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Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall
Atmospheric Research 232 (2020) 104680
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Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall A. Gusaina, S. Ghoshb,c,d, S. Karmakara,b,c,
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a
Environmental Science and Engineering Department, Indian Institute of Technology Bombay, Mumbai 400076, India Interdisciplinary Programme in Climate Studies, Indian Institute of Technology Bombay, Mumbai 400076, India c Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India d Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India b
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A B S T R A C T
Keywords: Atmospheric circulation CMIP models phase 6 General circulation models ISMR characteristics Multimodel average
This study assesses the improvements in climate model (CM) simulations for indicative characteristics representing complex dynamics of Indian summer monsoon rainfall for period 1951–2005 in CMIP6 (Coupled Model Intercomparison Project Phase 6). To our knowledge, this is the first preliminary study which compares the performance of models available in CMIP5 and CMIP6 consortium and their multi-model average (MMA). We find a significant improvement in CMIP6 models in capturing the spatiotemporal pattern of monsoon over Indian landmass, especially in the Western Ghats and North-East foothills of Himalayas. We also show that present-day global warming of 0.6 °C over Indian landmass is remarkably consistent with the changes in annual maximum 1day precipitation in MMA of CMIP6, which are in agreement with the observations. Our results suggest that added value in CMIP6 models precipitation simulations is not consistent within CMs used in present study. However, it still provides a precedent to the scientific community in performing future studies on climate change impact assessment.
1. Introduction Indian summer monsoon rainfall (ISMR) plays a pivotal role in shaping India's economic growth as it is mainly dependent on the agrarian society. Agriculture accounts for nearly 59% of the working population, which depends on monsoon rains (http://www.fao.org/ india/fao-in-india/india-at-a-glance/en/). Therefore, the rainfall pattern across the Indian subcontinent during ISMR etches its importance in the sustenance of diverse agro-ecological system and influencing food-water security, well-being, and prosperity of the country. The Indian summer monsoon is manifested with the development of low pressure region (acting as an abundant heat source) in the north-western part of Indian landmass due to seasonal migration of the intertropical convergence zone (ITCZ) which results into heavy precipitation during June, July, August and September (JJAS; Goswami and Chakravorty, 2017; Nair et al., 2018). Several studies speculated that large-scale teleconnections such as El Niño Southern Oscillation (ENSO; Kumar et al., 1999; Xavier et al., 2007; Li et al., 2017), Indian Ocean Dipole (IOD; Saji et al., 1999; Saha et al., 2014; Singh et al., 2017), Atlantic Multidecadal Oscillation (AMO; Goswami et al., 2006),
Atlantic zonal mode (AZO; Sabeerali et al., 2018), Eurasian snow cover (Zhang et al., 2019), equatorial Indian Ocean Oscillation (Webster et al., 1999), low pressure systems (Sandeep et al., 2018), and MaddenJulian Oscillation (MJO; Mishra et al., 2017) pose a notably strong influence on intraseasonal, interannual and multi-decadal variability of Indian monsoon. Numerous studies revealed that the second half of the twentieth century experienced a weakening of monsoon which resulted into decrease in mean precipitation (Roxy et al., 2014; Ghosh et al., 2016; Preethi et al., 2017). However, an increasing trend is observed in case of precipitation extremes (Goswami et al., 2006; Vittal et al., 2013; Hijioka et al., 2014; Fischer and Knutti, 2015; Ghosh et al., 2016) under a warming climate. Hence, it is crucial to detect and attribute the major drivers responsible for potential changes in ISMR pattern under changing climate for devising future adaptation policies in different sectors. Global circulation models (GCMs), which are developed by various modeling groups across the globe under the aegis of coupled model intercomparison project (CMIP), are widely used to study the impacts of past, present and future climate changes at global or synoptic scale (Eyring et al., 2016). These state-of-art climate models (CMs) have evolved over the past two decades to understand the complex climate
⁎ Corresponding author at: Environmental System Research Laboratory (ESRL), Room 211, Second floor, Environmental Science and Engineering Department, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India. E-mail address: [email protected] (S. Karmakar).
https://doi.org/10.1016/j.atmosres.2019.104680 Received 15 June 2019; Received in revised form 10 September 2019; Accepted 11 September 2019 Available online 15 September 2019 0169-8095/ © 2019 Elsevier B.V. All rights reserved.
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during the historical period (1951–2005). The GCM outputs are regridded to 0.25° spatial resolution as in observed dataset using a bilinear interpolation technique. Seasonal climatology for the study area is quantified by spatially averaging the long-term precipitation over entire Indian landmass during ISMR for climate model simulations and observations. Extreme value analysis is performed using the GCM simulations and observations to compare the performance of CMIP5 and CMIP6 models in capturing precipitation extremes over Indian subcontinent during ISMR. Here, extreme precipitation events are defined as values exceeding the threshold of 95th percentile delineated using peak over threshold (PoT) method to quantify extreme events at 1:25 year return period (RP; Gusain et al., 2019). Further, intra-seasonal variations in ISMR among model simulations and observation data are computed in terms of active (or wet) and break (or dry) spells (events corresponding to excess or low rainfall characterizing the fluctuations in monsoon strength over core monsoon zone) using three widely-used indices: total number of days per year (TND), frequency of occurrence (FoO), and maximum consecutive days (MCD). This analysis is performed over the core monsoon region roughly varying 18.0°- 28.0° N and 65.0° - 88.0° E in ISMR using the methodology as described in detail by Rajeevan et al. (2010). Finally, a two-step attribution method is used to examine the changes in precipitation extremes per degree of global warming. The wettest day of the year (annual maximum of daily precipitation, R × 1d) is considered as an index to describe precipitation extremes. We compute anomalies in observations and simulations for the period 1951–2005 with respect to a reference period, 1961–1990. It is further compared against the anomalies in global mean temperature derived from HadCRUT4 datasets (details in Table S2) to demonstrate consistency of simulated changes in precipitation extremes with observed data available for second half of 20th century (Bindoff et al., 2013; Fischer and Knutti, 2015).
