P r o j e c t : S h o r t T e r m C l i m a t e V a r i a b i l i t y a n d P r e d i c t i o n S u b - P r o j e c t : C l i m a t e V a r i a b i l i t y , P r e d i c t a b i l i t y a n d A p p l i c a t i o n s

Objectives

To understand mechanisms responsible for Indian monsoon variability on intraseasonal, interannual and inter-decadal time scale which can be achieved through diagnostic studies as well as modelling and simulation studies.

In Particular

To quantify the various aspects of climate change and variability on on various space-time scales related to the southwest and northeast monsoons.

To continue ongoing efforts in identifying regional and global climate drivers for monsoon interannual variability and to identify useful predictors.

To examine the teleconnections of Indian monsoon rainfall on intra-seasonal , interannual to decadal scale.

To develop forecast models for relevant climatic parameters over India on different spatio-temporal scales using empirical/statistical methods as well as downscaling of seasonal forecasts from General Circulation Models (GCMs) with high-resolution regional models.

To develop data products useful for various sectors.

Research Achievements

The basic aim of this project is to study variability of Indian summer monsoon rainfall and its pre-

diction using statistical techniques. Research has been carried out on these objectives and has been

published in reputed scientific journals.

Key Research Findings

(a) Seasonal prediction of summer monsoon rainfall over cluster regions of India (Kakade and

Kulkarni, JESS)

Shared Nearest Neighbour (SNN) cluster algorithm has been applied to seasonal (June–September)

rainfall departures over 30 sub-divisions of India to identify the contiguous homogeneous cluster re-

gions over India. Five cluster regions are identified. Rainfall departure series for these cluster re-

gions are prepared by area weighted average rainfall departures over respective sub-divisions in

each cluster. In order to consider the combined effect of North Atlantic Oscillation (NAO) and

Southern Oscillation (SO), an index called effective strength index (ESI) has been defined. It has

been observed that the circulation is drastically different in positive and negative phases of ESI-

tendency from January to April. Hence, for each phase of ESI-tendency (positive and negative),

separate prediction models have been developed for predicting summer monsoon rainfall over iden-

tified clusters.

It is observed that RMSE is less than the standard devia-

tion of summer monsoon rainfall departures over all

these rain-cluster regions of India. The anomaly correla-

tions are also more than 0.80 for all regions. Chi-square

statistics is calculated for the contingency table showing

qualitative agreement between observed and estimated

rainfall departures. It also suggests significant relation-

ship in qualitative prediction of monsoon rainfall over

these rain-cluster regions. The performance of these

models in predicting extreme summer monsoons of each

RC region of India is also examined. Figure-1 shows

observed and estimated rainfall departures (%) for ex-

treme monsoons over all RC regions of India. It can be

seen that this method is able to predict almost all ex-

treme monsoon rainfall years of five RC regions of In-

dia reasonably well.

(b) Mid-latitude Rossby wave modulation of the Indian summer monsoon (Yadav, QJRMS)

The dominant mode of July-August seasonal variability of Indian summer monsoon rainfall ob-

tained by performing an EOF analysis over India, excluding north-east India, for the period 1979-

2014 (Figure-2a) is highly correlated to the quasi-stationary mid-latitude Eurasian Rossby wave

train, known as the „Silk Road‟ pattern (Figure-2b). A Rossby wave train features successive upper-

tropospheric negative and positive geopotential height anomalies over the north-east of the Mediter-

ranean and north-west of India, respectively. The negative height anomaly decreases the sum of the

vertical integral of potential and internal energy, while this increases in the region of positive

geopotential height anomalies.

This is associated with middle to lower tro-

pospheric mid-latitude descent and ascent

anomalies at 40°E and 50°E, which cause

negative surface temperature anomalies over

Syria, Iraq, Jordan and Saudi Arabia, and a

positive surface temperature anomaly over

Iran, respectively (Figure-2c). The negative

and positive surface temperature anomalies

are associated with positive and negative sur-

face pressure anomalies over Saudi Arabia

and Iran, respectively, which generate

anomalous northwesterly winds over eastern

Saudi Arabia and Persian Gulf (Figure-2d).

Further downstream this anomalous north-

westerly wind combines with the clima-

tological background cross-equatorial south-

westerly flow in the Arabian Sea. The flow

further converges towards central and north-

west India. The flow also acts as a source of

moisture supply from the warm Arabian Sea

and Persian Gulf for the deep convection

over Western Ghats, north-west and central

India and hence reinforces active Indian

summer monsoon conditions (Figure-2d). JA

seasonal trend analysis for (e) 2mT and (f) MSLP

for the period 1979 – 2014.

