BRAIN STORMING WORKSHOP ON CLIMATE MODELING: NATIONAL STATUS
What: In total, 65 scientists from 24 institutions engaged in climate science research in India, assembled to discuss the outcome of the recently concluded network program on climate modeling undertaken by the Department of Science and Technology of the Government of India. The other important objectives of this brainstorming workshop were to assess the present status of climate modeling in India and plan for a future course of action.
When: 19–20 July 2016
Where: New Delhi, India
Weather and climate over India are dominated by the monsoonal system, namely, the southwest (June–September) and the northeast (October–December) monsoons, which are predominantly seasonal and are largely characterized by low-level westerly and easterly wind patterns, respectively. It is well known that the Indian summer monsoon (ISM) is a complex nonlinear multifaceted phenomenon, involving substantial variability at intraseasonal and interannual time scales. Many studies have demonstrated the rich spatiotemporal variations in rainfall across the homogeneous zones in India (e.g., Dash et al. 2002; Mooley and Parthasarathy 1984; Parthasarathy et al. 1995); furthermore, these variations are vulnerable to global warming and subsequent changes in the atmospheric circulation patterns (Dash et al. 2011, 2014). These climate-change-related space–time variations in temperature, rainfall, winds, and humidity in turn have a direct bearing on agriculture, human health, and other socioeconomic conditions. Numerical models provide an ideal avenue to assess these impacts. To this end, a good climate model ought to be able to simulate the current climate as accurately as possible so as to build confidence for its subsequent use for addressing future projections under different climate change scenarios. Over the past three decades there have been substantial advances in numerical models that have enabled us to simulate the seasonal and annual cycles as well as the mean monsoon rainfall reasonably well. However, today, no single model is successful in simulating the spatiotemporal variations in rainfall to the extent required for using their products in the context of climate change. Thus, Indian scientists have a great responsibility to develop a suitable climate model for India.
BACKGROUND.
In India, the Ministry of Earth Sciences (MoES), Government of India (GoI) is entrusted with the responsibility of excelling in augmenting knowledge and technology pertaining to Earth system science for public safety and socioeconomic benefits to society. Among the several objectives of MoES, the most important ones include i) conducting scientific and technical activities related to Earth system science; ii) early warning for hazards, such as cyclones and tsunamis; iii) weather forecasts, warnings, and advisories for diverse sectors, such as agriculture, aviation, water resources, and tourism; iv) climate change (CC) research and climate services; v) ocean observations, modeling, and ocean services for fishery and shipping; vi) exploration of sea for resources, including freshwater and development of technology; vii) assessment of marine productivity; viii) exploration of polar regions; and ix) earthquake monitoring and research. The weather- and climate-related activities of MoES have been undertaken by its organizations, namely, the India Meteorological Department (IMD), the National Centre for Medium Range Weather Forecasting (NCMRWF), and the Indian Institute of Tropical Meteorology (IITM).
Recognizing the importance of building reliable information and a strategic knowledge system in CC and the need for addressing issues concerning sustenance of the Himalayan ecosystem in this part of the world, the Department of Science and Technology (DST) of the GoI is coordinating and implementing two out of eight national missions on CC as part of the National Action Plan on Climate Change (NAPCC). These are i) the National Mission for Sustaining the Himalayan Ecosystem (NMSHE) and ii) the National Mission on Strategic Knowledge for Climate Change (NMSKCC). One of the deliverables of NMSKCC is to set up national network programs in different areas of CC science, adaptation, and mitigation. Climate modeling is one such priority area that is to be addressed by the mission. It has been realized that weather and climate scientists, especially the early career scientists, should be provided with adequate infrastructure in terms of computing facilities and free access to weather and climate data; this is so as to ensure that the current modeling efforts in the country can percolate to the level of universities and premier scientific organizations in addition to the developmental activities in the premier institutions under the MoES.
