s2d prediction activities at cccma(p)
DESCRIPTION
S2D Prediction Activities at CCCma(p). HFP2 (2-tier seasonal) basis of CMC operational forecasts Analyses Trend in SIP forecasts CHFP1 and 2 (1-tier seasonal/interannual) initial approach and results analyses and initialization development path DHFP (decadal) - PowerPoint PPT PresentationTRANSCRIPT
S2D Prediction Activities at CCCma(p)
HFP2 (2-tier seasonal) basis of CMC operational forecasts Analyses Trend in SIP forecasts
CHFP1 and 2 (1-tier seasonal/interannual) initial approach and results analyses and initialization development path
DHFP (decadal) potential predictability results WGCM/WGSIP/CMIP5 and decadal prediction
Retrospective forecasts are crucial for - establishing forecast skill - providing forecast climatology for bias correction - guiding forecast calibration and post-processing
Why retrospective forecasts?
Current EC 2-tier operational system: - 4 AGCMs x 10-ensemble - validated by 2nd Historical Forecast Project (HFP2) - 4-month retrospective forecasts initialized each month 1969-2003
Validate also 1-tier coupled forecasts in CHFP
HFP2 CMC deterministic and probabilistic
seasonal forecasts are based on HFP2 HFP2 (2nd Historical Forecasting Project)
retrospective 1-season forecast experiment operational context – no information from the
future 2-tier - SST forecast provides boundary conditions
for AGCM forecasts multi-model, multi-realization – each of 4 models
produces 10-member ensemble forecasts we use results from 33-year period 1970-2002
Seasonal forecastsJan-Feb-Mar 2009
DeterministicProbabilistic
Skill assessment of HFP2 forecasts analysis by S. Kharin et al. (2008) deterministic forecasts
continuous valued categorical combining multi-model ensemble mean forecast
information unweighted variance weighted (2 methods) regression improved
scale unweighted ensemble mean (single parameter) scale each model mean (four parameter)
skill measures: correlation and MSSS probabilistic categorical
combining multi-model forecast information count value gaussian adjusted gaussian
skill measures: Brier skill score benefits of Multi-model approach
Correlation and MSSS for Sfc air Tmethods of combiningmulti-model forecasts
1 parameter regression weighting4 parameter regression weighting
Percent correct, 3-category forecasts
Seasonal variation of skillNo dependence on combination method
3-category probabilistic forecasts
Count methodsGaussian methods
Normal
Below
Adjustedgaussian
Brier skill score
Reliability
The virtues of the MME approach
For same overallensemble sizemore models give better scores
More modelsare better
Diminishingreturns
Four models isa “practical”choice
Summary
Deterministic forecasts not critical how MME formed scaled MME result (1 parameter) increases MSSS but
degrades correlation scaled MME result (4 parameters) degrades scores
Probabilistic forecasts gaussian method better than count adjusted gaussian only better over ocean (on account of
persisted SSTAs) Multi-model methods
for same ensemble size more models the better because of diminishing returns, 4 models (as HFP2) is
reasonable “practical” choice
Climate trends and seasonal forecasts
trend in HFP2 no GHG or other forcing in HFP2 implicit in persisted SSTAs and initial
conditions characterization of trend statistical correction
trend in CHFP1 GHG forcing effect in coupled forecast model
Boer (2008)
Background
Observational studies show long timescale trends thought to be due to GHG and other forcing - together with long timescale variability
Externally forced trends should provide an additional seasonal forecasting signal - if present and properly forecast
Do we capture these trends in the 33-years of retrospective forecast data of HFP2 (and does it matter)?
