workshop funceme 2013

The Science and Practice of Seasonal Climate Prediction at FUNCEME

Liqiang Sun

January 22, 2013

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.

Weather vs. ClimateWEATHER

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?

CLIMATEThe 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?

The atmosphere is a dynamical system

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Weather Forecast vs. Climate Forecast

In general,

Advection Forcing

Weather Forecast – Initial Condition Problem

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

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).

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.

OUTLINE

Sources of Climate Predictability

Prediction Methodology

Forecast Product and Format

Forecast Verification

Improving the Forecasts

Summary

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

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.

Tropical Pacific – Average State

El NinoTrade winds get weakerWarm water flows back eastwardConvection moves eastwardWinds weaken further, etc.

La NiñaTrade winds get strongerMore warm water pushed westwardConvection enhanced in western PacificWinds strengthen further, etc.

“Expected” Climate

Anomalies during ENSO

Events

A real-time forecast

OUTLINE






Summary

Prediction ToolsEmpirical Models

Dynamical Models

AGCM (two-tiered process) CGCM (one-tiered process)

Prediction Systems:empirical vs. dynamical system

ADVANTAGES

Based on actual, real-worldobserved data. Knowledge ofphysical processes not needed.

Many climate relationshipsquasi-linear, quasi-Gaussian------------------------------------Uses proven laws of physics.Quality observational data not required (but helpful for val-idation). Can handle casesthat 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

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 inthe prediction; lack of driftthat can appear in 1-tier system

Reasonably effective for regionsimpacted 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

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

BelowNormal

AboveNormal

Near-Normal

OUTLINE






Summary

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

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

Seasonal Forecast

http://www.funceme.br/DEMET/index.htm

The UK Met Office 2009 summer forecast issued in April

Britain will have first decent ‘barbecue summer’ in three years with temperatures regularly above 80FBritain 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

Media’s Media’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?

OUTLINE






Summary

Forecast VerificationReliability 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

OUTLINE






Summary

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

climate variability.

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.

Quiz

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

Thank YouObrigado谢谢