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(seasonal) prediction systems Arun Kumar Climate Prediction Center - - PowerPoint PPT Presentation

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Design and framework of long-range (seasonal) prediction systems Arun Kumar Climate Prediction Center College Park, Maryland, USA arun.kumar@noaa.gov IITM-ICTP ESM Workshop 21 July, 2016 1/32 Outline What is long-range prediction and

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IITM-ICTP ESM Workshop 21 July, 2016 1/32

Design and framework of long-range (seasonal) prediction systems

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

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IITM-ICTP ESM Workshop 21 July, 2016 2/32

Outline

  • What is long-range prediction and what makes long-range

(seasonal) prediction possible?

  • Methods for making seasonal prediction
  • An example of seasonal prediction system: NCEP Climate Forecast

System version 2 (CFSv2)

  • Summary
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IITM-ICTP ESM Workshop 21 July, 2016 3/32

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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IITM-ICTP ESM Workshop 21 July, 2016 4/32

  • There is always a spread (uncertainty) in forecasts…
  • This forecast uncertainty is quantified using ensemble prediction

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

  • Evolution of individual forecasts in the ensemble results in a

collection of future outcomes which can be quantified using a probability density function (PDF)

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Example of forecast spread: ENSO Prediction

NCEP/CFS Nino 3.4 SST Prediction

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IITM-ICTP ESM Workshop 21 July, 2016 6/32

Characterizing seasonal prediction

  • There is a forecast PDF of possible outcomes for a specific season

(for which we intend to make prediction).

  • There is a climatological PDF based on aggregation of all seasons.
  • These PDF depend on

– Season – Variable – Location

  • Seasonal prediction depends our ability to differentiate PDF of

forecast PDF from the climatological PDF

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IITM-ICTP ESM Workshop 21 July, 2016 7/32

Characterizing seasonal prediction

Climatological PDF PDF for a Season (Red)

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IITM-ICTP ESM Workshop 21 July, 2016 8/32

What lends predictability in long-range predictions?

  • Initial conditions

– Weather prediction – ENSO prediction

  • Influence of boundary conditions

– Anomalous SSTs  Influence on atmospheric variability

  • Influence of external forcings

– Changes in CO2

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IITM-ICTP ESM Workshop 21 July, 2016 9/32

What provides skill in seasonal predictions

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

predicted from the corresponding climatological PDF

  • Differences in the PDF can come from differences in various

moments of the PDF

– Mean – Spread – Skewness

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IITM-ICTP ESM Workshop 21 July, 2016 10/32

Examples of high/low prediction skill

High Predictability Low Predictability Climo PDF FCST PDF

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IITM-ICTP ESM Workshop 21 July, 2016 11/32

Outline

  • What is seasonal prediction and what makes seasonal prediction

possible?

  • Methods for making seasonal prediction
  • An example of seasonal prediction system: NCEP Climate Forecast

System version 2 (CFSv2)

  • Summary
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IITM-ICTP ESM Workshop 21 July, 2016 12/32

Seasonal Prediction Methods

  • Empirical prediction tools

– Advantages

  • Trained based on historical observations
  • Unbiased
  • Simple and computationally efficient

– Disadvantages

  • Limited by observational data
  • Mostly depend on linear relationships
  • Non-stationarity in climate is hard to include
  • Cannot handle unprecedented situations
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Seasonal Prediction Methods

  • Dynamical Prediction Tools

– Advantages

  • Linearity and non-stationarity is not an issue
  • Easier to construct PDF of seasonal mean state
  • Easier to handle unprecedented situations

– Disadvantages

  • Computationally expensive and require a large infrastructure
  • Forecast systems have biases that requires special attention
  • Properties of empirical and dynamical prediction tools are complementary, and in

general, and generally both are used in the development of final forecast

  • This is the current practice used by several operational centers, e.g., prediction of

monsoon rainfall by the IMD

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IITM-ICTP ESM Workshop 21 July, 2016 14/32

Outline

  • What is seasonal prediction and what makes seasonal prediction

possible?

