South African Weather Service SWIOCOF-5 Pre-Forum. Seasonal Forecast, Water Resources and Expected Outcomes

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1 South African Weather Service SWIOCOF-5 Pre-Forum Seasonal Forecast, Water Resources and Expected Outcomes Marc de Vos, September 2016

2 South Africa: Context BCRE, 2016, adapted from Boebel et al.,

3 SAWS: An Introduction

4 SAWS: An Introduction 25 Weather Offices 205 Automatic Weather Stations 165 Automatic Rainfall Stations 11 Climate Stations 1159 Rainfall Stations 11 Upper Air 23 Sea Surface Temperature NFC, ~ 60 Weather Pretoria buoys in the South Atlantic, South Indian & Southern Oceans 24 Lightning Detection Stations 14 Meteorological Radar (C-, S- and X- band) 17 Air Quality Measuring and Monitoring Stations 2 Dobson Spectrophotometer 3 Aircraft 1 Baseline Surface Radiation Network Station in De Aar 1 Global Atmosphere Watch Station at Cape Point 12 Voluntary Observing Ships 13 Solar Radiations Global Producing Centre

5 Seasonal Forecast SAWS: 2 Seasonal Forecasting Systems 1. Dynamical global ensemble prediction system (EPS) ECHAM 4.5 AGCM 2. Statistical Forecasting System Model output statistics (MOS) approach Downscaled to SADC region Monthly Consensus discussion In house verification, based on an IRI methodology implemented some years ago.

6 Seasonal Forecast Ensemble 1 Ensemble 2 Ensemble 3 Ensemble n (e.g. ECHAM4.5) (e.g. CCM3) (e.g. CFS) (other forecast centre) N 1 members N 2 members N 3 members N n members Combining algorithm (not trivial!) Landman et al., 2008 Multi-model ensemble of N 1 +N 2 +N 3 + N n members

7 Seasonal Forecast 3 Central Interior DJF Simulations; ECHAM Ensemble Mean Observed Landman et al.,

8 Seasonal Forecast Model Output Statistics (MOS) applied to output from the following GCMs: ECHAM4.5 (SAWS; 12 ensemble members) CFS (NCEP; 40 ensemble members) CCM3 (IRI; 24 ensemble members) Forecast probabilities calculated by CPT Forecast probabilities averaged Landman et al., 2008

9 Seasonal Forecast: ECHAM4.5 at SAWS All runs performed on NEC SX-8 Climatological (6 members) and operational ensemble runs - 24hr LAF Atmospheric initial conditions from ECMWF (1979 to 1996) analysis Climatological dataset ( ) constructed using AMIP physics; model constrained by lower boundary conditions generated from a high resolution AMIP2 dataset for SST and sea-ice Operational set-up: persisted and forecast SSTs obtained from a high resolution observed SST (optimum interpolation v-2) and IRI (mean) respectively (6 members each) 12-member ensemble operational runs on 18 th of each month for 6 consecutive months (i.e., 0-6 months lead-time) Landman et al., 2008

10 Seasonal Forecast: CFS at NCEP CFS is run twice a day from initial conditions for the atmosphere and ocean, which are 7 days old The atmospheric initial conditions are obtained from NCEP Reanalysis-2 and the ocean initial condition is obtained from NCEP GODAS (Global Ocean Data Assimilation) The integrations are complete for the first partial month + 9 full months into the future 4 ensemble members are obtained each day for 10 days to create a 40 member ensemble Landman et al., 2008

11 Seasonal Forecast: CCAM at CSIR

12 Seasonal Forecast: CCM at IRI All runs performed at IRI Forced with persisted SST anomalies 24-member ensemble Landman et al., 2008

13 Seasonal Forecast: Interface

14 Seasonal Forecast: Interface

15 Seasonal Forecast Experimentation with one-tier (fully coupled) models e.g. Landman et al., tier Systems ECHAM 4.5v3 MOM3-DC2 ECHAM 4.5-GML-NCEP CFSSST Computationally expensive 2-tier System ECHAM 4.5 AGCM, forced with SST derived from statistical model Lower relative computational cost Landman et al., 2008

16 Seasonal Forecast Downscaled coupled systems outscored downscaled 2-tier systems Neither outscored reference system (AGCM forced with simultaneously-observed SST) Therefore, room for further development remains Acknowledged in SA that we need to spend time and resources developing coupled systems Landman et al., 2008

17 Current State & Outlook: Summary Neutral ENSO state Weak La Niña looking less likely Spring rainfall: lower than normal Indian Ocean conditions (negative IOD) consequence for moisture transport Higher than normal rainfall prediction uncertain (LT & La Niña) Spring & summer temperatures higher than normal

18 Current State & Outlook: Rainfall & Water Most of SA still experiencing drought conditions Likely to persist despite summer rainfall Forecast particularly uncertain Long LT Uncertainty regarding weak La Niña

19 Current State & Outlook: Rainfall & Water Dept. Water Affairs & Sanitation, 2016

20 Current State & Outlook: Rainfall & Water Province Nett FSC million m^3 This Week (%) Last Week (%) Last Year (%) Eastern Cape Free State Gauteng Kwazulu Natal Lesotho* Limpopo Mpumalanga North West Northern Cape Western Cape Total Dept. Water Affairs & Sanitation, 2016

21 Current State & Outlook: Temperatures Most of SA: higher than normal for spring into summer West & southern coasts: lower than normal temperatures

22 Current State & Outlook: Temperatures

23 Expected Outcomes Generally Insights into seasonal forecasting Statistical methods and approaches to atmospheric climate data Updates w.r.t current best practice to take back to my colleagues at the NFC Specifically Look for potential areas in which oceanographic section can add value Templ ref: PPT-ISO-colour.001 Doc Ref no:

24 References BCRE. (2016). Sea Atlas - Retroflection. Available: Last accessed 19th Sep Boebel, O., Rossby, T., Lutjeharms, J., Zenk, W. and Barron, C., Path and variability of the Agulhas Return Current. Deep Sea Research Part II: Topical Studies in Oceanography, 50(1), pp Dept. Water & Sanitation. (2016). Status of surface water storage.available: Last accessed 19th Sep Dept. Water & Sanitation. (2016). Status of surface water storage.available: Last accessed 19th Sep Johnston, P.A., Archer, E.R.M., Vogel, C.H., Bezuidenhout, C.N., Tennant, W.J. and Kuschke, R., Review of seasonal forecasting in South Africa: producer to end-user. Climate Research, 28(1), pp Landman, W.A., DeWitt, D., Lee, D.E., Beraki, A. and Lötter, D., Seasonal rainfall prediction skill over South Africa: one-versus two-tiered forecasting systems. Weather and Forecasting, 27(2), pp Landman, W.A., Kgatuke, M.J., Mbedzi, M., Beraki, A., Bartman, A. and Piesanie, A.D., Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective. International Journal of Climatology, 29(11), pp SAWS. (2016). Seasonal Forecast. Available: Last accessed 19th Sep 2016.

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