Seasonal Forecasting in Canada G.J. Boer Canadian Centre for Climate Modelling and Analysis Environment Canada

People involved Many people involved to a greater or lesser extent over the years…including - McGill - CCCma - RPN - CMC - others

Evolution of SP in Canada Long slow rather low priority evolution… Winter of 82/83 was a “wake up” call Traditional (subjective) forecasts were for cold winter Some (even in AES) pointed to a volcano and predicted cold winter

Early studies initial studies of “potential predictability” observed tropical SST anomaly imposed as boundary condition provides forcing international “response” experiments in ACRN we analyzed AGCM1 (T20) response served as test bed for seasonal forecast approach

Observed T850

Modelled T850 with specified SSTA

Early 2-tier forecast experiment Objective, operational approach 2-tier with no information from the future - 1st tier: SSTA forecast (by persistence) - 2nd tier: AGCM forecast with reanalysis initial conditions Ensembles of forecasts Retrospective forecasts for January from 79-86 Systematic error correction Measures of skill

Geographical distribution of skill and spread/skill relationships (Boer,1993: MWR)

History of SP/HFP CMC had need for operational seasonal forecasts but limited resources Based on previous work we proposed 1-season 2-tier forecasts with AGCM - subjective approach costly and experience lacking - statistical approach would need development - prefer physically-based approach with evolution path Externally funded loose (part time) “network” - University and Government researchers - CMC computers - Forum of researchers, meteorologists, management Result was the HFP and ultimately operational seasonal forecasts at CMC

Sacred Principles of SP/HFP Forecasts to be based on clearly described and justified objective methods Forecasts must provide quantitative results that may be objectively verified Forecasts must be accompanied by measures of historical skill Changes in forecast procedures require objective evidence of improvement

Historical forecasting project (HFP1) Stable system produces historical seasonal forecasts (i.e. hindcasts) Operational context; no information from the future Multi-model; 1 weather forecasting model (RPN) and 1 climate model (CCCma) Basis for CMC operational seasonal forecasts Connection to international programs - SMIP2/HFP - APCN/APCC

SMIP2 SMIP1 and initial SMIP2 were WGSIP projects to investigate “potential predictability” with imposed observed SSTs SMIP2/HFP extended to actual skill, either - 2 tier with forecast SST - 1 tier coupled SMIP2 analysis and results - this workshop - lessons for TFSP 1st experiment (CHFP)

CMC forecasts based on HFP2 Multi-model approach - 4 models GCM2 T32L10 GCM3 T63L32 SEF T95L27 GEM 1.8oL50 HFP2 35 years (from 1969) … extend to 2006 10 member ensembles for 4 models = 40 member ensemble 4 month forecast made every month 12 rolling seasons

Some HFP2 results Deterministic and probabilistic forecasts Multi-model and geographically distributed skill Bias correction, combination, calibration, verification Post-processing and added value

Deterministic quantitative forecasts Bias removed – deal with anomalies of observations X and forecasts Y How best to combine/scale results from 4 models? Try in 5 ways for ensemble mean forecasts {Y}: - YU => unweighted – grand mean of {Y} - YM => scale each {Y} with its variance - YT => scale each {Y} with total variance - YL => scale Yu linearly to minimize MSE - YR => multi-model regression on YM to minimize MSE

Deterministic forecasts Skill measures - correlation - MSSS = 1- MSE/MSEo Concentrate on DJF but available for all seasons Based mainly on cross-validated results from S. Kahrin

Temp: Correlation/MSSS – globe little difference in methods of combining model results … but linear (1 parameter) scaling degrades correlation while increasing MSSS multi-model (4 parameter) scaling degrades both Corr

T Correlation/MSSS - Canada similar result except scaling to reduce MSE - degrades correlation - also degrades MSSS - multi-model scaling is the worst reiterates the difficulty in scaling when sample size small and skill modest Corr

DJF Temperature Correlation MSSS MSSS- multi-model scaling MSSS-linear scaling

Summary Reasonable skill for first season forecasts Little difference based on methods of combining multi-model results Differences in scaling multi-model results - no scaling is OK - linear scaling (1 constant) improves global MSSS (i.e. over ocean) but degrades correlation - multi-model scaling (4 constants) degrades both MSSS and correlation For HFP, multi-model information apparently adds skill: - by averaging out noise (increased ensemble size) - by simple averaging out of error in the signal

Deterministic 3-category forecast next 3-month season (HFP1) short lead (or 1 month) disseminated via web historical skill (percent correct) map welded to forecast map Canada only (but results available for globe) May-June-July

Temperature - Percent Correct DJF Little sensitivity to MM combination method DJF

Probabilistic forecasts next 3-month season probabilities for each of 3 categories given in map form reliability diagram attached

May-June-July

Brier Skill Score (BSS) Where ● Three methods to estimate Pf (Kharin and Zwiers 2003):

Briar Skill Score (BSS) BN=below NN=normal AN=above C =count G =gaussian GA=gaussian “adjusted”

