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Climate Prediction in East Africa-Tanzania Use of High

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Climate Prediction in East Africa-Tanzania Use of High Tanzania experience on the use of Climate information/ seasonal prediction Sarah E.Osima Tanzania Meteorological Agency (Principal Meteorologist and climate scientist) Location of the United Republic of Tanzania Contents 1. Background Climatology of Tanzania Some evidences of extreme climatic events 2. Weather forecast Early warning 3. Prediction process Data used Experience and challenges 4. User Engagement Strategy Future direction Climatology of Tanzania ! Stations Bukoba Musoma 2 Ngara Mwanza B Arusha Shinyanga IM Moshi O KIASame 4 D Same A Kigoma L Tanga Tabora ) Pemba S ° ( e Dodoma d 6 u t Zanzibar i t a Morogoro L Iringa Dar es Salaam 8 UNIMODAL Mbeya Mahenge 10 Mtwara Songea 30 32 34 36 38 40 Longitude (°E) Rainfall distribution in Tanzania S Bukoba Musoma 2 Mwanza ArushaKilimanjaroMoshi Shinyanga 4 Kigoma Singida Same Tabora TangaPemba Handeni Pemba 6 Dodoma Zanzibar Morogoro Dar es Salaam Iringa Sumbawanga 8 Mahenge Mbeya 10 Mtwara Songea 12 E 30 31 32 33 34 35 36 37 38 39 40 41 Temperature distribution Tanzania 1 Bukoba S Musoma 2 Mwanza Lyamungu 3 Moshi Arusha Kilimanjaro Same 4 Kigoma Singida Tabora Tanga 5 Mlingano Pemba 6 Zanzibar Dodoma Ilonga Dar es Salaam 7 Morogoro Sumbawanga Iringa Mafia 8 Mbeya 9 Igeri Lindi 10 Mtwara 11 Songea 12 E 30 31 32 33 34 35 36 37 38 39 40 41 Rainfall climatology (1980-1999)_CORDEX (shared from PhD thesis res.) (a) Bimodal rainfall regime 220 200 180 160 140 120 100 80 60 40 20 0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC ECHAM5 ERAI HIRH5-ERAI HIRH5-44KM HIRH5-10KM CRU KIA FEWS (b) Unimodal rainfall regime 220 200 180 160 140 120 100 80 60 Rainfall (mm/month) Rainfall 40 20 0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC ECHAM5 ERAI HIRH5-ERAI HIRH5-44KM HIRH5-10KM CRU DODOMA IRINGA FEWS Rainfall projection (shared from PhD thesis res.) 70 a) Rainy days _MAM (Area B) 60 c) Rainy days _ OND (Area B) 60 50 50 40 40 30 30 Present Frequecy Frequency 20 Present 20 Near future 10 Near future 10 Far future 0 Far future 0 Rainfall (mm/day) 25 b) Projected rain days change (%) 35 d) Projected change in rainy days (%) 20 30 25 15 20 10 15 Nf minus Pre NF minus Pre 10 5 Frequency Ff minus Pre Ff minus Pre Frequecy 5 0 0 -5 -5 -10 -10 -15 Rainfall (mm/day) Rainfall (mm/day) Some evidences of extreme climatic events o It is known that Tanzania and most regions around Indian Ocean had experienced ENSO impacts. In 1997/1998 very heavy rainfall (floods) followed with prolonged drought 1999/2000 which lasted to 2004. In the year 2005 and 2010 East Africa experiences two catastrophes: floods and drought respectively.(Many studies including the IPCC’s reports). o Glacier retreat atop Mount Kilimanjaro . Feb 1, 2001 1960 Nov 28, 2009 1939 Landslide over Usambara mountains during EL-Nino year 1997/98. 7/28/2015 11 Extreme heavy rainfall Dec/2011 Extreme heavy rainfall during MAM rainfall season 2014 (Dar es Salaam) 2. Weather forecast •Daily weather forecast •10 day weather forecast •Monthly weather forecast •Seasonal weather forecast Early warning •Tanzania Meteorological Agency (TMA) is a center for early warning system •TMA collect, process, store and disseminate meteorological information •TMA provides and disseminates wx forecasts and unepected extremes wx events (floods/drought) and other wx related hazards information for safety of life and property •TMA collaborates with other institutions and various stakeholders including the Ministry of Agriculture and Food Security and Prime Minister’s Office (Disaster management sect), research institutions and NGO’s. Early warning cont.. •TMA has recently launched a bulletin called ‘ Climate status of Tanzania (released annually) •TMA takes part world wide in global exchange of meteorological data and products for the safety of humankind and to enhance the understanding of the global atmosphere 3. Prediction Process Observations ENSO Climate State patterns ENSO Background forecasts Average climate Assessment Global forecasts Forecasts ENSO (conversation) Climatology Statistical forecasts Regional, seasonal Outlook (temp, rain, flows) Products Data used in seasonal prediction •The data used in wx weather forecasting is the existing climate data on sea surface temperatures, which are then used in ocean-atmosphere dynamic models, coupled with the synthesis of physically plausible national and international models •The ocean variability is an important feature in manipulating climate variations and changes due to the ocean’s capacity to absorb from and release heat back into the atmosphere GLOBAL OCEANS COMMUNICATE Experiences and Challenge cont.. •Different models interpretations (daily weather forecast). -Needs for evaluation/verification of these models •How to communicate the uncertainty of the wx forecast to the stakeholders/user information (farmers) •Language used for farmers (other key stake holders) Indian Ocean Dipole (IOD) Source: Japan Agency for Marine-Earth science and Technology Seasonal SST Anomalies (oC): 22 May -20 August 2011 During the 22 May – 20 Aug 2011 period, equatorial SSTs were above- average in the eastern Pacific and western Indian Oceans, while neutral conditions over western Pacific and cooling over northern Atlantic oceans. OND 2011 RAINS: OUTCOME 4. User Engagement Strategy Improved weather & climate services Integrate user Understand requirements user into day to day requirements operations Tanzania Met User Engagement Users Agency Champion Market Understanding Stakeholders‘ participation Public Weather Services dissemination channels Vituo vya Luninga Vituo vya Radio Social Networks Magazeti Internet & Email Mobile phones How we reach the society (User Interface) • TMA uses Television, Radio, Blogs, Newspapers, journals, mobile phones and social networks such as facebook, tweeter and youtube to reach the society. (www.facebook.com/tmaservices) (www.twitter.com/tma_services) (www.youtube/tanzaniametagency) • Advisories websites -www.meteo.go.tz www.wamis.org • Feedback from clients shows that 75% are satisfied with TMA services. 27 FarmSMS FarmSMS implementation in Tanzania. This enables farmers to get a short message on wx forecast through mob.phone Communica ting weather information to farmers (mob.phone s) User Engagement Strategy cont. Needs for Agriculture Sector • Types of information – Farmers would like onset of rainfall (when to plant), total amount of rainfall, end of rainfall, duration of rainfall (when to harvest) – Plain / meaningful language related to farmer’s actions – Information needs to be localised / downscaled (districts and regions) – Integration of TMA climate information with indigenous knowledge – Preferred lead time: 1 month – need time to cascade to communities – Long term climate information for seed manufacturers – Historical climate information to understand crop / climate pattern • Communication Channels – Community Radio (special programs based on forecasts) – Agriculture extension workers (to explain impact to farmers) © Crown copyright Met Office Future Directions of TMA • Improve and expand network stations and research in climate change science •Improve better two way information flows –link scientists and decision makers and understand information needs (eg. indentify gaps and data needs) Thank you for your attention “Asante kunisikiliza” “MERCI BEAUCOUP” “AMESEGNALEHU” .
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