1. Canadian Centre for Climate Modelling and Analysis (CCCma) Victoria, BC Canada Environment Canada's seasonal forecasts: Current status and future directions Bill 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)

2. 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. Motivation for coupled vs 2-tier system Mar 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

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

8. 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 2-tier (atmosphere + specified ocean temps) coupled (interactive atmosphere + ocean)

10. 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 - 1.41  0.94  L40 - GM stirring, aniso visc - KPP+tidal mixing - Subsurface solar heating climatological chlorophyll SST bias vs obs (OISST 1982-2009) CC CC

11. J 0 -9 J 0 -8 J 0 -7 J 0 -6 J 0 -5 J 0 -4 J 0 -3 J 0 -2 J 0 -1 J0J0 J 0 -9 J 0 -8 J 0 -7 J 0 -6 J 0 -5 J 0 -4 J 0 -3 J 0 -2 J 0 -1 J0J0 J 0 -9 J 0 -8 J 0 -7 J 0 -6 J 0 -5 J 0 -4 J 0 -3 J 0 -2 J 0 -1 J0J0 GEM GCM2 J 0 -9 J 0 -8 J 0 -7 J 0 -6 J 0 -5 J 0 -4 J 0 -3 J 0 -2 J 0 -1 J0J0 GCM3 SEF Month 1Month 2Month 3Month 4 Two-tier initialization (1990s-2011) atmospheric analyses at 12-hour lags to 120 hours Forecasts atmospheric models

12. CanSIPS initialization assimilation runs Ensemble member Atmospheric assimilation SST nudging Sea ice nudging forecasts

13. 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)

14. 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

15. Data Sources: Hindcasts vs Operational (transitioning to daily CMC)

16. 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

17. 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

18. All-in-one probability maps Temperature probabilities: individual categories ucalibrated White = ‘equal chance’ (no category > 40%) Temperature probabilities: all-in-one Above Normal Near Normal Below Normal

19. Advantages of calibrated probability forecasts uncalibratedcalibrated 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)

20. 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) uncalibratedcalibrated

21. Calibrated probabilistic forecasts in the media Aug 21, 2013 Sep 2, 2014

22. Current operational configuration Day of month  Forecast months  Official forecast Backup forecast 11531 1 2 3 4 5 6 7 8 9 10 11 12 7 27 Mid-month “preview” forecast (+ lead 0.5 months for BoM ENSO, WMO, APCC)

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  Forecast months  Official forecast Backup forecast 11531 1 2 3 4 5 6 7 8 9 10 11 12 7 27 Mid-month “preview” forecast (+ lead 0.5 months for BOM ENSO WMO, APCC)

26. 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)

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

28. Contributions to international forecast compendia

29. WMO Global Producing Centres for Long Range Forecasts 2-tier (atmosphere + specified ocean temps) coupled (interactive atmosphere + ocean)

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

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

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 CanCM3 CanCM4

33. ENSO/Nino Index Forecasts

34. UK Met Office decadal forecast exchange http://www.metoffice.gov.uk/research/climate/ seasonal-to-decadal/long-range/decadal- multimodel

35. UK Met Office decadal forecast exchange http://www.metoffice.gov.uk/research/climate/ seasonal-to-decadal/long-range/decadal- multimodel

36. Annual (12-month average) forecasts

37. CanSIPS Probabilistic forecast Verification (1981-2010 percentile) + ACC 2011 2012 2013 2014 ACC skill Annual T2m forecasts climatological pdf forecast pdf Global mean forecast vs climatological PDF

38. Annual Forecast Skills for Canada Deterministic: Anomaly correlation Probabilistic: ROC area/below normalROC area/above normal January initialization Area-averaged score, all initialization months

39. Climate Indices

40. CanSIPS ENSO prediction skill lead 0 lead 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

41. 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

42. 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. 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 Index 4. Indian Summer Monsoon Circulation Index

43. 22.0% 26.1% 44.8% PDO index of PC of 1 st 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

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) 2002-2003 CanCM3 assimilation runs CanCM4 assimilation runs 1997-2007 climatology vs in situ obs Sospedra-Alfonso et al., in preparation

47. 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

48. Sea Ice Prediction

49. WMO Global Producing Centres for Long Range Forecasts 2-tier (atmosphere + specified ocean temps) coupled (interactive atmosphere + ocean) interactive sea ice  climatological sea ice  

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) raw values anomalies CanSIPS persistence Correlation skill 0 1

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

53. Aug 2012 ice concentrations NASA Team CMC - NASA TeamCMC - NASA Bootstrap NASA Bootstrap CMC

54. CanSIPS 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. 3% 12% 20% 32% 25% 8% 1-5 Jun 6-10 Jun 11-16 Jun 16-20 Jun 21-25 Jun 26-30 Jun Will require  New bias correction methods, e.g. seasonal cycle mapping  Historical verification data back to ~1981

56. Towards CanSIPSv2

57. 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?

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

59. 1 Mar 1981 1 Mar 2010 1 Sep 1981 1 Sep 2010 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

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

61. 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

62. Global vs regional model topography Global model:  x  300 kmRegional model:  x  25 km

63. 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: