Docstoc

Slide 1 - IRI - Columbia University

Document Sample
Slide 1 - IRI - Columbia University Powered By Docstoc
					   Seasonal Climate Forecast
               Sylwia Trzaska
International Institute for Climate and Society
    Earth Institute at Columbia University
                New York, USA

Basics of seasonal climate forecast
      - sources of uncertainty
Main methods
      - statistical
      - General Circulation Models
IRI Forecast system and products
Forecast verification
     Seasonal Climate Forecast
Global Seasonal Forecasts




                        Seasonal Climate Outlook Forums

                               PRESANOR


                               PRES-AO (11)
                                              GHACOF (22)

                                   PRES-AC (3)

                                                 Consensus Regional Forecast

                                      SARCOF (11)
            Example of climate information

Historical record of temperature
in South Africa




   Climate is not a steady state
   Varies on a range of scales
                  Scales and uncertainties                                        Initial &
                                                                                 Projected
                                                                                Atmospheric
                                                        Initial &               Composition
               Current
              Observed     Initial & Projected         Projected
                State     State of Atmosphere        State of Ocean             Climate Change

                                                                      Decadal
Uncertainty




                                                           Under research




  adapted from CLIVAR    Time Scale, Spatial Scale
  courtesy L. Goddard
Bases of Seasonal Forecast
   SST influence on Tropical atmosphere




 Modifications of boundary conditions (SST, Land
   surface) can influence weather characteristics
   (amplitude of an event, its persistence etc.,)
 Thus the characteristics of the season
      Bases of seasonal forecast – IC and BC

Example: predict the trajectory of an arrow
We do know the physical laws that govern the movement of the dart so what
determines the arrival point?

The exact conditions at the departure (the exact departure point, the exact
direction of the throw, the initial speed etc)
The conditons that can influence its trajectory (ex. Draft that could deflect the
trajectory)

The farther the target the more difficult the estimation of the arrival point

Weather forecast – horizon <10 days, depend mostly on the Initial Conditions (IC)
Seasonal forecats – horizon > month, depend mostly on the external influences
(Boundary Conditions – BC); SST are the key factor


      Uncertainty on Initial Conditions
      Uncertainty on the evolution of the system
  Bases of seasonal forecast – sources of
               uncertainty
 Uncertainty     in Initial Conditions
Estimated from observed data, mostly
surface stations, distributed unevenly,
with vast areas without data



           Surface observations density




                                          Satellite data do provide some
                                          information but need calibration
                                          against observed data


                                          Solution : ensemble technique
                                          With perturbed IC
                                    Ensemble Technique
                                                                            Deterministic Forecast
Uncertainty on Initial Conditions             Uncertainty on the forecats




                                                                                       X




 Met Analyses
                                Climatology



                                       time
  Bases of Seasonal Forecast – sources of
               uncertainty
  Uncertainty on the evolution of the system
  How do we forecast the future state of the climate?
  •Statistical Methods
  •Dynamical Methods

1. Statistical Methods: identify statistical relationships between
   variables in the past
 Ex. Relationship between a rainfall             Ex. 3 SST indices used to forecast the
 indexin E Africa and Nino3.4                    total JAS rainfall in the Sahel




Pb. The set of predictors and relationships based on the past
There are instabilities in the teleconections
  Bases of Seasonal Forecast – sources of
               uncertainty
2. Dynamical methods: Numerical Models of Climate System

                   General Circulation Models
Attempt to quantitavely represent all the proccesses in the climate system




 • Numerical Models
 • Based on well known physical equations
 • Spatio-temporal discretisations: the quantities are
 computed for a 3D box with a given time step
             General Circulation Models


                                         Constraints on computing
                                         ressources
                                         = constraintes on the resolytion

                                         Typical GCM resolution for climate
                                         purposes 250x250km
                                         Time step 15min




Sources of Errors:
 Scale of numerous phenomena < model grid scale
(ex. clouds)
 Models of different subsytems (ex. atmosphere,
ocean) developped separetly, assuming the other
component perfectly known/represented
                                             GCM
                A part of the world according to a GCM...



                                                            red: land
                                                           white: water

                                                           1 box=500 km




                    Can you recognize the region?
Courtesy Prof Deliang Chen, Goteborgs University, Sweden
                                             GCM

                                                1 box=250 km




                                                           1 box=180 km




                                  1 box=110 km

Courtesy Prof Deliang Chen, Goteborgs University, Sweden
   Changing Scales - Downscaling
                                                            Dynamical
                                                           downscaling


 Statistical
    and
 dynamical                                                  Statistical
downscaling                                                downscaling




Courtesy Prof Deliang Chen, Goteborgs University, Sweden
    Dynamical Systems for Seasonal Forecast
•Systems where the moatmospgeric and ocean modelas are coupled i.e.
Communicate with each other in both directions ( the state of the atmosphere is
modified based on ocean conditions and the state of the ocean is influenced by the
atmosphere )

Ex. 1 Tier System, ex. ECMWF
          the system (atm and oc) is initialised the is left to evolve on its own

•Uncoupled systems where the evolution of one of the components is prescribed
(ex. evolution of the SST) and influences the other component (atmosphere)without
being influenced back

