Research Project under National Monsoon Mission

1. Bias estimation and effort for removal of UM/CFS coupled model output with adaptive techniques for improving forecast skill of ISM. Research Project under National Monsoon Mission PI : Prof. Sutapa Chaudhuri Co-PIs : Prof. M.Majumdar& Dr.D.Lohar JRFs : Debanjana Das, SayantikaGoswami & Arumita Roy Chowdhury Department of Atmospheric Sciences University of Calcutta

2. The objectives of the Proposed research: Study and analysis of various components of monsoon and their seasonal and monthly variability (spatial and temporal) during June, July, August and September (JJAS). Seasonal variability of monsoon will be the major concern. Correlation analysis between monsoon rainfall and various components like SST anomaly, ENSO, NAO etc. Predictability of the phases (active or break) and intra-seasonal variability and identification of relevant predictors Error analyses for both CFS (v2) and Unified model (UM) of UKMO generated products and their comparison. Performance analysis of model outputs using various skill scores. Identification of the model bias due to various mesoscale weather components Error minimization of the model products. Scrutinizing the validation of model after bias correction and error minimization

3. The preliminary objectives (i) and (ii) of the project have been taken up and completed , which are Published in referred journals: Chaudhuri Sutapa, Sayantika Goswami, Debanjana Das & Anirban Middey (2014) Meta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model, TheorApplClimatol,116, 3-4, 585-595 (IF – 1.742) Chaudhuri Sutapa & Jayanti Pal (2014) The influence of El Niño on the Indian summer monsoon rainfall anomaly: a diagnostic study of the 1982/83 and 1997/98 events, MeteorolAtmos Phys, 124, 3-4, 183-194 (IF – 1.245) Chaudhuri Sutapa & Jayanti Pal (2014) Cloud–aerosol coupled index in estimating the break phase of Indian summer monsoon, TheorApplClimatol, 118, 3, 447- 464(IF – 1.742) Chaudhuri Sutapa, Jayanti Pal, SuchandraGuhathakurta(2015) The influence of galactic cosmic ray on all India annual rainfall and temperature, Advances in Space Research, 55, 4, 1158–1167 (IF – 1.238)

4. Research undertaken to attain the prime objectives of the project: (iii) Bias estimation for CFS (v2) model generated products Motivation Scientists have shown that the summer monsoon of India encounters with episodes of abundant precipitation during active phase and scarce precipitation during break phase. It has also been observed that the onset of Indian summer monsoon (ISM) is triggered by northward propagation of the wet phase of boreal summer intra-seasonal oscillation (BSISO) with considerable inter-annual variability. Approach Considering the crucial role of BSISO on ISM, proper simulation of this variability through the state – of – the - art coupled model is felt to be important for the short-term and long-term prediction of the ISM. In view of this, the efficiency of (CFSv2) over the Indian monsoon region is investigated with 50 years long run. The skill of the model in simulating the spatial distribution of Precipitation, SST, northward and eastward propagation of the system is estimated.

5. Mean JJAS Precipitation – Comparison between CFSv2 &OBS Seasonal mean rainfall is characterized by three strong convective zones over Indian landmass during monsoon season: Western Ghat Central India and North-east India CFSv2 model simulates the mean JJAS rainfall but overestimates rainfall over Western Ghat and north - east India. However, it underestimates the rainfall over central India. The model has a tendency to show more rain over regions having elevated orography. In central India as there is no remarkable elevation in orography, the model underestimates the actual rainfall pattern.

6. Mean JJAS Precipitation – Comparison between CFSv2 &OBS The oceanic rainfall is a major challenge for CFSv2 model simulation as it is clear from the rainfall plots. The significant wet rainfall bias is evident over western equatorial Indian Ocean (WIO) and Arabian Sea (AS). The presence of dry rainfall bias of similar magnitude over eastern equatorial Indian Ocean (EIO) and Bay of Bengal (BoB) is also observed.

7. Mean JJAS Precipitation & Mean JJAS SST - Comparison The wet rainfall biases over western equatorial Indian Ocean (WIO) and Arabian Sea (AS). coincides with the cold SST biases. Dry rainfall bias over eastern equatorial Indian Ocean (EIO) and Bay of Bengal (BoB) also coincides with cold SST bias Thus, CFSv2 model shows cold SST bias over ocean.

8. Intra-seasonal precipitation variance The variance of CFSv2 model output with observation of rainfall and SST during JJAS period shows the regions with maximum bias (variance). The plots show that Somali upwelling, which is a common feature during SW monsoon is over simulated by the model. Intra-seasonal SST variance

9. Lead Lag SST-precipitation regression Both the northward and eastward propagations are simulated by CFSv2. However, it is not very prominent. Northward Propagation Eastward Propagation

10. Findings The analyses show that the CFSv2 model successfully simulates many major features like precipitation, SST variation and propagation during ISM. However, the model overestimates precipitation over Western Ghats and North East India having elevated orography. The significant overestimate of precipitation is observed over the eastern equatorial Indian Ocean (EIO) and Arabian Sea (AS). The oceanic rainfall is a major impediment in the CFSv2 simulation as is evident from rainfall variation. The presence of significant dry bias (3 - 7 mm/day) over central India implies that CFSv2 underestimates the rainfall over the monsoon trough zone. The model has a tendency to underestimate the precipitation over central India having comparatively flat orography. The study further shows that CFSv2 model depicts cold SST bias over ocean.

