page 1© crown copyright 2004 a review of uk met office seasonal forecasts for europe (1-8 months...

© Crown copyright 2004

A Review of UK Met Office Seasonal forecasts for Europe (1-8 months ahead)

Andrew Colman, Richard Graham

Met Office Hadley Centre

Exeter UK http://www.metoffice.gov.uk/research/seasonal/index.html

Thanks also to Peter McLean, Margaret Gordon, Adam Scaife for providing some of the material presented


Inputs into the Met Office seasonal forecast

Dynamicalforecasting models

Analysis ofcurrent oceanobservations

Statisticalforecasting model

Analysis ofclimate trends

Skill assessed by past performance of the forecast methods

Monthly conference:

Climate Research/Ops Centre/Comms

(Met Office,ECWMF, EURO-SIP)

Research studies(e.g. PREDICATE, COAPEC, ENSO teleconnections)

Forecasts from other centres


Example: Winter 05/06 forecast: ‘…two-in-three chance of below-average temperatures over much of Europe…’

Statistical prediction, North Atlantic Oscillation

Observed May 05 SSTA

‘tripole’ pattern

Dynamical prediction from Sep05

Most-likely temperature category, DJF05/06

‘fickle’

500hPa anomaly DJF05/06

robust – but too weak (40%)

courtesy W. Norton

HadAM3 temperature response to idealised (‘May05-like’) forcing

Model studies

2005

Marked negative winter NAO predicted (-1.1)

Correct sign predicted in 2 years out of 3

Verification


Met Office winter forecast 2005/6

The forecast

A two in three chance of a colder-than-average winter for much of Europe. If this holds true, parts of the UK – especially southern regions – are expected to have temperatures below normal

There is also an indication for a drier-than-average winter over much of the UK.

Customers:• public • government (Cabinet office, EA)• planners in utilities, transport, finance & insurance, defence, aviation, local authorities• 71% of public aware, 13% took action

Observed Europe temperature anomalies

Observed UK rainfall anomalies

The outcome, DJF 2005/6


1. Forecast Methods and tools

2. Calibration (of probabilities)

3. Correcting for Climate change trend

4. Skill Assessments

5. Future plans

Focus mainly on winter and temperature

Contents


Forecast Methods and Tools (for Europe)

1.GloSea (Global Seasonal Forecast System) HadCM3 Hadley Centre coupled ocean-atmosphere climate model adapted for

seasonal forecasting 2.5° x 3.75° x 19 level AGCM coupled with (1.25° to 0.3°) x 1.25° x 40 level OGCM 41 Member Ensemble is run out to 6 months ahead (once per month) Initial conditions from 5 ocean surface wind stress perturbations x 8 SST

perturbations + unperturbed member

2.Statistical forecast of DJF NAO index from preceding May N Atlantic SST

Spring SST Anomalies are hidden by warm surface water in Summer but tend to re-emerge in Autumn when surface water cools

(Rodwell and Folland, Quarterly Journal of the Royal Meteorological Society, 2002, 128, 1413-1443)


Forecast Methods and Tools (for Europe)

3. Statistical forecasts of July-August Temperature from Winter and Spring N Atlantic SST (Colman and Davey I.J.Climatol. 19 513-536 ,1999)

4. ENSO teleconnections (EG. Toniazzo and Scaife Geophys. Res. Let., 33, L24704,

2006)

5 Corrections for climate trend (developed from Scaife et. al. Geophys. Res. Let., 32,

L18715, 2005)


Additional tools

DePreSys (Decadal Prediction System) run once or twice per year out to 10 years

EUROSIP model (ECMWF, Meteo-France Met Office) Also run out to 6 months on a monthly basis

NCEP CPC Model , IRI forecast viewed on internet


Raw ensemble and calibrated probabilities

Dynamical seasonal forecasts are usually produced as ensembles

(Raw Ensemble) Probabilities are based on the proportion of ensemble member forecasts within given category

Probabilities reflect uncertainty in initial conditions (used to distinguish ensemble members) but not non-linear errors in the model

To correct for non-linear model error the probabilities needs to be calibrated using historical observed data.

