1. Introduction to Long-Range Forecasting
Asmerom F. Beraki
Cobus Olivier
Long-Range Forecasting (LRF) Group SAWS
Willem Landman
NRE-CSIR
Training Material

2. Purpose of Presentation
Introduce long-range forecasting principles and their customized utilization in operational environments;
the contents of this material serve solely for the purpose of the AMESD project training needs; it shouldn’t be used for other purposes without the prior permission of the respective authors.
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 2

3. PART 1 Out lines Introduction
Numerical Weather Prediction on Longer Time-Scales
Atmospheric Circulation and Slowly evolving boundary forcing
SAWS experience in the area of climate modelling (operations and research)
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 3

4. introduction Global Climate models (1-tier and 2-tier systems):
Mathematical procedure to simulate the interactions of the atmosphere, oceans, land surface, and ice
The procedure involves:
Dynamic processes
Physical parameterization
Numerical approximations
Downscaling Issues
Why is it necessary?
Dynamical DS (e.g., nested climate
models, stretched grid models)
Empirical / Statistical DS (e.g., multiple regression,
Canonical Correlation Analysis …. )
altitude
GCM dynamics are represented by the governing atmospheric equation that include the equation of motion (Newton’s second law), the first law of thermodynamics, the law of conservation of mass or continuity equation, the equation of state (ideal gas law) and the conservation equation of the water substances such as moisture. Those processes not explicitly governed by the atmospheric equations in the model dynamics are integrated into the model by means of physical parameterisations
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 4

5. introduction Uncertainties in climate models (sources)
Initial states: from the same set of initial states, different models typically produce a different set of forecast outcomes
Our lack of understanding and imperfections in model formulations,
boundary forcing (2-tier)
How uncertainties are represented in forecasts?
Ensemble prediction system and Multi-Model System
EPS is collection of predictions which collectively “explore” the possible future outcomes, given the uncertainties inherent in the forecast process
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 5

6. introduction
The ensemble spread is increased relative to the individual model ensembles. Thus the observed outcome more frequently falls within the range of forecast solutions provided by the ensemble.
The Multiple-model provides a filter for the more skilful individual model (the best model will vary with season/variable/region). Thus the strengths of the individual models are exploited, improving capabilities for global climate prediction.
Benefits derive mainly from the use of additional models, but also from the increased ensemble size
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 6

7. Numerical Weather Prediction on Longer Time-Scales
Limitations
Great progress has been made to predict the day-to-day state of the atmosphere (e.g., frontal movement, winds, pressure)
However, day-to-day fluctuations in weather are not predictable beyond two weeks
Beyond that time, errors in the data defining the state of the atmosphere at the start of a forecast period grow and overwhelm valid forecast information
This so called “chaotic” behaviour is an inherent property of the atmosphere
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 7

8. Numerical Weather Prediction on Longer Time-Scales
Maturity of NWP on shorter time-scales
Skill improvements are the result of improvements:
dynamics and physics of numerical models
observational network
computational infrastructure - realization ensemble prediction system
understanding to atmosphere-ocean-land processes and their interactions (more pronounced on longer time scales).
Courtesy of ECMWF
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 8

9. Numerical Weather Prediction on Longer Time-Scales
Maturity of NWP on shorter time-scales
Training Material
Forecast error over the European domain
(500 hPa geopotential heights)
1998
1990
1980
1975
persistence
climatology 1 2 3 4 5 6 7 8 9 10
lead-time (days) 20 40 60 80
100
120
140
error in gpm
Progress! Forecast errors made by a 1998 model after 5 days, are similar to errors made after 2 days by a 1975 model.
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 9

10. How then possible predicting climate anomalies on longer time-scales?
Bases of climate modelling:
The presence of sufficient physical basis for predicting the mean state of the atmosphere on longer time-scales is documented in the literature (e.g., Shukla, 1981; Shukla and Gutzler, 1983; Mason, et al., 1999)
The source of predictability revolves around:
improving numerical models, initial conditions and the parameterization of physical processes
By adding slowly evolving boundary conditions to the system most notably SSTs (i.e., El Niño and La Niña) and its influence on the atmospheric circulation (more pronounced at seasonal time-scale)
Sea-surface temperature (SST) anomalies of September 1997 (El Niño of 1997/98)
Anomaly: departure from the mean or average
Sea-surface temperature (SST) anomalies of November 1988 (La Niña of 1988/89)
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 10

11. How then possible predicting climate anomalies on longer time-scales?
Bases of climate modelling :
In the context of seasonal forecasting, the forecast period, lead-time and persistence issues have a significant importance as far as the quality of a particular forecast assessment is concerned.
What is desired is best quality forecast for permissible longer lead-time to address complex climate application needs
climatology:
average taken over a long time; the forecast is that the average value will happen.
persistence:
‘today’s weather is what will happen tomorrow’.
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 11

12. Dynamical Forecasts: Monthly Forecasts
Daily Scores over Northern Hemisphere +
Monthly running mean Scores
Courtesy of Meteo France
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 12

