Willem A. Landman The evolution of seasonal forecasting in South Africa  Model/system development started in early 1990s –

Slides:



Advertisements
Similar presentations
DROUGHT MONITORING IN SOUTHERN AFRICA DEVELOPMENT COMMUNITY
Advertisements

Climate Prediction Division Japan Meteorological Agency Developments for Climate Services at Japan Meteorological Agency 1.
Willem A. Landman Francois Engelbrecht Ruth Park.
Meteorological stations within the system.
WA Landman. The RCOF concept  Towards the end of 1997 two important climate-related events occurred The record-breaking El Niño episode The holding of.
Willem A. Landman & Francois Engelbrecht.  Nowcasting: A description of current weather parameters and 0 to 2 hours’ description of forecast weather.
Willem A. Landman Ruth Park Stephanie Landman Francois Engelbrecht.
Consolidated Seasonal Rainfall Guidance for Africa, November 2013 Initial Conditions Issued 7 November 2013 Forecast maps Forecast Background – ENSO update.
South African Seasonal Rainfall Prediction Performance by a Coupled Ocean-Atmosphere Model Willem A. Landman 1, Dave DeWitt 2, Dong-Eun Lee 2, Asmerom.
INPE Activities on Seasonal Climate Predictions Paulo Nobre INPE-CCST-CPTEC WGSIP-12, Miami, January 2009.
Recent S/I Prediction Activities at IRI. IRI Climate Predictability Tool (CPT) Simon Mason.
Enhancing the Scale and Relevance of Seasonal Climate Forecasts - Advancing knowledge of scales Space scales Weather within climate Methods for information.
Recent advances in operational seasonal forecasting in South Africa: Models, infrastructure and networks Willem A. Landman Asmerom Beraki.
Impact of climate scenarios on vulnerability and coping Impact of climate scenarios on vulnerability and coping or “How does the climate modeler fit in.
THE IMPACT OF DIFFERENT SEA-SURFACE TEMPERATURE PREDICTION SCENARIOS ON SOUTHERN AFRICAN SEASONAL CLIMATE FORECAST SKILL Willem A. Landman Asmerom Beraki.
Workshop on Weather and Seasonal Climate Modeling at INPE - 9DEC2008 INPE-CPTEC’s effort on Coupled Ocean-Atmosphere Modeling Paulo Nobre INPE-CPTEC Apoio:
Temperature (ºC) wind field at 200hPa Performance of the HadRM3P model for downscaling of present climate in South American Lincoln Muniz Alves*, José.
Climate recap and outlook Nate Mantua, PhD University of Washington Climate Impacts Group Nate Mantua, PhD University of Washington Climate Impacts Group.
World Weather Open Science – 17 August 2014 The Impact of Land Surface on Sub-seasonal Forecast Skill Zhichang Guo and Paul Dirmeyer Center for Ocean-Land-Atmosphere.
02/08/2015Regional Writing Centre2 02/08/2015Regional Writing Centre3.
Application of seasonal climate forecasts to predict regional scale crop yields in South Africa Trevor Lumsden and Roland Schulze School of Bioresources.
The CCAM multi-scale variable-resolution modelling system at CSIR
IRI Experience Climate knowledge/information as a resource with many partners, developing the capacity to manage climate-related risks in key climate-sensitive.
Documenting Results of Dynamical Downscaling of Climate Forecasts over the Equatorial East Africa Using Regional Spectral Model Drs. Matayo Indeje, L.
Willem A. Landman Asmerom Beraki Francois Engelbrecht Stephanie Landman Supercomputing for weather and climate modelling: convenience or necessity.
Workshop on Theory and Use of Regional Climate Models, ICTP, Trieste, 26 May - 6 June ARIAL PROGRAMME ON REGIONAL CLIMATE VARIABILITY AND CHANGE.
Drought in the West: Short-Range Forecasts to Assist with Local and Regional Planning Douglas Le Comte NOAA/CPC Association of Bay Area Governments: Water/Land.
Development of a downscaling prediction system Liqiang Sun International Research Institute for Climate and Society (IRI)
Uncertainty in climate scenarios when downscaling with an RCM M. Tadross, B. Hewitson, W Gutowski & AF07 collaborators Water Research Commission of South.
Multi-model operational seasonal forecasts for SADC Willem A. Landman Asmerom Beraki Cobus Olivier Francois Engelbrecht.
Intraseasonal TC prediction in the southern hemisphere Matthew Wheeler and John McBride Centre for Australia Weather and Climate Research A partnership.
Aim high with the goal in mind. Exp 2 Exp 3 Exp 1Largest response:
FORECAST SST TROP. PACIFIC (multi-models, dynamical and statistical) TROP. ATL, INDIAN (statistical) EXTRATROPICAL (damped persistence)
Effect of Tropical Biases on ENSO Simulation and Prediction Ed Schneider and Ben Kirtman George Mason University COLA.
Forecast development at the IRI Michael K. Tippett.