Linear trends in <T850> for DJF
For global means
<T> = <a> + <T’> - fit linear trends by least squares
forecast trend weaker than trend in NCEP reanalysis data
we also fit trends to each point
Forecast
NCEP
years
Units: oC
-17 17
Trends in SAT from NCEP and HFP2
DJFNCEP
DJFHFP2
JJANCEP
JJAHFP2
Units oC/decade
Trends in Z500 from NCEP and HFP2
DJFNCEP
DJFHFP2
JJANCEP
JJAHFP2
Units m/decade
Trends
trends are larger at higher latitudes, over land and in winter – the signature of global warming
trends in forecasts are weaker than in reanalysis data
suggests that lack of GHG forcing may degrade the forecasts
spatial average of MSE over globe
3 components error in trend error in non-trend component cross-product
Time evolution of error
Dealing with anomalies minimizes MSE in DJF T850
Anomalies
Trends setto zero at t=0
global means MSE over globe
trend
trend
total
total
non-trend
non-trend
cross-product
NCEP
HFP2
Mean error
Mean error
Mean square error
Mean square error
Can we improve skill by adjusting trend?
try simple statistical adjustments based on scaling or trend adjustment
only cases where single parameter is estimated to avoid over-fitting
evaluate in cross-validation mode use MSSS and Correlation scores
Statistical adjustments
Scaled
Trendadded
Trendreplaced
Rawforecast
Trendreplaced
Rawforecast
Scaled
Trendadded
Trendreplaced
Rawforecast
Scaled
Trendadded
Global
Dec Jan Feb Mar DJF JFM
0.10
0.15
0.20
0.25
0.30
0.35
0.40
BP01WS01GHGWS01GHGA
Land
Dec Jan Feb Mar DJF JFM
0.05
0.10
0.15
0.20
0.25
0.30
Ocean
Dec Jan Feb Mar DJF JFM
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
No radiative forcing trend in modelWeak radiative forcing trendStronger radiative forcing trend
Temperature at 850hPa (T850) Anomaly Correlation1 December Initialization
Preliminary evidence of impact of radiative forcing trend on coupled model forecast skill
Forcing can affect both land and ocean in this case
Summary Observed/reanalysis trends have GHG signature Trends in seasonal forecasts are weak Statistically “correcting trends” gives some
improvement in skill over Asia but not, unfortunately, over North America
Suggests seasonal forecasting models should include radiative forcing due to GHG and aerosols - physically realistic (if properly inserted)- some hope for improved forecasts
For coupled CHFP forecasts SSTs may also drift and we have some evidence GHG forcing is needed
The Coupled Historical Forecast Project, version 1: Formulation, results, and progress towards CHFP2
Bill Merryfield, George Boer, Greg Flato , Slava Kharin , Woo-Sung Lee , Badal Pal , John Scinocca
Canadian Centre for Climate Modelling and AnalysisEnvironment Canada
Pilot Project: CHFP1
Based on CGCM3.1/T63 (IPCC AR4)
Simple SST nudging initialization after Keenlyside et al. (Tellus 2005):
- Strongly relax SST to observed 1970-2001 time series
- Anomalous wind stress tends to set up correct equatorial thermocline configuration:
AsiaAsia South South AmericaAmerica
warmcool
Anomalous wind stress
1 Sep assimilation run
31302928272625
AGCM
OGCM
AugForecast 1
Forecast 2
Forecast 3
Forecast 10
…
12 mos
forecast runs
lead 0 lead 1 lead 2
1 Aug1 Jul1 Jun
10 combinations of ACGM + OGCM initial conditions
1 Oct1 May
Construct 10 initial conditions for 1 Sep (e.g.) by combining atm and ocn states from preceding week:
Launch forecasts 1 Feb, 1June, 1 Sep, 1 Dec 1971-2000 (10 ensemble members) x (4 initializations yr-1) x 30 yrs 1200 years of coupled model integration
CHFP1 Ensemble Generation
CHFP1
Persistence
Damped persistence
CHFP1 Results (Ensemble size=10)
Anomaly correlation Mean square skill score
NINO3.4 skills
• Not too bad for off-the shelf model, crude initialization• Much room for improvement, improvement being realized…
Coupled Forecast System Development Path
CHFP2
“Off the shelf” CGCM
Simple SST nudging
initialization
Ocean data assimilation
CHFP1
CGCM development
2D Var after Tang
et al. (JGR 2004)Improved error covariances
