Unravelling the influence of subjectivity on ranking of <scp>CMIP6</scp> based climate models: A case study

Anil, Suram; Manikanta, Velpuri; Pallakury, Anand Raj

doi:10.1002/joc.7164

Cited by 28 publications

(14 citation statements)

References 58 publications

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“…The entropy technique is used to determine the weightage of each performance indicator used in the study [28,46]. The entropy for each indicator j is calculated by using Equation (2).…”

Section: Weight Computing Technique (Entropy Technique)mentioning

confidence: 99%

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Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach

et al. 2022

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For effective management practices and decision-making, the uncertainty associated with Regional Climate Models (RCMs) and their scenarios need to be assessed in the context of climate change. The present study analyzes the various uncertainties in the precipitation and temperature datasets of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) under Representative Concentrative Pathways (RCPs) 4.5 and 8.5 over the Munneru river basin, in India, using the Reliable Ensemble Averaging (REA) method. From the available 21 RCMs, the top five ranked are ensembled and bias-corrected at each grid using the non-parametric quantile mapping method for the precipitation and temperature datasets. The spatio-temporal variations in precipitation and temperature data for the future periods, i.e., 2021–2039 (near future), 2040–2069 (mid future) and 2070–2099 (far future) are analyzed. For the period 2021–2099, annual average precipitation increases by 233 mm and 287 mm, respectively, the in RCP 4.5 and RCP 8.5 scenarios when compared to the observed period (1951–2005). In both the RCP 4.5 and RCP 8.5 scenarios, the annual average maximum temperature rises by 1.8 °C and 1.9 °C, respectively. Similarly, the annual average minimum temperature rises by 1.8 °C and 2.5 °C for the RCP 4.5 and RCP 8.5 scenarios, respectively. The spatio-temporal climatic variations for future periods obtained from high-resolution climate model data aid in the preparation of water resource planning and management options in the study basin under the changing climate. The methodology developed in this study can be applied to any other basin to analyze the climatic variables suitable for climate change impact studies that require a finer scale, but the biases present in the historical simulations can be attributed to uncertainties in the estimation of climatic variable projections. The findings of the study indicate that NEX-GDDP datasets are in good agreement with IMD datasets on monthly scales but not on daily scales over the observed period, implying that these data should be scrutinized more closely on daily scales, especially when utilized in impact studies.

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“…The entropy technique is used to determine the weightage of each performance indicator used in the study [28,46]. The entropy for each indicator j is calculated by using Equation (2).…”

Section: Weight Computing Technique (Entropy Technique)mentioning

confidence: 99%

“…A lower L p indicates the best suitable RCM. Equation ( 6) represents the expression for the L p metric [46].…”

Section: Compromise Programmingmentioning

confidence: 99%

Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach

et al. 2022

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“…In order to rank the performance of earlier studies, GCM outputs were spatially re-gridded to a common grid resolution of 2°Â 2° (Ahmed et al 2019b;Noor et al 2019), 2.5°Â 2.5° (Johnson & Sharma 2009;Eghdamirad et al 2017;Raju et al 2017;Jiang et al 2019), and 3°Â 3° (Woldemeskel et al 2014). Due to the fact that some researchers preferred to re-grid the GCM data into a resolution of the observed gridded dataset (Tiwari et al 2014;Pour et al 2018;Hassan et al 2020;Homsi et al 2020;Anil et al 2021), the collected GCM outputs were spatially re-gridded to 0.25°Â 0.25°. However, the GCM outputs were bias-corrected using the empirical quantile mapping method after re-gridding the raw GCM outputs to the spatial resolution of 0.25°Â 0.25°to remove the inherent biases.…”

Section: Cmip6 Gcm Datasetsmentioning

confidence: 99%

“…(IPCC 2013;Tiwari et al 2014;McSweeney et al 2015). Apart from these uncertainties, reference datasets and performance evaluation metrics also influence the performance of GCMs (Anil et al 2021) used. Due to all these uncertainties associated with GCMs, the selection of GCMs is becoming a challenge in terms of ranking them using the simulation of the current climate (Raju et al 2017;Kamworapan & Surussavadee 2019).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

“…A number of works have been attempted to rank GCMs in Indian river basins using CMIP5 (Raju et al 2017;Das et al 2018) and CMIP3 (Rajeevan & Nanjundiah 2009;Raju & Nagesh Kumar 2014;Anandhi & Nanjundiah 2015) phase models. Recently, the Coupled Model Intergovernmental Panel on Climate Change Phase 6-based climate model data was made available for the evaluation of climate change studies (Eyring et al 2016), but very meagre studies have been conducted based on Coupled Model Intercomparison Project Phase 6 (CMIP6; Anil et al 2021) climate models in India. The CMIP6 climate data differ in terms of carbon emissions and forcing scenarios compared with CMIP5 and CMIP3 phases.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

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Deciphering the projected changes in CMIP-6 based precipitation simulations over the Krishna River Basin

Anil

2022

Journal of Water and Climate Change

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The impact of climate change on the Krishna River Basin (KRB) is significant due to the semi-arid nature of the basin. Herein, 21 global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) were examined to simulate the historical monthly precipitation over the 1951–2014 period in the KRB. The symmetrical uncertainty (SU) method and the multi-criteria decision method (MCDM) were employed to select the suitable GCMs for projecting possible changes in precipitation over the KRB. The biases in the climate projections were removed by using the empirical quantile mapping method. The reliability ensemble averaging (REA) method was used to generate the multi-model ensemble (MME) mean of projections and to analyse the spatio-temporal changes of precipitation under different shared socioeconomic pathways (SSPs). BCC-CSM2-MR, IPSL-CM6A-LR, MIROC6, INM-CM5-0, and MPI-ESM1-2-HR were found to be the most suitable GCMs for the KRB. The MME mean of the chosen GCMs showed significant changes in precipitation projection that occurs for a far future period (2071–2100) over the KRB. The projection changes of precipitation range from −36.72 to 83.05% and −37.68 to 95.75% for the annual and monsoon periods, respectively, for various SSPs. Monsoon climate projections show higher changes compared with the annual climate projections, which reveals that precipitation concentration is more during the monsoon period over the KRB.

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The effectiveness of machine learning‐based multi‐model ensemble predictions of CMIP6 in Western Ghats of India

Shetty

Umesh

Shetty

2023

Intl Journal of Climatology

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The popularity of cutting‐edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top‐performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi‐Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo‐climatic basins. Precipitation is best simulated by EC‐Earth3 and BCC‐CSM2‐MR. Maximum temperature by MPI‐ESM1‐2‐HR, EC‐Earth3‐Veg, INM‐CM5‐0 and MPI‐ESM1‐2‐LR. Minimum temperature by INM‐CM5‐0 and MPI‐ESM1‐2‐LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per‐humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms.

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Unravelling the influence of subjectivity on ranking of CMIP6 based climate models: A case study

Cited by 28 publications

References 58 publications

Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach

Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach

Deciphering the projected changes in CMIP-6 based precipitation simulations over the Krishna River Basin

The effectiveness of machine learning‐based multi‐model ensemble predictions of CMIP6 in Western Ghats of India

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