BSS DJF Temperature Above Below Normal

Reliability Above Normal Below Globe Canada

Summary for Probability Forecast Simple gaussian fit better than counting modest skill over Canada/Globe reasonably reliable forecasts – but not sharp “optimum” is gaussian fit over Canada(land) and adjusted over ocean (see J.-S. Fontecilla poster for more HFP2 results)

Prediction/predictability studies Stratospheric influence (QBO with K. Hamilton) Bayesian applications Diagnostic studies (see H. Lin’s poster) GOAPP: coupled analysis/forecasting - coupled processes - predictability studies (from days to decades) - ocean analysis - coupled prediction (see Wm Merryfield’s poster) - CHFP (=> TFSP project)

Diagnostics Other investigations based on HFP, HFP2 data e.g. SST and PNA prediction and connection with tropical SST Improved precipitation forecasts See Hai Lin poster Lin, Derome and Brunet

Initial coupled model NINO3 Initial coupled model NINO3.4 SST prediction attempts using the CCCma CGCM 1982-83 El Nino 1997-98 El Nino Obs. Obs. Ensemble Avg. SST ANOM Ensemble Avg. 1988-1989 La Nina 1975-1976 La Nina The pattern of the solar radiation of the AGCM3 is similar to that of the Era40. The maximum of the solar radiation is over the east Pacific because the convection over the west Pacific “warm pool” brings cloudiness over the area. On the equatorial east Pacific, there is a sinking motion and hence clear sky with more solar radiation. The difference field shows model overestimates radiation over the east Pacific (~30 W/m^2) and ITCZ and over the south central Pacific and underestimates off the equator. Obs. Ensemble Avg. SST ANOM Obs. Ensemble Avg. 1 2 3 4 5 6 7 8 9 10 11 12 Forecast range (months) 1 2 3 4 5 6 7 8 9 10 11 12 Forecast range (months)

Stratospheric aspects: the QBO More or less annual reversal of winds in tropical stratosphere One of the few long timescale processes in the atmosphere QBO may affect extratropics by affecting propagation of large scale waves Can a knowledge of the QBO add skill to extratropical seasonal forecasts?

QBO and QBO index U(p,q) Hamilton and Hsieh(2002) analyze equatorial zonal wind between 70-10hPa obtain “circular non-linear principle component” representation of QBO u(p,t) = U(p,q(t)) + u’ U(p, q) is a structure periodic in q which “optimally” represents the QBO m=2 m=4 phase q(t)

QBO index q(t) q(t) is QBO index from average U(p, q) over 30-50hPa filtered version of QBO intended to capture QBO “essential structure” long timescale make it easy to forecast

Observations and forecasts Can knowledge of QBO provide additional skill in N winter? For 4-model HFP2 ensemble mean DJF forecast Y and a single parameter adjustment X = aY + W , a = XY/sY2 Nominal skill is SHFP=rxy2

Skill SHFP of scaled HFP2 forecast 500 850 hPa Temperature 500 hPa geopotential

Additional skill We ask if information in some index I can improve the forecast beyond HFP2 For X = aY + W, attempt to account for remaining variance W using index I W = bI+ e, b=WI/sn2 For this approach skill is additive S = SHFP + SI skill from HFP2 + from index I Try two indices: n = NINO3.4, q = QBO

Additional skill Sn from Nino3.4 Index 500 500 hPa geopotential 850 hPa Temperature

Additional skill Sq from QBO index q 500 500 hPa geopotential 850 hPa Temperature 850 hPa Temperature 500 hPa geopotential Sq > 0.12 is nominally significant

Baldwin et al. 2001 Since the QBO is driven by “sub-grid” tropical waves it is missing in AGCMs HFP2 models rapidly loose their initial conditions and their representation of the QBO

QBO and skill HFP2 captures available skill from SSTs in extratropics (NINO3.4 doesn’t help) Apparently some modest extratropical signal from the QBO lacking in HFP2 results This additional skill, such as it is, is - in N winter - in North Atlantic region - might interact with NAO QBO skill could potentially be added statistically or by improving QBO in models

Coupled forecasting Follow the Sacred Principles GOAPP - coupled processes - predictability studies (from days to decades) - ocean analysis - coupled prediction - CHFP (=> TFSP project) Initial experiments (see Bill Merryfield’s poster)

GOAPP Theme I Theme II Analysis and mechanisms Ocean data assimilation Predictability of the coupled system Diagnostic Prognostic Coupled forecast initialization Coupled historical forecasting project Verification Ocean data assimilation Ocean Analysis and forecasting Regional Global Role of eddies Applications Data assimilation Coupling Analysis methods Modes of variability Limits to predictability Value of forecasts

Summary Reasonably sophisticated objective 2-tier multi-model forecast system developed for Canada Based on Sacred Principles we are undertaking: - comprehensive analysis of HFP2 results - optimum realistic correction, calibration, implementation Developments via GOAPP of CHFP including decadal prediction and predictability

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