Canadian Centre for Climate Modelling and Analysis (CCCma)

Victoria, BC Canada

Environment Canada's seasonal

forecasts: Current status and

future directionsBill Merryfield

RPN Seminar, 4 Sep 2014

In collaboration with: G. Boer, G. Flato, S. Kharin, W.-S. Lee, J. Scinocca… (CCCma) M. Alarie, B. Archambault, B. Denis, J.-S. Fontecilla, J. Hodgson… (CMC)

Predictability and Prediction

Predictability and Prediction

CanSIPS development and operations

Seasonal forecasting methods

• Earliest standard: empirical/statistical forecasts

• Later standard: two-tier model ensemble forecasts

- model sea surface temperature (SST) prescribed

- used by EC from 1995 until 2011 (anomaly persistence SST)

- forecast range limited to 4 months

• Current standard: coupled climate model ensemble forecasts

- fully interactive atmosphere/ocean/land/(sea ice)

- SSTs predicted as part of forecast

- potentially useful forecast range greatly extended

Motivation for coupled vs

2-tier systemMar 2006

Apr 2006

May 2006

Jun 2006

Jul 2006

Oct 2006

Observed SST anomaly

…

“Forecast” (persisted) SST anomaly

Example: consider 2-tier forecast (persisted SSTA) from 1 April 2006

2-tier system with persisted SSTA cannot predict El Niño or La Niña

Coupled forecast system development

• 2006 Funding from Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) to the Global Ocean-Atmosphere Prediction and Predictability (GOAPP) Network

• 2007-2008 Pilot project using existing AR4 model,

simple SST nudging initialization

• 2008-2009 Model development leading to CanCM3/4,

initialization development

• 2009-2010 Hindcast production

• Dec 2011 Operational implementation

The Canadian Seasonal to Interannual Prediction System (CanSIPS)

• Developed at CCCma

• Operational at CMC since Dec 2011

• 2 models CanCM3/4, 10 ensemble members each

• Hindcast verification period = 1981-2010

• Forecast range = 12 months

• Forecasts initialized at the start of every month

WMO Global Producing Centres for Long Range Forecasts

2-tier (atmosphere + specified ocean temps)

coupled (interactive atmosphere + ocean)

CanSIPS Models

CanAM3 Atmospheric model - T63/L31 (2.8 spectral grid) - Deep convection scheme of Zhang & McFarlane (1995) - No shallow conv scheme - Also called AGCM3

CanAM4 Atmospheric model - T63/L35 (2.8 spectral grid) - Deep conv as in CanCM3 - Shallow conv as per von Salzen & McFarlane (2002) - Improved radiation, aerosols

CanOM4 Ocean model - 1.410.94L40 - GM stirring, aniso visc - KPP+tidal mixing - Subsurface solar heating climatological chlorophyll

SST bias vs obs (OISST 1982-2009)

C C

J0-9J0-8J0-7J0-6J0-5J0-4J0-3J0-2J0-1J0

J0-9J0-8J0-7J0-6J0-5J0-4J0-3J0-2J0-1J0

J0-9J0-8J0-7J0-6J0-5J0-4J0-3J0-2J0-1J0

GEM

GCM2

J0-9J0-8J0-7J0-6J0-5J0-4J0-3J0-2J0-1J0

GCM3

SEF

Month 1 Month 2 Month 3 Month 4

Two-tier initialization (1990s-2011)

atmospheric analyses at 12-hour lags to 120 hours Forecasts

atmospheric models

CanSIPS initialization

assimilation runs

Ensemble member

Atmospheric assimilationSST nudgingSea ice nudging

forecasts

Impacts of AGCM assimilation: Improved land initialization

Correlation of assimilation run vs Guelph offline analysis

SST nudging + AGCM assimSST nudging only

Soil temperature(top layer)

Soil moisture(top layer)

Probabilistic soil moisture forecast Feb 2014 lead 0

1 Feb 2014

9 Feb 2014

28 Feb 2014

25 Feb 2014

Evidence CanSIPS soil moisture initialization is somewhat realistic

21 Jan 2014

Data Sources: Hindcasts vs Operational

(transitioning to daily CMC)

Previous default: Deterministic forecast map

• colours = tercile category of ensemble mean anomaly:

• Issues: - small differences in forecasted anomaly can lead to large differences in in map

- no probabilistic information (climate forecasts are inherently probabilistic)

- no guidance as to magnitude of anomaly, other than tercile category

below normal near normalabove normal

Previous default: Deterministic forecast map

• colours = tercile category of ensemble mean anomaly:

• Issues: - small differences in forecasted anomaly can lead to large differences in in map