  • Methods for making seasonal prediction
  • An example of seasonal prediction system: NCEP Climate Forecast

System version 2 (CFSv2)

  • Summary
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IITM-ICTP ESM Workshop 21 July, 2016 15/32

Components of a Seasonal Forecast System

  • Real-time forecasts
  • Initialization
  • Bias correction and calibration of real-time forecasts (uses hindcasts)
  • Forecast dissemination
  • Verification
  • Hindcasts
  • Skill assessment of the prediction system
  • Assessment of time-dependent biases
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IITM-ICTP ESM Workshop 21 July, 2016 16/32

Initialization

  • Various components of the forecast system need to be initialized from

their observed state

– Atmosphere (temperature; humidity; winds) – Ocean (temperature; salinity; ocean currents) – Land (soil moisture; snow) – Sea ice (extent; thickness)

  • Initialization is done from the Climate Forecast System Reanalysis (CFSR)

that provides a consistent 3-dimensional analysis of various components

  • f the Earth System
  • After initialization, forecast system is run to nine months into the future
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IITM-ICTP ESM Workshop 21 July, 2016 17/32

Real-time forecasts: CFSv2

  • Four nine month forecasts every day
  • 120 seasonal forecasts in a month
  • Real-time forecasts are constructed based on forecasts from latest

10 days of initial conditions, i.e., an ensemble of 40 forecasts is used for developing real-time seasonal predictions

  • Lagged ensemble provides an estimate of PDF of seasonal mean

states

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IITM-ICTP ESM Workshop 21 July, 2016 18/32

Real-time forecasts

  • Configuration of real-time forecasts generally differs from their

hindcast counterpart

– More frequent – Larger ensembles

  • Consistency in the analysis of initial conditions, particularly for

slowly varying components of the Earth System (SST, soil moisture) is crucial!

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IITM-ICTP ESM Workshop 21 July, 2016 19/32

Hindcasts

  • Hindcasts – Run the real-time forecast system over historical cases
  • Run the forecast system over last thirty years (1981-2010)
  • Four nine months forecast every 5th day of the calendar
  • 72 forecasts every year
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IITM-ICTP ESM Workshop 21 July, 2016 20/32

Hindcasts

  • What is the purpose of hindcasts?

– Provides an assessment of the skill of the seasonal forecast system – Because of model biases

  • Real-time forecasts have to be bias corrected
  • Hindcasts provide the data set for bias correction
  • Hindcasts are used to develop initial month, and lead-time dependent model

climatology

– Calibration of real-time forecasts

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IITM-ICTP ESM Workshop 21 July, 2016 21/32

Skill Assessments

  • Based on 30-year hindcast, skill of the CFSv2 can be assessed for

– Predicting sea surface temperature anomalies – Predicting various SST indices that are important for seasonal predictions, e.g., Nino 3.4 SST index – Surface quantities over land, e.g., precipitation and surface temperatures – Other variables

  • Soil moisture
  • Sea ice
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IITM-ICTP ESM Workshop 21 July, 2016 22/32

Skill Assessment: SST

Anomaly Correlation

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Skill Assessment: Surface Temperature

Anomaly Correlation

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Skill Assessment: Precipitation

Anomaly Correlation

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Bias Correction and Calibration

  • Bias correction

– Correct for differences in observed and predicted mean state – Adjust if variability between observations and predictions differs

  • Calibration

– Adjust predicted anomaly based on assessment of past skill (e.g., from hindcast data set) – If past skill is close to zero, make the forecast PDF same as the climatological PDF

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Model bias

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Forecast Dissemination

  • Graphical products

– Bias corrected seasonal mean anomalies – Normalized anomalies – Bias corrected anomalies with skill mask

  • Forecast and hindcast gridded data

– Real-time forecasts – Hindcast data available via several outlets – Data could be used for statistical downscaling

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Graphical Products: SST Anomaly

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Graphical Products: Standardized SST Anomalies

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Graphical Products: SST Anomalies with Skill Mask

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IITM-ICTP ESM Workshop 21 July, 2016 31/32

Outline

  • What is seasonal prediction and what makes seasonal prediction

possible?

  • Methods for making seasonal prediction
  • An example of seasonal prediction system: NCEP Climate Forecast

System version 2 (CFSv2)

  • Summary
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IITM-ICTP ESM Workshop 21 July, 2016 32/32

Summary

  • Seasonal prediction system are fairly mature
  • Skill of prediction is limited, but it is better than a random guess
  • Hindcast and real-time forecast data is a huge data base that can

be used for various research and analyses purposes, for example,

– Analysis and predictability of extremes – Influence of various climatic factors on extremes