Predictability, Prediction & Multi-decadal Variability of the Indian Summer Monsoon

Transcription

1 Predictability, Prediction & Multi-decadal Variability of the Indian Summer Monsoon B. N. Goswami Former Director, IITM, Pune INSA Senior Scientist, Cotton University, Guwahati

2 Our agriculture & food production critically epends on monsoon rain roughts and Floods have great impact on the economy and life in general

3 Sir Gilbert Walker, FRS Director General, IMD during From 1897 to 1920, India faced many monsoon failures and Droughts leading to famine. Sir Gilbert Walker was looking for a handle to predict such droughts. In doing so he discovered Southern Oscillation Trying to predict monsoon rainfall for nearly 100 years. However, it remained a grand challenge. Even today all model skill is rather limited!!

4 The Plan Challenges in Simulating the mean and in Seasonal Prediction of the Indian Monsoon Conceptual basis for prediction skill beyond limit on potential predictability Targeted improvement of Simulation and Prediction of the Indian monsoon under the Monsoon Mission of India An Air-Sea Interaction model for Multi-decadal Variability of the Indian Monsoon

5 Major Persisting Problem in Indian Monsoon Simulation From Sperber et al. 2013, Climate Dynamics

6 Summary of Main Problem in Simulating Indian Monsoon Severe underestimation of precipitation over land Overestimation of precipitation over the Ocean OR Dry Bias over land Wet Bias over Ocean Although climate models have improved in simulating the mean global climate and modes like ENSO, PDO etc, this problem of monsoon simulation has not improved over last 30 years!

7 Problem of Predicting Monsoon Rainfall Krishna Kumar et al, 2005, GRL Skill of ISMR prediction by ENSEMBLES models Rajeevan et al. 2011, Clim. Dyn.

8 Summary of Main Problem in Predicting Indian Monsoon Bad News: 1.Perfect model experiments show that the Potential limit on IMS predictability is limited to a skill of ~0.65! 2.This skill being barely useful, if we can not go beyond the limit on potential predictability limit, we stiil have a big problem! May have been responsible deterring investment of resources in monsoon research in recent times (my personal take!) 3.Model skill were significantly below this limit! Good News: With modal skill of ENSEMBLES being much higher than the earlier generation of models indicate that improvement of models lead to improvement of skill.

9 Possible Reasons for not Improvement of Indian Monsoon in CGCMs Different CGCMs have different biases and each group tries to improve their major bias. Biases of Indian monsoon simulation was not getting priority Targeted improvement of biases in A CGCM related to the Indian Monsoon is required. India could not contribute to this effort significantly as it neither had the computing resource nor enough trained manpower to do this. However, this changed with The Monsoon Mission

10 Is Seasonal Prediction of ISM doomed? The internal variability of ISM is large ( Goswami 1998) resulting in a low Potential Predictability limit, a perfect model skill of ~0.65! Thus, even if we achieve perfect model skill, it is barely useful Therefore, the seasonal prediction of ISM could be useful only if we can achieve skill equal to or better than Potential Predictability limit!! Conventional wisdom says, this may be impossible! Hence, ISM prediction appeared to be doomed!

11 A Potential Resolution of this Dialema If the Internal variability which is conventionally considered unpredictable, is also modulated by a slowly varying predictable forcing like the ENSO, then that part of internal variability becomes predictable. Large fraction of ISM internal variability is caused by the Monsoon Intraseasonal Oscillations (MISOs). Thus, if MISOs are modulated by ENSO, then we can expect higher prediction skill by models if models simulate the MISO with high fidelity. Model skill could go to equal Potential Predictability limit or even beyond!

12 A Ray of Hope! We discover that MISO statistics is indeed modulated by the ENSO, (Dwivedi, Goswami and Kucharski,2015, GRL)! EL Nino, the frquency of Long Break spells > Short Break spells < Long Actve spells < Short Active spells > La Nina, the frquency of Long Break spells < Short Break spells > Long Actve spells > Short Active spells < Thus, the Internal variability contributed by the MISos appear to be predictable!

13 Thus, skill of ISM prediction equal to or greater than PPL is Achievalble! A Ray of Hope! We we also know that the MISO modulates the ISM synoptic events like the LPS, Goswami et al. 2003, GRL, 30, doi: /2002gl Implication: The contribution of synoptic events to the seasonal mean is also modulated by the ENSO!

