VIEWS: 1 PAGES: 29 POSTED ON: 10/17/2012
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
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