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Home arrow Geography arrow Observed Climate Variability and Change over the Indian Region

Regional Climate Change Scenarios

J. Sanjay, M.V.S. Ramarao, M. Mujumdar and R. Krishnan

Introduction

The information about long-term (century-scale) climate change is needed to develop national or regional adaptation and mitigation policies. The reliable climate information will be useful and actionable only when they are available at the sub-continental to regional scales. The coarser spatial resolution and systematic error (called bias) of global climate models (GCMs) limits the examination of possible impacts of climate change and adaptation strategies on a smaller scale. The grid interval of atmosphere-ocean coupled GCMs (AOGCMs) used in the Coupled Model Intercomparison Project phase 5 (CMIP5; Taylor et al. 2012) ranges from 1.0° to 3.8°. The CMIP5 models indicate large bias in the monthly cycle of near surface air temperature and precipitation climate over the Indian region, particularly at elevations over the Himalayas (Flato et al. 2013). Recently, the stand-alone atmospheric GCMs run at higher resolution than AOGCMs have been made possible with high-performance computer systems to provide complementary regional-scale climate information. The advantages of a 20-km grid atmospheric GCM in simulating the Indian climate have been identified, especially the complex features of the Indian summer monsoon, including improved regional precipitation (Rajendran and Kitoh 2008; Krishnan et al. 2013). Another approach is the use of an atmospheric GCM with variable grid-point resolution zooming over the region of interest. The global simulation using telescopic zooming to a high resolution of more than 35 km over India was shown to provide improved representation of the organized convective activity over the South Asian monsoon region and in realistically capturing the regional details of the precipitation variability and their links to monsoon circulation (Sabin et al. 2013). However, as large computer resources are needed, the model integration period and number of ensemble members is

J. Sanjay (H) • M.V.S. Ramarao • M. Mujumdar • R. Krishnan

Indian Institute of Tropical Meteorology, Earth System Science Organization, Pune, India e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

© Springer Science+Business Media Singapore 2017 285

M.N. Rajeevan and S. Nayak (eds.), Observed Climate Variability and Change Over the Indian Region, Springer Geology, DOI 10.1007/978-981-10-2531-0_16

limited for such high-resolution atmospheric GCMs. Therefore, modellers rely on dynamical and/or statistical downscaling methods to make available higher-resolution, long-term climate data for impact and adaptation studies in specific regions.

The dynamical downscaling method using high resolution limited area regional climate models (RCMs) utilizes the outputs provided by AOGCMs as lateral boundary condition to provide physically consistent spatiotemporal variations of climatic parameters at spatial scales much smaller than the AOGCMs’ grid. The RCMs by resolving the topographical details, coastlines, and land-surface heterogeneities allow the reproduction of small-scale processes and information that are most useful for impact assessment and in decision making for adaptation (Flato et al. 2013). Past research have studied the ability of individual RCMs to capture the general features of the Indian climate, particularly the summer monsoon climate (e.g. Krishnakumar et al. 2011; Dash et al. 2013 and the references therein). These studies showed that in general RCMs reproduce the precipitation seasonal mean and annual cycle quite accurately, although individual models can reveal significant biases in some sub-regions and seasons. In addition to the RCM-related bias, more errors and uncertainties in the downscaled climate can be inherited from the driving AOGCMs through the lateral boundary conditions. Parts of these uncertainties for the present climate can be addressed by generating a collection of simulations by driving the same RCM with different AOGCMs. The other sources of uncertainty in our understanding of the future climate change need to be addressed using the RCMs in a similar manner were addressed in CMIP5 using AOGCMs. Several different emission scenarios such as the Representation Concentration Pathways (RCPs) need to be used to force the RCMs so as to properly sample the uncertainty linked to future changes of the external forcing of the climate system. Similarly, by using several RCMs or a group of simulations with one RCM perturbed in its representation of the physics, parts of the uncertainties associated with how changes in external forcing factors control the climate need to be assessed. Finally, several simulations with each RCM driven with the same RCP scenario but with different initial conditions need to be generated to understand to what extent is the future climate change signal masked/amplified by natural variations of the climate system. The detailed evaluation of RCM-produced projections and a full classification of these underlying uncertainties are necessary for providing valuable information for assessing regional climate change vulnerability and impact application studies. The computer resources needed to generate such large ensembles of RCM outputs vary, depending on a number of factors (e.g. time step; horizontal and vertical resolutions; domain size; time period of simulation; and coupling system with additional models for ocean, land surface or terrestrial vegetation), but are quite demanding.