Ex. 2 Tier System, ex. IRI
          The evolution of the SST over the next 12 months is forecasted externally
          - Tropical Pacific SST forecasted using Cane-Zebiak model
          - SST in other basins :      1/ persistance
                                       2/ statistical methods


Finally : the forecatst use multimodel-ensembling and each model is
integrated with several perturbed initial conditions
    IRI operational Seasonal Climate
                Forecast
 IRI is issuing operationally each month (around 15th of each month)
                                                 …
the seasonal forecast for a number of forthcoming seasons


                                         …
                                 …
     M       J      J       A      S      O       N

  Available on the web as maps for
 RR and temp
  some data available for partners
IRI Dynamical Climate Forecast System
                           2-tier system
     OCEAN                 ATMOSPHERE
       PERSISTED              GLOBAL
        GLOBAL              ATMOSPHERIC
                              MODELS
           SST
                                             10 Persisted
        ANOMALY                              24    SST
                             ECPC(Scripps)   24
                                                Ensembles
                                                3 Mo. lead
                                             10                   POST
  FORECAST SST              ECHAM4.5(MPI)
                                                               PROCESSING
   TROP. PACIFIC             CCM3.x(NCAR)
(multi-models, dynamical                                       MULTIMODEL
                                             12
    and statistical)           NCEP(MRF9)                      ENSEMBLING
                                             24 Forecast
 TROP. ATL, INDIAN            NSIPP(NASA)    24     SST
                                                Ensembles
     (statistical)                           30
                                   COLA2     12 3/6 Mo. lead
  EXTRATROPICAL
                                             30
 (damped persistence)               GFDL
                                             30
  Post-processing of multi-ensemble multi-model

•Compute the seasonal mean (ex. JAS) for different variables (precipitation,
temperature) in each grid point
•Compute the main statistics (mean, median, std)
•Comparison with climatological distribution => estimation of probabilites
Past climatology of the ensemble
           Mean
           JAS
                                                                                  forecast

                     Dispersion



     33%    33%     33%                                  High probability of rainfall in the above normal category
           terciles



                      Automatic Corrections
                      • Each model grid point is weight in function of the past
                      performance
                      • Spatial smoothing applied to the ewights
                      • More recently – baysian approach
ENSO Forecasts
IRI Seasonal Forecasts
New forecast format - probability of
           excedence
                          ECHAM4.5 2m Temperature:
                          JFM 1983 – El Nino



                        Climatology based on 1950-2001 period.




                                     (Courtesy Lisa Goddard)
         Forecast Verification

 It is important to provide the user with some
assessment of the quality of the forecast

   Quality – degree to which a forecast corresponds to observations

   Value – degree to which a forecast helps the decision maker
           realize incremental economic (other) benefit

          • High quality forecast – little value: clear sky over Sahara
          • Low quality forecast - high value : prediction of fog at
          airports => even if overcast (false alarms)
      Assessing Forecast Quality
  Probabilistic forecasts address the two fundamental
  questions:
             • What is going to happen?
             • How confident can we be that it is
             going to happen?
  Both these aspects require verification
  There is no single measure that gives a comprehensive
  summary of forecast quality.

NB. As soon as a forecast is expressed probabilistically, all possible outcomes
are forecast
Probabilistic forecasts cannot be right or wrong, but they can be over- or
under-confident.
    Assessing Forecast Quality
•How do we decide whether a forecast was “correct”?
•How do we decide whether a set of forecasts is correct
 consistently enough to be considered “good”?
•What is the difference between an “accurate” forecast, and
 a “skilful” forecast, and a “reliable” forecast?

  Accurate – close to observations
  Reliable – average agreement with observations
  Skilful – average accuracy with respect to reference
   forecast (climatology, persistence)


 Skill score – comparison with a reference forecast
 Verification score – comparison with observations
   Probabilistic Forecast Verification
                method1

Whenever a forecaster says there is a high probability of rain
tomorrow, it should rain more frequently than when the forecaster
says there is a low probability of rain.

   Reliability
   A consistency between the forecast probabilities for an event
   and the observed relative frequencies of this event.

   If the proportion of times that the event occurs is the same as
   the forecast probability for all values of the prior probability, the
   system is reliable (or well calibrated).

   Murphy, A. H., 1973: A new vector partition of the probability score. Journal of Applied
   Meteorology, 12, 595–600.
   Murphy, A. H., 1993: What is a good forecast? An essay on the nature of goodness in
   weather forecasting. Weather and Forecasting, 8,         281–293.
Reliability Diagram
Multimodel ensemble verification
 Major Goal of
 Probabilistic
 Forecasts
 Reliability!
Forecasts should
“mean what they say”.




                        Courtesy Tony Barnston
    Probabilistic Forecast verification
                 method 2
       Relative Operating Characteristics (ROC)

•   Mainly addresses the following questions:
    – For how many of the events were warnings correctly
       provided?
    – For how many of the non-events were warnings
       incorrectly provided?
•   ROC measures how good forecasts are in the
    context of a very simple decision-making model
    from the perspective off the user.
•   ROC recognizes that forecast quality cannot be
    measured by a single number
ENSO Forecasts

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:1
posted:10/17/2012
language:English
pages:29