11. The presence of bias in the CFSv2 model products in simulating ISM motivated to Investigate on the ambiguity in predictability of Summer Monsoon in various climate models along with CFSv2 Preamble Characterizing and quantifying ambiguity in simulation of Summer Monsoon is of primary importance not only for the purposes of detection and attribution, but also for tactical approaches for adaptation and mitigation. Uncertainty in prediction derives from three main sources: forcing, model response, and internal variability (Hawkins and Sutton 2009; Tebaldi and Knutti 2007). Internal variability is the natural variability of the climate system that occurs in the absence of external forcing, and includes processes inherent to the atmosphere, the ocean, and the coupled ocean-atmosphere system. Internal atmospheric variability, also termed as ‘‘climate noise’’ (Madden 1976; Schneider and Kinter 1994;Wunsch 1999; Feldstein 2000), arises from chaotic dynamical processes inherent to the atmosphere. Internal variability, on the other hand, depends on dynamics. Approach Predictability of CFSv2 model assessment is done along with few other existing global model with the help of signal to noise ratio. Attempt is made to calculate and analyze the predictability limit. The Internal variability of model is evaluated. The Predictability is computed at the significance level of 99.9%, 99%, 95%.

14. Inter comparison of Model bias during JJAS for (b) sea surface temperature Inter comparison of Model bias during JJAS for Total precipitation CFSv2 model products show cold bias almost over the entire tropical ocean except some parts of west pacific. Different models show different kinds of biases in some region. Each model suffers from cold as well as warm biases throughout the globe.

15. Inter comparison of Model signal during JJAS for SST Inter comparison of Model signal during JJAS for Total precipitation SST signal is reasonably captured by the four global coupled models. NASA GMAO model has serious problem in the polar region.

16. Inter comparison of Model noise during JJAS for (b) sea surface temperature Inter comparison of Model noise during JJAS for Total precipitation Among the four models CFSv2 shows maximum internal variability that is the differences in the realization. This is also observed to be high over the tropics in each model. In CFSv2 the noise is highest over the tropical Indian ocean and over tropical Pacific.

17. Inter comparison of Model SNR during JJAS for Total precipitation Inter comparison of Model SNR during JJAS for sea surface temperature SNR is highest in the tropics as signal is highest in the tropics and noise is also high for precipitation. However, the signal is higher than the noise thus, SNR is high.

18. Inter comparison of Model Signal to total square root during JJAS for (a) Total precipitation Inter comparison of Model Signal to total square root during JJAS for (b) sea surface temperature.

19. Inter comparison of predictability at different significance level during JJAS for (a) Total precipitation Inter comparison of predictability at different significance level during JJAS for (b) sea surface temperature the theoretical predictability of seasonal mean monsoon is quite high

20. Findings • SNR is highest in the tropics as signal is highest in the tropics and noise is also high for precipitation. However, the signal is higher than the noise thus, SNR is high. • Location of Maximum signal to Noise is different in different models. The main reason behind this is that the signal is changing from model to model. Signal to Noise is high in tropics. This means that boundary condition of SST are the main forcing behind producing large signal. • SST is high in the tropics mostly because noise is low in the tropics. Signal is higher than the noise here. But in the mid-latitudes Noise is higher than signal • Region of cool and warm SST bias vary in model to model. Bias is minimum in ENSO region.CFSv2 is having cool SST bias • It is the measure of internal variability. Internal variability depends on dynamics. It is very surprising that noise is very different in different models. • Noise in the CFS is highest among all the models. This noise is highest in the Indian Ocean and Pacific Ocean region. • It is not known that which one is correct because the noise is not known in observation as the observation has only one realization and therefore in observation, noise as well as signal is mixed. In nature there is no ensemble member. • SST is highly predictable during JJAS almost over the whole globe. • From the analysis it is clear that predictability is higher in tropics. • Signal is well captured in CFSv2 but at the same time internal variability(noise) is also maximum in case of CFSv2. • Orographay and moist convection seem to be most important factors in simulating monsoon rainfall.

21. The most important finding of this study is that the theoretical predictability of seasonal mean monsoon is quite high; however it is known from practical experience that actual skill of seasonal operation prediction by coupled models is not very high. Thus, the question is why the predictability is high but the prediction skill is low? The answer to this question is beyond the scope of this study but it has been suggested by many authors that it is partly because of the large intra-seasonal variability within the monsoon season and partly because the models have very large biases. The speciality of CFSv2 model is that the internal dynamics - generated noise is much higher than other models at the same time signal is also highest among the other models or closest to observation.