The World Climate Research Programme CORDEX (Coordinated Regional climate Downscaling Experiment; Giorgi et al. 2009; http://wcrp-cordex.ipsl. jussieu.fr/) initiative aims to foster international partnership in order to produce an ensemble of high-resolution past and future climate projections at regional scale, by downscaling several AOGCMs participating in the CMIP5. This ensemble can be used to demonstrate uncertainties on the regional scale or to obtain probabilistic climate change information in a region. The CORDEX South Asia component led by the Centre for Climate Change Research (CCCR) at the Indian Institute of Tropical Meteorology (IITM) aims to develop such multi-model ensemble of high-resolution (50-km) past and future climate projections over the South Asia region. The goal of this collaborative initiative is to generate robust national climate change information through dissemination of CORDEX South Asia datasets for regional climate change impact assessments and for developing adaptation strategies. The available RCM outputs under the CORDEX South Asia initiative are archived and published on the CCCR-IITM climate data portal (http://cccr.tropmet. res.in/cordex/files/downloads.jsp). Table 1 provides the basic references for these RCMs, their driving AOGCMs and the contributing partner modelling institutes. These datasets will shortly be accessible from the CCCR-IITM Earth System Grid Federation (ESGF) data node, which was the climate model data dissemination mechanism used for CMIP5. Some of these simulations have been analysed in terms of Indian precipitation extreme indices, its associated intermodal variability,

Table 1 List of CORDEX South Asia regional climate model (RCM) experiments

Experiment

name

RCM description

Driving GCM

Contributing institute

LMDZ4

(IPSL)

Institut Pierre-Simon Laplace (IPSL) Laboratoire de Me'te' orologie Dynamique Zoomed version 4 (LMDZ4) atmospheric general circulation model (Sabin et al. 2013)

IPSL Coupled Model version 5 (IPSL-CM5-LR; Dufresne et al. 2013)

Centre for Climate Change Research (CCCR), Indian Institute of Tropical Meteorology (IITM), India

CCLM4

(MPI)

COnsortium for Small-scale MOdelling (COSMO) model in CLimate Mode version 4.8 (CCLM; Dobler and Ahrens 2008)

Max Planck Institute for Meteorology, Germany, Earth System Model (MPI-ESM-LR; Giorgetta et al. 2013)

Institute for Atmospheric and Environmental Sciences (IAES), Goethe University, Frankfurt am Main (GUF), Germany

RCA4

(ICHEC)

Rossby Centre regional atmospheric model version 4 (RCA4; Samuelsson et al. 2011)

Irish Centre for High-End Computing (ICHEC), European Consortium ESM (EC-EARTH; Hazeleger et al. 2012)

Rosssy Centre, Swedish Meteorological and Hydrological Institute (SMHI), Sweden

RegCM411

(GFDL)

RegCM445

(GFDL)

The Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climatic Model version 4 (RegCM4; Giorgi et al. 2012)

Geophysical Fluid Dynamics Laboratory, USA, Earth System Model

(GFDL-ESM2 M-LR; Dunne et al. 2012)

CCCR, IITM

REMO

(MPI)

MPI Regional model 2009 (REMO2009; Teichmann et al. 2013)

MPI-ESM-LR (Giorgetta et al. 2013)

Climate Service Center, Hamburg, Germany

and up to 100-year return periods (Mishra et al. 2014). Here, we compare the outputs of the downscaled RCMs to those of the driving AOGCMs over the recent climate. It is important first to generally assess the ability of the CORDEX RCMs to simulate the general characteristics of the Indian climate (e.g. seasonal distribution of temperature and precipitation, and summer monsoon season climatology) and, second, to examine whether the downscaled simulations add value to those by the driving GCMs. Therefore, we focus not only on the main climate statistics, but we inspect also the ability of the RCMs to reproduce the temperature and precipitation seasonal cycle in sub-regions over India. Further, we analyse the changes in seasonal mean 2-m air temperature and precipitation to show the spread in average climate conditions by the middle of the century (2031-2060) for the Indian sub-continent.

 
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