systems, especially the dynamics of monsoon pattern under changing climate. Extensive research to understand future changes in Indian monsoon and its teleconnections (Naveendrakumar et al., 2019) started with release of CMIP3 models (Kripalani et al., 2007; Salvi et al., 2013; Asharaf and Ahrens, 2015) which escalated even further with the release of its improved successor CMIP5 (Sperber et al., 2013; Shashikanth et al., 2014; Song and Zhou, 2014; Su et al., 2016). Though several models in the recent phase (CMIP5) incorporated new components such as dynamic vegetation, indirect effects of aerosols, etc. (Taylor et al., 2012) but their coarse spatial resolution fails to capture the influence of local scale features (such as topography, land-surface feedback, land use changes, etc.) in reproducing present climatic conditions (Ghosh et al., 2016; Sharma et al., 2018; Jain et al., 2019). Several studies also pointed the drawback of CMIP5 models to over- or underestimate the monsoon characteristics over South Asian and Indian subcontinent inconsistently for different precipitation indices thus lowering the confidence in future projections (Saha et al., 2014; Sharmila et al., 2015). Various downscaling approaches, statistical and dynamical (Kannan and Ghosh, 2013; Salvi et al., 2013; Xue et al., 2014 and references therein), were proposed in the last decade to alleviate the simulations at local scale but they did not add any significant improvement in all cases studies and deteriorated to even worse in few (Singh et al., 2017; Sharma et al., 2018). To overcome these challenges, improved climate model simulations under sixth phase of CMIP is released by few modeling groups at present: Beijing Climate Center (BCC; Wu et al., 2018), Centre National de Recherches Météorologiques (CNRM; Voldoire et al., 2019), and Institut Pierre-Simon Laplace (IPSL), etc. The future climate projections involve use of improved emissions, land use scenarios, improved model parameterization, and physical processes, etc. driven by shared socioeconomic pathways (SSPs) based scenarios (Eyring et al., 2016; O'Neill et al., 2016). It is essential to understand the improvements in available CMIP6 models in comparison to their corresponding version in CMIP5 and evaluate its performance before using its climatic projections for decision- and policymaking. In this work, we focus on the evaluation of the performance of two generations of climate models; CMIP5 and CMIP6, to compare their ability in simulating different ISMR characteristics for the historical period, 1951–2005. Here, our objective is to analyze the improvements in the multi-model ensemble (MME) from CMIP6 consortium considered in this study for monsoon characteristics such as mean and extreme precipitation, intraseasonal variability, and seasonal climatology. In addition to it, we also inspect the ability of available CMIP6 models to capture the recent changes in observations that are weakening of ISMR and increasing precipitation extremes. To the best of our knowledge, this is the first effort to assess the ability of CMIP6 models in reproducing complex dynamics of ISMR, which always have been a challenging task for the climate modeling community.
3. Results To comprehend the improvements in CMs under computationallyexpensive CMIP phase 6 to simulate ISMR, we analyze various monsoon characteristics. The modeled seasonal precipitation data (JJAS) from CMIP5 and CMIP6 consortium are compared with observations (APHRODITE), masking precipitation over the oceanic areas. The Indian subcontinent receives heavy precipitation events during summer monsoon over the windward side of Western Ghats as well as at the foothills of the Himalayas in Northeast (NE) India (Fig. 1a and S1) due to the orographic effect of the mountain range. Fig. 1b and c illustrate the multimodel average (MMA) of CMIP5 and CMIP6 models showing mean precipitation (Prmean) across the Indian subcontinent, respectively. Notably, an improvement is observed in CMIP6 over CMIP5 in simulating the spatial variability of mean precipitation over the dry areas and high rainfall receiving areas. The coarser resolution of GCMs in CMIP5 fails to capture the orographic effects and local changes in the landmass that influences the spatial variability and distribution of rainfall (also reported in Shashikanth et al., 2014; Jain et al., 2019). A significant improvement is observed in CMIP6 models and their MMA over Central and North India, where high precipitation over these areas was underestimated by the majority of CMIP5 models as reported in Jain et al. (2019). Here we find that the precipitation simulations for individual CMIP6 models show improvement in mean, especially in case of BCC-CSM2-MR (Fig. S2e) and CNRM-CM6–1 (Fig. S2 g) where orography of the Western Ghats and NE India is simulated reasonably to some extent. Improvements in ISMR simulations in BCC-CSM2-MR and CNRM-CM6–1 can be possibly attributed to the updated convective parameterization schemes, cloud fraction estimation methods and inclusion of indirect effects posed by aerosols onto the formation of clouds and precipitation (details provided in Wu et al., 2018 and Voldoire et al., 2019, respectively). Such improvements have resulted into a better representation of global and synoptic-scale processes such
2. Data and methods Here we use daily raw precipitation outputs of four GCMs from CMIP5 (Taylor et al., 2012) and CMIP6 (Eyring et al., 2016) consortium extracted from models sharing same parent dynamical core structure, experiment ID (historical runs), and simulation variant (r1i1p1). The post-1850s precipitation outputs are obtained from Earth System Grid Federation (ESGF) portal as per the availability of data in the low and medium resolution of CMIP6 models (till March 2019) and their corresponding versions in CMIP5 (Description in Table S1, S2, supplementary information). The model simulations are compared with the gridded observation dataset provided by Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources project, Japan (APHRODITE) to demonstrate their ability to emulate present monsoon characteristics. We focus our analyses entirely on precipitation over the entire Indian subcontinent at 0.25° resolution for Indian summer monsoon (June to September) 2
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Fig. 1. Comparison of mean monsoon precipitation (mm/day) as MMA quantified from the outputs of raw CMIP5 (b), and CMIP6 (c) models with respect to observed precipitation (a) for the period 1951–2005. Fig. 1(e) and (f) show bias in MMA of CMIP5 and CMIP6 models from mean observed precipitation. Fig. 1(d) shows the scatter plot between error in mean ISMR simulated by MMA of CMIP5 and CMIP6 models where probability density functions (here, non-parametric kernel distribution) represent the spatial variability of error across the Indian subcontinent.
changes (explained in supplementary information; Fig. S5 and Table S3). We find that no substantial improvement occurs in CMIP6 models in capturing this decreasing (or weakening) changes of observed data in recent past except over a few scattered regions. The seasonal climatology of summer monsoon months for observed data, CMIP5 and CMIP6 simulations and their respective MMA is plotted in Fig. 2. Improvement is seen in case of MMA of CMIP6 (Fig. 2a) in following the observed climatology which is also reflected in case of individual CMIP6 models except for CNRM-CM6–1 (Fig. 2d) where CMIP5 version is capturing the observation very well. Also, the timings of onset, peak, and withdrawal of ISMR monsoon in the seasonal cycle is captured quite well in case of CMIP6 models which are comparable to the observed climatology except for IPSL-CM6A-LR where the onset and peak are highly underestimated. The majority of CMIP5 models captures the present-day climatological pattern of ISMR; however it underestimates the average precipitation magnitude during
as quasi-biennial oscillations (QBO) and Madden-Julian oscillations (MJO) which influences the organization of monsoon conditions over tropical latitudes and sustenance of ISMR during JJAS. We also compute the bias in model simulations with the observations where improvement can be seen in MMA of CMIP6 as the error in Prmean reduces over Central region of India and Western Ghats (Fig. 1e and f). Further, the improvement in CMIP6 over CMIP5 simulations are represented as probability density function (PDF)-scatter plots derived from bias in individual models (Fig. S3) and their MMA (Fig. 1d). Scatter points lie close to the 45° line showing no significant improvement in error although the distribution of error has shifted from dry bias (> − 10 mm/day) to low dry (−5 to 0 mm/day) and wet bias (0 to 5 mm/day) which can be clearly visualized from the non-parametric distribution function on the axes. The ability of CMs in CMIP5 and CMIP6 to simulate recent changes in mean precipitation is also evaluated to understand the credibility of models in projecting future 3
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Fig. 2. Seasonal climatology of ISMR as simulated by raw precipitation outputs of CMIP 5 and CMIP6 models where red line corresponds to CMIP5 model and solid blue line corresponds to CMIP6 model which is compared to the observed climatology. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
pattern of precipitation extreme. Improvements in CMIP6 over CMIP5 in terms of better model parameterization, convective schemes, etc. has led to non-uniform spatial improvements (over specific regions) in the simulation of precipitation extremes among different models. Further, the recent changes in 1:25 years RP extreme event is evaluated between the same two timeslices as used in case of mean precipitation using the bootstrapping method proposed by Kharin and Zwiers (2005) (Fig. S6 and Table S4). Spatially non-uniform changes are obtained in both the cases (CMIP5 and CMIP6) in comparison to the observations which shows the failure of improved low- and mediumresolution CMIP6 models to estimate decreasing changes in the observed dataset for the historical period at a finer scale which is also reported by Ghosh et al. (2012) and Singh et al. (2017) in case of CMIP5 models. ISMR is highly influenced by the characteristics of lower level atmospheric activities (particularly convergence and divergence), which characterizes the monsoon fluctuations during peak period over core monsoon zone (Singh et al., 2014). As a measure of intra-seasonal variability, we quantified these fluctuations in terms of active and break spells during peak monsoon months (July and August) which are presented in Fig. 4. TND, FoO, and MCD for active spells are higher than for break spells in the observed data. FoO is slightly better simulated by MMA of CMIP6 in comparison to CMIP5 for both, active and break spells (Fig. 4b). However, TND for active and break spells is predicted slightly higher in case of CMIP5 with no significant improvement in the case of CMIP6 (MMA) when compared to observations (Fig. 4a). MCD for active and break spell is not well captured in CMIP5, but little improvement is observed in CMIP6 (Fig. 4c) even though it is predicted
