Variability and Predictability in Long- Range Predictions Arun - - PowerPoint PPT Presentation

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Variability and Predictability in Long- Range Predictions Arun - - PowerPoint PPT Presentation

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Variability and Predictability in Long- Range Predictions Arun Kumar Climate Prediction Center College Park, Maryland, USA arun.kumar@noaa.gov ICTP-IITM ESM Workshop 18 July, 2016 1/33 Outline What is weather and climate variability?

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ICTP-IITM ESM Workshop 18 July, 2016 1/33

Variability and Predictability in Long- Range Predictions

Arun Kumar Climate Prediction Center College Park, Maryland, USA arun.kumar@noaa.gov

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ICTP-IITM ESM Workshop 18 July, 2016 2/33

Outline

  • What is weather and climate variability?
  • What is predictability?
  • How is predictability quantified?
  • Sources of predictability
  • Estimating predictability
  • Realizing predictability (or prediction skill)
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Weather and Climate Variability

  • Temperature tomorrow is not the same as today
  • Monthly (seasonal) mean precipitation for June-July-August

seasonal average over India is not the same in 2014 as in 2015

  • Average precipitation over India for a 10-year average changes

from one decade to another

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2014 2015

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Annual Mean All India Temperature Anomaly

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Quantifying Variability

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Variance of 200-mb DJF Seasonal Mean Height

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Predictability

  • Predictability: From the knowledge of the current state of the
  • cean, our ability to anticipate its future evolution
  • Prediction for a particular time-scale, what fraction of variability

can be anticipated?

– Predictability varies between 0-100% of variability

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Why all the variability is not predictable?

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There is always a spread (uncertainty) in forecasts!

  • Non-linear dynamical systems sensitivity to specification of initial

conditions

  • Deterministic chaos
  • Uncertainty could be better quantified, but can never be removed
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Example of Seasonal Prediction

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Example of Climate Projection

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  • The forecast spread (uncertainty) can be quantified using ensemble

prediction approach where a collection of forecasts is initiated from small perturbations in the initial conditions

  • In a nutshell

– The reason for a limit on predictability stems from limits on the accuracy of predictions on shorter time-scales – One cannot always predict the state of the atmosphere ∆t from now with 100% accuracy no matter how small ∆t is.

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How is Predictability Quantified?

  • Spread in forecast outcomes from different initial conditions can be

quantified as probability density function (PDF)

  • It is our ability to distinguish PDF of outcomes for the event to be

predicted from the climatological PDF

  • Differences in the PDF can come from differences in various

moments of the PDF

– Mean – Spread – Skewness

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How is Predictability Quantified?

Climatological PDF PDF for a Season (Red)

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High predictability

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Low predictability

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Why it is Important to Understand and Quantify Predictability?

  • Helps gauge limits of prediction skill and manage expectations
  • Helps pinpoint sources of predictability, e.g., SST  for

atmospheric variability

  • How do climate models simulate processes, physics and

interactions to better predict “sources” of predictability?

  • Provides one way to focus model improvements
  • Where to place limited resources (ensemble size, model resolution,

analysis, perturbations,…)

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Sources of Predictability

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Sources of Predictability

  • Weather – Atmospheric initial conditions
  • Seasonal – Boundary conditions (upper oceans, soil moisture, snow, sea-ice…)
  • Decadal – deeper oceans,…
  • Climate projections – CO2,…
  • For different lead time, the relative contribution from sources of predictability

differs

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Influence of Various Factors on the PDF

…initial conditions …boundary conditions …external conditions

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Seasonal-to-Interannual - ENSO

Sea Surface Temperature Anomaly

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Decadal - PDO

Sea Surface Temperature Anomaly (shading)

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Estimating Predictability

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Methods for Estimating Predictability

  • Observational data Daily time-series

– Predictor – Predictand relationships – Analogs – Daily time-series

  • Simple; unbiased, but non-linearity is hard to incorporate

DJF Z700 Correlation with SST index

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Methods for estimating predictability

  • Models

–Ensemble of integrations

  • Spread among the ensemble members is the

unpredictable component

  • Ensemble mean (the common part) is the

predictable component

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Model Simulations

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Decomposing Total Variability

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Ratio of Predictable and Unpredictable Component 200mb Z

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Realizing Predictability

  • Predictability  Prediction skill
  • Requires a real-time forecast system
  • To realize predictability that exists, forecast systems need to have certain

attributes

  •  Design and framework of long-range prediction systems (Thursday)
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Realizing Predictability

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Implication of Limited Predictability

  • Since future outcomes are

not certain, forecasts have to be probabilistic

  • Decision making under

probabilistic information context is hard

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Summary

  • There is variability associated with all time-scales
  • All variability cannot be anticipated in advance – Predictability
  • There are physical reasons that allow us to anticipate variability –

sources of predictability

  • Predictability can be estimated either from observational data or

model simulations

  • Forecast systems allow to realize predictability as prediction skill