Ranking of CMIP5-based global climate models using standard performance metrics for Telangana region in the southern part of India

Sreelatha, Koppala; Raj, P. Anand

doi:10.1080/09715010.2019.1634648

Cited by 31 publications

(17 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When selecting appropriate GCMs, care should be taken as it creates significant uncertainty leading to either overestimation or underestimation of the projected climatic variables (Knutti and Sedláček, 2013; Shivam et al ., 2019). In India, numerous studies have been carried out to select appropriate GCMs for climatic parameters such as maximum temperature, minimum temperature, sea surface temperature, precipitation, and spatially and temporally mean surface temperature (Perkins et al ., 2007; Maxino et al ., 2008; Sreelatha and AnandRaj, 2019, 2020).…”

Section: Methodsmentioning

confidence: 99%

Regional analysis of drought severity‐duration‐frequency and severity‐area‐frequency curves in the Godavari River Basin, India

Kumar

AnandRaj

Sreelatha

et al. 2021

Intl Journal of Climatology

View full text Add to dashboard Cite

India is one of the most drought‐ravaged countries in the world and faces at least one drought in one region or another in every 3 years. There is no single reliable approach in characterizing future droughts. To understand future drought risk, potential changes of drought properties and characteristics are analysed in this study. Using Fuzzy c‐means clustering approach, homogeneous drought regions are identified in the Godavari river basin and therefore, optimum number of clusters were assigned as four. The 12‐month standardized precipitation index (SPI) using precipitation data from India Meteorological Department (IMD) and Global Climate Model (GCM)—MIROC‐ESM‐CHEM is calculated for the homogeneous regions of the Godavari basin. The best fit copula for observed and simulated severity and duration are: Region 1—Clayton, Regions 2 and 3—Gumbel, Region 4—Frank copula. Severity‐duration‐frequency (SDF) and severity‐area‐frequency (SAF) curves were developed and analysed using the best fit copulas. The research findings conclude that moderate and severe droughts are frequently increasing for future periods (2006–2099) compared to the historic period (1962–2005). Droughts with high severity and high mean interarrival time are observed as expected in the future. For the Godavari basin, the SDF curves were concave upwards indicating an increase in severity with an increase in duration. The rate of increase of severity is small for shorter durations compared to that of longer‐duration drought. Thus, more prolonged drought events in the 21st century are likely to occur. The SAF curves with steeper slopes and high variability in topographical and hydrological characteristics have been observed over the Godavari basin. From these curves, for a specified percentage of area and return period, the drought severity can be calculated and the information can be used for crop management and agricultural water demands. Overall, the findings of this research offer a view of likely scenarios of drought in the Godavari basin.

show abstract

Section: Methodsmentioning

confidence: 99%

Regional analysis of drought severity‐duration‐frequency and severity‐area‐frequency curves in the Godavari River Basin, India

Kumar

AnandRaj

Sreelatha

et al. 2021

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…This indicates that NorESM1-M performed well in the different climate of Middle Mountain zone of the Koshi River Basin (Supplementary Table S6), and for Trans-Himalayas zones, CSIRO-Mk3.6.0 ranked first for FD and R95 which indicates this model simulate well for intensity-based climate indices (Supplementary Table S7). Sreelatha & Anand Raj (2019) ranked 36 CMIP5 GCMs using compromise programming for the Telangana region in Southern India. The models MIROC5, CNRM-CM5, HadGEM2-A0, ACCESS 1.0, and BCC-CSM1.1 were observed to be ranked as the first five most suitable, based on average temperature (T avg ).…”

Section: Ranking Of the Gcmsmentioning

confidence: 99%

“…Recently, the evaluation of GCMs has been attempted using the strength values of individual models ( Johnson et al 2011;Sperber et al 2013). Various studies have also used weighting schemes of multi-model ensembles with varying outcomes (Fordham et al 2011;Sreelatha & Anand Raj 2019).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the CMIP5 general circulation models for simulating the precipitation and temperature of the Koshi River Basin in Nepal

Pradhan

Shrestha

Mohanasundaram

et al. 2021

Journal of Water and Climate Change

View full text Add to dashboard Cite

This study evaluates the performance of 12 different general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to simulate precipitation and temperature in the Koshi River Basin, Nepal. Four statistical performance indicators: correlation coefficient, normalised root-mean-square deviation (NMRSD), absolute NMRSD, and average absolute relative deviation are considered to evaluate the GCMs using historical observations. Seven different climate indices: consecutive dry days, consecutive wet days, cold spell duration index, warm spell duration index, frost days, very wet days, and simple daily intensity index are considered to identify the most suitable models for the basin and future climate impact assessment studies. Weights for each performance indicator are determined using the entropy method, with compromise programming applied to rank the GCMs based on the Euclidian distant technique. The results suggest that CanESM2 and CSIRO-MK3.6.0 are the most suitable for predicting extreme precipitation events, and BCC-CSM 1.1, CanESM2, NorESM1-M, and CNRM-CM5 for extreme temperature events in Himalayan river basins. Overall, IPSL-CM5A-MR, CanESM2, CNRM-CM5, BCC-CSM 1.1, NorESM1-M, and CSIRO-Mk3.6.0 are deemed suitable models for predicting precipitation and temperature in the Koshi River Basin, Nepal.

show abstract

“…Studies have been conducted in assessing the performances of GCMs in many parts of the globe using different statistical measures (Rivera and Arnould, 2020;Sreelatha and Anand Raj, 2019). However, there can be challenges in decision making as to which GCMs performed best due to contradictions in the outputs from different statistical measures (Ayugi et al, 2021b;Klutse et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of CMIP6 global climate models for selecting models for climate projection over Nigeria

Shiru

Chung

2021

Theor Appl Climatol

View full text Add to dashboard Cite

This study assessed the performances of 13 GCMs of the CMIP6 in replicating precipitation and maximum and minimum temperatures over Nigeria during 1984Nigeria during -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.

show abstract

Ranking of CMIP5-based global climate models using standard performance metrics for Telangana region in the southern part of India

Cited by 31 publications

References 39 publications

Regional analysis of drought severity‐duration‐frequency and severity‐area‐frequency curves in the Godavari River Basin, India

Regional analysis of drought severity‐duration‐frequency and severity‐area‐frequency curves in the Godavari River Basin, India

Evaluation of the CMIP5 general circulation models for simulating the precipitation and temperature of the Koshi River Basin in Nepal

Performance evaluation of CMIP6 global climate models for selecting models for climate projection over Nigeria

Contact Info

Product

Resources

About