- no probabilistic information (climate forecasts are inherently probabilistic)

- no guidance as to magnitude of anomaly, other than tercile category

below normal near normalabove normal

All-in-one probability mapsTemperature probabilities:

individual categories

ucalibrated

White = ‘equal chance’(no category > 40%)

Temperature probabilities: all-in-one

AboveNormal

NearNormal

BelowNormal

Advantages of calibrated probability forecasts

uncalibrated calibrated

• uncalibrated probabilities:

- high probabilities predicted far more frequently than observed

- overconfident, especially for precipitation and near- normal category

- near-normal grossly overpredicted

• calibrated* probabilities:

- much more reliable (forecast probability observed frequency)

- less overconfident

- near-normal less overpredicted

Temperature

perfect forecast

Brier skill score = 0

no resolution

*Kharin et al. , A-O (2009)

Advantages of calibrated probability forecasts

Precipitation

perfect forecast

Brier skill score = 0

no resolution

• uncalibrated probabilities:

- high probabilities predicted far more frequently than observed

- overconfident, especially for precipitation and near- normal category

- near-normal grossly overpredicted

• calibrated* probabilities:

- much more reliable (forecast probability observed frequency)

- less overconfident

- near-normal less overpredicted*Kharin et al. , A-O (2009)

uncalibrated calibrated

Calibrated probabilistic forecasts in the media

Aug 21, 2013 Sep 2, 2014

Current operational configuration

Day of month

Forecastmonths

Official forecast

Backup forecast

1 15 31123456789101112

7

27

Mid-month “preview” forecast(+ lead 0.5 months for BoM ENSO, WMO, APCC)

Fall/Winter/Spring/Summer WPM Briefingsled by Marielle Alarie

…(23 pp., Fr & En)

Daily seasonal forecasts JJA 2014 (unofficial)

Optimal combination = ?

Proposed operational configuration

Day of month

Forecastmonths

Official forecast

Backup forecast

1 15 31123456789101112

7

27

Mid-month “preview” forecast(+ lead 0.5 months for BOM ENSO WMO, APCC)

Benefits of multi-model ensemble (1)

• A desirable property (reliability) of prediction e.g. of ENSO indices is that Ensemble Spread RMSE

• Ensemble Spread << RMSE for each model individually overconfident

• Ensemble Spread RMSE for the two-model combination (except shortest leads)

Benefits of multi-model ensemble (2)Experiment: compare CanSIPS (10xCanCM3 + 10xCanCM4) vs 20xCanCM4 (Jan initialization only): 10xCanCM3 + 10xCanCM4

20xCanCM4

Temperature anomaly correlation:slight advantage for 20xCanCM4 (except lead 0)

Temperature mean-square skill score: big advantage for 10xCanCM3 + 10xCanCM4

Contributions to international forecast

compendia

WMO Global Producing Centres for Long Range Forecasts

2-tier (atmosphere + specified ocean temps)

coupled (interactive atmosphere + ocean)

Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC)

• 7 models: CMCC, MSC_CanCM3, MSC_CanCM4, NASA, NCEP, PMU, POAMA

• month 1-3 and 4-6 probabilistic & deterministic forecasts at ~0.5-1 month lead

CanCM3 CanCM4

• Currently 8 models including CanCM3 and CanCM4

• Temperature forecast for SON 2014 lead 1 shown here

• Besides contributing to combined NMME forecast, enables comparisons between performance of different models

• Temperature anomaly correlation skills for SON lead 1 month shown here

CanCM3 CanCM4

ENSO/Nino Index Forecasts

UK Met Office decadal forecast exchange

http://www.metoffice.gov.uk/research/climate/seasonal-to-decadal/long-range/decadal-multimodel

UK Met Office decadal forecast exchange

http://www.metoffice.gov.uk/research/climate/seasonal-to-decadal/long-range/decadal-multimodel

Annual (12-month average) forecasts

CanSIPS Probabilistic forecast Verification (1981-2010 percentile) + ACC

201

120

12

201

320

14

AC

C skill

Annual T2m forecasts

climatological pdf

forecast pdf

Glo

bal

mea

n f

ore

cas

t v

s c

lim

ato

log

ical

PD

F

Annual Forecast Skills for CanadaDeterministic:

Anomaly correlationProbabilistic:

ROC area/below normal ROC area/above normal

January initialization

Area-averaged score, all initialization months

Climate Indices

CanSIPS ENSO prediction skill

lead 0lead 9

…

0.55 < AC < 0.84 at 9-month lead

Nino3.4 anomaly correlation skill:

Does this translate to long lead skill over Canada?

