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Canadian Centre for Climate Modelling and Analysis (CCCma) Victoria, BC Canada

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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Canadian Centre for Climate Modelling and Analysis (CCCma) Victoria, BC Canada

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  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. 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

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

  10. 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

  11. 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

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

  13. 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)

  14. 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

  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 Above Normal Temperature probabilities: all-in-one ucalibrated Near Normal White = ‘equal chance’ (no category > 40%) Below Normal

  19. 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)

  20. 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  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

  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 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

  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 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

  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

  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. 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

  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

  39. Climate Indices

  40. 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?

  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. 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

  43. 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

  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

  48. 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)

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