JJAS due to intrinsic model bias due to coarse horizontal and vertical structure, poor representation of physical processes, and inadequate boundary conditions and initialization reference datasets as reported in Jayasankar et al. (2015). These improvements can also be attributed to changes in convective parametrization schemes with enhanced convective microphysical processes to estimate convective cloud cover that ameliorates the diurnal cycle during the summer monsoon period in the tropics (Wu et al., 2018; Voldoire et al., 2019). Precipitation extremes, which tend to be another important characteristic of monsoon precipitation, show a significant improvement in MME (CMIP6) and its MMA from their CMIP5 counterparts (Fig. 3). We use PoT method to delineate extremes that are fitted with generalized Pareto distribution (GPD) for computing 1:25 years RP events. Heavy precipitation events can be seen in the Western Ghats and few parts of Northeast India in the observed data (Fig. 3a). Indian landmass experiences non-uniform changes in CMs while simulating extremes in comparison to observed data. Overestimation of extremes is found in some parts of the central, and southern zone of India in MMA of CMIP5 and CMIP6 models. However, the extremes in the windward side of Western Ghats, Northwestern, and Central India are captured quite well (Fig. 3c). The dry bias in case of extreme precipitation reduced in the western zone, and the northern part of the central zone from CMIP5 to CMIP6 (Fig. 3d and e). Furthermore, the direction of precipitation extremes (increase/decrease) is not consistent among the models, which lead to poor representation of spatial pattern across India (Fig. S4). MMA of CMIP6 models show an improvement in capturing the spatial variability of 1:25 year RP event in the observed dataset; however the individual models (CMIP5 and CMIP6) fails to preserve this spatial 4
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Fig. 3. MMA of 1:25 years RP precipitation event estimated using PoT from raw precipitation outputs of CMIP5 and CMIP6 models and percentage error with respect to observed datasets.
changes in extremes in observational data due to higher bias in simulated precipitation extremes. However, the CMIP6 (MMA) tends to capture the internal variability of the changes in precipitation extremes thus providing a realistic representation of daily precipitation during JJAS months which maintains a consistent trend with the regional average of observed data. This result states a noticeable improvement in CMIP6 models to detect ISMR response to global warming due to anthropogenic climate change in the observations at a regional scale. A similar assessment was reported in Wu et al. (2018), where the long-term trends in BCC CMIP5 and CMIP6 models were compared and observed a high correlation between CMIP6 model and the HadCRUT dataset.
slightly higher than the observations. Improvement in the estimation of active and break spells in CMIP6 models can be linked with the better predictability and representation of MJO which is strongly related to the onset of active and break spells during ISMR (Pai et al., 2011). The recent changes in intra-seasonal variability for historical monsoon period is also computed for observations and individual models for these indices (Fig. S7). No single model in CMIP5 and CMIP6 can simulate decreasing changes in active and break spells for all three indices calculated using observed data. This result indicates the low performance of medium- and low-resolution CMIP6 models in capturing recent changes in intra-seasonal monsoon characteristics during the historical period, which is comparable to CMIP5 models with similar spatial resolutions. Similar results were observed by Singh et al. (2017), where the performance of CMIP5 models was compared with their corresponding CORDEX model outputs. To further evaluate the performance of CMIP6 models over CMIP5, we also evaluated the regional changes in 1-day annual maxima precipitation extreme over Indian subcontinent per degree global warming, as shown in Fig. 5. Individual models in CMIP5 and CMIP6 under- and over-estimate the observed trend in R × 1d computed from APHRODITE gridded dataset. CMIP5 models and their MMA fails to attribute the
4. Discussion and conclusions In this study, we have demonstrated the performance of available medium- and low- resolution climate models (CMs) in the sixth phase of CMIP project in simulating complex dynamics of ISMR which is considered as one of the most difficult atmospheric circulation system to be understood by the scientific community. The results are compared illustratively with their corresponding model versions in the CMIP5 to 5
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performance of high-resolution models from the available low, medium, and high-resolution models in CMIP5 performed better with a high degree of agreement to the observations. However, these improvements are mostly limited to extra-tropics and higher latitudes, where the contribution to total precipitation by the resolved large-scale processes increases with resolution (Kooperman et al., 2018). Thus, it can be noted that the improved model physical parameterization along with higher horizontal and vertical resolution may further reduce the wet bias of CMs to reproduce the extreme precipitation events at daily scale more reasonably. We further find that CMIP6 models reasonably capture the seasonal climatology of observed data well, which is also reflected in its MMA over corresponding CMIP5 model versions except for the CNRM-CM6–1. However, the ability of CMs to simulate seasonal climatology does not necessarily correspond its credibility in emulating the climate change response in the observations (Racherla et al., 2012). The intra-seasonal variations are another important monsoon characteristic that explains the dynamics of ISMR, which is computed in this study. Here, we find that there is no consistency among the models, CMIP5 or CMIP6, to capture the active or break spell variations in the observations, but MMA provides a better estimate of intra-seasonal variations with respect to individual models. We further perform an investigation to test the ability of CMIP6 models to capture the recent significant changes in the observations for decreasing mean and increasing extreme precipitation (Figs. S5-S7). The study revealed no apparent improvement in CMIP6 models in capturing the spatial pattern and direction of changes in the observation data. However, CMIP6 models show a significant improvement in capturing the changes in precipitation extremes over Indian subcontinent per degree global temperature rise, which is highly consistent with the observations. In summary, CMIP6 models do show significant improvement in emulating the behavior of some of the monsoon characteristics over a few specific regions in Indian sub-continent; however the spatial improvement is inconsistent among the models. Modified deep convective schemes, advanced microphysics parameterization options, improved spatial and vertical resolution, incorporating indirect effects of aerosols in cloud formation, and improved ocean-ice models are some of the noteworthy changes in CMIP6 dynamical core structure which resulted into reduction of bias in CMs. These modifications have added value to CMIP6 model simulations, which captures the seasonal dynamics and intra-seasonal variations during monsoon comparable to that of observations, especially in tropical monsoon systems (Gerber and Manzini, 2016). Furthermore, the improved land surface parameterization in CMIP6 models provide a realistic configuration for simulating actual regional land-surface processes in simulating monsoon progression over India while taking into account the aspects of sub-grid variability. Our analysis also concludes MMA as a better representation of spatial variations across the Indian landmass rather than individual models as it reduces unnecessary background noise which arises in the multi-model ensemble (Santer et al., 2009; Sachindra et al., 2014; Jain et al., 2019). However, the usage of weighted averaging methods such as Bayesian model averaging, distance weighting method, etc. can further reduce the error in the model average by assigning higher weights to models having a higher degree of closeness to the observations (Duan and Phillips, 2010; Shashikanth et al., 2018). Here, the analysis is restricted to 4 GCMs from a group of nearly 70 complex models in CMIP6, and 53 models in CMIP5 project for which data is available for historical simulations. Also, the study does not involve the assessment of changes in future projections as model data is not publicly available for all CMs used in the study. Due to the unavailability of data from high-resolution models in CMIP6 GCM consortium at present, the direct usage of low or medium resolution climate model outputs is not recommended for decision- and policy-making studies at a finer scale. Hence, the scientific community has to rely on multiple downscaling approaches (statistical or dynamical), which can further add uncertainty under certain conditions. The inclusion of multiple model simulations at fine-resolution in High-resolution model
Fig. 4. Intra-seasonal oscillations of ISMR as computed from observed data with the evaluation of MMA of raw CMIP5 and their corresponding CMIP6 GCMs precipitation outputs. The average TND, FoO, and MCD in a year with the standardized anomaly of active and break spells are presented in (a), (b), and (c) respectively for the duration 1951–2005.