OISST obs

Oceanic Indices (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)Pacific :1.Niño1+2 : SST Anomalies in the box 90°W - 80°W, 10°S - 0°.2.Niño3 : SST Anomalies in the box 150°W - 90°W, 5°S - 5°N.3.Niño4 : SST Anomalies in the box 160°E - 150°W, 5°S - 5°N4.Niño3.4 : SST Anomalies in the box 170°W - 120°W, 5°S - 5°N5.SOI : difference of SLP anomalies between Tahiti and Dawin6.El Niño Modoki Index (EMI) EMI = SSTA(165E-140W, 10S-10N)-0.5*SSTA (110W-70W, 15S-5N)-0.5*SSTA (125E-145E, 10S-20N Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007 : El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, doi:10.1029/2006JC003798.

Atlantic :1. North Atlantic Tropical SST index(NAT) ; SST anomalies in the box 40°W - 20°W, 5°N - 20°N.2. South Atlantic Tropical SST index(SAT) SST anomalies in the box 15°W - 5°E, 5°S - 5°N.3. TASI = NAT – SAT4. Tropical Northern Atlantic index(TNA) SST anomalies in the box 55°W - 15°W, 5°N -25°N.5. Tropical Southern Atlantic index(TSA) SST anomalies in the box 30°W - 10°E, 20°S - EQ.

Indian Ocean :1. Western Tropical Indian Ocean SST index (WTIO) : SST anomalies in the box 50°E - 70°E, 10°S - 10°N2. Southeastern Tropical Indian Ocean SST index(SETIO) : SST anomalies in the box 90°E - 110°E, 10°S - 0°3. South Western Indian Ocean SST index(SWIO) : SST anomalies in the box 31°E - 45°E, 32°S - 25°S4. Indian Ocean Dipole Mode Index (IOD) : WTIO - SETIO

Monsoon Indices

Pacific :

1. Western North Pacific Monsoon Index WNPMI = U850 (5ºN -15ºN, 90ºE-130ºE) – U850 (22.5ºN - 32.5ºN, 110ºE-140ºE) Wang, B., and Z. Fan, 1999: Choice of South Asian summer monsoon indices. Bull. Amer. Meteor. Soc., 80, 629–638.2. Australian Summer Monsoon Index AUSMI = U850 averaged over 5ºS-15ºS, 110ºE-130ºE Kajikawa, Y., B. Wang and J. Yang, 2010: A multi-time scale Australian monsoon index, Int. J. Climatol, 30, 1114-11203. South Asia Monsoon Index SAMI= V850-V200 averaged over 10ºN -30ºN, 70ºE-110ºE Goswami, B. N., B. Krishnamurthy, and H. Annama lai, 1999: A broad-scale circulation index for interannual variability of the Indian summer monsoon. Quart. J. Roy.. Meteorol. Soc., 125, 611- 633.4. East Asian Monsoon Index EASMI= U850(22.5°–32.5°N, 110°–140°E) - U850 (5°–15°N, 90°–130°E) Wang, Bin, Zhiwei Wu, Jianping Li, Jian Liu, Chih-Pei Chang, Yihui Ding, Guoxiong Wu, 2008: How to Measure the Strength of the East Asian Summer Monsoon. J. Climate, 21, 4449–4463. doi: http://dx.doi.org/10.1175/2008JCLI2183.1

Indian :

1. Indian Monsoon Index IMI=U850(5ºN -15ºN, 40ºE-80ºE) – U850(20ºN -30ºN, 70ºE-90ºE) Wang, B., R. Wu, and K-M. Lau, 2001: Interannual variability of Asian summer monsoon: Contrast between the Indian and western North Pacific–East Asian monsoons. J. Climate, 14, 4073–4090.2. Webster-Yang Monsoon Index WYMI=U850-U200 averaged over 0-20ºN, 40ºE-110ºE Webster, P. J., and S. Yang, 1992: Monsoon and ENSO: Selectively interactive systems. Quart. J. Roy. Meteor. Soc., 118, 877-926.3. All Indian Rainfall Index4. Indian Summer Monsoon Circulation Index

22.0%

26.1%

44.8%

• PDO index of PC of 1st EOF of North Pacific SST

• Comparison of obs and CanSIPS EOF patterns:

Pacific Decadal Oscillation (PDO)

Woo-Sung Lee plots

Obs

CanSIPS lead 0

CanSIPS lead 5

(a) Monthly Mean

AC

C

0.0

0.2

0.4

0.6

0.8

1.0

CanCM3 CanCM4 CanCM34

MEAN

0.6630.6930.703

(b) Seasonal Mean

AC

C

0.0

0.2

0.4

0.6

0.8

1.0

CanCM3 CanCM4 CanCM34

MEAN

0.6950.7210.731

Averaged PDO anomaly correlation skill for all initial months (1979-2010)