(c) Changes in seasonality index over India (Nandargi et al, IJCAR)

The variation in seasonality in rainfall over the Indian

region is examined using monthly rainfall values for the

period 1951 to 2015 of 34 meteorological sub-divisions

excluding two Sea Islands. A seasonality index (SI) of a

monthly rainfall is computed on monthly, seasonal (June

to September) and annual scale. It is observed that sea-

sonality index of rainfall of 34 sub-divisions for all

months are in the range 0.37 (Jammu & Kashmir) to

1.56 (Saurashtra Kutch & Diu).

The results show that rainfall is markedly seasonal with a long dry season and most rainfall in

less than three months. Most of the rainfall occurs in monsoon months. The seasonality index

for monsoon season is computed (Figure-3) and it varies from 0.19 (Nagaland, Manipur,

Mizoram,Tripura) to 0.59 (Saurashtra Kutch & Diu, Rajasthan, Punjab, Haryana and

Chandigarh, Delhi) resulting in rainfall spread throughout the year, but with a definite wetter sea-

son. Trends of this index through the 65-year period are identified and indicate that seasonality is

increasing in Uttaranchal, Himachal Pradesh, Gujarat Region-Dadra & Nagar Haveli; Saurashtra-

Kutch & Diu, Konkan & Goa, Madhya Maharashtra, Marathwada, Chattisgarh, Tamilnadu &

Pondicherry. The analysis clearly showed the climate change impact on northwest sub-divisions of

the country showing increase in SI values

leading to dryness during the monsoon season. The negative trend in SI values was observed in Sub

-Himalayan West Bengal, Haryana-Delhi-Chandigarh, Punjab, Jammu & Kashmir, West and east

Rajasthan, coastal Andhra Pradesh showing increasing wetness for an already wet months although

rainfall occurs in a very short period of just a month or two.

(d) Western Himalaya Trees Growth Study and its Association with Droughts in India: A Case

Study (Somaru Ram et al, Global J of Bot Sci)

Tree ring-width index chronology based on a well replicated tree core samples from the western

Himalaya showed significant positive relationship with standardized precipitation potential

evapotranspiration (SPEI) and standardized soil index (SSI) during summer season (April-June).

However, SSI that describes the drought index over the region is found more compatible with tree

growth variations than SPEI in controlling the annual ring-width patterns. It shows high temporal

stability with trees growth compared to SPEI. The results showed that the SSI which is an indicator

of drought index has strong localized effects on patterns of annual tree growth and forest dynamic,

working as booster in limiting of trees growth over western Himalaya.

(e) Recent trends in tropospheric temperature over India during the period 1971–2015

(Kothawale and Singh, Earth and Space Science)

All-India mean annual temperature shows increasing

trend from surface to 500 hPa and little negative or no

trend at 200 and 150 hPa levels. The trends are statisti-

cally significant at surface, 700 hPa, and 500 hPa levels

of 0.2°C, 0.19°C, and 0.12°C/decade, respectively. On

the seasonal scales, winter temperature shows significant

increasing trend from surface to 500 hPa levels, and

highest trend is observed at 700 hPa, while nebulous or

no trend at 200 and 150 hPa levels. It is worth to note

that the surface temperature increases significantly over

all the above-mentioned regions during all the seasons.

Climate Applications - WEB PORTAL : CLIMINFO ( Kulkarni, Patwardhan, Sapre, Jagtap)

Develop weather and climate gridded data products on various space-time scales

Communicate with policy makers and government officials

Develop weather and climate data library for agricultural, health and hydrological applications

Provides all the information on rainfall and temperature variability over a region in one click.

The spatial scale : sub-divisions, cities and districts

The time scale : daily , pentad, fortnight, monthly and seasonal

The products are so designed as to be used by general public as well as farmers.

District level : nakshatras (fortnight) ; pentad

Products : Empirical Distribution ; Standardized time series ; rainfall limits , extremes, fre-

quency/intensity of wet/dry spells

Publications

1. Kulkarni A; 2017; Homogeneous clusters over India using probability density function of daily rainfall,

Theoretical and Applied Climatology, 2017, 129 , 633-643.

2. Kakade S.B. & Kulkarni A., 2017, Seasonal prediction of summer monsoon rainfall over cluster re-

gions of India, J Earth Syst Sci, 126: 34. doi:10.1007/s12040-017-0811-5.

3. Kakade S.B. & Kulkarni A., 2017, Association between Arctic and Indian Summer Monsoon Rain-

fall, J Climatol Weather Forecasting 5:207. doi:10.4172/2332-2594.1000208.