Fig. 5. 20-year moving window of anomalies in regional average of R × 1d, over Indian subcontinent versus global mean temperature change (°C) for the period (1951–2005) as simulated by the individual CMIP5 and CMIP6 models (black lines) and their respective multi-model average, MMA (green and blue lines), and as observed in APHRODITE precipitation dataset (red line). 20-yr moving average for APHRODITE, CMIP models, and their respective MMA are plotted against observed global mean (land and ocean) from HadCRUT4 where anomalies are estimated with respect to the reference period 1961–90. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
evaluate their ability in simulating different monsoon characteristics such as mean and extreme precipitation, intra-seasonal variability, etc. In case of mean precipitation, a significant reduction in dry bias over major parts of Indian landmass is found in CMIP6 models and their MMA, especially over the Western Ghats, thus capturing the spatial variability of precipitation in observations. However, this improvement is not consistent among all CMIP6 models spatially for a 1:25 years RP extreme precipitation event (Fig. S3). Also, wet bias is further intensified over Southern and some parts of Eastern zone in India (Fig. 3). These results point to the improvement in ISMR simulation for mean and extremes in CMIP6 model simulations over the entire Indian subcontinent; however, the regional consistency among models is still a major challenge. Kim et al. (2019) reported that extreme precipitation simulations over Indian subcontinent are highly dependent on the horizontal resolution of the CMs. The study showed that the
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intercomparison project (HighResMIP v1.0) would significantly improve the confidence in CMIP6 models for devising adaptation strategies under changing climate (Haarsma et al., 2016). These are some limitations of this preliminary study and can be considered as a potential area of research in near-future which heralds the beginning of a new era of high-resolution CMIP6 climate models. With the release of more model outputs, a similar assessment could be performed to get a greater insight into the improvements within CMIP6 models which would commensurate with a higher horizontal and vertical resolution to comprehend the complex atmospheric systems such as ISMR.
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Declaration of Competing Interest None. Acknowledgments The work is supported by the Department of Science & Technology (SPLICE – Climate Change Programme), Government of India, Project reference number DST/CCP/CoE/140/2018, Grant Number: 00000000000010013072 (UC ID: 18192442). The authors like to thank the World Climate Research Programme (WCRP) for making CMIP5 and CMIP6 model data publicly available. The authors would also like to thank Olivier Boucher and Voldoire-Petithomme Aurore (developers of CNRM-CM6-1 and IPSL-CM6A-LR, respectively) for sharing information about improvements in these CMIP6 models through personal communication over e-mail. The authors sincerely thank the Indian Institute of Technology Bombay (IIT Bombay) for providing all the computational facilities. The authors are grateful to the editors and the anonymous reviewers for their suggestions. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosres.2019.104680. References Asharaf, S., Ahrens, B., 2015. Indian summer monsoon rainfall processes in climate change scenarios. J. Clim. 28 (13), 5414–5429. Bindoff, N.L., Stott, P.A., AchutaRao, K.M., Allen, M.R., Gillett, N., Gutzler, D., Hansingo, K., Hegerl, G., Hu, Y., Jain, S., Mokhov, I.I., 2013. Detection and attribution of climate change: from global to regional. In: Climate Change 2013: The Physical Science Basis. IPCC Working Group I Contribution to AR5. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. Duan, Q., Phillips, T.J., 2010. Bayesian estimation of local signal and noise in multimodel simulations of climate change. J. Geophys. Res. Atmos. 115, p1–15 (D18123. Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J., Taylor, K.E., 2016. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. (Online) 9, p1–44 (LLNLJRNL-736881. Fischer, E.M., Knutti, R., 2015. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Chang. 5 (6), 560–565. Gerber, E.P., Manzini, E., 2016. The dynamics and variability model intercomparison project (DynVarMIP) for CMIP6: assessing the stratosphere-troposphere system. Geosci. Model Dev. 9, 3413–3425. Ghosh, S., Das, D., Kao, S.C., Ganguly, A.R., 2012. Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes. Nat. Clim. Chang. 2 (2), 86–91. Ghosh, S., Vittal, H., Sharma, T., Karmakar, S., Kasiviswanathan, K.S., Dhanesh, Y., Sudheer, K.P., Gunthe, S.S., 2016. Indian summer monsoon rainfall: implications of contrasting trends in the spatial variability of means and extremes. PLoS One 11 (7), e0158670. Goswami, B.N., Chakravorty, S., 2017. Dynamics of the Indian summer monsoon climate. In: Oxford Research Encyclopedia of Climate Science, (Retrieved from http://oxfordre.com/climatescience/view/10.1093/acrefore/9780190228620.001.0001/ acrefore-9780190228620-e-613). Goswami, B.N., Madhusoodanan, M.S., Neema, C.P., Sengupta, D., 2006. A physical mechanism for North Atlantic SST influence on the Indian summer monsoon. Geophys. Res. Lett. 33 (2), 1–4. Gusain, A., Vittal, H., Kulkarni, S., Ghosh, S., Karmakar, S., 2019. Role of vertical velocity in improving finer scale statistical downscaling for projection of extreme precipitation. Theor. Appl. Climatol. 137 (1–2), 791–804.
