Vol. 60: 103–117, 2014 CLIMATE RESEARCH Published online June 17 doi: 10.3354/cr01222 Clim Res Ranking of global climate models for India using multicriterion analysis K. Srinivasa Raju1, D. Nagesh Kumar2,3,* 1Department of Civil Engineering, Birla Institute of Technology and Science-Pilani, Hyderabad campus, India 2Center for Earth Sciences, Indian Institute of Science, 560012 Bangalore, India 3Present address: Department of Civil Engineering, Indian Institute of Science, 560012 Bangalore, India ABSTRACT: Eleven GCMs (BCCR-BCCM2.0, INGV-ECHAM4, GFDL2.0, GFDL2.1, GISS, IPSL- CM4, MIROC3, MRI-CGCM2, NCAR-PCMI, UKMO-HADCM3 and UKMO-HADGEM1) were evaluated for India (covering 73 grid points of 2.5° × 2.5°) for the climate variable ‘precipitation rate’ using 5 performance indicators. Performance indicators used were the correlation coefficient, normalised root mean square error, absolute normalised mean bias error, average absolute rela- tive error and skill score. We used a nested bias correction methodology to remove the systematic biases in GCM simulations. The Entropy method was employed to obtain weights of these 5 indi- cators. Ranks of the 11 GCMs were obtained through a multicriterion decision-making outranking method, PROMETHEE-2 (Preference Ranking Organisation Method of Enrichment Evaluation). An equal weight scenario (assigning 0.2 weight for each indicator) was also used to rank the GCMs. An effort was also made to rank GCMs for 4 river basins (Godavari, Krishna, Mahanadi and Cauvery) in peninsular India. The upper Malaprabha catchment in Karnataka, India, was chosen to demonstrate the Entropy and PROMETHEE-2 methods. The Spearman rank correlation coefficient was employed to assess the association between the ranking patterns. Our results sug- gest that the ensemble of GFDL2.0, MIROC3, BCCR-BCCM2.0, UKMO-HADCM3, MPI- ECHAM4 and UKMO-HADGEM1 is suitable for India. The methodology proposed can be extended to rank GCMs for any selected region. KEY WORDS: Entropy · Performance indicators · PROMETHEE-2 · River basin Resale or republication not permitted without written consent of the publisher 1. INTRODUCTION and consequently their impacts, e.g. on hydrologic systems (Smith & Chiew 2010, Pitman et al. 2012). Changes in the concentration of greenhouse gases The climate system is represented in a simplified and in the radiative balance of the atmosphere cause form in GCMs, with combinations of models for dif- corresponding changes in temperature and precipi- ferent components of the climate system. While tation patterns (Bates et al. 2008). One of the impor- GCMs demonstrate significant skill at continental tant impacts of future climate changes on society will and hemispheric spatial scales and incorporate a be changes in regional water availability for various large proportion of the complexity of the global sys- purposes such as drinking water supply and irriga- tem (Reichler & Kim 2008), they are inherently un - tion, and the occurrence of extreme events, including able to represent local subgrid-scale features and floods and droughts. To address this problem, global dynamics. However, in considering impacts of global climate models (GCMs) have been developed to sim- climate changes, the focus is primarily on societal ulate the present climate and are gaining importance responses to the local and regional consequences of due to their ability to project future climate changes the projected large-scale changes. *Corresponding author: [email protected] © Inter-Research 2014 · www.int-res.com 104 Clim Res 60: 103–117, 2014 Xu (1999) inferred that the accuracy of GCMs (1) tal scale from ensembles of 9 different atmosphere− de creases with increasingly finer spatial and tempo- ocean general circulation model simulations with 2 ral scales; (2) decreases from free tropospheric vari- emission scenarios. They considered 2 reliability cri- ables to surface variables, while the variables at the teria: (1) performance of the model in reproducing ground surface have direct use in water balance present-day climate and (2) convergence of the sim- computations; (3) decreases from climate-related ulated changes across models. The REA method was variables — i.e. wind, temperature, humidity and air applied to mean seasonal temperature and precipita- pressure — to precipitation, evapotranspiration, run - tion changes for the late decades of the 21st century, off and soil moisture, while the latter variables are of over 22 land regions of the world. Similar studies key importance in hydrologic regimes. were reported by Murphy et al. (2004) and Dessai et The uncertainties associated with the formulation al. (2005). of GCMs arise due to a number of factors and lead to Perkins et al. (2007) evaluated 14 GCMs using a skill