# Seasonal forecasting with the NMME with a focus on Africa

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1 Seasonal forecasting with the NMME with a focus on Africa Bill Merryfield Canadian Centre for Climate Modelling and Analysis (CCCma) Victoria, BC Canada School on Climate System Prediction and Regional Climate Information Dakar, Nov 2016

2 Topics covered Fundamentals of seasonal forecasting - Deterministic vs probabilistic ensemble forecasts - Forecast skill - How seasonal forecasts are produced - El Niño impacts - ENSO prediction Multi-model ensembles (MMEs) North American Multi-Model Ensemble (NMME)

3 Fundamentals of seasonal forecasting

4 Necessary conditions for useful climate predictions 1) The phenomenon being forecast must be predictable 2) Prediction method must have ability to capitalize on natural predictability If these two conditions are met then there is potential for skillful predictions

5 Daily weather is not very predictable after 7-10 days Example: Daily weather predictions for Paris in February 2017, retrieved on 20 November 2016!

6 Daily weather is not very predictable after 7-10 days Example: Daily weather predictions for Paris in February 2017, retrieved on 20 November 2016! However, longer term averages over a week, month or season may be predictable depending on location, lead time, etc.

7 Predictability and Prediction

8 Probabilities based on an ensemble of forecasts Ensemble of forecasts When uncertainties are large, a single deterministic forecast tells us very little need an ensemble of forecasts to estimate the probabilities of different outcomes Ensemble average provides a deterministic forecast for the average outcome Better are probabilistic forecasts describing the likelihood of different outcomes

9 Deterministic vs probabilistic ensemble forecasts

10 Ensemble deterministic forecasts Example: Seasonal mean temperature for JFM 2016 Deterministic forecast (single location) The average temperature in Victoria, Canada during JFM 2016 will be 0.85 C above normal relative to the average of all years in Deterministic forecast map However, these products contain no indication of uncertainty

11 Probabilistic forecast (single location) Seasonal mean temperature Here the forecast probability distribution or PDF is described in terms of probabilities that forecast seasonal mean temperature will fall into climatologically equi-probable tercile categories: below normal near normal above normal

12 Probabilistic forecast (single location) Seasonal mean temperature Here the forecast probability distribution or PDF is described in terms of probabilities that forecast seasonal mean temperature will fall into climatologically equi-probable tercile categories: below normal near normal above normal

13 Probabilistic forecast (single location) Note: here the ensemble of forecast values has been fit to a normal distribution. Probabilities can also be obtained from raw forecast values Seasonal mean temperature Here the forecast probability distribution or PDF is described in terms of probabilities that forecast seasonal mean temperature will fall into climatologically equi-probable tercile categories: below normal near normal above normal

14 Probabilistic forecast maps Probabilities in each category Above Normal Highest probability at each location Near Normal Below Normal White = equal chance (no category > 40%)

15 Reliability of probabilistic forecasts Consider many probabilistic forecasts from different times, locations Compare forecast probabilites with observed frequencies Forecasts underconfident: forecast probability < observed frequency Forecasts reliable: forecast probability = observed frequency Forecasts overconfident: forecast probability > observed frequency climatological frequency = 1/3 for tercile forecasts

16 Reliability of probabilistic forecasts Consider many probabilistic forecasts from different times, locations Compare forecast probabilites with observed frequencies no skill Forecasts underconfident: forecast probability < observed frequency skill > 0 Forecasts overconfident: forecast probability > observed frequency Forecasts reliable: forecast probability = observed frequency no skill climatological frequency = 1/3 for tercile forecasts

17 Advantages of calibrated probability forecasts uncalibrated probabilities: - high probabilities predicted far more frequently than observed Forecast Seasonal precipitation forecast Reliability perfect forecast - overconfident, especially for precipitation and nearnormal category - near-normal grossly overpredicted Brier skill score = 0 no resolution calibrated probabilities: - much more reliable (forecast probability observed frequency) - less overconfident - near-normal less overpredicted

20 Flexible probabilistic forecasts from IRI Useful if tercile below/near/above normal probabilities are not specific enough Example: probability that JFM 2016 mean temperature will exceed 80 th percentile relative to (Options are 10, 15, 85, 90 percentiles)