Woo-Sung Lee plots

Snow Prediction

Evidence CanSIPS snow initialization is somewhat realistic

Example: BERMS Old Jack Pine Site (Saskatchewan, Canada)

2002

-200

3CanCM3 assimilation runs CanCM4 assimilation runs

1997-2007 climatology vs in situ obs

Sospedra-Alfonso et al. , in preparation

3-category probabilistic forecast (left)

MERRA verification(right)

JFM 2012 (lead 0)

SWE (left)

2m temperature (right)

Anomaly correlation

JFM (lead 0)

Higher than for T2m in snowy regions!

SWE T2m

CanSIPS snow water equivalent (SWE)

forecasts & skill

Sea Ice Prediction

WMO Global Producing Centres for Long Range Forecasts

2-tier (atmosphere + specified ocean temps)

coupled (interactive atmosphere + ocean) interactive sea ice climatological sea ice

CanSIPS predictions (hindcasts)Predictions of Arctic sea ice area: Anomaly correlation skill

Trend included Trend removed

Skill of anomaly persistence “forecast” Value added by CanSIPS

Sigmond et al. GRL (2013), Merryfield et al. GRL (2013)

Regional verification of CanSIPS sea ice forecastsWoo-Sung Lee, CCCma/UVic

Subregions of the Arctic Oceanas defined by the Navy/NOAA Joint Ice Center

Example: Beaufort Sea

Monthly Climatology

Forecast time series (lead 0)raw values

anomalies

CanSIPS

persistence

Correlation skill

0

1

CanSIPS predictions (forecasts)Prediction of monthly Arctic sea ice extent from 1 June 2012

Aug 2012 ice concentrationsNASA Team

CMC - NASA TeamCMC - NASA Bootstrap

NASA BootstrapCMC

CanSIPS predictions (forecasts)What of we adjust for higher CMC ice cover?

Original prediction

Original predictionminus

mean(CMC-NSIDC)

sea ice forecasts aligned with North American Ice Service products

• Initially, attempt to develop probabilistic forecasts for freeze-up and breakup dates, e.g.

3%12%

20%32%

25%

8%

1-5Jun

6-10Jun

11-16Jun

16-20Jun

21-25Jun

26-30Jun• Will require

New bias correction methods, e.g. seasonal cycle mapping Historical verification data back to ~1981

Towards CanSIPSv2

CanSIPS Development Efforts

• Improved ocean initialization

• Improved sea ice initialization

• Improved land initialization based on EC’s Canadian Land Data Assimilation System (CaLDAS)

• Improved climate model components (atmosphere, ocean, land, sea ice)

• New coupled model based on MSC’s GEM weather prediction model

• Regional downscaling of global model forecasts?

Current CanCM3/4 ice model grid

OPA/NEMOORCA1 grid

OPA/NEMOORCA025grid

Planned CanSIPS ice/ocean model improvements

1 M

ar 1

981

1 M

ar 2

010

1 S

ep 1

981

1 S

ep 2

010

• Based on relaxation to (not very realistic) model seasonal thickness climatology

• Unlikely to accurately capture thinning trend

Sea ice thickness on first day of forecasts (~initial values)

meters

Current CanSIPS sea ice thickness initialization

Real-time sea ice thickness estimation through statistical relationships to observables

Arlan Dirkson, UVic grad student

Thickness reconstructions based on 3 SVD modes

Sep 1996

2012Sep

Experimental downscaling of CanSIPS forecasts

• CanRCM4 = Canadian Regional Climate Model version 4• CORDEX North America grid – 0.22 ~ 25 km resolution• RCM runs will be initialized from downscaled assimilation runs• Atmospheric scales > T21 spectrally nudged in interior domain• Global model output files = RCM input global, downscaled forecasts run concurrently

Soil moisture probabilistic forecast on CanSIPS global grid

Surface temperature on CanRCM4 0.22 CORDEX North America grid

Global vs regional model topography

Global model: x 300 km Regional model: x 25 km

Summary• CanSIPS has reliably produced EC’s seasonal forecasts to a range

of 12 months since December 2011

• Multi-model approach appears to have been justified

• CanSIPS contributes to many international forecast compendia

• Many new products are under development

• CanSIPS R & D includes development of improved and new models (including GEM/NEMO), improvements in initialization (e.g. sea ice thickness), and downscaling to 25 km resolution using CanRCM4

Research supported by: