Consensus Seasonal Weather Outlook November, December and January (NDJ) Seasonal Rainfall for Sri Lanka

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1 Consensus Seasonal Weather Outlook November, December and January (NDJ) Seasonal Rainfall for Sri Lanka This consensus outlook have been developed through an expert assessment of The prevailing global climate conditions Forecasts from different climate models from around the world. Statistical downscaling of Global Circulation Model (GCM) output using Climate Predictability Tool (CPT) Issued by Centre for Climate Change Studies (CCCS) And 1 P a g e Research Division

2 Prevailing Global Climate Conditions Currently strong El Niño conditions prevail in the Pacific Ocean and it is almost certain that El Nino conditions will continue to prevail during the boreal winter season. However, it is recognized that there is uncertainty about the impacts of El Niño on the seasonal rainfall patterns over Sri Lanka. The Strongest influence of ENSO extremes is evident during Second Inter Monsoon season(sim) (October-November) with excess of seasonal rainfall over most parts of the island during El Nino years (Fig 1). In contrast to SIM, deficit of seasonal rainfall is evident over North western, North central and central parts of the Island in Northeast monsoon (NEM) (December-February) during El Nino years (Fig 2). Due to prevailing El Nino condition, enhance rainfall activity can be expected during month of November and December 2015 and slightly reduced rainfall activity can be expected in month January Fig : 1 Composites of seasonal rainfall probabilities (shading) for SIM season during El Nino (Rainfall probabilities refer to the chance of seasonal rainfall exceeding the median, expressed as a ratio with the mean probability (nominally 50%) (Left) Climatological SIM seasonal median rainfall (mm) (Right), (Source : Hapuarachchi, 2015 Unpublished). Fig : 2 composites of Seasonal rainfall probabilities (shading) for NEM season during El Nino (Rainfall probabilities refer to the chance of seasonal rainfall exceeding the median, expressed as a ratio with the mean probability (nominally 50%)). (left). Climatological SIM seasonal median rainfall (mm) (right) (Source : Hapuarachchi, 2015 Unpublished). 2 P a g e

3 ENSO Conditions over the Pacific Ocean The El Niño/Southern Oscillation (ENSO) is one of the global scale climate phenomena that have significant influence on the year-to-year variability of the northeast monsoon rainfall as well as the surface temperatures over South Asia. In March 2015, weak El Nino conditions were established over the equatorial Pacific which strengthened to moderate level in early June and then to strong level in middle of July. Currently, the atmospheric conditions over the Pacific also reflect patterns consistent with El Niño conditions. Latest forecasts indicate that the El Niño conditions are nearly certain to persist until early part of the next year. Models and expert opinion also suggest that sea surface temperatures in the east-central tropical Pacific Ocean are likely to exceed 2 Celsius above average, potentially placing this El Niño event among the four strongest events since 1950 ( , , ). El Niño conditions are known to strengthen the northeast monsoon circulation and enhance the rainfall over the region. However, their impact on the regional rainfall distribution varies from year to year. Forecasts from different climate models from around the world. November, December, January (NDJ) season Figure 3 shows the probabilistic multi model ensemble forecast using dynamical models from 12 global producing centers (GPC) for November, December, January (NDJ) season. There is a higher probability of receiving above normal rainfall for NDJ season. Majority of Individual GPC forecast ( 9 out of 11) gives above normal rainfall for NDJ season (Fig 4) Fig 3 : Probabilistic multi model ensemble forecast for NDJ using dynamical models from 12 WMO Global Producing Centers (GPC). 3 P a g e

4 Fig 4 : Individual forecast for NDJ season by dynamical models from 12 WMO global producing centers (GPC). Month of November 2015 Figure 5 shows the probabilistic multi model ensemble forecast using dynamical models from 12 global producing centers (GPC) for November There is a higher probability of receiving above normal rainfall for November Majority of Individual GPC forecast ( 8 out of 11) gives above normal rainfall for month November (Fig 6). Fig 5 : Probabilistic multi model ensemble forecast for November 2015 using dynamical models from 12 WMO global producing centers (GPC). 4 P a g e

5 Fig 6 : Individual forecast for November 2015 by dynamical models from 12 WMO global producing centers (GPC) For Month of December 2015 Figure 7 shows the probabilistic multi model ensemble forecast using dynamical models from 12 global producing centers (GPC) for December There is a higher probability of receiving above normal rainfall for December Majority of Individual GPC forecast ( 8 out of 11) gives above normal rainfall for December 2015 (Fig 8). Fig 7 : Probabilistic multi model ensemble forecast for December 2015 using dynamical models from 12 WMO global producing centers (GPC) 5 P a g e

6 Fig 8 : Individual forecast for December 2015 by dynamical models from 12 WMO global producing centers (GPC) For Month of January 2016 Figure 9 shows the probabilistic multi model ensemble forecast using dynamical models from 12 global producing centers (GPC) for January There is no clear signal during month of January Out of 11 Individual GPC forecast for January 2016, 5 GPCs indicates above normal rainfall and 5 GPCs indicate below normal rainfall and 1 GPC does not show a clear signal. Fig 9 : Probabilistic multi model ensemble forecast for January 2016 using dynamical models from 12 WMO global producing centers (GPC) 6 P a g e

7 Fig 10 : Individual forecast for January 2016 by dynamical models from 12 WMO global producing centers (GPC) Statistical downscaling of CFSv2 global forecast output using Climate Predictability tool (CPT) November 2015 The probabilistic rainfall forecast for November 2015 for Sri Lanka by downscaling CFSv2 SST using CPT is given in the table 1 below. The district wise average rainfall is given in the column 2 of the table 1. Chance (probability) of receiving below/about/above average is given in the columns 3, 4, and 5 respectively in the table 1. All the districts except Jaffna have more chance (higher probability) of receiving above average rainfall during the month of November P a g e

8 District Average rainfall (mm) (November) Probability % Below Normal Above Colombo Kalutara Galle Matara Hambantota Ampara Batticaloa Trincomalee Mullaithivu Jaffna Killinochchi Mannar Puttalam Gampaha Kegalle Ratnapura Monaragala Badulla Pollonnaruwa Vavuniya Anuradapura Kurunegala Matale Kandy Nuwaraeliya Summary SUMMARY of MODEL FORECAST for SRI LANKA Season WMO LC MME WMO GPC prevailing global climate CPT FINAL conditions For NDJ season AN AN AN AN For November AN AN AN AN AN 2015 For December AN AN AN AN 2015 For January 2016 No Signal No Signal BN Neutral BN: Below Normal N: Normal AN: Above Normal CP: Climatological Probability 8 P a g e Table 2 : Summary of Model forecasts for Sri Lanka

9 There is a higher probability of receiving above normal rainfall for NDJ season as well as for November 2015 and December Near Normal rainfall can be expected for January It was also recognized that there is moderate seasonal predictability for the NDJ season (latter part of Second inter monsoon season and early part of Northeast monsoon season) as the seasonal variability is strongly influenced by the slowly varying boundary forcing like sea surface temperatures. However, the predictability is limited to some extent due to the strong day to day atmospheric variability caused by the passage of the synoptic scale systems such as easterly waves, lows, depressions, cyclones etc. The seasonal predictability of the northeast monsoon over the region is also limited by the Madden Julian Oscillation (MJO), which represent the major global scale intraseasonal variability pattern. 9 P a g e