Dynamics of Indian summer monsoon in different phases
by Smrutishree Lenka, Krushna Chandra Gouda, Rani Devi & C. M. Joseph
Abstract
Better understanding on the phases of Indian summer monsoon i.e., onset, progress and withdrawal helps in the decision making and proper management. In this paper, date of onset of monsoon (DOM) and date of withdrawal of monsoon (DWM) is estimated using the long-term daily rainfall data observed by the India Meteorological Department (IMD) and cumulative rainfall anomaly concept, as the onset/withdrawal represents a signature of rainfall transition. The results are promising with a better match between the estimated DOM and IMD announced DOM over Kerala. The length of monsoon season variability is also analysed. The concept of onset at grid point (OGP) in India is also evaluated and progress of monsoon is monitored by considering the latitudinal variation, persistent & strength of rainfall in the continental India. The zonal classification of the spread of monsoon in terms of the intensity and time (duration) is presented. Zone wise pentad analysis of onset clearly signifies the progress of monsoon and its dependency on various physical factors like SST, OLR, TTG, ITCZ, Energy budget and wind circulation. The impact of ENSO on the OGP is brought out, which clearly shows that DOM, DWM and progress is almost influenced by the largescale processes. The intensity–duration–frequency analysis quantified the actual ISM length, seasonal rainfall and rainy days at all locations in India. This study will help modellers in better understanding of onset and progress of monsoon dynamics in India and can be integrated with NWP for the accurate and advance prediction of DOM and the progress.
Source: https://doi.org/10.1007/s00382-023-06925-1
Dynamical influence of MJO phases on the onset of Indian monsoon
by S Lenka, Krushna Chandra Gouda, Rani Devi and C M Joseph
Abstract
There is a need to understand the onset of monsoon dynamics as the date of onset of monsoon (DOM) is an important parameter in framing all the policy for the imminent season like crop choice, sowing schedule, disaster management, power distribution etc It is observed that the interannual variability of the DOM in India is about 7–8 days, making it more challenge to predict this at long lead. The MJO phases are linked with the different convection centres and hence, influences the global circulation process and the rainfall. In this paper the dynamical influence of the different phases of MJO are being quantified on DOM and its progress in continental India by using the multi-source atmospheric and oceanic parameters like wind structure, outgoing longwave radiation (OLR), sea surface temperature (SST). The linkage of the active and inactive phases of MJO along with the favourable conditions for DOM is obtained by using the pentad analysis of associated parameters in different clusters for both the wet and dry phases of MJO along with the strength for the period 1980–2018. Also the dynamics are studied for the early, normal and late onset years separately to understand the relation better. It is inferred that the wet (dry) phase leads to early (late) monsoon onset over Kerala (MOK) in India. To address the progress of monsoon the DOM in Rajasthan (MOR) is considered and the rainfall anomalies during MOK-MOR period are linked to the MJO phases. It is inferred that the wet MJO phase with negative OLR anomaly triggers the fast progress of monsoon over India. This understanding will surely help operational researchers and the NWP modellers for improving the methodologies for the advanced and accurate prediction of DOM.
Source: https://iopscience.iop.org/article/10.1088/2515-7620/acde3a
Does increasing the spatial resolution in dynamical downscaling impact climate change projection of Indian summer monsoon, population and GDP?
by C B Jayasankar, K Rajendran and Sajani Surendran
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) program. Quantitative estimations show that majority of CORDEX-SA models exhibit large dry bias (< − 4 mm/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 (~ 1 mm/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 ± 0.9 mm/day and CCSM4-WRF projects 0.67 mm/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.
Do seasonal forecasts of Indian summer monsoon rainfall show better skill with February initial conditions?
by K Rajendran, Sajani Surendran, Stella Jes Varghese and A Chakraborty
Prediction for Indian summer monsoon rainfall (ISMR) is generated by integrating model from initial conditions (ICs) of weather at some time prior to season. We examine the factors responsible for the widely reported highest ISMR forecast skill for February ICs in climate forecast system (CFSv2) model. Skill for February ICs is highest only based on correlation between observed and predicted year-to-year variation of ISMR, whereas other skill scores indicate highest skill for late-April/early-May ICs having shorter yet useful forecast lead-time. Higher correlation for February ICs arises from correct forecasting of 1983 ISMR excess, which is however due to wrong forecast of La Niña and correlation drops to lower value than that for late-April/early-May ICs if 1983 is excluded. Forecast skill for sea surface temperature variation over equatorial central Pacific (ENSO) in Boreal summer is lowest for February ICs indicating role of dynamical drift induced by long forecast lead-time. Model deficiencies such as oversensitivity of ISMR to ENSO and unrealistic ENSO influence on variation of convection over equatorial Indian Ocean (EQUINOO) lead to errors in ISMR forecasting. In CFSv2, ISMR is mostly decided by ENSO whereas in observation it is influenced by ENSO and EQUINOO independently.
Source: https://www.currentscience.ac.in/Volumes/120/12/1863.pdf
Page 23 of 33
