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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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slide1

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

slide5

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

slide6

Observed SST anomaly

“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
  • 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
slide8

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
slide9

WMO Global Producing Centres for Long Range Forecasts

coupled (interactive atmosphere + ocean)

2-tier (atmosphere + specified ocean temps)

cansips models
CanSIPS Models

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

slide11

J0-9

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

SST nudging

Sea ice nudging

Ensemble member

assimilation runs

forecasts

CanSIPS initialization
slide13

Impacts of AGCM assimilation:

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)

slide14

21 Jan 2014

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

slide15

Data Sources: Hindcasts vs Operational

(transitioning to daily CMC)

slide16

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 normal

above normal

slide17

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 normal

above normal

slide18

All-in-one probability maps

Temperature probabilities:

individual categories

Above

Normal

Temperature probabilities: all-in-one

ucalibrated

Near

Normal

White = ‘equal chance’

(no category > 40%)

Below

Normal

slide19

Advantages of calibrated probability forecasts

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)

slide20

Advantages of calibrated probability forecasts

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)

current operational configuration
Current 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

fall winter spring summer wpm briefings
Fall/Winter/Spring/Summer WPM Briefings

led by Marielle Alarie

…(23 pp., Fr & En)

slide24

Daily seasonal forecasts

JJA 2014 (unofficial)

 Optimal combination = ?

proposed operational configuration
Proposed 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)
  • 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)

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

slide29

WMO Global Producing Centres for Long Range Forecasts

coupled (interactive atmosphere + ocean)

2-tier (atmosphere + specified ocean temps)

slide30

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
slide31

Currently 8 models including CanCM3 and CanCM4

  • Temperature forecast for SON 2014 lead 1 shown here

CanCM3

CanCM4

slide32

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

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

CanCM4

CanCM3

slide34

UK Met Office decadal forecast exchange

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

slide35

UK Met Office decadal forecast exchange

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

slide37

Annual T2m forecasts

CanSIPS Probabilistic forecast

Verification (1981-2010 percentile) + ACC

2011

forecast pdf

climatological pdf

2012

Global mean forecast vs climatological PDF

2013

ACC skill

2014

slide38

Annual Forecast Skills for Canada

Deterministic:

Anomaly correlation

Probabilistic:

ROC area/below normal

ROC area/above normal

January initialization

Area-averaged score, all initialization months

slide40

CanSIPS ENSO prediction skill

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?

slide41

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

slide42

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, 1114-1120

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

slide43

CanSIPS lead 0

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

slide44

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

Woo-Sung Lee plots

slide46

Evidence 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

slide47

CanSIPS 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

slide49

WMO Global Producing Centres for Long Range Forecasts

coupled (interactive atmosphere + ocean)

interactive sea ice

climatological sea ice

2-tier (atmosphere + specified ocean temps)

slide50

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)

slide51

Regional verification of CanSIPS sea ice forecasts

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

slide52

CanSIPS predictions (forecasts)

Prediction of monthly Arctic sea ice extent from 1 June 2012

slide53

Aug 2012 ice concentrations

NASA Bootstrap

CMC

NASA Team

CMC - NASA Bootstrap

CMC - NASA Team

slide54

CanSIPS predictions (forecasts)

What of we adjust for higher CMC ice cover?

Original

prediction

Original prediction

minus

mean(CMC-NSIDC)

slide55

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
slide57
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?

CanSIPS Development Efforts

slide58

Current CanCM3/4

ice model grid

OPA/NEMO

ORCA1 grid

OPA/NEMO

ORCA025

grid

Planned CanSIPS ice/ocean model improvements

slide59

meters

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)

slide60

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
  • 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

Global model: x  300 km

Regional model: x  25 km

slide63

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:

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