13. Dynamical Forecasts: Seasonal Forecasts
Daily Scores over Northern Hemisphere +
Seasonal running mean Scores
Courtesy of Meteo France
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 13

14. Dynamical Forecasts: Seasonal Forecasts
Daily Scores over Northern Hemisphere +
Ensemble forecast, Seasonal running mean and SST forecast
Courtesy of Meteo France
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 14

15. Atmospheric Circulation and Slowly evolving boundary forcing
Atmospheric Circulation is large-scale movement of mass and energy and triggered by thermal gradient
Hadley cell: circular motion of air masses toward poles at tropopause (trough; ITCZ) and toward equator at the surface (trade winds) characterized by rising unstable warm and moist air and subsiding dry air (ridge).
Ferrell cell: eddy-driven mid-latitude circulation though not closed cell (causes upper and low level westerlies) with no strong source heat, cold sink . The course of westerlies is easily overridden by moving weather system
Polar cell: circular motion driven by thermal gradient; Polar easterlies are the result of this cell and Corolis effect.
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 15

16. Atmospheric Circulation and Slowly evolving boundary forcing
Walker circulation:
Many overturning equatorial zonal cells are also present (such as equatorial Africa, Central and South America and Indonesian region)
Of which equatorial Pacific is most dominant and referred to as Walker Circulation
caused by longitudinal SST variations as a consequence of wind-driven Ocean currents.
Produce zonally asymmetric atmospheric circulation and in some regions may dominate the Hadley Cell
East-west pressure gradient mainly associated with WC undergoes an irregular interannual variation
This global scale variation in pressure and consequential changes in wind, temperature and precipitation patterns named Southern oscillation by Walker
During La Niña; After Webster and Chang, 1988
A schematic view of the three-dimensional Walker Cell circulation. The Walker Cell circulation consists of trade winds blowing from east to west across the tropical Pacific Ocean (blue arrow), bringing moist surface air to the west. In the western tropical Pacific, the moist air rises, forming clouds. The rising air becomes drier as much of its moisture falls to the surface as rain. Winds a few miles high blow from west to east, moving the now drier air toward South America. The air descends back to the surface in the eastern tropical Pacific, dry and relatively cloud free, completing the circulation loop. Atmospheric sea level pressures are higher under the dry sinking air in the eastern Pacific than in the more warmer and more humid west.
courtesy of IPCC AR4
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 16

17. Atmospheric Circulation and Slowly evolving boundary forcing
Source of Predictability:
Slowly evolving boundary forcings notably equatorial Pacific SST gradient are therefore the major players in modulating the meridional (Hadley Cell) and Zonal (Walker) overturning atmospheric circulations
Extreme phases of ENSO (major coupled ocean–atmosphere phenomenon) represents the single most prominent mode of climate variability at seasonal and interannual time scales.
Equatorial Atlantic Ocean Dipole (AOD), Equatorial Indian Ocean Dipole (IOD), Land surface forcing (such as soil moisture…) also among contributors though relatively less understood
In the Southern African context, these SST gradients are believed to modulate the relative annual position of the Inter-tropical convergence zone (ITCZ), the South Atlantic anticyclone, and the midlatitude westerlies albeit not extensively investigated or well understood
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 17

18. SAWS experience in the area of climate modelling (operations and research)
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 18

19. SAWS Ensemble Prediction system
SAWS experience in the area of climate modelling (operations and research)
SAWS Ensemble Prediction system
Global Precip. and Temp. Forecasts for 3 seasons (up to 5 months ahead)
Uninitialized System
Forced with persisted and forecast SST scenarios
12 ensemble members
Provides operational probabilistic forecast for different regions
Feeds information to the Multi-Model System (SAWS)
Forcing regional climate models (RecCM3)
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 19

20. SAWS experience in the area of climate modelling (operations and research)
SAWS as GPC for LRF
Fixed production cycles and time of issuance
Provision of limited set of products
Provision of verifications as per the WMO Standard “Standardized Verification System for Long-Range Forecast (SVS-LRF)
Provide up-to-date information on methodology used by the GPC
Accessibility of products
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 20

21. New Seasonal Forecasting System
SAWS experience in the area of climate modelling (operations and research)
New Seasonal Forecasting System
enhancements relative to the existing operational forecasting system:
the model knows the actual state of the atmosphere during initialization
SST scenarios with better description of uncertainties
Land surface model is initialized with realistic soil moisture
1) Description of initial conditions
The atmospheric initial conditions are suitably transformed (in a manner that respects numerical stability) – source NCEP/DOE (Kanamitsu et al., 2002) that involves:
Horizontal interpolation
vertical interpolation based on the vertical integration of the hydrostatic equation with some adjustments that maintains geostrophic balance and mass conservation
Grid to spectral transformation (T42L19)
Uncertainties are accounted by taking 10 consecutive daily NCEP atmospheric states back from the forecast date in each year (i.e., October 26 – November 4).
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 21

22. New Seasonal Forecasting System
SAWS experience in the area of climate modelling (operations and research)
New Seasonal Forecasting System
Description of boundary Conditions
The NCEP CFS SST ensemble forecasts background error that accounts different lead-times is identified from the dominant mode of Principal Component Analysis (PCA)
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 22

