Earth's climate variability is due to both inherent fluctuations (natural variability) within the climate system and through external forcing. These fluctuations can occur on a variety of time scales, from seasonal and annual, to longer time scales. Internal processes cause variability on all time scales. Atmospheric internal processes operate on time scales ranging from virtually instantaneous to years. On the other hand, the ocean and the large ice sheets over land cause climate variability on much longer time scales. In addition, the internal variability is also generated due to the complex coupled interactions between various climate components, such as the El Niño Southern Oscillation (ENSO).
We propose to understand the impact of changing global atmospheric conditions on the Asian summer monsoon circulation in general and Indian monsoon circulation and associated rainfall in particular. Predictability of Indian summer monsoon is limited by the ‘climate noise’ or ‘internal’ interannual variability (IAV), generated in the region. In order to improve the prediction skill, it is important to understand the physical processes responsible for the ‘climate noise’. It is proposed to unravel the physical processes responsible for ‘internal’ IAV of monsoon
In order to improve the forecast models achieving the limit on potential predictability of seasonal mean monsoon, it is important to isolate and quantify the contribution from different climate drivers like ENSO, IOD, PDO, AMO, etc. in relation to the ‘internal’ IAV of the monsoon. Using available observations and high resolution coupled-ocean-atmosphere models, we attempt to isolate the contribution of various climate drivers of IAV of the Indian monsoon. The findings of such studies will guide us to develop or identify better models for predicting monsoon climate.
To examine the impact of changing climate on short term climate variability is a key scientific problem. We examine the variability of Indian monsoon on the intra-seasonal, sub-seasonal, inter-annual to decadal time scales to address such issues. In view of the large spatial variability of Indian monsoon the efforts are being made to predict the summer monsoon rainfall on smaller spatial scales such as homogeneous regions, sub-divisions etc.
- Development of web portal RAINFO and TEMPINFO: A web portal ‘RAINFO and TEMPINFO’ is being developed which will provide all the information on rainfall and temperature variability over a region in one click. The products are being prepared on the spatial scale of sub-divisions, cities and districts and on the time scale of daily, monthly and seasonal. The products are so designed as to be used by the general public as well as farmers. These products would be very useful as inputs to impact assessment groups.
Prediction of summer monsoon rainfall over India and its homogeneous regions
The coherent regions for various meteorological parameters (sea level pressure, temperature, geopotential height and zonal wind anomalies) at the surface, 850, 500 and 200 hPa levels in pre-monsoon months and seasons have been identified by applying the shared nearest neighbour (SNN) algorithm. The time series were constructed by averaging the parameters over the respective clusters. The relationship between these time series and the summer monsoon rainfall over India and over well-defined homogeneous regions over India, (northwest India, central northeast India, northeast India, west central India and peninsular India) was examined during the positive and negative phases of effective strength index (ESI) tendency using multiple regression. Fig. 1 depicts the observed and estimated summer monsoon rainfall over India for 1951-2012. Root mean square error (RMSE) on the domain 1951-2012 is 4.25, whereas CC between the observed and estimated rainfall departure is 0.90.
All estimated rainfall departure values in deficit/excess years are shown in Fig. 2 and it is observed that extreme rainfall departures are qualitatively well predicted. The unprecedented droughts in 2002 and 2009, where all models failed to predict, are quantitatively well captured by this strategy. [Kakade S., Kulkarni Ashwini, Prediction of summer monsoon rainfall over India and its homogeneous regions, Meteorological Applications, 23, January 2016, DOI:10.1002/met.1524]
Fig. 1: Estimated (white column) and observed (black column) summer monsoon rainfall percentage departures over All India (top panel) and subsequently followed over North west India, West central India, Central north east India, North east India and Peninsular India respectively; using separate equations depending upon positive or negative phase of ESI-tendency for 1951-2012.
Fig. 2: Observed (black) and estimated (red) all-India summer monsoon rainfall departures (%) during (a) deficient and (b) excess monsoon years during 1951-2012.
Fig. 3: Annual cycle of monthly maximum temperatures.
Fig. 4: Lead lag correlation of NDJ(0/1) Niño 3.4 index with JJAS(0) rainfall anomalies (a, d), JJAS(1) rainfall anomalies (b, e) and simultaneous correlation of NIO SST anomalies with JJAS rainfall anomalies (c, f) for epoch-1 and epoch-2. The boxes in (f) are named (A) Southern box (74°E–78.5°E, 14°N–21°N), (B) Northern box (79°E–88.5°E, 20°N–25°N). The shaded region are significant above 95 % level
Fig. 5: Schematic diagram that shows factors responsible for changes in ISM rainfall during (a) ED seasonal mean, (b) June and July in MD years, (c) August and September in MD years and (d) ND seasonal mean. Green arrow represents low level circulation and blue arrows represent Walker circulation. Rectangular box dark red (light red) represents high (low) troposphere temperature (H-TT and L-TT). Thickness or brightness of color represents the intensity. ACC is anticyclone circulation, CC is cyclonic circulation and CEF is cross equatorial flow
Monsoon variability, the 2015 Marathwada drought and rainfed agriculture
The analysis of 145 years of summer monsoon rainfall over Marathwada shows that the two successive droughts of 2014-2015 and also the good monsoon of 2016 are not the effect of climate change and are well within the limits of monsoon variability over this region. It has also been shown that the Marathwada rainfall has strong relationship with all-India summer monsoon rainfall as well as ENSO. (Ashwini Kulkarni, Sulochana Gadgil, Savita Patwardhan , CURRENT SCIENCE, VOL. 111, NO. 7, 10 OCTOBER 2016)
Project: Climate Variability and Data Assimilation Research
Project Director: Dr. C. Gnanaseelan, Scientist-F
Dr. C. Gnanaseelan
Phone No - +91-(0)20-25904271
Dr. Anant Parekh
Air-sea interaction, IO variability
Phone No - +91-(0)20-25904264
Dr. J.S. Chowdary
Air-sea interactions, Monsoon Variability and Predictability
Phone No - +91-(0)20-25904273
Shri. Prem Singh
Ocean Modelling and Simulation Studies
Phone No - +91-(0)20-25904279
Smt. J. S. Deepa
Phone No - +91-(0)20-25904230
Ku Rashmi Arun Kakatkar
Phone No - +91-(0)20-25904239
Dr. Sreenivas Pentakota
Phone No - +91-(0)20-25904303
Project: Climate Variability, Predictability and Applications
Project Director: Dr. Ashwini Kulkarni, Scientist-E,
Dr. Ashwini Kulkarni
Monsoon Variability and Teleconnections
Phone No - +91-(0)20-25904541
Monsoon Variability and Teleconnections
Phone No - +91-(0)20-25904444
Dr. Nayana Deshpande
Phone No - +91-(0)20-25904540
Shri. S.D. Bansod
Monsoon Variability and Teleconnections
Phone No - +91-(0)20-25904533
Phone No - +91-(0)20-25904358
Dr. Ramesh Kumar Yadav
Phone No - +91-(0)20-25904353
Dr. S.B. Kakade
Monsoon Variability and Prediction
Phone No - +91-(0)20-25904229
Phone No - +91-(0)20-25904844
Shri. S.P. Ghanekar
Monsoon variability and predictability
Phone No - +91-(0)20-25904228
Phone No - +91-(0)20-25904344
Dr. S. G. Narkhedkar
Objective Analysis (Satellite data in weather forecasting)
Phone No - +91-(0)20-25904238