In this regard, DST had launched a National Network Programme on Climate Modeling in 2012, wherein a number of institutions working in the area of climate modeling participated. The tenure of the network has come to an end this year. With a view to initiate the second phase of the network program on climate modeling, a brainstorming workshop titled “Climate Modeling: National Status” was jointly organized by the Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, and DST on 19 and 20 July 2016 at IIT Delhi. The basic purpose of the workshop, attended by 65 scientists from 24 institutions engaged in climate science research, was to discuss the outcome of the recently concluded DST network program on climate modeling and, more importantly, to plan for a future course of action. The workshop focused on the present status and future prospects of climate modeling in the national context, scientific and technical challenges, identification of thrust areas, and the way forward. The workshop identified some broad themes concerning climate modeling in India, potential groups of investigators, and institutions who would synergistically participate in the proposed National Network Program on Climate Modeling.
STATUS OF CLIMATE MODELING IN INDIA.
A talk on DST’s initiatives on building science and technology capacities in climate change kicked off the first scientific session of the workshop, titled “Building National Capacity in Climate Modelling: Status and Strategies.” Following this, a representative of MoES presented an overarching view of the initiatives and programs on climate modeling supported by the ministry. This was followed by talks on specific climate and weather modeling efforts at the three major organizations that come under the umbrella of MoES, namely, IMD, NCMRWF, and IITM.
The latter three talks are summarized below. IMD provides the operational weather services in the country, and its specific products depending on numerical weather prediction (NWP), including services related to weather, agrometeorology, cyclone forecasting, aviation, hydrology, nowcasting, power, health, mountain expedition, and polar meteorology. For these services, IMD uses models such as the Global Forecasting System (GFS) T574L64 (25 km), Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW; 9 km, 3 km), Advanced Regional Prediction System (ARPS; 9 km), and Warning Decision Support System–Integrated Information (WDSS-II) for forecasting in the time scales of 7 days, 1 day to 36 h, 6–24 h, and 0–2 h, respectively. It is planned to use GFS at T1534 resolution and the coupled model Coupled Forecast System (CFS), version 2, for extended range forecast by 2017 and the Hurricane WRF with coupler by 2018. Block-level weather forecast is aimed at by 2018. The climate-related activities at IITM have been undertaken by its Centre for Climate Change Research (CCCR). The IITM Earth System Model (ESM; Swapna et al. 2015) has been successfully implemented, and it will hopefully make an important contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) simulations. The IITM is also involved in the Coordinated Regional Downscaling Experiment (CORDEX) program of the World Climate Research Programme (WCRP) with the aim of developing an international coordinated framework to generate improved regional climate change projections. It is planned to start with an atmosphere–ocean coupled model with realistic mean climate, which eventually will have the fidelity to capture the global and monsoon climate, and that also has realistic representation of monsoon interannual variability and important features of ocean–atmosphere coupled interactions. In the future, ESM will include modules on biogeochemistry, interactive sea ice, and aerosol and chemistry transport. Finally, NCMRWF is an important institution under the aegis of MoES that has been completely engaged with NWP models for improvements in weather forecasts in the medium range. It uses the NCMRWF Unified Model (NCUM; 17 km) and global Earth Prediction System (EPS) based on NCUM (33/44 km) for forecasts in the range of a week to 10 days. Currently, NCUM-R (with explicit rain processes) is run at 4-km resolution, with NCUM-G (global) inputs at 0000 UTC for 72-h forecasts daily. Similar runs at 1.5-km resolution are currently in the testing phase. Four-dimensional variational data assimilation (4DVar) for regional 4-km NCUM is expected to be ready soon. NCMRWF has developed a robust data monitoring mechanism for all observations that include conventional, satellite, and radar retrievals. Future plans also include a global model at 12 km, global EPS at 12 km with 44-member ensemble, and coupled model at 25 km (atmosphere) and 0.25 km (ocean).
IDENTIFICATION OF THEMES.
