Dr Liqiang Sun talks about climate outlooks.

1. The Science and Practice of Seasonal
Climate Prediction at FUNCEME
Liqiang Sun
January 22, 2013

2. If we can’t predict the weather next
week, why do we think we can make
prediction for next season?
We can’t predict the weather for
next season, but under some
conditions, we can say something
useful about the climate for next
season.

3. Weather vs. Climate
WEATHER
Weather is the day to day evolution of the atmosphere. We experience it as rain
or sunny, hot or cold, windy or calm.
weather worries:
Should I bring my umbrella to
work today?

4. CLIMATE
The most basic aspect of climate is the long term average of weather. Its what we
expect for a particular region at a particular time of year (for example, hot and
muggy in NYC during summer).
climate concerns, on average:
Should I live in NYC because its so hot and muggy in the summer?
Climate also includes the range of possibilities (for example, the warmest and
coldest temperature ever).
climate concerns, on variability:
Should I buy new snow tires for my car, in case it's a bad winter?

5. The atmosphere is a dynamical system
H
D
p
C
con
Q
p
C
rad
Q
)
p
T
p
κTω(Tv
t
T +++
∂
∂−−∇⋅−=
∂
∂
q
DCE
p
q
ωqv
t
q
+−+
∂
∂
−∇⋅−=
∂
∂
∂v

∂t = −v

×∇v

−ω ∂v

∂p + f k

× v

− ∇Φ + MD
 
Weather Forecast vs. Climate Forecast
In general,
Advection Forcing
∂X

∂t
= −v

×∇X

+ F(X

,b)

6. Weather Forecast –
Initial Condition Problem

7. Weather Forecast -
Predictability of the First Kind
 Sensitivity to initial conditions
 Predictability depends on state of the
system
 The memory of the atmosphere to initial
conditions is limited to approximately 10
days

8. Climate Forecast (2-tiered)–
Primarily External Forcing Problem
(Predictability of the Second Kind)
The atmosphere is so strongly
forced by the underlying
ocean that integrations with
fairly large differences in the
atmospheric initial conditions
converge, when forced by the
same SST (Shukla and Kinter
2006).

9. Seasonal Climate Prediction
 Exact sequence of daily weather during a
season (e.g. 3 month) is impossible to
predict. (beyond deterministic predictability
limit)
 We predict “statistics” of weather during a
season.

10. OUTLINE
 Sources of Climate Predictability
 Prediction Methodology
 Forecast Product and Format
 Forecast Verification
 Improving the Forecasts
 Summary

11. Prediction and Predictability
 Predictability is a physical characteristic of the
natural system, and not altered by forecasting
methodologies.
 Estimated predictability is system dependent.
 Predictability varies with location and season
 Predictability is the top limit of the actual
prediction skill

12. Sources of Climate Predictability
– External Forcing
 Changes in boundary conditions can influence the
characteristics of weather, and thus influence the seasonal
climate.
 If future evolution in the boundary conditions can be anticipated,
then from the knowledge of their influences on global
atmospheric circulation, skillful seasonal predictions are
possible.
 A key requirement in making successful seasonal climate
forecasts is understanding atmospheric responses to a broad
range of anomalous boundary forcings.
 SST forcing is principle among the boundary conditions
influencing atmospheric seasonal variability. Others include soil
moisture, snow cover, volcano eruption, and etc.

13. Tropical Pacific – Average State

14. El Nino
Trade winds get weaker
Warm water flows back eastward
Convection moves eastward
Winds weaken further, etc.
La Niña
Trade winds get stronger
More warm water pushed westward
Convection enhanced in western Pacific
Winds strengthen further, etc.

15. “Expected”
Climate
Anomalies
during ENSO
Events

16. A real-time forecast

17. OUTLINE
 Sources of Climate Predictability
 Prediction Methodology
 Forecast Product and Format
 Forecast Verification
 Improving the Forecasts
 Summary

18. Prediction Tools
Empirical Models
Dynamical Models
 AGCM (two-tiered process)
 CGCM (one-tiered process)
X

(t0 +τ ) = AY

(t0 )+ b
∂X

∂t
= −v

×∇X

+ F(X

,b)

