Short Term Climate Variability and Prediction
Project Directors: Dr. C. Gnanaseelan, Scientist F and Dr. Ashwini Kulkarni, Scientist E
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.
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.
Changes in climate extremes over major river basins of IndiaHigh-resolution gridded daily rainfall data (1951-2014) and gridded daily temperature data (1951-2013) are used to examine the temporal changes in the extreme rainfall and temperatures on daily time scales in major river basins of India. Trend analysis is carried out to examine the temporal changes in the frequency, area covered by extreme events and their intensities. Rainfall of 10 cm during summer monsoon (JJAS), maximum temperature of 40°C during summer season (MAM) and minimum temperature of 10°C during the winter season (DJF) were used as the thresholds to define the extreme weather events of rainfall and temperatures. Analysis indicates that during monsoon season, zero rainfall days are increasing in all the river basins except some parts of the Peninsular river basins. River basins located in the central parts of India show significant increase in the area covered by the heavy rainfall episodes and their intensity. Substantial rise in the monthly maximum temperatures is seen in the Krishna, Peninsular and West Coast river basins (Fig. 3). Frequency, area coverage and intensity of hot days during summer season are increasing significantly in the Peninsular river basins, while no substantial change was observed for cold days during winter season in any river basins of the study. [Deshpande N.R., Kothawale D.R., Kulkarni Ashwini, Changes in climate extremes over major river basins of India, International Journal of Climatology, online, February 2016, DOI: 10.1002/joc.4651]
Fig. 3: Annual cycle of monthly maximum temperatures.
Combined influence of remote and local SST forcing on Indian Summer Monsoon Rainfall variabilityThe combined influence of tropical Indian Ocean (TIO) and Pacific Ocean (TPO) sea surface temperature (SST) anomalies on Indian summer monsoon rainfall (ISMR) variability is studied in the context of mid-1970s regime shift. The rainfall pattern on the various stages of monsoon during the developing and decaying summer of El Niño is emphasized. Analysis reveals that ISMR anomalies during El Niño developing summer in epoch-1 (1950–1979) are mainly driven by El Niño forcing throughout the season, whereas TIO SST exhibits only a passive influence (Fig. 4). On the other hand in epoch-2 (1980–2009) ISMR does not show any significant relation with Pacific during the onset phase of monsoon whereas withdrawal phase is strongly influenced by El Niño. Again the eastern Indian Ocean cooling and westward shift in northwest Pacific (NWP) cyclonic circulation during epoch-2 have strong positive influence on the rainfall over the central and eastern India during the matured phase of monsoon. ISMR in the El Niño decaying summer does not show any significant anomalies in epoch-1 as both Pacific and Indian Ocean warming dissipate by the summer. On the other hand in epoch-2 ISMR anomalies are significant and display strong variability throughout the season. In the onset phase of monsoon, central and east India experience strong negative precipitation anomalies due to westward extension of persistent NWP anticyclone (forced by persisting Indian Ocean warming). The persistent TIO warming induces positive precipitation anomalies in the withdrawal phase of monsoon by changing the atmospheric circulation and modulating the water vapour flux. Moisture budget analysis unravels the dominant processes responsible for the differences between the two epochs. The moisture convergence and moisture advection are very weak (strong) over Indian land mass during epoch-1 (epoch-2) in El Niño decaying summer. The changing moisture availability and convergence play important role in explaining the weakening of ENSO monsoon relation in the recent years. The local TIO SST forcing and NWP circulation are prominent forcing factors for the interannual variability of ISMR during epoch-2. [Chakravorty, S., Gnanaseelan, C. and Pillai, P.A. (2016) Climate Dynamics 47: 2817. doi:10.1007/s00382-016-2999-5]
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
Indian summer monsoon rainfall variability in response to differences in the decay phase of El NiñoIn general the Indian summer monsoon (ISM) rainfall is near normal or excess during the El Niño decay phase. Nevertheless the impact of large variations in decaying El Niño on the ISM rainfall and circulation is not systematically examined. Based on the timing of El Niño decay with respect to boreal summer season, El Niño decay phases are classified into three types in this study using 142 years of Sea Surface Temperature (SST) data, which are as follows: (1) early-decay (ED; decay during spring), (2) mid-summer decay (MD; decay by mid-summer) and (3) no-decay (ND; no decay in summer). It is observed that ISM rainfall is above normal/excess during ED years, normal during MD years and below normal/deficit in ND years, suggesting that the differences in El Niño decay phase display profound impact on the ISM rainfall. Tropical Indian Ocean (TIO) SST warming, induced by El Niño, decays rapidly before the second half of the monsoon season (August and September) in ED years, but persists up to the end of the season in MD years, whereas TIO warming maintained up to winter in ND case. Analysis reveals the existence of strong sub-seasonal ISM rainfall variations in the summer following El Niño years. During ED years, strong negative SST anomalies develop over the equatorial central-eastern Pacific by June and are apparent throughout the summer season accompanied by anomalous moisture divergence and high sea level pressure (SLP). The associated moisture convergence and low SLP over ISM region favour excess rainfall (mainly from July onwards). This circulation and rainfall anomalies are highly influenced by warm TIO SST and Pacific La Niña conditions in ED years. Convergence of southwesterlies from Arabian Sea and northeasterlies from Bay of Bengal leads to positive rainfall over most part of the Indian subcontinent from August onwards in MD years. ND years are characterized by negative rainfall anomaly spatial pattern and weaker circulation over India throughout the summer season, which are mainly due to persisting El Niño related warm SST anomalies over the Pacific. Schematic diagram (Fig. 5) illustrates the factors responsible for changes in ISM rainfall during ED, MD and ND years. During ED years the seasonal rainfall over the Indian subcontinent is positive due to (1) establishment of La Nina like conditions over the central and eastern Pacific corroborated by enhanced Walker circulation with anomalous convection over the ISM and the Indo-western Pacific region, (2) anomalous TIO warming enhanced convection over ISM region, (3) increased cross equatorial flow and eastward shift in NW Pacific anticyclone with the progress of summer monsoon supports ISM rainfall and (4) warm TT anomalies over the Indian subcontinent and enhanced low level convergence further uphold positive rainfall anomalies over ISM region. [Chowdary, J.S., Harsha, H.S., Gnanaseelan, C., Srinivas, G., Parekh, A., Pillai, P. and Naidu, C.V., (2016) Climate Dynamics. doi:10.1007/s00382-016-3233-1]
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)
Associates : Dr. S. G. Narkhedkar, Sci-D and Dr. Sreenivas Pentakota, Sci-D