The OCEANS and Indian Monsoon. Climate Variability

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1 The OCEANS and Indian Monsoon Weather, Climate and the OCEANS Climate Variability B. N. Goswami Indian Institute of Tropical Meteorology, Pune 3 rd OSICON, Nov, 2013, IITM, Pune

2 The Sun-Earth System

3 Climate : Incoming solar heat radiated loss of heat from earth-atmos. Broadly three factors influence

4 Net heat flux at the top of the atmosphere is Positive over Tropics and Negative over the polar region

5 Top-of-atmosphere net (solar minus Earth longwave) radiative flux 100 Wm

6 Atmospheric general circulation: moving energy from near the equator toward the poles

7 How is equilibrium of temperature maintained? Cold polar region-high pressure Hot tropics-low pressure Circulation is setup due to pressure gradients under the Coriolis force Transports heat from tropics to the polar region Large north-south temperature gradient also gives rise to some unstable planetary waves. They also transport heat pole ward. From an initial ONE cell, a THREE cell meridional structure emerges Dynamic equilibrium is maintained. Cold polar region-high pressure

8 F TA T Atmospheric transport A Required Heat Transport T O / Oceanic transport r cos F The net heat balance at the TOA also indicates that, for the earth s climate to be in equilibrium, there must be mechanisms in place that continously transports heat from equatorial regions to the polar regions. TA d

9 Transport of energy required by observed heat balance

10

11 The thermohaline circulation of the world ocean

12 External forcing, namely solar forcing has long term variations, In time scales of solar forcing has oscillations in 21 thousand years 43 thousand years (Milankovich cycles) 100 thousand years But no significant short tern variations! However, there are significant short-term climate variability Ex. ENSO approx 4 years : TBO -- aprox 2 years PDO years & years AMO and Indian monsoon aprox 60 years What is responsible for these short term climate variability?

13 Ocean and Atmosphere interact to produce Climate Variability on a variety of time scales! ENSO IODM

14 To Understand these interactions, we must understand; How Ocean forces Atmospheric motion How Atmosphere forces Changes in SST distribution in Ocean

15 How does Ocean forces Atmospheric Motion? Atmosphere feels the Ocean through SST SST modulates moisture supply through evaporation Atmosphere Heating forces surface winds Moisture supply modulates Atmos. Heating Distr.

16 How does Atmosphere forces Changes in SST? Atmospheric surface winds forces Ocean currents Upper ocean currents redistribute water and influence SST distr. Atmospheric convection and winds influence Qnet at surface

17 How does atmosphere and ocean interact in the tropics? Changes in SST T s Changes in evaporation E s Surface stress drives ocean currents Changes in atmospheric heating Q Changes in atmospheric circulation C (surface stress)

18 Where is this Air-sea Interactions most Effective? TROPICS For this, Small Change in SST large change in moisture availability High mean SST in tropics makes it possible. Low SST in middle lat makes it less effective Available moisture should result in latent heat release Conditionally unstable thermal structure makes it possible in the tropics

19 Another Example Ocean-Atmosphere Interaction also produce one Multi-decadal Mode of Indian Monsoon Variability!

20 JJAS All India Rainfall (AIR) Interannual Variability Mean : 86 cm; S.D. : 8.5 cm Climatological mean JJAS Precipitation Decreasing trend of AIR between No long term increasing trend A year multi-decadal variability? Decreasing trend in the last 5 decades!

21 A Big Question! What is responsible for the recent decreasing trend ISMR? Is this trend forced by anthropogenic forces (GHG, aerosols etc) or is part of a natural multi-decadal oscillation?

22 Bollasina, Ming and Ramaswamy, 2011, Science, 334, 502 Claims that this trend is forced by anthropogenic aerosols based on A series of experiments with a coupled model with active aerosols!

23 However, the model produces strong decreasing trend in south China where observed trend is increasing! Hence, the model trend over Indian region could not be trusted!

24 Goswami et al. 2006, GRL, vol.33, L02706 Atlantic mutlidecadal variability (AMO) and Indian monsoon SST

25 + (-) AMO phases of NA SST Strong (weak) Indian summer monsoon Higher frequency of Strong + (-) NAO events Persistent increase (decrease) Meridional gradient of TT over monsoon region. Changes in the Jet stream and storm tracks Persistent + (-) TT anomaly over N. India and S. Eurasia How does AMO modulates South Asian Monsoon?

26 11-year running mean of AIR and an AMO index

27 Trend in JJAS SST Interdecadal mode of AIR and Eq. IO SST extracted using singular spectrum analysis (SSA) EQIO SST is always out of phase with AIR, strongly so in recent decades.

28 TISM : Integral of positive gradient of TT LRS Onset Withdraw Scatter plot of LRS AIR and TISM and resultant Correlation. Xavier, Marzin and Goswami, 2007, QJRMS

29 EQIO SST can also influence AIR through TT gradient Trend of TT in the Northern box Trend of TT in the South box (black) and trend of EQIO SSTA Trend of TT in the Northern box (solid) compared to that in the south box (dashed)

30 Increasing Trend of EQIO SST Strong Increasing Trend of EQIO TT Decreasing Trend of TISM AIR Decreasing Trend of TT, as TT in the northern Indian box has weaker increasing trend Increasing trend NA SST in recent decades increasing TT in the north box Increasing trend of Eastern Equatorial Pacific SST (ENSO) decreasing trend of TT in the north box

31 AGCM experiments: CTL AGCM forced by global SST with increasing trend, ( )

32 AGCM experiments forced by SST trends globally and trends removed from IO SST shows that, The trend in the NB is weak while the increasing trend in the SB is much stronger, as in the observations. Thus, the large scale air-sea interactions make

33 What is responsible for increasing trend of IO SSTA? IOSST increase weakens ISM further increases IOSST weak ISM Thus, the recent increasing trend of IOSST is result of air-sea interaction! Swapna, Krishnan and Wallace, 2013, Clim.