21 Forecast skill

22 o o Some terminology Forecast lead time Lead 0 months Forecast issued Skill scores Forecast valid month 1 month 2 month 3 month 4 Example: Anomaly correlation AC= Lead 1 month <Fʹ Oʹ > σ(fʹ ) σ(oʹ ) Fʹ = forecast anomaly Oʹ = observed anomaly _ Perfect No skill f f f AC=0.9 AC=0.5 AC=0.3

23 Global anomaly correlation skills (from Canadian Seasonal to Interannual Prediction System) DJF (Lead 0 months) JJA (Lead 0 months) Near-surface temperature Precipitation General behavior Higher in tropics than extratropics Higher over oceans than land Higher in winter than summer Much lower for precipitation then temp

24 Skill dependence on lead time and averaging period (from Canadian Seasonal to Interannual Prediction System) Example: Anomaly correlation for near-surface temperature from Dec DJF lead 0 month lead 0 monthly skill > lead 0 seasonal skill atmospheric initial conditions contribute to skill in first month skill decreases (usually) as lead time increases Dec lead 0 month Jan lead 1 months Feb lead 2 months

25 Skill dependence on lead time and averaging period (from Canadian Seasonal to Interannual Prediction System) Example: Anomaly correlation for near-surface temperature from Dec JFM lead 1 month lead 1 seasonal skill > lead 1,2,3 monthly skill seasonal averaging improves skill after lead 0 when atmospheric initial conditions are forgotten Jan lead 1 months Feb lead 2 months Mar lead 3 months

26 Anomaly correlations averaged over Africa vs predicted season & lead Seasonal near-surface temperature Seasonal precipitation lead 0 months lead 1 month lead 2 months lead 3 months Monthly near-surface temperature Monthly precipitation There are lots of other skill scores including probabilistic, not enough time to cover here,

27 Guiding principles of climate (e.g. seasonal) forecasting 1) Forecasts should communicate uncertainty Probabilities ensemble forecasts 2) Forecasts should be interpreted in the context of past performance (skill) need many years of hindcasts to calculate skill

28 Purposes of hindcasts Estimate lead-time dependent model biases ( drift ) so that they can be corrected for more in lab session Estimate historical skill Hindcasts enable us to Calibrate probabilistic forecasts Notes: When estimating in-sample corrections and skill, cross validation should be applied to avoid inflated estimates of skill (won t worry about it in the lab, unless you want to) WMO currently recommends as hindcast base period 30 years 12 initialization months 10 ensemble members = 3600 years of model integration per hindcast! (assuming 12 mon range)

29 How seasonal forecasts are produced

30 Computer models of the Earth s climate: tools for assessment and prediction IBM Supercomputer

31 How dynamical seasonal forecasts are made Weather forecast 1-10 days Climate projection years Atmosphere/land models Observations of current global conditions used to initialize model Atmosphere/ocean/ landsea ice models Initial conditions not crucial

32 How dynamical seasonal forecasts are made Weather forecast 1-10 days Climate projection years Atmosphere/land models Observations of current global conditions used to initialize model Atmosphere/ocean/ landsea ice models Initial conditions not crucial Seasonal forecast 1-12 months

33 1 tier (coupled) vs 2 tier forecasts 1 tier forecast 2 tier forecast atmosphere atmosphere land ocean land ocean atmosphere interacts with land SSTs specified (no ocean model) For example, some systems simply persist the SST anomaly present before the forecast 1 tier systems cannot forecast El Niño/La Niña atmosphere interacts with land and ocean coupled climate model includes ocean component future SSTs are forecast by model 2 tier systems potentially can predict El Niño/La Niña (and often do)

34 Problem with 1 tier forecasts Consider 2-tier forecast (persisted SSTA) from 1 April 2006 SST forecast Observed SST anomaly (persisted SST anomaly) Mar 2006 Apr 2006 (lead 0) May 2006 (lead 1) Jun 2006 (lead 2) Jul 2006 (lead 3) Oct 2006 (lead 6)

35 Problem with 1 tier forecasts Consider 2-tier forecast (persisted SSTA) from 1 April 2006 The 1 tier forecast persists the La Niña present before the start of the forecast SST forecast Observed SST anomaly (persisted SST anomaly) Mar 2006 Apr 2006 (lead 0) May 2006 (lead 1) Jun 2006 (lead 2) Jul 2006 (lead 3) Oct 2006 (lead 6)

