climate projection. The study uses the monthly full reanalysis precipitation product Version 6 of Global Precipitation Climatology Centre and the maximum and minimum temperature CRU version TS v. 3.23 products of Climatic Research Unit as reference data. The study applied five statistical indices namely, normalized root mean square error, percentage of bias, Nash-Sutcliffe efficiency, and coefficient of determination; and volumetric efficiency. Compromise programming (CP) was then used in the aggregation of the scores of the different GCMs for the variables. Spatial assessment, probability distribution function, Taylor diagram, and mean monthly assessments were used in confirming the findings from the CP. The study revealed that CP was able to uniformly evaluate the GCMs even though there were some contradictory results in the statistical indicators. Spatial assessment of the GCMs in relation to the observed showed the highest ranked GCMs by the CP were able to better reproduce the observed properties. The least ranking GCMs were observed to have both spatially overestimated or underestimated precipitation and temperature over the study area. In combination with the other measures, the GCMs were ranked using the final scores from the CP. IPSL-CM6A-LR, NESM3, CMCC-CM2-SR5, and ACCESS-ESM1-5 were the highest ranking GCMs for precipitation. For maximum temperature, INM.CM4-8, BCC-CSM2-MR, MRI-ESM2-0, and ACCESS-ESM1-5 ranked the highest while AWI-CM-1-1-MR, IPSL-CM6A-LR, INM.CM5-0, and CanESM5 ranked the highest for minimum temperature.

scholarly journals CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles

2020 ◽  
Author(s):  
Chiranjib Chaudhuri ◽  
Colin Robertson
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Global Climate ◽  
Similarity Index ◽  
Wasserstein Distance ◽  
Regional Level ◽  
Global Climate Models ◽  
Annual Maximum ◽  
Maximum Precipitation ◽  
Structural Loss

Despite numerous studies in statistical downscaling methodology, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products.

Statistical Downscaling for Climate Science

2019 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Statistical Downscaling ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Scale ◽  
Climatic Changes ◽  
Global Climate Models ◽  
Small Scale ◽  
Climate Projections

Global climate models are our main tool to generate quantitative climate projections, but these models do not resolve the effects of complex topography, regional scale atmospheric processes and small-scale extreme events. To understand potential regional climatic changes, and to provide information for regional-scale impact modeling and adaptation planning, downscaling approaches have been developed. Regional climate change modeling, even though it is still a matter of basic research and questioned by many researchers, is urged to provide operational results. One major downscaling class is statistical downscaling, which exploits empirical relationships between larger-scale and local weather. The main statistical downscaling approaches are perfect prog (often referred to as empirical statistical downscaling), model output statistics (which is typically some sort of bias correction), and weather generators. Statistical downscaling complements or adds to dynamical downscaling and is useful to generate user-tailored local-scale information, or to efficiently generate regional scale information about mean climatic changes from large global climate model ensembles. Further research is needed to assess to what extent the assumptions underlying statistical downscaling are met in typical applications, and to develop new methods for generating spatially coherent projections, and for including process-understanding in bias correction. The increasing resolution of global climate models will improve the representation of downscaling predictors and will, therefore, make downscaling an even more feasible approach that will still be required to tailor information for users.

scholarly journals Nonparametric Statistical Downscaling of Precipitation from Global Climate Models

10.5772/32910 ◽  
2012 ◽  
Author(s):  
Paul Nyeko-Ogiramoi ◽  
Patrick Willems ◽  
Gaddi Ngirane-Katashaya ◽  
Victor Ntegek
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Nonparametric Statistical

Maize productivity analysis in response to climate change under different nitrogen management strategies

2021 ◽  
Vol 23 (3) ◽  
pp. 279-285
Author(s):  
NISHANT K SINHA ◽  
M. MOHANTY ◽  
J. SOMASUNDARAM ◽  
R. S. CHAUDHARY ◽  
H. PATRA ◽  
...  
Keyword(s):  
Management Practices ◽  
Climate Models ◽  
Global Climate ◽  
N Uptake ◽  
Management Strategies ◽  
Central India ◽  
Maize Yield ◽  
Baseline Period ◽  
Global Climate Models ◽  
Productivity Analysis

The evaluation of climatic change impact on maize grain and biomass yield under different N management practices through a well-calibrated and validated APSIM model in Vertisol of central India has been made. Climate scenarios were derived from seven global climate models (GCM) for two representative concentration pathways (RCPs), i.e. RCP4.5 and RCP8.5, and two-time slices, i.e. 2050 and 2080. The five N scenarios, namely N0%, N50%, N100%, N150%, and 100% organic, were studied in different climatic scenarios. The probability of exceedance showed that N0%, N50%, N100%, N150%, and 100% organic treatments have a 50% chance of yield greater than 1.0, 3.40, 4.20, 4.45 and 3.84 t ha-1, respectively. The average reduction of maize yield was -44.4, -20, -19.7 - 17.9 and 22.5 per cent in N 0%, N 50%, N 100%, N 150%, 100% organic, respectively under RCP4.5 over the baseline period (1980-2010). For RCP8.5, the average reduction of maize yield in N 0%, N 50%, N 100%, N 150%, 100% organic was 41.2, 21.2, 20.8 20.6 and 23.1 per cent, respectively. Simulation results suggested that a higher decrease of maize yield in 100 per cent organic treatments than inorganic treatments is due to variability in N uptake.

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