The second scientific session, titled “Prioritization of Research & Capacity Needs, and Identification of Themes for the Proposed Network Programme,” began with an outline of the rationale behind the proposed Network Programme on Climate Modeling. This was followed by talks from the conveners of the four identified themes (see below); these themes essentially form the thrust areas that require a clear and present attention from the climate modeling community in India. The identified themes are
process and phenomenological studies through modeling,
understanding extremes,
climate projections, and
regional climate modeling.
There were breakaway sessions during the morning session of the second day, and discussions were held on the following seven salient points for each of the four themes mentioned above:
vital scientific questions to be addressed,
intended potential deliverables,
broad infrastructure requirements,
goal for capacity building,
approach for the defined objectives,
overall institutional linkages, and
road map for strategic knowledge sharing.
Process and phenomenological studies through modeling.
Results from phase 5 of the Coupled Model Intercomparison Project (CMIP5) presented in the IPCC AR5 suggest that climate models have large uncertainty in regional climate projections. The story remains the same for India as well. Large biases over India may be attributed to many factors; perhaps the most important factor is that most, if not all, models have been developed in other countries and simulation of Indian climate is not their leading priority.
The need of the hour demands a community-based climate model customized for the Indian region, although there are many challenges in realizing this need. Major challenges include i) insufficient computing power to carry out multidecadal runs at standard resolutions and decadelong runs at very high resolutions; ii) lack of understanding of climatic aspects of India, such as intraseasonal variation, the diurnal cycle, and convection–large-scale interactions; and iii) lack of data for model validation and process understanding. One of the major challenges in customizing a given climate model over the Indian region is our lack of understanding of some of the processes that form an important part of our regional climate system. Present-day models such as those used in CMIP5 and CORDEX have unacceptably large biases and fail to simulate many important facets of the Indian climate system.
As a part of this theme, model improvement will be carried out through process understanding, customization, and tuning. A state-of-the-art climate model will be selected that is scalable up to a large number of cores and supported by a large developer/user community. Sources of model biases will be identified through targeted numerical experiments, with special emphases on process integration, resolution dependence, dynamical core, surface processes, and convection and cloud parameterization (e.g., Mishra 2011; Mishra and Sahany 2011a,b; Mishra et al. 2011; Sahany et al. 2012, 2014). The model will be improved so as to minimize the errors discussed above by modifying existing modules using observations and theoretical constraints and adding new modules as necessary. Unless the underlying processes are well understood and represented in the models, customization and tuning exercises will be of limited utility. For example, the spatiotemporal distribution of diabatic heating over the subcontinent is poorly simulated in the models. Given its importance in regulating the Indian monsoons, this aspect will be thoroughly examined from satellite retrievals, reanalysis, and numerical experiments. Sources of errors will be identified and appropriate changes will be made to the modules governing the diabatic heating profiles, such as the convective parameterization.
Understanding extremes.
The footprint of warming on rainfall is seen most prominently as an increase in extreme rainfall, often attributed to an increase in the atmospheric moisture capacity (e.g., Held and Soden 2006). There has been sufficient observational evidence to this end over the past few decades, especially in regional rain extremes (see, e.g., Groisman et al. 2005; AR5). The physical basis of the observed increase of extremes in a warming world, in general, has been explored in detail (see, e.g., O’Gorman and Schneider 2009); however, the sensitivity of precipitation extremes to warming is still not completely well understood, especially when convection is important (O’Gorman 2015). Another consequence of warming is the oft-quoted “wet–wetter, dry–drier” paradigm, which suggests that wet (dry) regions experience wetter (drier) conditions with increasing temperature. While there may be support for this paradigm from observations (Liepert and Previdi 2009; Allan et al. 2010; Chou et al. 2013) and simulations (e.g., Lau et al. 2013), an analysis of historical in situ rainfall data casts a shadow on this (Greve et al. 2014); in fact, the land and ocean response to increasing temperatures is found to be different. Compounding this is the fact that many parts of the tropics feel the influence of long-term natural variability [see, e.g., Sukhatme and Venugopal (2016) for an investigation of the role of the phases of El Niño–Southern Oscillation (ENSO) in modulating extreme rain].
