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Environment Canada's seasonal forecasts: Current status and future directions. Bill Merryfield. Canadian Centre for Climate Modelling and Analysis (CCCma) Victoria, BC Canada. In collaboration with: G. Boer, G. Flato, S. Kharin, W.-S. Lee, J. Scinocca… (CCCma)

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Environment Canada's seasonal forecasts: Current status and future directions

Bill Merryfield

Canadian Centre for Climate Modelling and Analysis (CCCma)

Victoria, BC Canada

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)

RPN Seminar, 4 Sep 2014





Seasonal forecasting methods future directions

  • 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


Observed SST anomaly future directions

“Forecast” (persisted) SST anomaly

Motivation for coupled vs2-tier system

Mar 2006

Apr 2006

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

May 2006

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

Jun 2006

Jul 2006

Oct 2006


Coupled forecast system development
Coupled forecast system development future directions

  • 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 (CanSIPS)

coupled (interactive atmosphere + ocean)

2-tier (atmosphere + specified ocean temps)


Cansips models
CanSIPS Models (CanSIPS)

CanAM4Atmospheric model

- T63/L35 (2.8 spectral grid)

- Deep conv as in CanCM3

- Shallow conv as per von

Salzen & McFarlane (2002)

- Improved radiation, aerosols

CanAM3Atmospheric model

- T63/L31 (2.8 spectral grid)

- Deep convection scheme of

Zhang & McFarlane (1995)

- No shallow conv scheme

- Also called AGCM3

CanOM4 Ocean model

- 1.41°0.94°L40

- GM stirring, aniso visc

- KPP+tidal mixing

- Subsurface solar heating

climatological chlorophyll

SST bias vs obs (OISST 1982-2009)

C

C


J (CanSIPS)0-9

J0-9

J0-9

J0-9

J0-8

J0-8

J0-8

J0-8

J0-7

J0-7

J0-7

J0-7

J0-6

J0-6

J0-6

J0-6

J0-5

J0-5

J0-5

J0-5

GEM

J0-4

J0-4

J0-4

J0-4

J0-3

J0-3

J0-3

J0-3

J0-2

J0-2

J0-2

J0-2

J0-1

J0-1

J0-1

J0-1

J0

J0

J0

J0

GCM2

GCM3

SEF

Month 1

Month 2

Month 3

Month 4

Two-tier initialization (1990s-2011)

atmospheric models

Forecasts

atmospheric analyses at 12-hour lags to 120 hours


Cansips initialization

Atmospheric assimilation (CanSIPS)

SST nudging

Sea ice nudging

Ensemble member

assimilation runs

forecasts

CanSIPS initialization


Impacts of AGCM assimilation: (CanSIPS)

Improved land initialization

Correlation of assimilation run vs Guelph offline analysis

SST nudging only

SST nudging + AGCM assim

Soil temperature

(top layer)

Soil moisture

(top layer)


21 Jan 2014 (CanSIPS)

1 Feb 2014

Probabilistic soil moisture forecast Feb 2014 lead 0

9 Feb 2014

Evidence CanSIPS soil moisture initialization is somewhat realistic

28 Feb 2014

25 Feb 2014


Data Sources: Hindcasts vs Operational (CanSIPS)

(transitioning to daily CMC)


Previous default: Deterministic forecast map (CanSIPS)

  • 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 normal

above normal


Previous default: Deterministic forecast map (CanSIPS)

  • 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 normal

above normal


All-in-one probability maps (CanSIPS)

Temperature probabilities:

individual categories

Above

Normal

Temperature probabilities: all-in-one

ucalibrated

Near

Normal

White = ‘equal chance’

(no category > 40%)

Below

Normal


Advantages of calibrated probability forecasts (CanSIPS)

Temperature

  • 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

uncalibrated

calibrated

perfect forecast

Brier skill score = 0

no resolution

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


Advantages of calibrated probability forecasts (CanSIPS)

Precipitation

  • 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

uncalibrated

calibrated

perfect forecast

Brier skill score = 0

no resolution

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


Calibrated probabilistic forecasts in the media
Calibrated probabilistic forecasts in the media (CanSIPS)

Sep 2, 2014

Aug 21, 2013


Current operational configuration
Current operational configuration (CanSIPS)

Day of month 

1

15

27

31

1

2

3

4

Forecast

months

5

6

7

7

8

Mid-month “preview” forecast

(+ lead 0.5 months for BoM ENSO, WMO, APCC)

9

10

11

12

Backup forecast

Official forecast


Fall winter spring summer wpm briefings
Fall/Winter/Spring/Summer WPM Briefings (CanSIPS)

led by Marielle Alarie

…(23 pp., Fr & En)


Daily seasonal forecasts (CanSIPS)

JJA 2014 (unofficial)

 Optimal combination = ?