23. New Seasonal Forecasting System
SAWS experience in the area of climate modelling (operations and research)
New Seasonal Forecasting System
The skill of ECHAM 4.5 AGCM in predicting austral summer total precipitation qualitatively; model simulation (left) and CMAP-CPC (right).
The skill of ECHAM 4.5 AGCM in predicting total precipitation probabilistically; ROC area (left) below-normal and (right) above-normal computed using model hindcasts against CMAP-CPC. Each forecast case were to be issued on the 4th of November each year ( ).
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 23

24. SAWS experience in the area of climate modelling (operations and research)
Extended-Range forecast: migration from subjective to objective probabilistic forecasts
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 24

25. Toward a Coupled System (ECHAM4.5-MOM3)
SAWS experience in the area of climate modelling (operations and research)
Toward a Coupled System (ECHAM4.5-MOM3)
Courtesy of Magdalena Balmaseda ECMWF
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 25

26. Toward a Coupled System (ECHAM4.5-MOM3)
SAWS experience in the area of climate modelling (operations and research)
Toward a Coupled System (ECHAM4.5-MOM3)
Anomalously coupled system (lacking sea-ice model)
AGCM and OGCM are coupled using the multiple-program multiple-data (MPMD) paradigm.
Exchange information via data files every model simulation day.
No flux adjustment or empirical SST corrections is applied.
ECHAM-MOM 2009/2010 summer season SST forecast
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 26

27. Further Readings
AMS Statement, 2001: Seasonal to interannual climate prediction (adopted by AMS Council 14 January 2001). Bull. Amer. Meteo. Soc., 82: , 710.
Goddard, L., S. J. Mason, S. E. Zebiak, C. F. Ropelewski, R, Basher, and M. A. Cane (2001) Current approaches to seasonal-to-interannual climate predictions. International Journal of Climatology, 21, 1111–1152.
Kalnay, E., S.J. Lord & R.D. McPherson, 1998: Maturity of Operational Numerical Weather Prediction: Medium Range, Bull. Amer. Meteo. Soc., 79,
Shukla, J. (1981) Dynamical predictability of monthly means. J. Atmos.Sci. 38: 2547–2572.
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 27

28. SAWS MULITI-MODEL SYSTEM AND ITS INTERPRETATION
PART 2
SAWS MULITI-MODEL SYSTEM AND ITS INTERPRETATION
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 28

29. SAWS Operational Multi-Model System
GCM Raw Forecast at Coarse Resolution (~2.5°)
Statistical Downscaling to a Fine Resolution (~0.5°)
Statistical Forecast at a Fine Resolution (~0.5°)
Combination of Individual GCM Forecasts
Current Multi-Model Setup
Current GCM’s in use
ECHAM4.5 – SAWS
CFS – NCEP
ECHAM4.5-MOM3 – IRI
Current Statistical Software used
Climate Predictability Tool (CPT) – IRI
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 29

30. Example of Multi-Model Seasonal Forecast
Currently only probability maps are produced
Forecasts for 3 rolling (monthly) 3-month seasons
Current variables include Total Precipitation, Minimum and Maximum Temperature
0.5 Degree resolution from 5N-35S and 5E-52.5E
Various Formats can be provided
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 30

31. Example of Multi-Model Seasonal Forecast
Verification Maps for the First Season
Tercile Climate Maps for Each Season
Daily Rainfall Maps from
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 31

32. Interpretation of Seasonal Forecasts – Certainty vs. Uncertainty
Very Uncertain Forecast - Equal Chance for any of the three categories to occur
AB=33-40
NN=20-33
BN=33-40
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 32

33. Interpretation of Seasonal Forecasts – Certainty vs. Uncertainty
Uncertain Forecast – Although there is a slight favor for the Below-Normal category to occur
AB=33-40
NN=15-27
BN=40-45
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 33

34. Interpretation of Seasonal Forecasts – Certainty vs. Uncertainty
Certain Forecast – The Below-Normal category is heavily favored to occur
AB=<33
NN=0-50
BN=>50
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 34

35. Interpretation of Seasonal Forecasts – Skillful vs. Non-Skillful
Uncertain Forecast – Although there is a slight favor for the Below-Normal category to occur and there is skill in predicting the Below-Normal Category
AB=33-40
NN=15-27
BN=40-45
ROC= >0.5
Medium confidence for Below-Normal to occur
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 35

36. = + + = + = = + Confidence levels Low Probability High/Low Skill
Low Confidence + =
High Probability
Low Skill
Low Confidence + =
Medium Probability
High Skill
Medium Confidence = +
High Probability
High Skill
High Confidence
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 36

37. Further Readings
Landman, W. and A. Beraki: 2010: Multi-model forecast skill for mid-summer rainfall over southern Africa, accepted, International Journal of Climatology
Document Reference: RES-LRF-AMESD
Document Template Reference: TQM-PRE Date of last revision: 26 May 2010 37