14 Hypothesis A Model with high fidelity simulations of the MISOs will have high skill of Seaonal prediction of ISM, equal to or higher than the PPL. Therefore: Target improving the biases in similating the MISO in models. Potential double benefits: It would reduce biases in mean simulation Improve skill of Seasonal Prediction

15 The Monsoon Mission: A mission mode project to deliver quantifiable improved forecast of Seasonal mean monsoon rainfall Objectives To set up and operationalize a Dynamical coupled Sesaonal Prediction System with hindcast skill To set up and operationalize a Dynamical coupled Extended Range Prediction System with hindcast skill Improve model simulation and data assimilation to improve forecasts The Ministry of Earth Sciences, Govt. of India To be led and coordinated by IITM (2009)

16 Strategy Select a Prediction system involving A CGCM and make systematic improvement on THAT CGCM and on improved data assimilation. The improvements on the CGCM will be targeted to improve the deficiency (bias) of the model in simulating Indian monsoon. To achieve this, improve modeling capacity in the country through collaboration with Academic community, National and International Improve the Computing Infrastructure significantly to be comparable to the best International climate prediction Centres.

17 Under the Monsoon Mission Overarching Hypothesis of Model Development Improve simulation of MISO Improve Mean ISM as well as skill of Seasonal Prediction We selected CFSv.2 as the base model for development and use in prediction of Indian Monsoon. We got Indian as well as International Academic Climate community involved (through a series of extramural projects) in improving THE CFSv.2 As CFSv.2 with high atmospheric resolution (T382) showed improvement in MISO simulations, the CFSv.2 with T382 atmosphere was selected for the initial Seasonal Prediction System.

18 Currently the Monsoon Mission Prediction System has the best skill for Seasonal Prediction of monsoon

19 Model Development:Improvement of CFSv.2 Three major developments: 1.Modification of the Land Surface Model by adding a 5-layer snow model, improves north-south T gradient, improves MISO, improves mean monsoon 2.A new modified cloud microphysics scheme implemented with a modified convection scheme. Improves MISO, mean monsoon 3.Implemented a multi-cloud stochastic parameterization in CFSv.2. Improves Equatorial waves, MJO, MISO, also mean monsoon

20 Now published in the AGU : Journal of Advances in Earth System Modeling 1. Hazra Anupam, H. S. Chaudhari1, S. K. Saha, S. Pokhrel and B. N. Goswami (2017) Progress towards achieving the Challenge of Indian Summer Monsoon Climate Simulation in a Coupled Ocean-Atmosphere Model, J. Adv. Earth System Modeling, doi: /2017MS Saha, S. K., K. Sujith, S. Pokhrel, H. S. Chaudhari, and A. Hazra (2017), Effects of multilayer snow scheme on the simulation of snow: Offline Noah and coupled with NCEP CFSv2, J. Adv. Model. Earth Syst., 9, , doi: /2016ms Goswami B. B., B Khouider, R Phani, P Mukhopadhyay, and A.J. Majda, 2017: Implementation and calibration of a stochastic multicloud convective parameterization in the NCEP Climate Forecast System (CFSv2), J. Adv. Mod. Earth Sys. doi: /2017MS Abhik, S., R. P. M. Krishna, M. Mahakur, M. Ganai, P. Mukhopadhyay, and J. Dudhia (2017), Revised cloud processes to improve the mean and intraseasonal variability of Indian summer monsoon in climate forecast system: Part 1, J. Adv. Model. Earth Syst., 09, doi: /2016ms

21 Bias in Snow Depth (cm) from CFSv2 Freerun

22 Snow Cover Area

23 JJAS Rainfall

24 TRMM GPCP CTL day day Space-time spectra of the low frequency day mode. MCMv.1 MCMv.2 Total intraseasonal variance (ISV) of and day filtered precipitation (JJAS) Hazra et al., (2017), JAMES, DOI: /2017MS Northward propagation of ISOs using lead/lag regressed days filtered rainfall anomaly (JJAS)

25 Representation of organized convection in CFSv2 via Stochastic Multi-Cloud Model (CFSsmcm) Bidyut B. Goswami 1,4, R. Phani 2, B. Khouider 1, P. Mukhopadhyay 2, and A. J. Majda 3 1 University of Victoria, Victoria, Canada; 2 Indian Institute of Tropical Meteorology, Pune, India; 3 Courant Institute for Mathematical Sciences, New York, USA 4 New York University Abu Dhabi, UAE (current location) Details can be found in a series of three papers: Goswami et al 2017a, b, c