Raw ensemble probabilities are not calibrated. 2

2

1,3

exp[ 0.5 ( )]( )

exp[ 0.5 ( )]t

tu

u

D xP x

D x

Linear Discriminant Analysis is ourprincipal tool for combining andcalibrating forecasts

Discriminant equations are calculatedfrom historical data like regressionequations but the output is probabilitiesfor a set of forecast categories

Can take weighted mean of calibratedand uncalibrated probabilities to maximise

skill.

x

( )tP x2 ( )tD x

-vector of predictor values

-predicted NAO index

-GloSea ensemble T2m and precip

- probability of category ‘t’

-generalised squared ‘distance’ from hindcast

- predictor values when ‘t’ is observed

(takes account of skill)


Prob(abv) 1/d

Prob(avg) 1/d

Prob(blw) 1/d

Principles of linear discriminant calibration

Historical predictions

eg 2 metre temperature at nearest grid-point to predicted location (plus statistical prediction for summer or winter)

mean

mean

mean

d

d

d

real-time prediction (e.g. single ensemble member)

Predicted values when above-normal category observed

Predicted values when near-normal category observed

Predicted values when below-normal category observed

‘skilful’ system


Example of different ways of weighting calibrated (discriminant) and raw ensemble probabilities ( from DJF 06/07 forecast of temperature anomaly sign)

Raw Ensemble +NAO 67% calibrated+NAO 100% calibrated+NAO

Raw Ensemble (0% calibrated) 67% calibrated 100% calibrated


Correcting for climate change trend

Trend correction needed because GloSea model (and statistical predictions) do not include

recent changes in radiative forcing

(b) Requirement to express forecasts relative to historical climatologies eg 1971-2000 which may be significantly different to present due to climate change

(c) Evidence of negative bias in recent forecasts

Trend Correction Equation FTR=FA+ CL+ TC

FTR= trend corrected forecast FA= Forecast anomaly CL= Climatology (from which forecast anomaly is calculated) TC= Trend correction (for greenhouse forcing)


Evidence of need for trend correction (from 2/3 calibrated

predictions of Sept-Nov UK T2M as example)

Similar results for other seasons

With Correction Without correction

FORECASTS FORECASTS WITH TREND OBSERVATIONS

(0.15C per decade since 1975)


HadCRUT3 observations (dashed) and HadAM3 (EMULATE) 18 member Ensemble mean simulated temperatures (solid) for Europe and trend estimate

0.15C per decade trend since 1975 initially estimated by Scaife et al (2005. Geophys. Res. Let. 32, L18715) using HadAM3 ensemble mean simulated temperatures for N Europe (Observed trend not used because of NAO contribution)

Trend curve extended backwards in time at 0.075C per decade

Summer (JJA) trend in simulations similar to winter

Linear approximation with “hinge” fit made to model

simulations

Trend also takes account of pre-1950 data and is a conservative estimate (assumes trend is always constant or increasing)


Example probability forecast maps Calibrated GloSea; +NAO; +trend DJF T2m


ROC skill maps for 4 seasons from GloSea 2mT: 1 month lead

MAM from Feb JJA from May SON from Aug DJF from Nov


ROC skill plots: Impact of adding trend correction to Winter (DJF) temperature forecasts from August 1959-2001

Adding NAO, trend correction and raw ensemble weight improves skill

100% Calibrated 67% Calibrated 50% Calibrated Raw Ensemble

GloSea + NAO; No trend correction

Glosea + NAO; Trend corrected

GloSea only; No trend correction


Summary and future plans

SUMMARY

The Met Office use primarily a dynamical seasonal forecast system (GloSea) which has some useful skill all year round

GloSea is supplemented with SST based statistical predictions for summer and winter which enhanced skill and lead to the successful winter forecasts of 2005-6 and 2006-7

A correction for climate trend is now added which also enhances skill

FUTURE PLANS

GloSea is due to be upgraded in the next 18 months with a version of the HadGEM3 model

The new GloSea should include variable radiative forcing, hence there should no longer be a need for trend correction.

PACE project is investigating European Winter predictability


Example T2m forecast for Sep-Nov from the combination of 3 different inputs: Observed (1-17 Sept), Medium range (18-30 Sept) and Glosea for Oct-Nov


Questions ?

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