34 Based on these analyses, we propose that the recent decreasing trend of AIR is due to an airsea interaction involving IO SST, Pacific SST as well as NA SST. Could the recent decreasing trend of AIR be part a natural mode of variability? Is there a preferred periodicity of the Multidecadal Variability of the Indian Monsoon?

35 India Tree-ring Speleothem δ 18 O Thailand Tree-ring India Tree-ring : (A.D ); Palaeo-3, Borgaonkar et al Thailand Tree-ring (A. D ); Clim.Dyn, Buckley et al Speleothem δ 18 O: (A.D ); GRL, Sinha et al, 2011 (Dandak and Jhumar caves)

36 Asian Monsoon Proxies A - RWI-India ( ) B - δ18o-ci ( ) C - RWI-Thailand ( ) With Borgaonkar, Kriplani and Preethi, Unpublished! Red line : 21-year moving average

37 Multi-decadal Periodicities of Asian Monsoon RWI-India δ18o-ci RWI-Thailand

38 Can this Oscillation be considered a Mode of variability? Empirical Mode Decomposition (EMD) Decomposes the data time series into finite number of Intrinsic Mode Functions (IMFs) each associated with a unique frequency The EMD is implemented through following steps (sifting process) 1. Identify all extrema of x(t) 2. Interpolate the local maxima to form an upper envelope u(x) 3. Interpolate the local minima to form an lower envelope l(x) 4. Calculate the mean envelope: m(t) = (u(x)+l(x))/2 5. Extract the mean from the signal: h(t)=x(t)-m(t) 6. Check whether h(t) satisfies the IMF stoppage criteria, YES: h(t) is an IMF, stop sifting NO: let x(t)=h(t), keep sifting An IMF has the following properties: 1. In the whole data set, the number of extrema and the number of zero-crossings must be either equal or differ at most by one; 2. At any time point, the mean value of the upper envelope (defined by the local Maxima) and the lower envelope (defined by the local minima) must be zero. 3. The EMD method avoids spurious harmonics and the components of the EMD are usually physically meaningful. Ref : Huang et al. (1998)

39 RWI-India δ18o-ci RWI-Thailand Intrinsic Mode Functions (IMFs) of Asian Monsoon Proxies

40 RWI-India δ18o-ci RWI-Thailand Periodicities of IMFs of Asian Monsoon Proxies Blue line : 95% Confidence level Green line : 5% Confidence level

41 Staistical Significance of IMFs RWI-India δ18o-ci Fig. The spread function. The groups of the dots from upper left to the lower right are the energy density as a function of the spectrum weighted mean period of IMFs 1-9 for 1024 samples with 1024 data points. The black solid line is the theoretical line. Black dots correspond for the pairs of averaged mean energy density and averaged extrema-counting mean period. Dashed lines represent first and 95 th percentiles. Wu and Huang (2005). RWI-Thailand Blue line : 95% Confidence level Green line : 5% Confidence level

42 AMO ENSO PDO Intrinsic Mode Functions (IMFs) Global SST Proxies of AMO, ENSO and PDO Mann et al. 2009, Science

43 AMO ENSO PDO Periodicities ofof IMFs of Global SST Proxies AMO ENSO PDO Staistical Significance of IMFs Blue line : 95% Confidence level Green line : 5% Confidence level

44 IMF 5 of RWI-India and IMF 2 of AMO Coherence between Multi-decadal Mode of Asian Monsoon and Global SSTs IMF 5 of RWI-India and IMF 3 of ENSO IMF 5 of RWI-India and IMF 2 of PDO

45 HadGEM2-AO MPI-ESM GFDL-CM3 Simulation of Indian monsoon rainfall (JJAS) by three coupled climate model participating in CMIP5 under two RCP scenarios each.

46 HadGEM2-AO MPI-ESM GFDL-CM3 Wavelet spectra detrended Indian monsoon rainfall index for the three coupled climate models for (top) natural + RCP4.5 and (bottom) natural + RCP8.5

47 HadGEM2-AO MPI-ESM GFDL-CM3 Wavelet spectra detrended AMO index for the three coupled climate models for (top) natural + RCP4.5 and (bottom) natural + RCP8.5

48 The Good News The weakening trend of the Indian monsoon during the past five decades is driven by the warming trend of the equatorial Indian Ocean (EQIO) SST. The discovery that Indian summer monsoon has a year multi-decadal Mode of variability as an integral part of a global coupled ocean-atmosphere year multi-decadal Mode of variability, indicates that the current decreasing trend of AIR is part of this natural multi-decadal variability. The increasing trend of IO SST may be part of a multidecadal global coupled ocean-atmosphere mode. This means that the decreasing trend may recover within the next couple of decades!

49 The Challenge The existence of the year mode of variability indicates that there will some decadal predictability of AIR. However, due to the broad band nature of the mode, the predictability will be limited. The current climate models to provide reliable projection of monsoon climate requires, They need to simulate the year multi-decadal mode correctly. Even on seasonal time scale some predictability comes from extra-tropical SST. The climate models need to explore this earnestly.

50 Thank you

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