4. Kothawale D. R. and Singh H. N., 2017, Recent trends in tropospheric temperature over India

during the period 1971-2015, AGU PUBLICATIONS , Earth and Space Science, doi;

10.1002/2016EA000246.

5. Kothawale D. R. and Rajeevan M.: Monthly, Seasonal and Annual Rainfall Time series for All-India,

Homogeneous Regions and Meteorological Subdivisions : 1871-2016 IITM Research Report No. RR-

138, 169pp

6. Ramesh Kumar Yadav; 2017; Mid-latitude Rossby wave modulation of the Indian summer monsoon;

Quarterly Journal of the Royal Meteorological Society (QJRMS); 143: 2260-2271; DOI: 10.1002/

qj.3083.

7. Maheshwar Pradhan, Ramesh Kumar Yadav, A. Ramu Dandi, Ankur Srivastava, M. K. Phani and

Suryachandra A. Rao; 2017; Shift in MONSOON-SST teleconnections in the tropical Indian Ocean and

ENSEMBLES climate models‟ fidelity in its simulation; International Journal of Climatology; 37: 2280

-2294; DOI: 10.1002/joc.4841.

8. Ramesh Kumar Yadav and Bhupendra Bahadur Singh; 2017; North Equatorial Indian Ocean Convec-

tion and Indian Summer Monsoon June Progression: a Case Study of 2013 and 2014; Pure and Applied

Geophysics; 174 (2); 477-489; DOI: 10.1007/s00024-016-1341-9.

9. Ramesh Kumar Yadav; 2017; On the relationship between east equatorial Atlantic SST and ISM

through Eurasian wave; Climate Dynamics; 48 (1), 281-295; DOI: 10.1007/s00382-016-3074-y.

10. S.S.Nandargi and S.S.Mahto and S.Ram, 2017; Changes in Seasonality Index over sub-divisions of In-

dia during 1951-2015, by Open Atmospheric Science Journal, US, Vol.11, pp.105-120

[DOI: 10.2174/1874282301711010105].

11. Somaru Ram, H. P. Borgaonkar and S. S. Nandargi, 2017; Western Himalaya Trees Growth Study and

its Association with Droughts in INDIA: A Case Study, Global Journal of Botanical Science, Vol.5,

No.1, pp 33-38.

12. S.S.Nandargi and K. Aman, Precipitation concentration changes over India during 1951-2014, Inter-

national J. of Scientific Research and Essays, Global Impact Factor(2015) 0.564 (Accepted)

13. S.S.Nandargi and K. Aman, Computation of the Standardized Precipitation Index (SPI) for assessing

droughts over India, International Journal of Current Advanced Research, SJIF Scientific Journal

Impact Factor 2016: 5.995 (Accepted).

Book Chapters

In “Observed Climate Variability and Change over the Indian Region” , Eds Rajeevan, Nayak, 2017

1. AK Shivastava, DR Kotahwale, M Rajeevan : Variability and Long-Term Changes in Surface Air Tem

peratures Over the Indian Subcontinent, 17-36pp

2. DS Pai, P Guhathakurta, A Kulkarni, M Rajeevan : Variability of Meteorological Droughts Over India ,

73-88 pp

Figure-1: Observed (black) and

estimated (red) summer monsoon

rainfall departures (%) for deficit

(left panels) and excess (right

panels) over five rain clusters

over the period 1951-2012.

Figure-3: Spatial distributtion of

Jun-Sept seasonality Index over

sub-divisions.

Figure-4: Annual and seasonal

trend profiles at different isobaric

levels (levels 1 = surface, 2 = 850,

3 = 700, 4 = 500, 5 = 200, 6 = 150

hPa; significant trend is shown by

first letter of the season, in red

color).

Figure-2: a) The first leading EOF of JA rainfall

for the period 1979 – 2014. The shading indicates

the rainfall anomalies. Simultaneous CC of PC1R

with gridded (b)250 hPa GPH (colour shade) and

zonal wind (black contours), and 500 hPa vertical

velocity (green contours), (c) 2mT, and (d) MSLP

(colour shade). Contours in(b), (c) and (d) for ab-

solute CC larger than 0.33 are 95% statistical sig-

nificant. The black arrows in (d) represent the si-

multaneous regression of 850 hPa wind ontoPC1R

(m s−1). JA seasonal trend analysis for (e) 2mT

and (f) MSLP for the period 1979 – 2014.

(Team members: Ashwini Kulkarni, Savita Patwardhan, Nayana Deshpande, SD Bansod, RK Yadav, SB Kakade, SP Ghanekar, MD Chipade, Shobha Nandargi, HN Singh, Associate : SG Narkhedkar)