36 Problem with 1 tier forecasts Consider 2-tier forecast (persisted SSTA) from 1 April 2006 The 1 tier forecast persists the La Niña present before the start of the forecast But the La Niña soon disappears! SST forecast Observed SST anomaly (persisted SST anomaly) Mar 2006 Apr 2006 (lead 0) May 2006 (lead 1) Jun 2006 (lead 2) Jul 2006 (lead 3) Oct 2006 (lead 6)

37 Steps for producing seasonal forecasts Run ensemble of forecasts from slightly different initial conditions Subtract the hindcast climatology to obtain anomalies, from the climatological mean and correct for model biases and drift Deterministic forecast = ensemble mean anomaly To construct probabilistic forecast, must - count ensemble members in each tercile of observed climatology, or - fit ensemble values to a distribution (better), or - calibrate the forecast distribution to produce a more reliable forecast (best)

38 El Niño impacts

39 Equatorial Pacific climate Normal SST C

40 Equatorial Pacific climate Normal SST C El Niño

41 Equatorial Pacific climate Normal SST C El Niño SST anomaly C

42 El Niño direct impacts shift in deep convection dry wet dry

43 El Niño teleconnections heating

44 upper tropospheric response: quasi-stationary Rossby wave El Niño teleconnections strengthened Aleutian Low polar jet stream shifted north Northern winter subtropical jet stream extended & amplified Trenberth et al., JGR (1998) Horel & Wallace, MWR (1981) heating

45 Historical El Niño/La Niña variability A widely used indicator of El Niño/La Niña activity is Nino3.4 = mean SST anomaly in 5N-5S, 120W-170W The Oceanic Nino Index (ONI) consists of a 3-month rolling average of Nino3.4 Nino3.4 index

46 Historical El Niño/La Niña variability Very strong El Niños DJF-averaged SST anomalies from NCEP/OISST

47 Historical El Niño/La Niña variability Very strong El Niños Eastern Pacific Moderate El Niños Central Pacific or Modoki DJF-averaged SST anomalies from NCEP/OISST

48 Global El Niño impacts

49 Global El Niño impacts

50 Global La Niña impacts

51 ENSO impacts on Africa precipitation DJF El Niño composite MAM El Niño composite DJF La Niña composite MAM La Niña composite

52 ENSO impacts on Africa precipitation JJA El Niño composite SON El Niño composite JJA La Niña composite SON La Niña composite

53 ENSO Prediction

54 Nino3.4 ensemble plumes from Nov 2016 similar message: weak La Niña transitioning to possible weak to moderate El Niño by Summer 2017

55 Historical CanSIPS ENSO predictions Seasonal mean Nino3.4 index: observed vs 0-9 month lead times OISST obs lead 0 mon lead 9 mon Nino3.4 anomaly correlation skill Some false alarms, such as and However, no misses for El Niño/La Niña events exceeding ±1.5 C, except for unusual summer-peaked 1987 El Niño

56 Multi-model ensembles (MMEs)

57 Why multi-model ensembles? 1) Different models have different strengths and weaknesses - model errors will tend to cancel each other out - higher skill for multi models than for single model, for a given ensemble size N - this example considers 4 models with 10 ensemble members each 2) More ensemble members available by combining models than from individual models Skill 4 models 3 models 2 models 1 model Kharin et al., Atm.-Ocn. (2009)

58 WMO multi-model ensemble 1 tier (coupled) 2 tier 12 Global Producing Centres (GPCs) representing different meteorological services Forecast information provided to Regional Climate Centres (RCCs) and Climate Outlook Forums (COFs) Maps and data password protected

59 APCC multi-model ensemble Models include CMCC, CanCM3, CanCM4, NASA, NCEP, PMU, POAMA Month 1-3 and 4-6 probabilistic & deterministic forecast maps publicly available Data password protected

60 EUROSIP multi-model ensemble Four models at ECMWF: ECMWF System 4 Met Office HADGEM model, Met Office ocean analyses Météo-France Météo-France model, Mercator ocean analyses NCEP CFSv2 Common operational schedule (products released at 12Z on 15 th ) Some charts (ENSO plumes, tropics) publicly available at

61 North American Multi-Model Ensemble (NMME)

62 What is the NMME? Ensemble of opportunity: US, Canadian operational forecast systems + US research systems Hindcasts and real time forecasts Real time forecasting since Aug 2011 All data openly accessible Requirements for inclusion - ensemble system, range 9 months - must provide hindcast data for commitment to provide real time forecasts by 8 th of each month (CPC operational schedule)

