1 / 64

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)

rhelton
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)

More Related