Title: Canadian Centre for Climate Modelling and Analysis (CCCma)
1Environment 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
2Predictability and Prediction
3Predictability and Prediction
4CanSIPS development and operations
5Seasonal 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
6Forecast (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
7Coupled 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
-
8The 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
9WMO Global Producing Centres for Long Range
Forecasts
coupled (interactive atmosphere ocean)
2-tier (atmosphere specified ocean temps)
10CanSIPS 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
11Two-tier initialization (1990s-2011)
atmospheric models
Forecasts
atmospheric analyses at 12-hour lags to 120 hours
12CanSIPS initialization
13Impacts 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)
141 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
15Data Sources Hindcasts vs Operational
(transitioning to daily CMC)
16Previous 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
17Previous 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
18All-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
19Advantages 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)
20Advantages 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)
21Calibrated probabilistic forecasts in the media
Sep 2, 2014
Aug 21, 2013
22Current operational configuration
Day of month ?
Official forecast
Backup forecast
23Fall/Winter/Spring/Summer WPM Briefings
led by Marielle Alarie
(23 pp., Fr En)
24Daily seasonal forecasts JJA 2014 (unofficial)
? Optimal combination ?
25Proposed 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
26Benefits 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)
27Benefits 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
28Contributions to international forecast compendia
29WMO Global Producing Centres for Long Range
Forecasts
coupled (interactive atmosphere ocean)
2-tier (atmosphere specified ocean temps)
30Asia-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
31- 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
33ENSO/Nino Index Forecasts
34UK Met Office decadal forecast exchange
http//www.metoffice.gov.uk/research/climate/seaso
nal-to-decadal/long-range/decadal-multimodel
35UK Met Office decadal forecast exchange
http//www.metoffice.gov.uk/research/climate/seaso
nal-to-decadal/long-range/decadal-multimodel
36Annual (12-month average) forecasts
37Annual 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
38Annual Forecast Skills for Canada
Deterministic Anomaly correlation
Probabilistic
ROC area/below normal
ROC area/above normal
January initialization
Area-averaged score, all initialization months
39Climate Indices
40CanSIPS 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
42Monsoon 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
43CanSIPS 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
44Averaged PDO anomaly correlation skill for all
initial months (1979-2010)
Woo-Sung Lee plots
45Snow Prediction
46Evidence 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
47CanSIPS 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
48Sea Ice Prediction
49WMO Global Producing Centres for Long Range
Forecasts
?
?
?
?
?
?
?
?
coupled (interactive atmosphere ocean)
interactive sea ice
climatological sea ice
2-tier (atmosphere specified ocean temps)
50CanSIPS 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)
51Regional 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
52CanSIPS predictions (forecasts)
Prediction of monthly Arctic sea ice extent from
1 June 2012
53Aug 2012 ice concentrations
CMC - NASA Team
CMC - NASA Bootstrap
54CanSIPS predictions (forecasts)
What of we adjust for higher CMC ice cover?
Original prediction
Original prediction minus mean(CMC-NSIDC)
55 sea ice forecasts aligned with North American
Ice Service products
- Initially, attempt to develop probabilistic
forecasts for freeze-up and breakup dates, e.g.
- New bias correction methods, e.g. seasonal cycle
mapping - Historical verification data back to 1981
56Towards CanSIPSv2
57CanSIPS 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?
58Current 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)
60Real-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
61Experimental 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
62Global vs regional model topography
Global model ?x ? 300 km
Regional model ?x ? 25 km
63Summary
- 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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