Atmospheric initializationInsertion of reanalyses Atm data assimilation
Land initialization
GHG forcing,
trend correction, etc
Off-line forcing by bias-correctedreanalysis
S assim after Troccoli et al. (MWR 2002)
AGCM, OGCM
Analysis and Verification
Obs SSTA Nov 1982 Deterministic forecast SSTA Nov 1982New AGCM, OGCM Lead=11 mo
• Much improved model ENSO with new AGCM, OGCM• Exemplified by “hit” for 11-month lead prediction of 1982/83 El Nino:
NINO3 Power Spectrum
CGCM4
CGCM3.8
CGCM3.1
OBS1970-99
OBS1940-69
PERIOD (Y)100 10 1 10-1
Ocean Data Assimilation
Initially use approach of Tang et al. (JGR 2004): - input ocean reanalysis in lieu of observations - simple variational assimilation level-by-level (2D
Var) - background error covariances of Derber &
Rosati (JPO, 1989)
Assimilate multiple ocean analyses
Explore methods to improve error covariances
MULTI-ANALYSIS EXP_ATMOS EXP_OCEAN
Ensemble member 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
Atmosphere Initial State
8/318/31
8/30
8/29
8/28
8/27
8/26
8/31
Ocean Initial state
8/31 8/318/31
8/30
8/29
8/28
8/27
8/26
Used Reanalysis Data for ocean assimilation
GODAS ECMWF GFDL SODA INGV METUK GODAS GODAS
Ocean Initialization by multi-analysis assimilation
Experiment: compare NINO3.4 skill and ensemble spread for three
ensemble initialization strategies:
- Multi-analysis: off-line assimilation of 6 ocean analysis products (same atm)
- Exp_atmos: 6 AGCM states from consecutive days prior to forecast start (same ocn)
- Exp_ocean: 6 OGCM states from consecutive days prior to forecast start (same ocn)
1980-2001: 22 years of Sep 1–initialized forecasts
Lead Month
0 1 2 3 4 5 6 7 8 9 10 11
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
multi_analysisexp_atmosexp_ocean
Cor
rela
tion
multi-reanalysisexp_atmosexp_ocean
0 1 2 3 4 5 6 7 8 9 10 11
0.0
0.2
0.4
0.6
0.8
1.0
Lead month
Ensemble Spread RMS Error
NINO3.4 skill and ensemble spread
Lead month
Lines : Ensemble mean
Symbols : Ensemble members
Improved skill at longer leads Larger ensemble spread in first two months
SST Forecast Skill
Multi-analysis ocean initialization leads to
Atmospheric Initialization
SST nudging informs AGCM of boundary forcing, but not correct synoptic configuration, i.e. weather
major loss of skill in first month of forecast
Two approaches are being pursued:
- Direct insertion of atmospheric analysis (cf. HFP2)
- Simple assimilation of analysis into AGCM
Land Initialization• CFCAS/GOAPP funded collaboration with A. Berg (Guelph)
• Force land surface model with bias-corrected reanalyses
after Berg et al. (Int J Clim 2005)
.95
.85
.75
.65
.55
>.5
Berg et al., 2003: 2005
Correlation of NCEP monthly precip with gauge-based measurements in USA:
before bias correction after bias correction
Coupled forecasts offer means for seasonal forecasting at longer leads, where future evolution of SSTA is critical
Prototype CHFP1 competitive with 4-model HFP2 at 1-month lead, but has only simplest initialization
CHFP1 provides a benchmark against which model and initialization improvements leading to CHFP2 can be assessed
CCCma participation in international CHFP through CLIVAR/WGSIP
Summary
Many scientific “opportunities”
improved models analysis of variability and of modes of variability improved analysis methods especially in the
ocean for model initialization for verification for model development
ensembles ensemble generation multi-model ensembles
prediction studies and the “DHFP” WGSIP/WGCM/CMIP5 coordinated project
Coupled Forecast System Development Path
CHFP2DHFP1
“Off the shelf” CGCM
Simple SST nudging
initialization
Ocean data assimilation
CHFP1
CGCM development
2D Var after Tang
et al. (JGR 2004)Improved error covariances
Atmospheric initializationInsertion of reanalyses Atm data assimilation
Land initialization
GHG forcing,
trend correction, etc
Off-line forcing by bias-correctedreanalysis
S assim after Troccoli et al. (MWR 2002)
AGCM, OGCM
Analysis and Verification
End of presentation