Proposed operational configuration
Proposed (CanSIPS) operational configuration

Day of month 

1

15

27

31

1

2

3

4

Forecast

months

5

6

7

7

8

Mid-month “preview” forecast

(+ lead 0.5 months for BOM ENSO WMO, APCC)

9

10

11

12

Backup forecast

Official forecast


Benefits of multi model ensemble 1
Benefits of multi-model ensemble (1) (CanSIPS)

  • 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
Benefits of multi-model ensemble (2) (CanSIPS)

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



WMO Global Producing Centres for Long Range Forecasts (CanSIPS)

coupled (interactive atmosphere + ocean)

2-tier (atmosphere + specified ocean temps)


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


CanCM4

CanCM3


ENSO/Nino Index Forecasts comparisons between performance of different models


UK Met Office decadal forecast exchange comparisons between performance of different models

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


UK Met Office decadal forecast exchange comparisons between performance of different models

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


Annual 12 month average forecasts

Annual (12-month average) forecasts comparisons between performance of different models


Annual T2m forecasts comparisons between performance of different models

CanSIPS Probabilistic forecast

Verification (1981-2010 percentile) + ACC

2011

forecast pdf

climatological pdf

2012

Global mean forecast vs climatological PDF

2013

ACC skill

2014


Annual Forecast Skills for Canada comparisons between performance of different models

Deterministic:

Anomaly correlation

Probabilistic:

ROC area/below normal

ROC area/above normal

January initialization

Area-averaged score, all initialization months


Climate indices

Climate Indices comparisons between performance of different models


CanSIPS ENSO prediction skill comparisons between performance of different models

OISST obs

lead 0

lead 9

Nino3.4 anomaly correlation skill:

0.55 < AC < 0.84 at 9-month lead

Does this translate to long lead skill over Canada?


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°N

4.Niño3.4 : SST Anomalies in the box 170°W - 120°W, 5°S - 5°N

5.SOI : difference of SLP anomalies between Tahiti and Dawin

6.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 – SAT

4. 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°N

2. 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°S

4. Indian Ocean Dipole Mode Index (IOD)

: WTIO - SETIO


Monsoon Indices (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)

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, 1114-1120

3. South Asia Monsoon Index

SAMI= V850-V200 averaged over 10ºN -30ºN, 70ºE-110ºE

Goswami, B. N., B. Krishnamurthy, and H. Annamalai, 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 Index

4. Indian Summer Monsoon Circulation Index


CanSIPS lead 0 (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)

Pacific Decadal Oscillation (PDO)

26.1%

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

  • Comparison of obs and CanSIPS EOF patterns:

Obs

22.0%

CanSIPS lead 5

44.8%

Woo-Sung Lee plots


Averaged PDO anomaly correlation (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)skill for all initial months (1979-2010)

Woo-Sung Lee plots


Snow prediction

Snow Prediction (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)


Evidence (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)CanSIPS snow initialization is somewhat realistic

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

CanCM3 assimilation runs

CanCM4 assimilation runs

2002-2003

1997-2007 climatology vs in situ obs

Sospedra-Alfonso et al. , in preparation


CanSIPS (http://ioc-goos-oopc.org/state_of_the_ocean/sur/) snow water equivalent (SWE) forecasts & skill

JFM 2012 (lead 0)

3-category probabilistic forecast (left)

MERRA verification

(right)

Anomaly correlation

JFM (lead 0)

SWE (left)

2m temperature (right)

  • Higher than for T2m

  • in snowy regions!

SWE

T2m


Sea ice prediction

Sea Ice Prediction (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)


WMO Global Producing Centres for Long Range Forecasts (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)

coupled (interactive atmosphere + ocean)

interactive sea ice

climatological sea ice

2-tier (atmosphere + specified ocean temps)


CanSIPS predictions (hindcasts) (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)

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 forecasts (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)

Woo-Sung Lee, CCCma/UVic

Subregions of the Arctic Ocean

as defined by the Navy/NOAA Joint Ice Center

Example: Beaufort Sea

Monthly Climatology

Forecast time series (lead 0)

Correlation skill

1

raw values

CanSIPS

anomalies

persistence

0


CanSIPS predictions (forecasts) (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)

Prediction of monthly Arctic sea ice extent from 1 June 2012


Aug 2012 ice concentrations (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)

NASA Bootstrap

CMC

NASA Team

CMC - NASA Bootstrap

CMC - NASA Team


CanSIPS predictions (forecasts) (http://ioc-goos-oopc.org/state_of_the_ocean/sur/)

What of we adjust for higher CMC ice cover?

Original

prediction

Original prediction

minus

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.

32%

25%

20%

12%

8%

3%

26-30

Jun

1-5

Jun

11-16

Jun

21-25

Jun

6-10

Jun

16-20

Jun

  • Will require

  • New bias correction methods, e.g. seasonal cycle mapping

  • Historical verification data back to ~1981



Improved ocean initialization products

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?

CanSIPS Development Efforts


Current CanCM3/4 products

ice model grid

OPA/NEMO

ORCA1 grid

OPA/NEMO

ORCA025

grid

Planned CanSIPS ice/ocean model improvements


meters products

1 Mar 1981

1 Sep 1981

1 Sep 2010

1 Mar 2010

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

  • Unlikely to accurately

    capture thinning

    trend

Current CanSIPS sea ice thickness initialization

Sea ice thickness on first day of forecasts

(~initial values)


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
Experimental downscaling of CanSIPS forecasts relationships to observables

  • 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 vs regional model topography relationships to observables

Global model: x  300 km

Regional model: x  25 km


Summary relationships to observables

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:


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