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projection of Indian summer monsoon variability under climate change scenario: an assessment from CMIP5 climate models. Glob. Planet. Chang. 124, 62–78. Shashikanth, K., Madhusoodhanan, C.G., Ghosh, S., Eldho, T.I., Rajendran, K., Murtugudde, R., 2014. Comparing statistically downscaled simulations of Indian monsoon at different spatial resolutions. J. Hydrol. 519, 3163–3177. Shashikanth, K., Ghosh, S., Vittal, H., Karmakar, S., 2018. Future projections of Indian summer monsoon rainfall extremes over India with statistical downscaling and its consistency with observed characteristics. Clim. Dyn. 51 (1–2), 1–15. Singh, D., Tsiang, M., Rajaratnam, B., Diffenbaugh, N.S., 2014. Observed changes in extreme wet and dry spells during the South Asian summer monsoon season. Nat. Clim. Chang. 4 (6), 456. Singh, S., Ghosh, S., Sahana, A.S., Vittal, H., Karmakar, S., 2017. Do dynamic regional models add value to the global model projections of Indian monsoon? Clim. Dyn. 48 (3–4), 1375–1397. Song, F., Zhou, T., 2014. Interannual variability of East Asian summer monsoon simulated by CMIP3 and CMIP5 AGCMs: Skill dependence on Indian Ocean–western Pacific anticyclone teleconnection. J. Clim. 27 (4), 1679–1697. Sperber, K.R., Annamalai, H., Kang, I.S., Kitoh, A., Moise, A., Turner, A., Wang, B., Zhou, T., 2013. The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Clim. Dyn. 41 (9–10), 2711–2744. Su, B., Huang, J., Gemmer, M., Jian, D., Tao, H., Jiang, T., Zhao, C., 2016. Statistical downscaling of CMIP5 multi-model ensemble for projected changes of climate in the Indus River Basin. Atmos. Res. 178, 138–149.
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Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall A. Gusaina, S. Ghoshb,c,d, S. Karmakara,b,c,
T
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a
Environmental Science and Engineering Department, Indian Institute of Technology Bombay, Mumbai 400076, India Interdisciplinary Programme in Climate Studies, Indian Institute of Technology Bombay, Mumbai 400076, India c Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India d Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India b
A R T I C LE I N FO
A B S T R A C T
Keywords: Atmospheric circulation CMIP models phase 6 General circulation models ISMR characteristics Multimodel average
This study assesses the improvements in climate model (CM) simulations for indicative characteristics representing complex dynamics of Indian summer monsoon rainfall for period 1951–2005 in CMIP6 (Coupled Model Intercomparison Project Phase 6). To our knowledge, this is the first preliminary study which compares the performance of models available in CMIP5 and CMIP6 consortium and their multi-model average (MMA). We find a significant improvement in CMIP6 models in capturing the spatiotemporal pattern of monsoon over Indian landmass, especially in the Western Ghats and North-East foothills of Himalayas. We also show that present-day global warming of 0.6 °C over Indian landmass is remarkably consistent with the changes in annual maximum 1day precipitation in MMA of CMIP6, which are in agreement with the observations. Our results suggest that added value in CMIP6 models precipitation simulations is not consistent within CMs used in present study. However, it still provides a precedent to the scientific community in performing future studies on climate change impact assessment.
1. Introduction Indian summer monsoon rainfall (ISMR) plays a pivotal role in shaping India's economic growth as it is mainly dependent on the agrarian society. Agriculture accounts for nearly 59% of the working population, which depends on monsoon rains (http://www.fao.org/ india/fao-in-india/india-at-a-glance/en/). Therefore, the rainfall pattern across the Indian subcontinent during ISMR etches its importance in the sustenance of diverse agro-ecological system and influencing food-water security, well-being, and prosperity of the country. The Indian summer monsoon is manifested with the development of low pressure region (acting as an abundant heat source) in the north-western part of Indian landmass due to seasonal migration of the intertropical convergence zone (ITCZ) which results into heavy precipitation during June, July, August and September (JJAS; Goswami and Chakravorty, 2017; Nair et al., 2018). Several studies speculated that large-scale teleconnections such as El Niño Southern Oscillation (ENSO; Kumar et al., 1999; Xavier et al., 2007; Li et al., 2017), Indian Ocean Dipole (IOD; Saji et al., 1999; Saha et al., 2014; Singh et al., 2017), Atlantic Multidecadal Oscillation (AMO; Goswami et al., 2006),
Atlantic zonal mode (AZO; Sabeerali et al., 2018), Eurasian snow cover (Zhang et al., 2019), equatorial Indian Ocean Oscillation (Webster et al., 1999), low pressure systems (Sandeep et al., 2018), and MaddenJulian Oscillation (MJO; Mishra et al., 2017) pose a notably strong influence on intraseasonal, interannual and multi-decadal variability of Indian monsoon. Numerous studies revealed that the second half of the twentieth century experienced a weakening of monsoon which resulted into decrease in mean precipitation (Roxy et al., 2014; Ghosh et al., 2016; Preethi et al., 2017). However, an increasing trend is observed in case of precipitation extremes (Goswami et al., 2006; Vittal et al., 2013; Hijioka et al., 2014; Fischer and Knutti, 2015; Ghosh et al., 2016) under a warming climate. Hence, it is crucial to detect and attribute the major drivers responsible for potential changes in ISMR pattern under changing climate for devising future adaptation policies in different sectors. Global circulation models (GCMs), which are developed by various modeling groups across the globe under the aegis of coupled model intercomparison project (CMIP), are widely used to study the impacts of past, present and future climate changes at global or synoptic scale (Eyring et al., 2016). These state-of-art climate models (CMs) have evolved over the past two decades to understand the complex climate
⁎ Corresponding author at: Environmental System Research Laboratory (ESRL), Room 211, Second floor, Environmental Science and Engineering Department, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India. E-mail address: [email protected] (S. Karmakar).
https://doi.org/10.1016/j.atmosres.2019.104680 Received 15 June 2019; Received in revised form 10 September 2019; Accepted 11 September 2019 Available online 15 September 2019 0169-8095/ © 2019 Elsevier B.V. All rights reserved.
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during the historical period (1951–2005). The GCM outputs are regridded to 0.25° spatial resolution as in observed dataset using a bilinear interpolation technique. Seasonal climatology for the study area is quantified by spatially averaging the long-term precipitation over entire Indian landmass during ISMR for climate model simulations and observations. Extreme value analysis is performed using the GCM simulations and observations to compare the performance of CMIP5 and CMIP6 models in capturing precipitation extremes over Indian subcontinent during ISMR. Here, extreme precipitation events are defined as values exceeding the threshold of 95th percentile delineated using peak over threshold (PoT) method to quantify extreme events at 1:25 year return period (RP; Gusain et al., 2019). Further, intra-seasonal variations in ISMR among model simulations and observation data are computed in terms of active (or wet) and break (or dry) spells (events corresponding to excess or low rainfall characterizing the fluctuations in monsoon strength over core monsoon zone) using three widely-used indices: total number of days per year (TND), frequency of occurrence (FoO), and maximum consecutive days (MCD). This analysis is performed over the core monsoon region roughly varying 18.0°- 28.0° N and 65.0° - 88.0° E in ISMR using the methodology as described in detail by Rajeevan et al. (2010). Finally, a two-step attribution method is used to examine the changes in precipitation extremes per degree of global warming. The wettest day of the year (annual maximum of daily precipitation, R × 1d) is considered as an index to describe precipitation extremes. We compute anomalies in observations and simulations for the period 1951–2005 with respect to a reference period, 1961–1990. It is further compared against the anomalies in global mean temperature derived from HadCRUT4 datasets (details in Table S2) to demonstrate consistency of simulated changes in precipitation extremes with observed data available for second half of 20th century (Bindoff et al., 2013; Fischer and Knutti, 2015).