significant variability across model simulations of fu - score approach which is based on their ability to simu- ture climates. These factors include effects of aero - late daily rainfall and daily minimum and maximum sols that are differently parameterised in different temperatures for 12 regions of Australia. The evalua- GCMs, initial and boundary conditions for each tion was based on how well each climate model could GCM, para meter and model structure of GCMs, ran- capture the observed probability density functions for domness, and future greenhouse gas emissions. each variable and each region. They concluded that These uncertainties accumulate from various levels, theskillscoremethodologyisusefulinassessingwhich from the GCM to the downscaling levels and may of the climate models should be used by the impact propagate down to the local levels, which may, in groups. Similar studies were reported by Maxino et al. turn, affect the adaptation studies that would be used (2008), Perkins et al. (2009, 2013) and Anandhi et al. as the basis for implementation. In other words, cli- (2011). Suppiah et al. (2007) assessed the performance mate model simulations at a local or regional scale of 23 models with respect to how well they reproduced can be highly uncertain (Mu jum dar & Nagesh Kumar patterns of seasonal average temperature, mean sea 2012). Moreover, in many regional hydrologic assess- level pressure and rainfall over the Australian conti- ments, time and resource constraints limit the num- nent. They reduced the size of the sample to 15 by ber of GCMs that can be used in determining impacts rejecting those models which frequently failed to meet (Wilby & Harris 2006). An additional complexity is certain root mean square error (RMSE) and spatial cor- the lack of ob served data to evaluate the GCM out- relation thresholds across the 4 seasons. Gleckler et puts in modelling a future climate. All of these al. (2008) developed the model climate performance aspects necessitate evaluating the available GCMs index (MCPI) and the model variability index (MVI), with more accuracy. Knutti et al. (2010) suggested whicharebasedontherelativeerror/RMSEmethodol- that the skill or performance of the models needs to ogy. They suggested developing a broad suite of met- be defined by comparing simulated patterns of rics to characterize the model performance from which present-day climate with the observed data. it may be possible to identify the optimal subsets for Simple, effective and meaningful metrics, indi - various applications. Tang et al. (2008) assessed the cators, measures or criteria are required to rank the model convergence by exploring en semble mean GCMs across time and space to evolve a subset of square, ensemble spread and the ratio of signal-to- models that can be employed for hydrological model- noise, and concluded that predictions from different ling applications (Randall et al. 2007, Tebaldi & models vary markedly if the model convergence is Knutti 2007). These metrics may strengthen the con- poor. Mujumdar & Ghosh (2008) fo cused on modelling fidence level of outputs of GCMs, as these are the GCM and scenario uncertainty using the possibility main inputs to the regional climate models or down- theory in projecting stream flow of the Mahanadi scaling methods, and the evolved forecasts of hydro- River in Hirakud, India. Three GCMs with 2 green- logic variables incorporating the impact of climate house emission scenarios were used. They used the changes are used in planning mitigation measures. A C-coefficient, which is based on a cumulative distribu- brief literature review related to the various perform- tionfunctionofthedataasaperformancemeasure,and ance metrics is presented next. ranked the GCMs by scenario. Giorgi & Mearns (2002) introduced the reliability Smith & Chandler (2009) adopted a similar ap - ensemble averaging (REA) method for calculating proach to that of Suppiah et al. (2007), but restricted average, uncertainty range and a measure of reliabil- their assessment to rainfall only to assess the per- ity of simulated climate changes at the sub-continen- formance of 22 GCMs, for the case of Australia. John- Raju & Nagesh Kumar: Climate model ranking for India 105 son & Sharma (2009) developed a variable conver- skill score approaches were jointly ap plied for rank- gence score (VCS) method to rank 8 variables based ing GCMs. No decision-making ap proach has been on the coefficient of variation of an ensemble of 9 used to date for ranking. In addition, a number of per- GCMs for the case study of Australia. They men- formance metrics, used by various researchers else- tioned that this methodology allows for quantitative where, have not been used for Indian climate condi- assessment between different