Numerical and Statistical Approaches to ShortTerm Climate Prediction

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Numerical and Statistical Approaches to
Short-Term Climate Prediction

• R. Saravanan
• Department of Atmospheric Sciences
• Texas AM University

Collaborators ATMO Salil Mahajan
STAT Marc Genton, Mandy Hering,
Bani Mallick, Souparno Ghosh
Funding NSF Collaboration in Mathematical
Geosciences
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Climate prediction

• Climate is what you expect.
• Weather is what you get!
• i.e., Climate is the expectation value of weather
• Short-term climate prediction
• Predicting seasons to years ahead
• Atlantic hurricane frequency, Sahel droughts, El
  Niño,
• Verifiable using 50-100 years of the instrumental
  data record
• Long-term climate prediction
• Predicting centuries ahead
• Climate change/global warming
• More appropriately termed climate projections,
• because of the need to model human behaviour

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Hurricane frequency over time
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El Niño Mechanism
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NINO 3.4 index(Kaplan SST data set base period
1951-1980)
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El Niño Teleconnections
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Approaches short-term climate prediction

• Numerical/physical approach
• Numerically solve the physical equations
  governing the climate system
• Statistical/empirical approach
• Train a statistical model using available data

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Typical numerical model for El Niño prediction

• Atmospheric Model
• 2.8 x 2.8 degree (300 km x 300 km) horizontal
  grid
• 26 vertical levels
• 20 minute time step
• Parameterizations of turbulence, mixing, clouds
  etc.
• Ocean Model
• 1 x 1 degree (100 km x 100 km) horizontal gid
• 25 vertical levels
• 1 hour time step
• Parameterizations of turbulence, boundary layers
  etc.
• Land Model
• Coupler to connect atmosphere, ocean, and land

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Numerical simulation of sea surface
temperatures(Davey et al., 2002)
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Typical statistical model for El Niño
prediction(Xue et al. 2000)
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Comparing different approaches to climate
prediction
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El Niño prediction skill (correlation)(van
Oldenborgh et al. 2006)
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Open Questions in Short-Term Climate Prediction

• Statistical Approaches
• Handle nongaussianity/nonlinearity
• Dimensionality reduction/mode truncation
• Optimal order of complexity (i.e., avoiding
  overfitting)
• Using data for training vs. validation
• Best measures of forecast skill
• Numerical Approaches
• Initial state estimation/data assimilation
• Model parameter estimation
• Using multimodel ensembles
• Optimal ensemble size
• Empirical model corrections to alleviate
  systematic errors

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Ongoing work in statistical prediction at Texas
AM

• Nongaussian methods (with M. Genton and M.
  Hering)
• Use skew-t distribution
• Best measure of forecast skill?
• Conventional measures do not discriminate very
  well between gaussian and non-gaussian behaviour
• Bayesian VAR models (with B. Mallick and S.
  Ghosh)
• Estimate order of VAR process

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Forecast probability of malaria annual incidence
for Botswana(Thomson et al., Nature, 2006)
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Tropical Atlantic Variability
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Predictability of North Tropical Atlantic Sea
Surface Temperatures(Saravanan Chang, 2004)
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Low frequency variability of Sahel Rainfall
Giannini, Saravanan, and Chang (2003)
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El Niño-Southern Oscillation
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Deterministic Nonperiodic Flow (Chaos)Lorenz
(1963)

• Does the flap of a butterflys wings in Brazil
  set off a tornado in Texas? Lorenz (1972)

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Ensemble prediction in the Lorenz Attractor
UK MetOffice
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Typical statistical model for El Niño
prediction(Oldenborgh et al. 2006)
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Did the ECMWF seasonal forecast model outperform
statistical ENSO forecast models over the last 15
years?(van Oldenborgh et al., 2006)
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Vostok core
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van Oldenborgh et al., 2006
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Climatic PredictabilityLorenz (1975)

• Predictability of the First Kind (weather
  prediction)
• Effect of uncertainty in initial conditions
• Predictability of the Second Kind (climate
  prediction)
• Response of climate system to boundary conditions
• Solar radiation
• Sea surface temperature
• Soil wetness
• Sea ice distribution
• Carbon dioxide concentration

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El Niño Forecast Skill(Goddard DeWitt, 2005
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El Niño Prediction Skill
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El Niño prediction at 12 month lead timeGoddard
DeWitt (2005)