systems, especially the dynamics of monsoon pattern under changing climate. Extensive research to understand future changes in Indian monsoon and its teleconnections (Naveendrakumar et al., 2019) started with release of CMIP3 models (Kripalani et al., 2007; Salvi et al., 2013; Asharaf and Ahrens, 2015) which escalated even further with the release of its improved successor CMIP5 (Sperber et al., 2013; Shashikanth et al., 2014; Song and Zhou, 2014; Su et al., 2016). Though several models in the recent phase (CMIP5) incorporated new components such as dynamic vegetation, indirect effects of aerosols, etc. (Taylor et al., 2012) but their coarse spatial resolution fails to capture the influence of local scale features (such as topography, land-surface feedback, land use changes, etc.) in reproducing present climatic conditions (Ghosh et al., 2016; Sharma et al., 2018; Jain et al., 2019). Several studies also pointed the drawback of CMIP5 models to over- or underestimate the monsoon characteristics over South Asian and Indian subcontinent inconsistently for different precipitation indices thus lowering the confidence in future projections (Saha et al., 2014; Sharmila et al., 2015). Various downscaling approaches, statistical and dynamical (Kannan and Ghosh, 2013; Salvi et al., 2013; Xue et al., 2014 and references therein), were proposed in the last decade to alleviate the simulations at local scale but they did not add any significant improvement in all cases studies and deteriorated to even worse in few (Singh et al., 2017; Sharma et al., 2018). To overcome these challenges, improved climate model simulations under sixth phase of CMIP is released by few modeling groups at present: Beijing Climate Center (BCC; Wu et al., 2018), Centre National de Recherches Météorologiques (CNRM; Voldoire et al., 2019), and Institut Pierre-Simon Laplace (IPSL), etc. The future climate projections involve use of improved emissions, land use scenarios, improved model parameterization, and physical processes, etc. driven by shared socioeconomic pathways (SSPs) based scenarios (Eyring et al., 2016; O'Neill et al., 2016). It is essential to understand the improvements in available CMIP6 models in comparison to their corresponding version in CMIP5 and evaluate its performance before using its climatic projections for decision- and policymaking. In this work, we focus on the evaluation of the performance of two generations of climate models; CMIP5 and CMIP6, to compare their ability in simulating different ISMR characteristics for the historical period, 1951–2005. Here, our objective is to analyze the improvements in the multi-model ensemble (MME) from CMIP6 consortium considered in this study for monsoon characteristics such as mean and extreme precipitation, intraseasonal variability, and seasonal climatology. In addition to it, we also inspect the ability of available CMIP6 models to capture the recent changes in observations that are weakening of ISMR and increasing precipitation extremes. To the best of our knowledge, this is the first effort to assess the ability of CMIP6 models in reproducing complex dynamics of ISMR, which always have been a challenging task for the climate modeling community.
3. Results To comprehend the improvements in CMs under computationallyexpensive CMIP phase 6 to simulate ISMR, we analyze various monsoon characteristics. The modeled seasonal precipitation data (JJAS) from CMIP5 and CMIP6 consortium are compared with observations (APHRODITE), masking precipitation over the oceanic areas. The Indian subcontinent receives heavy precipitation events during summer monsoon over the windward side of Western Ghats as well as at the foothills of the Himalayas in Northeast (NE) India (Fig. 1a and S1) due to the orographic effect of the mountain range. Fig. 1b and c illustrate the multimodel average (MMA) of CMIP5 and CMIP6 models showing mean precipitation (Prmean) across the Indian subcontinent, respectively. Notably, an improvement is observed in CMIP6 over CMIP5 in simulating the spatial variability of mean precipitation over the dry areas and high rainfall receiving areas. The coarser resolution of GCMs in CMIP5 fails to capture the orographic effects and local changes in the landmass that influences the spatial variability and distribution of rainfall (also reported in Shashikanth et al., 2014; Jain et al., 2019). A significant improvement is observed in CMIP6 models and their MMA over Central and North India, where high precipitation over these areas was underestimated by the majority of CMIP5 models as reported in Jain et al. (2019). Here we find that the precipitation simulations for individual CMIP6 models show improvement in mean, especially in case of BCC-CSM2-MR (Fig. S2e) and CNRM-CM6–1 (Fig. S2 g) where orography of the Western Ghats and NE India is simulated reasonably to some extent. Improvements in ISMR simulations in BCC-CSM2-MR and CNRM-CM6–1 can be possibly attributed to the updated convective parameterization schemes, cloud fraction estimation methods and inclusion of indirect effects posed by aerosols onto the formation of clouds and precipitation (details provided in Wu et al., 2018 and Voldoire et al., 2019, respectively). Such improvements have resulted into a better representation of global and synoptic-scale processes such
2. Data and methods Here we use daily raw precipitation outputs of four GCMs from CMIP5 (Taylor et al., 2012) and CMIP6 (Eyring et al., 2016) consortium extracted from models sharing same parent dynamical core structure, experiment ID (historical runs), and simulation variant (r1i1p1). The post-1850s precipitation outputs are obtained from Earth System Grid Federation (ESGF) portal as per the availability of data in the low and medium resolution of CMIP6 models (till March 2019) and their corresponding versions in CMIP5 (Description in Table S1, S2, supplementary information). The model simulations are compared with the gridded observation dataset provided by Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources project, Japan (APHRODITE) to demonstrate their ability to emulate present monsoon characteristics. We focus our analyses entirely on precipitation over the entire Indian subcontinent at 0.25° resolution for Indian summer monsoon (June to September) 2
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Fig. 1. Comparison of mean monsoon precipitation (mm/day) as MMA quantified from the outputs of raw CMIP5 (b), and CMIP6 (c) models with respect to observed precipitation (a) for the period 1951–2005. Fig. 1(e) and (f) show bias in MMA of CMIP5 and CMIP6 models from mean observed precipitation. Fig. 1(d) shows the scatter plot between error in mean ISMR simulated by MMA of CMIP5 and CMIP6 models where probability density functions (here, non-parametric kernel distribution) represent the spatial variability of error across the Indian subcontinent.