Building Asian Climate Change Scenarios by Multi-Regional Climate Models Ensemble S. Wang, D. Lee, J. McGregor, W. Gutowski, K. Dairaku, X. Gao, S. Hong,
World Climate Research Programme Climate Information for Decision Making Ghassem R. Asrar Director, WCRP.
IRI Seasonal Forecasting Update. Models Run at IRI: 2-Tier ECHAM4.5 T42L19 GHG Forcing will be added New SST scenario strategy ECHAM5 T42L19 GHG Forcing.
Meteorology 485 Long Range Forecasting Friday, January 23, 2004.
© Crown copyright Met Office Climate change and variability - Current capabilities - a synthesis of IPCC AR4 (WG1) Pete Falloon, Manager – Impacts Model.
Verification of IRI Forecasts Tony Barnston and Shuhua Li.
Seasonal Prediction of Bushfire contributions from C. Lucas, B. Murphy and D. Jones.
Course Evaluation Closes June 8th.
Regional Climate Modelling over Southern Africa Mary-Jane M. Kgatuke South African Weather Service.
Report to WGSIP on VACS activities. Tanzania Workshop The Tanzania Meteorological Agency (TMA) hosted the Variability of the African Climate System (VACS)
Willem A. Landman. Objective multi-model forecast Subjective consensus forecast.
© Crown copyright Met Office WMO CBS operational collection/display of seasonal (and sub-seasonal) forecasts Richard Graham, Met Office Hadley Centre.
Dr Mark Cresswell Statistical Forecasting [Part 2] 69EG6517 – Impacts & Models of Climate Change.
Dynamical Prediction of Indian Monsoon Rainfall and the Role of Indian Ocean K. Krishna Kumar CIRES Visiting Fellow, University of Colorado, Boulder Dynamical.
The 2002/3 Season in South Africa Complex stresses, complex warnings & responses Ackn: SEI, Millennium Ecosystem Assessment, NOAA-OGP, USAID Emma Archer.
El Niño Forecasting Stephen E. Zebiak International Research Institute for climate prediction The basis for predictability Early predictions New questions.
LASG/IAP Collaboration between CLIVAR/AAMP and GEWEX/MAHASRI A proposal to foster interaction l Coordinated GCM/RCM Process study on Monsoon ISO l Multi-RCM.
Southern Africa Workshop on Regional Climate Modeling University of Cape Town South Africa 22 January - 2 February 2001.
Consolidated Seasonal Rainfall Guidance for Global Tropics, January 2016 Initial Conditions Issued 14 January 2016 Forecast Background – ENSO update –
Caio A. S. Coelho, S. Pezzulli, M. Balmaseda (*), F. J. Doblas-Reyes (*) and D. B. Stephenson Forecast calibration and combination: A simple Bayesian approach.
WMO Lead Centre for Long-Range Forecast Multi-Model Ensemble Seasonal Prediction for Summer 2014 Erik Swenson South Asian Climate Outlook Forum (SASCOF-5)
The Great 20 th Century Drying of Africa Ninth Annual CCSM Workshop Climate Variability Working Group 9 July 2004, Santa Fe Jim Hurrell, Marty Hoerling,
1/39 Seasonal Prediction of Asian Monsoon: Predictability Issues and Limitations Arun Kumar Climate Prediction Center
IRI Climate Forecasting System
IRI Experience with many partners, developing the capacity to manage climate-related risks in key climate-sensitive sectors: agriculture, food security,
S2S Activities (and potential) in South(ern) Africa
IRI Multi-model Probability Forecasts
GPC Pretoria: Progress Report
Changes in Precipitation and Drought
Seasonal Prediction Activities at the South African Weather Service
INDIA METEOROLOGICAL DEPARTMENT
IRI forecast April 2010 SASCOF-1
IRI’s ENSO and Climate Forecasts Tony Barnston, Shuhua Li,
Presentation transcript:

Willem A. Landman

The evolution of seasonal forecasting in South Africa  Model/system development started in early 1990s – SAWS, UCT, UP, Wits (statistical forecast systems)  South African Long-Lead Forecast Forum  SARCOF started in 1997 – consensus through discussions  Late 1990s – started to use AGCMs and post- processing At SAWS (COLA T30, then ECHAM4.5) At UCT (HadAM3P) At UP/CSIR (CSIRO-II/III, then CCAM)  Global Forecasting Centre for Southern Africa – 2003  Objective multi-model forecast systems – 2008  Coupled model considerations – 2010 onwards

Statistical modelling

AGCMs at SAWS COLA T30 ECHAM4.5

DJF rainfall forecasts using RCM  Operational regional climate model forecast for southern Africa  ECHAM4.5-RegCM3

RCM - MOS MOS - SSTMOS - GCM RCM - SST

3x

New objective multi-model forecast Old subjective consensus forecast ECHAM4.5 (IRI) + CCAM (UP)

Coupled model work: SAWS

Crop and streamflow predictions

To summarize  Seasonal forecasting in South Africa since early 1990s  Model/system development (mostly focussed on operational forecast production) MOS RCMs Multi-models Coupled models Model skill has improved incrementally Simple applications models  International and national collaboration significant