22. (iii) Bias estimation for UKMO Unified model (UM) generated products Motivation The Indian summer monsoon and associated rainfall is a matter of concern for decades. Modeling aspects and improved plotting platforms are coming up besides statistical methods for better understanding of the model products in simulating rainfall. The main purpose of this study is to estimate the skill of United Kingdom Met Office - Unified Model (UKMO - UM) in estimating the Indian summer monsoon rainfall (ISMR) from June to September (JJAS), 2011 within the confine of 5º to 36º latitude and 60º to 100º longitude over India. Approach Firstly Orography (m) dataset which is an adapted data in the Unified Model is collected. For the verification Terrain-Base (T-base) etopo2 orography (m) dataset from NOAA/NGDC site (http://dss.ucar.edu/datasets/ds759.3) is also collected. The resolution of the Orography dataset for both T-Base and Unified Model is converted into 0.25x0.25 grid resolution. Secondly, three important surface parameters produced by the model have been taken into account for analyzing the Orography induced effect. Those are total precipitation amount for Indian Summer Monsoon Rainfall (ISMR), Surface temperature (ST) and Surface vector wind (SVW). Total precipitation amount (kg/m2/TS, equivalent to mm/TS), 6-hourly data set is prepared for JJAS period as accumulated precipitation in mm/day (ISMR), Surface Temperature (ST) is computed in degree celcius (ºC) from its absolute value and surface vector wind (SVW) is in m/s.

23. Data Arrangements

24. The performance of UKMO-UM to produce precipitation w.r.t. IMD observation Precipitation (ISMR) observed by TRMM-merged IMD data (left), UKMO-UM model output (middle), estimated bias (right)

25. Zonal divisions of India WG EG GWB CPI HR

26. ISMR from IMD observation (left), UKMO-UM output (mid) and estimated model bias (right), over WG T-base elevation from NOAA/NGDC (left), UKMO-UM output (middle), and estimated bias in Elevation (right) over WG ST from NCEP/NCAR Reanalysis (left), UKMO-UM output (mid) and estimated bias in ST (right) over WG

27. SVW from NCEP/NCAR Reanalysis (left) and UKMO-UM output (right) over WG

28. ISMR from IMD observation (left), UKMO-UM output (mid) and estimated bias in ISMR (right) over EG T-base elevation from NOAA/NGDC (left), UKMO-UM output (middle), and estimated bias in Elevation (right) over EG ST from NCEP/NCAR Reanalysis (left), UKMO-UM output (mid) and estimated bias in ST (right) over EG

29. SVW from NCEP/NCAR Reanalysis (left) and UKMO-UM output (right) over EG

30. ISMR from IMD observation (left), UKMO-UM output (mid) and estimated bias in ISMR (right) over GWB T-base elevation from NOAA/NGDC (left), UKMO-UM output middle), and estimated bias in Elevation (right) over GWB ST from NCEP/NCAR Reanalysis (left), UKMO-UM output (mid) and estimated bias in ST (right) over GWB

31. SVW from NCEP/NCAR Reanalysis (left) and UKMO-UM output (right) over GWB

32. ISMR from MD observation (left), UKMO-UM output (mid) and estimated bias in ISMR (right) over CPI T-base elevation from NOAA/NGDC (left), UKMO-UM output (middle), and estimated bias in Elevation (right) over CPI ST from NCEP/NCAR Reanalysis (left), UKMO-UM output (mid) and estimated bias in ST (right) over CPI

33. SVW from NCEP/NCAR Reanalysis (left) and UKMO-UM output (right) over CPI

34. ISMR from IMD observation (left), UKMO-UM output (mid) and estimated bias in ISMR (right) over HR T-base elevation from NOAA/NGDC (left), UKMO-UM output (middle), and estimated bias in Elevation (right) over HR ST from NCEP/NCAR Reanalysis (left), UKMO-UM output (mid) and estimated bias in ST (right) over HR

35. SVW from NCEP/NCAR Reanalysis (left) and UKMO-UM output (right) over HR

36. Planned activities for the remaining period to meet the remaining objectives On going Research • Study of internal variability with high resolution data. • Estimation of orography induced bias on ISMR with UM model products • Future Plan • Long-range forecast of Indian Summer Monsoon Rainfall using adaptive neuro-fuzzy inference system and validation with CFSv2 and UKMO model products • The model bias arises due to various mesoscale weather components will be taken care off

37. Acknowledgement I thank the Ministry of Earth Science (MoES), GOI for giving me the opportunity to work for National Monsoon Mission I thank the team of Monsoon Mission Scientists of IITM , Pune for their kind cooperation and help provided to the research fellows of the project, whenever needed. I thank the University of Calcutta for giving me the facilities for the success of the project. I thank my research fellows of the project for hard work and taking keen interest for the success of the mission. I thank IITM-ICTP TTA activityfor enhancing the technical efficiency .

38. Thank You “Those who hate rain hate life”  ― Dejan Stojanovic