changes (explained in supplementary information; Fig. S5 and Table S3). We find that no substantial improvement occurs in CMIP6 models in capturing this decreasing (or weakening) changes of observed data in recent past except over a few scattered regions. The seasonal climatology of summer monsoon months for observed data, CMIP5 and CMIP6 simulations and their respective MMA is plotted in Fig. 2. Improvement is seen in case of MMA of CMIP6 (Fig. 2a) in following the observed climatology which is also reflected in case of individual CMIP6 models except for CNRM-CM6–1 (Fig. 2d) where CMIP5 version is capturing the observation very well. Also, the timings of onset, peak, and withdrawal of ISMR monsoon in the seasonal cycle is captured quite well in case of CMIP6 models which are comparable to the observed climatology except for IPSL-CM6A-LR where the onset and peak are highly underestimated. The majority of CMIP5 models captures the present-day climatological pattern of ISMR; however it underestimates the average precipitation magnitude during
as quasi-biennial oscillations (QBO) and Madden-Julian oscillations (MJO) which influences the organization of monsoon conditions over tropical latitudes and sustenance of ISMR during JJAS. We also compute the bias in model simulations with the observations where improvement can be seen in MMA of CMIP6 as the error in Prmean reduces over Central region of India and Western Ghats (Fig. 1e and f). Further, the improvement in CMIP6 over CMIP5 simulations are represented as probability density function (PDF)-scatter plots derived from bias in individual models (Fig. S3) and their MMA (Fig. 1d). Scatter points lie close to the 45° line showing no significant improvement in error although the distribution of error has shifted from dry bias (> − 10 mm/day) to low dry (−5 to 0 mm/day) and wet bias (0 to 5 mm/day) which can be clearly visualized from the non-parametric distribution function on the axes. The ability of CMs in CMIP5 and CMIP6 to simulate recent changes in mean precipitation is also evaluated to understand the credibility of models in projecting future 3
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Fig. 2. Seasonal climatology of ISMR as simulated by raw precipitation outputs of CMIP 5 and CMIP6 models where red line corresponds to CMIP5 model and solid blue line corresponds to CMIP6 model which is compared to the observed climatology. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
pattern of precipitation extreme. Improvements in CMIP6 over CMIP5 in terms of better model parameterization, convective schemes, etc. has led to non-uniform spatial improvements (over specific regions) in the simulation of precipitation extremes among different models. Further, the recent changes in 1:25 years RP extreme event is evaluated between the same two timeslices as used in case of mean precipitation using the bootstrapping method proposed by Kharin and Zwiers (2005) (Fig. S6 and Table S4). Spatially non-uniform changes are obtained in both the cases (CMIP5 and CMIP6) in comparison to the observations which shows the failure of improved low- and mediumresolution CMIP6 models to estimate decreasing changes in the observed dataset for the historical period at a finer scale which is also reported by Ghosh et al. (2012) and Singh et al. (2017) in case of CMIP5 models. ISMR is highly influenced by the characteristics of lower level atmospheric activities (particularly convergence and divergence), which characterizes the monsoon fluctuations during peak period over core monsoon zone (Singh et al., 2014). As a measure of intra-seasonal variability, we quantified these fluctuations in terms of active and break spells during peak monsoon months (July and August) which are presented in Fig. 4. TND, FoO, and MCD for active spells are higher than for break spells in the observed data. FoO is slightly better simulated by MMA of CMIP6 in comparison to CMIP5 for both, active and break spells (Fig. 4b). However, TND for active and break spells is predicted slightly higher in case of CMIP5 with no significant improvement in the case of CMIP6 (MMA) when compared to observations (Fig. 4a). MCD for active and break spell is not well captured in CMIP5, but little improvement is observed in CMIP6 (Fig. 4c) even though it is predicted
JJAS due to intrinsic model bias due to coarse horizontal and vertical structure, poor representation of physical processes, and inadequate boundary conditions and initialization reference datasets as reported in Jayasankar et al. (2015). These improvements can also be attributed to changes in convective parametrization schemes with enhanced convective microphysical processes to estimate convective cloud cover that ameliorates the diurnal cycle during the summer monsoon period in the tropics (Wu et al., 2018; Voldoire et al., 2019). Precipitation extremes, which tend to be another important characteristic of monsoon precipitation, show a significant improvement in MME (CMIP6) and its MMA from their CMIP5 counterparts (Fig. 3). We use PoT method to delineate extremes that are fitted with generalized Pareto distribution (GPD) for computing 1:25 years RP events. Heavy precipitation events can be seen in the Western Ghats and few parts of Northeast India in the observed data (Fig. 3a). Indian landmass experiences non-uniform changes in CMs while simulating extremes in comparison to observed data. Overestimation of extremes is found in some parts of the central, and southern zone of India in MMA of CMIP5 and CMIP6 models. However, the extremes in the windward side of Western Ghats, Northwestern, and Central India are captured quite well (Fig. 3c). The dry bias in case of extreme precipitation reduced in the western zone, and the northern part of the central zone from CMIP5 to CMIP6 (Fig. 3d and e). Furthermore, the direction of precipitation extremes (increase/decrease) is not consistent among the models, which lead to poor representation of spatial pattern across India (Fig. S4). MMA of CMIP6 models show an improvement in capturing the spatial variability of 1:25 year RP event in the observed dataset; however the individual models (CMIP5 and CMIP6) fails to preserve this spatial 4
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Fig. 3. MMA of 1:25 years RP precipitation event estimated using PoT from raw precipitation outputs of CMIP5 and CMIP6 models and percentage error with respect to observed datasets.
changes in extremes in observational data due to higher bias in simulated precipitation extremes. However, the CMIP6 (MMA) tends to capture the internal variability of the changes in precipitation extremes thus providing a realistic representation of daily precipitation during JJAS months which maintains a consistent trend with the regional average of observed data. This result states a noticeable improvement in CMIP6 models to detect ISMR response to global warming due to anthropogenic climate change in the observations at a regional scale. A similar assessment was reported in Wu et al. (2018), where the long-term trends in BCC CMIP5 and CMIP6 models were compared and observed a high correlation between CMIP6 model and the HadCRUT dataset.
slightly higher than the observations. Improvement in the estimation of active and break spells in CMIP6 models can be linked with the better predictability and representation of MJO which is strongly related to the onset of active and break spells during ISMR (Pai et al., 2011). The recent changes in intra-seasonal variability for historical monsoon period is also computed for observations and individual models for these indices (Fig. S7). No single model in CMIP5 and CMIP6 can simulate decreasing changes in active and break spells for all three indices calculated using observed data. This result indicates the low performance of medium- and low-resolution CMIP6 models in capturing recent changes in intra-seasonal monsoon characteristics during the historical period, which is comparable to CMIP5 models with similar spatial resolutions. Similar results were observed by Singh et al. (2017), where the performance of CMIP5 models was compared with their corresponding CORDEX model outputs. To further evaluate the performance of CMIP6 models over CMIP5, we also evaluated the regional changes in 1-day annual maxima precipitation extreme over Indian subcontinent per degree global warming, as shown in Fig. 5. Individual models in CMIP5 and CMIP6 under- and over-estimate the observed trend in R × 1d computed from APHRODITE gridded dataset. CMIP5 models and their MMA fails to attribute the
4. Discussion and conclusions In this study, we have demonstrated the performance of available medium- and low- resolution climate models (CMs) in the sixth phase of CMIP project in simulating complex dynamics of ISMR which is considered as one of the most difficult atmospheric circulation system to be understood by the scientific community. The results are compared illustratively with their corresponding model versions in the CMIP5 to 5
