Regional Optimization of Global Climate Models for Maximum and Minimum Temperature

2020 ◽  
pp. 63-71
Author(s):  
K. Sreelatha ◽  
P. AnandRaj
Keyword(s):  
Minimum Temperature ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models

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 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 Future Assessment of Precipitation and Temperature for Developing Urban Catchment Under Impact of Climate Change

2020 ◽  
Vol 8 (5) ◽  
pp. 3395-3404
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Future Climate ◽  
Time Slice ◽  
Maximum Temperature ◽  
Future Time ◽  
Hadcm3 Model

In this study, the attempt is made to investigate the impact of future climate changes related to three weather parameter maximum temperature (Tmax), minimum temperature (Tmin) and precipitation for study area were projected for two future time slice (2017–2058), and (2059–2100) from the three Global Climate Models (GCMs), CanESM2, CGCM3 and HadCM3 under different representative concentration pathway (RCPs) scenarios (RCP2.5, RCP4.5, and RCP8.5) using statistical downscaling model (SDSM). The predictor variables are downloaded from National Center for Environmental Prediction/Atmospheric Research (NCEP/NCAR) and simulations from the three Global Climate Models (GCMs), Second Generation Canadian Earth System Model (CanESM2), Canadian Centre for Climate Modelling and Analysis (CGCM3) and Hadley Centre for Climate Prediction and Research/Met Office (HadCM3) variability and changes in Tmax, Tmin and precipitation under different (RCPs) scenarios have been presented for two future time slice. The performance for three models showed maximum/minimum temperature increases in future for almost all the (RCPs) scenarios. Also precipitation of the entire catchment was found to increasing trends for all scenarios. In case of HadCM3 model, under RCP8.5 scenarios for the period (2017-2058), changes in max temperature, min temperature, and precipitation are forecasted as 0.72 °C, 1.42 °C, and 2.82 mm and for the period (2059-2100) are 1.16 °C, 2.14 °C, and 6.85 mm.The results obtained from HadCM3 model is higher side as compared with CanESM2, CGCM3.These results can provide understanding of the hydrologic role of future climate change scenarios, which is essential for probable impacts of climate change for planning and management of appropriate choice for designing the storm water drainage system and infrastructure for newly growing urbanization under climate change are of great concern to hydrologists, water managers, and policymakers

Selection of global climate models for India using cluster analysis

2016 ◽  
Vol 7 (4) ◽  
pp. 764-774 ◽  
Author(s):  
K. Srinivasa Raju ◽  
D. Nagesh Kumar
Keyword(s):  
Cluster Analysis ◽  
Minimum Temperature ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Optimal Number ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Number Of Clusters ◽  
Optimal Number Of Clusters

Global climate models (GCMs) are gaining importance due to their capability to ascertain climate variables that will be useful to develop long, medium and short term water resources planning strategies. The applicability of K-Means cluster analysis is explored for grouping 36 GCMs from Coupled Model Intercomparison Project 5 for maximum temperature (MAXT), minimum temperature (MINT) and a combination of maximum and minimum temperature (COMBT) over India. Cluster validation methods, namely the Davies–Bouldin Index (DBI) and F-statistic, are used to obtain an optimal number of clusters of GCMs for India. The indicator chosen for evaluation of GCMs is the probability density function based skill score. It is noticed that the optimal number of clusters for MAXT, MINT and COMBT scenarios are 3, 2 and 2, respectively. Accordingly, suitable ensembles of GCMs are suggested for India for MAXT, MINT and COMBT individually. The suggested methodology can be extended to any number of GCMs and indicators, with minor modifications.

Extreme Events in High Resolution CMCC Regional and Global Climate Models

2011 ◽  
Author(s):  
Enrico Scoccimarro ◽  
Silvio Gualdi ◽  
Antonella Sanna ◽  
Edoardo Bucchignani ◽  
Myriam Montesarchio
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models

scholarly journals Global climatology and trends in convective environments from ERA5 and rawinsonde data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mateusz Taszarek ◽  
John T. Allen ◽  
Mattia Marchio ◽  
Harold E. Brooks
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Convective Available Potential Energy ◽  
Global Climate Models ◽  
Convective Precipitation ◽  
The Past ◽  
The Tropics ◽  
Rawinsonde Data

AbstractGlobally, thunderstorms are responsible for a significant fraction of rainfall, and in the mid-latitudes often produce extreme weather, including large hail, tornadoes and damaging winds. Despite this importance, how the global frequency of thunderstorms and their accompanying hazards has changed over the past 4 decades remains unclear. Large-scale diagnostics applied to global climate models have suggested that the frequency of thunderstorms and their intensity is likely to increase in the future. Here, we show that according to ERA5 convective available potential energy (CAPE) and convective precipitation (CP) have decreased over the tropics and subtropics with simultaneous increases in 0–6 km wind shear (BS06). Conversely, rawinsonde observations paint a different picture across the mid-latitudes with increasing CAPE and significant decreases to BS06. Differing trends and disagreement between ERA5 and rawinsondes observed over some regions suggest that results should be interpreted with caution, especially for CAPE and CP across tropics where uncertainty is the highest and reliable long-term rawinsonde observations are missing.

scholarly journals Regions of intensification of extreme snowfall under future warming

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lennart Quante ◽  
Sven N. Willner ◽  
Robin Middelanis ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Observational Data ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Average Intensity ◽  
High Latitudes ◽  
Future Warming

AbstractDue to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century.

scholarly journals Reassessing Existing Reservoir Supply Capacity and Management Resilience under Climate Change and Sediment Deposition

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1819
Author(s):  
Eleni S. Bekri ◽  
Polychronis Economou ◽  
Panayotis C. Yannopoulos ◽  
Alexander C. Demetracopoulos
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Vital Role ◽  
Sediment Deposition ◽  
Global Climate Models ◽  
Exceedance Probability ◽  
Climate Change Effects ◽  
Management Scenario ◽  
Supply Capacity

Freshwater resources are limited and seasonally and spatially unevenly distributed. Thus, in water resources management plans, storage reservoirs play a vital role in safeguarding drinking, irrigation, hydropower and livestock water supply. In the last decades, the dams’ negative effects, such as fragmentation of water flow and sediment transport, are considered in decision-making, for achieving an optimal balance between human needs and healthy riverine and coastal ecosystems. Currently, operation of existing reservoirs is challenged by increasing water demand, climate change effects and active storage reduction due to sediment deposition, jeopardizing their supply capacity. This paper proposes a methodological framework to reassess supply capacity and management resilience for an existing reservoir under these challenges. Future projections are derived by plausible climate scenarios and global climate models and by stochastic simulation of historic data. An alternative basic reservoir management scenario with a very low exceedance probability is derived. Excess water volumes are investigated under a probabilistic prism for enabling multiple-purpose water demands. Finally, this method is showcased to the Ladhon Reservoir (Greece). The probable total benefit from water allocated to the various water uses is estimated to assist decision makers in examining the tradeoffs between the probable additional benefit and risk of exceedance.

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