26 CFSv2 SAS SMCM

27 The Model CFSv2 SMCM 40x40 ~ 2.5 km ~ 100 km Q tot (z) = H d φ d (z) + H c φ c (z) + H s φ s (z) For further details please refer to : Goswami et al 2016

28 Annual WK-spectra MJO power is seen in the WK-spectra for both the models. However we found that, the structure and dynamics of the CFSsmcm Thursday, simulated February 08, MJO is much improved and realistic.

29 Boreal summer ST spectra (For north-south spectra: k=1 corresponds to 20S-27N=48 degrees)

30 Climatological Annual Mean, Annual Cycle : CFSsmcm_139

31 Taylor Diagram of skill of ISM hindcasts with Control and Improved Models

32 Other Major Spin-off developments under the Monsoon Mission Development of the First Indian Earth System Model for long term climate simulations Coupled the atmospheric component of CFSv.2 with MOM4p1 and corrected the global energy imbalance For the first time India will participate in simulations and projections climate for IPCC AR6 The IITM Earth System Model, conversion of a seasonal prediction model to long term climate model Swapna et al+ B. N. Goswami. Bull. Am. Met. Soc, 2014

33 Indian summer monsoon (ISM) proxies indicate a year multi-decadal variability of the ISM A - RWI-India ( ) B - δ18o-ci ( ) C - RWI-Thailand ( ) Red line : 21-year moving average Goswami, Kripalani, Borgaonkar, and Preethi, 2015: Cliamte Change; Multidecadal and Beyond, Eds. C. P. Chang et al. Chap. 21, pp Also, Sinha Ashis, et a. (2015). Trends and oscillations in the Indian summer monsoon rainfall over the last two millennia, Nat. Commun. 6:6309 doi: /ncomms7309

34 Normalized Departure of JJAS Rainfall over India The ISM has a multi-decadal variability with decreasing trend since 1955 and below normal phase of multi-decadal variability since 1960.

35 Increasing trend of IO SST is causing decreasing trend of Indian monsoon? SST Interdecadal component of AIR and EQIO SST (10S-5N,50-100E) Roxy et al, 2016, Nature Communications

36 A very weak increasing trend of TT in the north box A strong increasing trend of TT in the south box Increasing trend lines of TT in the north (dotted) and south (solid) boxes Roxy et al, 2016, Nature Communications

37 Is the monsoon influencing the increasing trend of tropical IO SST? Trends in SST and Surface winds Time series of SST (C) bars and ERA zonal wind anomalies (m s-1, red lines) averaged over the equatorial IO (50E 100E, 5S 5N). Swapna et al. 2013: Clim. Dynamics, DOI /s

38 Thus, the increasing trend of IO SST decreasing trend of Indian monsoon rainfall increasing trend of IO SST! Could this unstable air-sea interaction lead to a runaway collapse of the Indian monsoon? In the absence of a negative feedback to nullify the positive feedback, the Indian summer monsoon may indeed face a runaway collapse!

39 Thus, there is evidence that extended below normal spell like the present one between had occurred in the past. Therefore, it is possible that the present decreasing trend of the Indian monsoon may not be a runaway process and could still be part of the monsoon multidecadal year mode! However, to achieve this we have to identify a negative feedback mechanism that would counter the positive feedback from IO SST!

40 As the Indian Monsoon is weakening and the IO SST is increasing, is there something happening in the Indo-Pacific basin that could lead to a negative feedback? Associated with the weakening trend of Asian Monsoon convection, eastern Equatorial IO convection is strengthening! Goswami and Chakravorty : work in progress

41 As a result, along with strengthening of Westerlies over Equatorial Indian Ocean, the Easterlies in the Equatorial Pacific strengthening!

42 Low Frequency modes of Pacific Ocean SST EOF1=PDO SST detrend as removing the global average SST OF2=NPGO (North Pacific Gyre Oscillation) (Di Lorenzo et al., 2007,2009)

43 El Nino (La Nina) produces ve (+ve) TT anom in north India, weaken (strengthen) N-S TT gradient and the Indian Monsoon

44 Thank you