Canadian Centre for Climate Modelling and Analysis (CCCma)

1
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
2
Predictability and Prediction
3
Predictability and Prediction
4
CanSIPS development and operations
5
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

6
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
7
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

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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

9
WMO Global Producing Centres for Long Range
Forecasts
coupled (interactive atmosphere ocean)
2-tier (atmosphere specified ocean temps)
10
CanSIPS Models
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
CanAM3 Atmospheric 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 - KPPtidal mixing -
Subsurface solar heating climatological
chlorophyll
SST bias vs obs (OISST 1982-2009)
?C
?C
11
Two-tier initialization (1990s-2011)
atmospheric models
Forecasts
atmospheric analyses at 12-hour lags to 120 hours
12
CanSIPS initialization
13
Impacts of AGCM assimilation Improved land
initialization
Correlation of assimilation run vs Guelph offline
analysis
SST nudging AGCM assim
SST nudging only
Soil temperature (top layer)
Soil moisture (top layer)
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1 Feb 2014
21 Jan 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
15
Data Sources Hindcasts vs Operational
(transitioning to daily CMC)
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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
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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
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All-in-one probability maps
Temperature probabilities individual categories
Above Normal
Temperature probabilities all-in-one
ucalibrated
Near Normal
White equal chance (no category gt 40)
Below Normal
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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)
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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)
21
Calibrated probabilistic forecasts in the media
Sep 2, 2014
Aug 21, 2013
22
Current operational configuration
Day of month ?
Official forecast
Backup forecast
23
Fall/Winter/Spring/Summer WPM Briefings
led by Marielle Alarie
(23 pp., Fr En)
24
Daily seasonal forecasts JJA 2014 (unofficial)
? Optimal combination ?
25
Proposed operational configuration
Day of month ?
1
15
31
27
1
2
3
4

5
6
7
7
8
Mid-month preview forecast ( lead 0.5 months
for BOM ENSO WMO, APCC)
9
10
11
12
Official forecast
Backup forecast
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Benefits of multi-model ensemble (1)

• A desirable property (?reliability) of prediction
  e.g. of ENSO indices is that Ensemble Spread ?
  RMSE
• Ensemble Spread ltlt RMSE for each model
  individually ? overconfident
• Ensemble Spread ? RMSE for the two-model
  combination (except shortest leads)

27
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
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Contributions to international forecast compendia
29
WMO Global Producing Centres for Long Range
Forecasts
coupled (interactive atmosphere ocean)
2-tier (atmosphere specified ocean temps)
30
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

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• Currently 8 models including CanCM3 and CanCM4
• Temperature forecast for SON 2014 lead 1 shown
  here

CanCM3
CanCM4
32

• 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
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ENSO/Nino Index Forecasts
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UK Met Office decadal forecast exchange
http//www.metoffice.gov.uk/research/climate/seaso
nal-to-decadal/long-range/decadal-multimodel
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UK Met Office decadal forecast exchange
http//www.metoffice.gov.uk/research/climate/seaso
nal-to-decadal/long-range/decadal-multimodel
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Annual (12-month average) forecasts
37
Annual T2m forecasts
CanSIPS Probabilistic forecast
Verification (1981-2010 percentile) ACC
2011
forecast pdf
climatological pdf
2012
Global mean forecast vs climatological PDF
2013
2014
ACC skill
38
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
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Climate Indices
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CanSIPS ENSO prediction skill
OISST obs
lead 0

lead 9
Nino3.4 anomaly correlation skill
0.55 lt AC lt 0.84 at 9-month lead
Does this translate to long lead skill over
Canada?
41
Oceanic Indices (http//ioc-goos-oopc.org/state_o
f_the_ocean/sur/) Pacific 1.Niño12 SST
Anomalies in the box 90W - 80W, 10S -
0. 2.Niño3 SST Anomalies in the box 150W -
90W, 5S - 5N. 3.Niño4 SST Anomalies in the
box 160E - 150W, 5S - 5N 4.Niño3.4 SST
Anomalies in the box 170W - 120W, 5S -
5N 5.SOI difference of SLP anomalies between
Tahiti and Dawin 6.El Niño Modoki Index (EMI)
EMI SSTA(165E-140W, 10S-10N)-0.5SSTA
(110W-70W, 15S-5N)-0.5SSTA (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, doi10.1029/2006JC003798. Atlantic
1. North Atlantic Tropical SST index(NAT)
SST anomalies in the box 40W - 20W, 5N -
20N. 2. South Atlantic Tropical SST index(SAT)
SST anomalies in the box 15W - 5E, 5S -
5N. 3. TASI NAT SAT 4. Tropical Northern
Atlantic index(TNA) SST anomalies in the box
55W - 15W, 5N -25N. 5. Tropical Southern
Atlantic index(TSA) SST anomalies in the box
30W - 10E, 20S - EQ. Indian Ocean 1.
Western Tropical Indian Ocean SST index (WTIO)
SST anomalies in the box 50E - 70E, 10S -
10N 2. Southeastern Tropical Indian Ocean SST
index(SETIO) SST anomalies in the box 90E -
110E, 10S - 0 3. South Western Indian Ocean
SST index(SWIO) SST anomalies in the box 31E
- 45E, 32S - 25S 4. Indian Ocean Dipole Mode
Index (IOD) WTIO - SETIO
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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, 629638. 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. 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(22.532.5N, 110140E) - U850 (515N,
90130E) 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,
44494463. doi http//dx.doi.org/10.1175/2008JCLI
2183.1 Indian 1. Indian Monsoon Index
IMIU850(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 PacificEast Asian monsoons. J.
Climate, 14, 40734090. 2. Webster-Yang Monsoon
Index WYMIU850-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
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CanSIPS lead 0
Pacific Decadal Oscillation (PDO)

• PDO index of PC of 1st EOF of North Pacific SST
• Comparison of obs and CanSIPS EOF patterns

Obs
CanSIPS lead 5
Woo-Sung Lee plots
44
Averaged PDO anomaly correlation skill for all
initial months (1979-2010)
Woo-Sung Lee plots
45
Snow Prediction
46
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
47
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
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Sea Ice Prediction
49
WMO Global Producing Centres for Long Range
Forecasts
?
?
?
?
?
?
?
?
coupled (interactive atmosphere ocean)
interactive sea ice
climatological sea ice
2-tier (atmosphere specified ocean temps)
50
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)
51
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
52
CanSIPS predictions (forecasts)
Prediction of monthly Arctic sea ice extent from
1 June 2012
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Aug 2012 ice concentrations
CMC - NASA Team
CMC - NASA Bootstrap
54
CanSIPS predictions (forecasts)
What of we adjust for higher CMC ice cover?
Original prediction
Original prediction minus mean(CMC-NSIDC)
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sea ice forecasts aligned with North American
Ice Service products

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

• Will require

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

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Towards CanSIPSv2
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CanSIPS Development Efforts

• Improved ocean initialization
• Improved sea ice initialization
• Improved land initialization based on ECs
  Canadian Land Data Assimilation System (CaLDAS)
• Improved climate model components (atmosphere,
  ocean, land, sea ice)
• New coupled model based on MSCs GEM weather
  prediction model
• Regional downscaling of global model forecasts?

58
Current CanCM3/4 ice model grid
OPA/NEMO ORCA1 grid
OPA/NEMO ORCA025 grid
Planned CanSIPS ice/ocean model improvements
59

• 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)
60
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
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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 gt 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
62
Global vs regional model topography
Global model ?x ? 300 km
Regional model ?x ? 25 km
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Summary

• CanSIPS has reliably produced ECs 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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