scholarly journals Monsoon Climate Change Projection for the Orographic West Coast of India Using High-Resolution Nested Dynamical Downscaling Model

Author(s):  
C. B. Jayasankar ◽  
Kavirajan Rajendran ◽  
Sajani Surendran
2021 ◽  
Author(s):  
Jayasankar C B ◽  
Rajendran K ◽  
Sajani Surendran

Abstract High-resolution regional climate model (RCM) simulations are found to be very useful in deriving realistic climate change projection information. This study uses high-resolution dynamical downscaling framework (CCSM4-WRF) for India. To delineate the advantage of high resolution, we compared the results of 9-km resolution CCSM4-WRF simulations against the 50-km resolution RCM simulations under Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) programme. Quantitative estimations show that majority of CORDEX-SA models exhibit large dry bias (<-4mm/day) and low pattern correlation coefficient (PCC) over the Western Ghats (WG). Mean climatology of Indian summer monsoon (ISM) rainfall simulated by high-resolution CCSM4-WRF outperforms the CORDEX-SA RCMs with low negative biases (~ 1mm/day) and high PCC (≥ 0.755). This skill of CCSM4-WRF provides better confidence in its future projection at local scale. CCSM4-WRF projects future intensification of monsoon rainfall over most parts of India and reduction over southern WG, which is consistent with recent observed trends, but none of the CORDEX-SA RCMs could simulate this rainfall reduction. For all-India rainfall, ensemble mean of CORDEX-SA models projects an increase by 1.3 ± 0.9mm/day and CCSM4-WRF projects 0.67mm/day. Projected changes in socioeconomic variables such as population and gross domestic product (GDP) exhibit future enhancement over most parts of India but with spatial heterogeneity. Shared Socioeconomic Pathways scenarios show pronounced future population growth over Indian coastal areas, and large enhancement in productivity over urban areas. Therefore, climate change projection information of ISM rainfall, together with enhanced future population and GDP, is useful for taking necessary steps for adaptation and mitigation in a sustainable manner.


2012 ◽  
Vol 48 (5) ◽  
Author(s):  
Sungwook Wi ◽  
Francina Dominguez ◽  
Matej Durcik ◽  
Juan Valdes ◽  
Henry F. Diaz ◽  
...  

Author(s):  
Filippo Giorgi

Dynamical downscaling has been used for about 30 years to produce high-resolution climate information for studies of regional climate processes and for the production of climate information usable for vulnerability, impact assessment and adaptation studies. Three dynamical downscaling tools are available in the literature: high-resolution global atmospheric models (HIRGCMs), variable resolution global atmospheric models (VARGCMs), and regional climate models (RCMs). These techniques share their basic principles, but have different underlying assumptions, advantages and limitations. They have undergone a tremendous growth in the last decades, especially RCMs, to the point that they are considered fundamental tools in climate change research. Major intercomparison programs have been implemented over the years, culminating in the Coordinated Regional climate Downscaling EXperiment (CORDEX), an international program aimed at producing fine scale regional climate information based on multi-model and multi-technique approaches. These intercomparison projects have lead to an increasing understanding of fundamental issues in climate downscaling and in the potential of downscaling techniques to provide actionable climate change information. Yet some open issues remain, most notably that of the added value of downscaling, which are the focus of substantial current research. One of the primary future directions in dynamical downscaling is the development of fully coupled regional earth system models including multiple components, such as the atmosphere, the oceans, the biosphere and the chemosphere. Within this context, dynamical downscaling models offer optimal testbeds to incorporate the human component in a fully interactive way. Another main future research direction is the transition to models running at convection-permitting scales, order of 1–3 km, for climate applications. This is a major modeling step which will require substantial development in research and infrastructure, and will allow the description of local scale processes and phenomena within the climate change context. Especially in view of these future directions, climate downscaling will increasingly constitute a fundamental interface between the climate modeling and end-user communities in support of climate service activities.


2010 ◽  
Vol 37 (9-10) ◽  
pp. 2005-2020 ◽  
Author(s):  
Lin-Lin Pan ◽  
Shu-Hua Chen ◽  
Dan Cayan ◽  
Mei-Ying Lin ◽  
Quinn Hart ◽  
...  

Author(s):  
K. Vinod ◽  
A. Anasu Koya ◽  
V. A. Kunhi Koya ◽  
P. G. Silpa ◽  
P. K. Asokan ◽  
...  

Mangroves are keystone ecosystems which provide numerous environmental services. Mangroves assume significance as standing stores of sequestered atmospheric carbon and are therefore, important in the light of climate change mitigation. In this study, we attempted to assess the biomass of mangroves in the Kadalundi wetland, south-west coast of India and evaluated the potential of these mangroves to sequester and store carbon. The C-stocks of above-ground and root biomass were 83.32±11.06 t C ha-1 and 34.96±4.30 t C ha-1 respectively, while the C-stock in sediment was estimated to be 63.87±8.67 t C ha-1. The estimates of mean combined C-stocks in the mangrove biomass and sediment of Kadalundi shows that this estuarine mangrove wetland stored 182.15 t C ha-1, which was equivalent to 668.48 t CO2 ha-1. The mangroves which cover an area of 13.23 ha in the Kadalundi wetland is assumed to have a potential to sequester and store a substantial quantity of 2,409.84 t C which is equivalent to 8,844.11 t CO2. The study underscores the importance of these intertidal forests for climate change mitigation and stresses the importance of protecting the mangroves which provide many other important ecosystem services that benefit communities.


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