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performance of high-resolution models from the available low, medium, and high-resolution models in CMIP5 performed better with a high degree of agreement to the observations. However, these improvements are mostly limited to extra-tropics and higher latitudes, where the contribution to total precipitation by the resolved large-scale processes increases with resolution (Kooperman et al., 2018). Thus, it can be noted that the improved model physical parameterization along with higher horizontal and vertical resolution may further reduce the wet bias of CMs to reproduce the extreme precipitation events at daily scale more reasonably. We further find that CMIP6 models reasonably capture the seasonal climatology of observed data well, which is also reflected in its MMA over corresponding CMIP5 model versions except for the CNRM-CM6–1. However, the ability of CMs to simulate seasonal climatology does not necessarily correspond its credibility in emulating the climate change response in the observations (Racherla et al., 2012). The intra-seasonal variations are another important monsoon characteristic that explains the dynamics of ISMR, which is computed in this study. Here, we find that there is no consistency among the models, CMIP5 or CMIP6, to capture the active or break spell variations in the observations, but MMA provides a better estimate of intra-seasonal variations with respect to individual models. We further perform an investigation to test the ability of CMIP6 models to capture the recent significant changes in the observations for decreasing mean and increasing extreme precipitation (Figs. S5-S7). The study revealed no apparent improvement in CMIP6 models in capturing the spatial pattern and direction of changes in the observation data. However, CMIP6 models show a significant improvement in capturing the changes in precipitation extremes over Indian subcontinent per degree global temperature rise, which is highly consistent with the observations. In summary, CMIP6 models do show significant improvement in emulating the behavior of some of the monsoon characteristics over a few specific regions in Indian sub-continent; however the spatial improvement is inconsistent among the models. Modified deep convective schemes, advanced microphysics parameterization options, improved spatial and vertical resolution, incorporating indirect effects of aerosols in cloud formation, and improved ocean-ice models are some of the noteworthy changes in CMIP6 dynamical core structure which resulted into reduction of bias in CMs. These modifications have added value to CMIP6 model simulations, which captures the seasonal dynamics and intra-seasonal variations during monsoon comparable to that of observations, especially in tropical monsoon systems (Gerber and Manzini, 2016). Furthermore, the improved land surface parameterization in CMIP6 models provide a realistic configuration for simulating actual regional land-surface processes in simulating monsoon progression over India while taking into account the aspects of sub-grid variability. Our analysis also concludes MMA as a better representation of spatial variations across the Indian landmass rather than individual models as it reduces unnecessary background noise which arises in the multi-model ensemble (Santer et al., 2009; Sachindra et al., 2014; Jain et al., 2019). However, the usage of weighted averaging methods such as Bayesian model averaging, distance weighting method, etc. can further reduce the error in the model average by assigning higher weights to models having a higher degree of closeness to the observations (Duan and Phillips, 2010; Shashikanth et al., 2018). Here, the analysis is restricted to 4 GCMs from a group of nearly 70 complex models in CMIP6, and 53 models in CMIP5 project for which data is available for historical simulations. Also, the study does not involve the assessment of changes in future projections as model data is not publicly available for all CMs used in the study. Due to the unavailability of data from high-resolution models in CMIP6 GCM consortium at present, the direct usage of low or medium resolution climate model outputs is not recommended for decision- and policy-making studies at a finer scale. Hence, the scientific community has to rely on multiple downscaling approaches (statistical or dynamical), which can further add uncertainty under certain conditions. The inclusion of multiple model simulations at fine-resolution in High-resolution model
Fig. 4. Intra-seasonal oscillations of ISMR as computed from observed data with the evaluation of MMA of raw CMIP5 and their corresponding CMIP6 GCMs precipitation outputs. The average TND, FoO, and MCD in a year with the standardized anomaly of active and break spells are presented in (a), (b), and (c) respectively for the duration 1951–2005.
Fig. 5. 20-year moving window of anomalies in regional average of R × 1d, over Indian subcontinent versus global mean temperature change (°C) for the period (1951–2005) as simulated by the individual CMIP5 and CMIP6 models (black lines) and their respective multi-model average, MMA (green and blue lines), and as observed in APHRODITE precipitation dataset (red line). 20-yr moving average for APHRODITE, CMIP models, and their respective MMA are plotted against observed global mean (land and ocean) from HadCRUT4 where anomalies are estimated with respect to the reference period 1961–90. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
evaluate their ability in simulating different monsoon characteristics such as mean and extreme precipitation, intra-seasonal variability, etc. In case of mean precipitation, a significant reduction in dry bias over major parts of Indian landmass is found in CMIP6 models and their MMA, especially over the Western Ghats, thus capturing the spatial variability of precipitation in observations. However, this improvement is not consistent among all CMIP6 models spatially for a 1:25 years RP extreme precipitation event (Fig. S3). Also, wet bias is further intensified over Southern and some parts of Eastern zone in India (Fig. 3). These results point to the improvement in ISMR simulation for mean and extremes in CMIP6 model simulations over the entire Indian subcontinent; however, the regional consistency among models is still a major challenge. Kim et al. (2019) reported that extreme precipitation simulations over Indian subcontinent are highly dependent on the horizontal resolution of the CMs. The study showed that the
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Declaration of Competing Interest None. Acknowledgments The work is supported by the Department of Science & Technology (SPLICE – Climate Change Programme), Government of India, Project reference number DST/CCP/CoE/140/2018, Grant Number: 00000000000010013072 (UC ID: 18192442). The authors like to thank the World Climate Research Programme (WCRP) for making CMIP5 and CMIP6 model data publicly available. The authors would also like to thank Olivier Boucher and Voldoire-Petithomme Aurore (developers of CNRM-CM6-1 and IPSL-CM6A-LR, respectively) for sharing information about improvements in these CMIP6 models through personal communication over e-mail. The authors sincerely thank the Indian Institute of Technology Bombay (IIT Bombay) for providing all the computational facilities. The authors are grateful to the editors and the anonymous reviewers for their suggestions. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosres.2019.104680. References Asharaf, S., Ahrens, B., 2015. Indian summer monsoon rainfall processes in climate change scenarios. J. Clim. 28 (13), 5414–5429. Bindoff, N.L., Stott, P.A., AchutaRao, K.M., Allen, M.R., Gillett, N., Gutzler, D., Hansingo, K., Hegerl, G., Hu, Y., Jain, S., Mokhov, I.I., 2013. Detection and attribution of climate change: from global to regional. In: Climate Change 2013: The Physical Science Basis. IPCC Working Group I Contribution to AR5. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. Duan, Q., Phillips, T.J., 2010. Bayesian estimation of local signal and noise in multimodel simulations of climate change. J. Geophys. Res. Atmos. 115, p1–15 (D18123. Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J., Taylor, K.E., 2016. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. (Online) 9, p1–44 (LLNLJRNL-736881. Fischer, E.M., Knutti, R., 2015. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Chang. 5 (6), 560–565. Gerber, E.P., Manzini, E., 2016. The dynamics and variability model intercomparison project (DynVarMIP) for CMIP6: assessing the stratosphere-troposphere system. Geosci. Model Dev. 9, 3413–3425. Ghosh, S., Das, D., Kao, S.C., Ganguly, A.R., 2012. Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes. Nat. Clim. Chang. 2 (2), 86–91. Ghosh, S., Vittal, H., Sharma, T., Karmakar, S., Kasiviswanathan, K.S., Dhanesh, Y., Sudheer, K.P., Gunthe, S.S., 2016. Indian summer monsoon rainfall: implications of contrasting trends in the spatial variability of means and extremes. PLoS One 11 (7), e0158670. Goswami, B.N., Chakravorty, S., 2017. Dynamics of the Indian summer monsoon climate. In: Oxford Research Encyclopedia of Climate Science, (Retrieved from http://oxfordre.com/climatescience/view/10.1093/acrefore/9780190228620.001.0001/ acrefore-9780190228620-e-613). Goswami, B.N., Madhusoodanan, M.S., Neema, C.P., Sengupta, D., 2006. A physical mechanism for North Atlantic SST influence on the Indian summer monsoon. Geophys. Res. Lett. 33 (2), 1–4. Gusain, A., Vittal, H., Kulkarni, S., Ghosh, S., Karmakar, S., 2019. Role of vertical velocity in improving finer scale statistical downscaling for projection of extreme precipitation. Theor. Appl. Climatol. 137 (1–2), 791–804.
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