Closer to home, there has been substantial evidence of a long-term significant increase in short-duration extremes of the Indian monsoon rainfall (e.g., Goswami et al. 2006; Rajeevan et al. 2008; Dash et al. 2009; Singh et al. 2014). Significant modeling effort has been devoted to determining the several causal elements that can lead to an increase in extreme monsoon rainfall. An additional challenge over the Indian region is that this secular increase of extreme rainfall is in the face of a decreasing mean monsoon, which has been debatably attributed to the presence of enhanced aerosol loading over the past several decades (Bollasina et al. 2011).
To this end, a clear and present (although long term) challenge in the context of Indian monsoon rainfall remains that of separating the contribution of the various drivers, namely, warming, aerosols, and natural variability, to changes in extreme rainfall, especially of the short-duration kind (e.g., Trenberth 2011; O’Gorman 2015), which have a substantial societal impact. In addition, special attention needs to be paid to understanding orographically driven rainfall, extremes of which have resulted in substantial damage over the past decade (e.g., Uttarakhand floods in 2013; see Cho et al. 2016).
Climate projections.
Recent literature documents the signatures of the impact of recent global warming on the Indian climate (e.g., Singh et al. 2014; Roxy et al. 2015). Climate change projections play a critical role in climate change adaptation. The deliberations during the workshop identified several critical issues to focus on, under the broad theme of climate-change-related projections. A major goal is to arrive at a better understanding of the climate drivers of the Indian summer monsoon at state to district levels in India and the relevant dynamics of teleconnection in the context of recent climate change. For example, the ENSOs have been changing over the last three to four decades (e.g., Ashok et al. 2007, 2012), and their impact on the Indian summer monsoon is waning (Kumar et al. 1999; Kucharski et al. 2008). A new understanding is emerging that, in addition to ENSO, there are other important potential drivers from the tropical Indian Ocean and from the Atlantic, (Kucharski et al. 2008; Krishnaswamy et al. 2015). Further, it is also important to study whether the impact of global warming on sea ice and sea level will in turn affect the Indian summer monsoon variability, and if so, attempt to understand the potential underlying mechanisms. These issues will be addressed through model sensitivity studies and also by analyzing various observational and reanalyzed datasets.
Another important aspect is to quantify the uncertainties in the downscaled climate change projections for subregional levels in India through dynamical and statistical–dynamical downscaling approaches. This also naturally involves an estimation of the uncertainties in the available observational datasets and reconciling the model uncertainties with those from observations.
It has been also highlighted that there have not been any modeling studies that address the past climate variability and change from the context of monsoon and its teleconnections. Modeling studies for the Holocene period have been identified as an important challenge. Studies that evaluate the relevance of natural and anthropogenic factors for decadal variability and predictability will also be carried out.
RECOMMENDATIONS.
At the end of the 2-day workshop, it was strongly believed by all the participants that there is an urgent need to address the climate modeling issues specific to India. The Indian institutions engaged in climate science besides the dedicated institutions under MoES will definitely benefit from the DST-proposed external support exclusively for climate modeling. The role of MoES in terms of active scientific and logistic support will further strengthen the climate modeling effort in the country. It was concluded that the conveners would submit the final list of topics and institutions in consultation with interested scientists irrespective of their involvement in this workshop. After getting their feedback, projects would be invited from interested scientists from different organizations. DST will encourage and fund scientists engaged in climate science research in different organizations in the country with a view to develop capacity in climate modeling and research in the country. The National Supercomputing Mission already launched jointly by DST and the Department of Electronics and Information Technology (DeitY) will immensely help in supporting climate modeling efforts in India.
ACKNOWLEDGMENTS
The Department of Science and Technology, Government of India, and IIT Delhi are acknowledged for providing financial and logistical support, respectively, for the workshop.
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