19. Prediction Systems:
empirical vs. dynamical system
ADVANTAGES
Based on actual, real-world
observed data. Knowledge of
physical processes not needed.
Many climate relationships
quasi-linear, quasi-Gaussian
------------------------------------
Uses proven laws of physics.
Quality observational data not
required (but helpful for val-
idation). Can handle cases
that have never occurred.
DISADVANTAGES
Depends on quality and
length of observed data
Does not fully account
for climate change, or
new climate situations.
------------------------------
Some physical laws must
be abbreviated or statis-
tically estimated, leading
to errors and biases.
Computer intensive.
Empi-
rical
-------
Dyna-
mical

20. Dynamical Prediction System:
2-tiered vs. 1-tiered forecast system
ADVANTAGES
Two-way air-sea interaction,
as in real world (required
Where fluxes are as important as
large scale ocean dynamics)
--------------------------------------
More stable, reliable SST in
the prediction; lack of drift
that can appear in 1-tier system
Reasonably effective for regions
impacted most directly by ENSO
DISADVANTAGES
Model biases amplify
(drift); flux corrections
Computationally
expensive
------------------------------
Flawed (1-way) physics,
especially unacceptable
in tropical Atlantic and
Indian oceans (monsoon)
1-tier
------
2-tier

21. Forecast Mean
Climate Forecast: Signal + Uncertainty
“SIGNAL”
The SIGNAL represents the ‘most likely’ outcome.
The NOISE represents internal atmospheric chaos,
uncertainties in the boundary conditions, and
errors in the models.
“NOISE”
Historical distribution
Climatological Average
Forecast distribution
Below
Normal
Above
Normal
Near-Normal

25. OUTLINE
 Sources of Climate Predictability
 Prediction Methodology
 Forecast Product and Format
 Forecast Verification
 Improving the Forecasts
 Summary

26. Forecast Product
 3-month mean precipitation and surface temperature
 SST anomalies
 Soil Moisture
 Extreme Events (heat wave, cyclone, …)
 Weather within Climate (dry spell, wet spell, precipitation
frequency)
 Onset of Rainy Season
 Monsoon (index)
 Crop Growing Period
 Evaporation
 Ground Solar Radiation

27. Forecast Format
 Tercile probability
 Probability Distribution Function (PDF)
 Forecast in Context

28. Seasonal Forecast
http://www.funceme.br/DEMET/index.htm

30. 

31. The UK Met Office 2009 summer forecast
issued in April

32. Britain will have first decent ‘barbecue summer’ in three years with
temperatures regularly above 80F
Britain is expected to bask in a hot and dry summer with temperatures regularly reach
86F(30C), forecasters have predicted.
The Telegraph, April 30, 2009
Media’s interpretation of UKMO forecast

33. Media’sMedia’s reaction toward the forecast
As millions of Britons holiday
at home after that promise of a
‘barbecue summer’, how did
the Met Office get it so
wrong?
Daily Mail, 30 July 2009
UK Met Office becomes
Wet Office?

34. OUTLINE
 Sources of Climate Predictability
 Prediction Methodology
 Forecast Product and Format
 Forecast Verification
 Improving the Forecasts
 Summary

35. 24Seasonal predictability - lecture | Andreas Weigel
ETH | 28 February 2011
Verification of probabilistic forecasts
Real-valued
observations
Probabilistic
forecasts
• Need many samples
• Need probabilistic skill metrics

36. Forecast Verification
Reliability and resolution are general attributes of probabilistic
forecasts, and need to be verified.
Reliability - agreement between forecast probability and mean
observed frequency
Resolution - A category should occur more frequently as its
probability increases, and less frequently as the probability
decreases
Reliability & resolution are independent attributes

40. OUTLINE
 Sources of Climate Predictability
 Prediction Methodology
 Forecast Product and Format
 Forecast Verification
 Improving the Forecasts
 Summary

41. Improving the Forecasts
 model development,
 improve observation coverage and accuracy,
 enhance data assimilation techniques, and
 advance our understanding of seasonal
climate variability.

42. Summary
 Seasonal forecasting relies on boundary conditions and
exploits predictability of second kind
 ENSO is the most important source of seasonal
predictability.
 Multi-model ensemble technique has become the
common practice in seasonal climate forecasts.
 The verification of ensemble forecasts requires a
sufficient number of verification samples and involves
the application of probabilistic skill metrics.
 Seasonal climate forecast remains a challenge. It is
essential to continue model development, improve
observation coverage and accuracy, enhance data
assimilation techniques, and advance our understanding
of seasonal climate variability.


43. Quiz
If you want to predict the climate over
Ceara next season, what do you think
you'd need to know?

44. Thank You
Obrigado
谢谢