63 NMME Operational Centers Research Centers NCEP ECCC GFDL NASA NCAR Hindcasts 8 th of each month Real-time Forecasts Research Community User/applications Community

64 Currently contributing models Model Center Ensemble size CFSv2 NCEP 24 (28) CanCM3 EC/CMC 10 CanCM4 EC/CMC 10 FLOR GFDL 24 CM2.1 GFDL 10 CCSM4 NCAR 10 GEOS-5 NASA 11 CESM1 NCAR 10 Total ensemble size 109 (113)

66 Individual model forecasts Individual model skills

67 Deterministic and probabilistic forecasts Prate 2015 OND from Deterministic Models weighted equally Probabilistic Ensemble members weighted equally* *Anomalies and tercile boundaries computed separately for each model

68 Raw and calibrated probabilistic forecasts Prate 2016 DJF from Raw probabilistic (overconfident) Calibrated probabilistic (more reliable)

69 Raw and calibrated probabilistic forecasts Prate 2016 DJF from Raw probabilistic (overconfident) Calibrated probabilistic (more reliable)

70 NMME Nino3.4 plumes weak La Niña in possible El Niño in 2017 N D J F M A M J

71 NMME for International Regions (web search nmme international )

72

73 Precipitation forecasts from Nov 2016 DJF JFM FMA MAM AMJ

74 NMME International Data Data is freely accessible!

75 NMME Data at IRI Hindcasts + real time forecasts Data is freely accessible!

76

77

78 CanSIPS Explorer Developed and maintained at CCCma by Slava Kharin Displays all monthly, seasonal hind/forecasts + verifications 1979-present + skills Probabilistic/deterministic forecasts (maps & local PDFs) for many variables, regions (including Africa), indices username: cccmasf password: seasforum sf2_daily Monthly to 12 mon Daily N-day, monthly & seasonal forecasts

79 What is El Niño?

80 Equatorial atmosphere/ocean Equator

81 Equatorial atmosphere/ocean Equator

82 Equatorial atmosphere/ocean Coriolis (trade winds)

83 Equatorial atmosphere/ocean westward current Coriolis (trade winds)

84 Equatorial atmosphere/ocean westward current Coriolis (trade winds) Coriolis

85 Equatorial atmosphere/ocean westward current Coriolis (trade winds) Coriolis cooler water

86 Typical buildup of a strong El Niño: the role of westerly wind bursts (WWB) low pressure climatological easterly high pressure surface Indonesia warm upwelling thermocline cool South America

87 Typical buildup of a strong El Niño: the role of westerly wind bursts (WWB) climatological easterly westerly wind burst (WWB) surface Indonesia warm upwelling thermocline cool South America

88 Typical buildup of a strong El Niño: the role of westerly wind bursts (WWB) climatological easterly westerly wind burst (WWB) surface Indonesia warm upwelling Rossby wave ~1 m/s downwelling Kelvin wave 2-3 m/s upwelling thermocline cool South America

89 2-3 months later climatological easterly cooler anomalous westerly (Bjerknes feedback) surface warmer Indonesia upwelling Rossby wave ~1 m/s thermocline upwelling South America

90 2-3 months later climatological easterly cooler anomalous westerly (Bjerknes feedback) surface warmer Indonesia upwelling Rossby wave ~1 m/s thermocline upwelling South America

91 2-3 months later climatological easterly cooler anomalous westerly (Bjerknes feedback) surface warmer Indonesia upwelling Rossby wave ~1 m/s thermocline upwelling South America SST anomaly - El Niño SLP anomaly - Southern Oscillation El Niño Southern Oscillation (ENSO)

92 Example: 1997 Low-level zonal wind anomalies NOAA/NCEP/CPC Monthly Ocean Briefing

93 Example: 1997 Low-level zonal wind anomalies Westerly wind bursts NOAA/NCEP/CPC Monthly Ocean Briefing

94 Example: 1997 Low-level zonal wind anomalies Mean temperature to 300m depth anomalies Kelvin waves NOAA/NCEP/CPC Monthly Ocean Briefing

95 Example: 1997 Low-level zonal wind anomalies Mean temperature to 300m depth anomalies SST anomalies SST increase NOAA/NCEP/CPC Monthly Ocean Briefing

96 Example: 1997 Low-level zonal wind anomalies Mean temperature to 300m depth anomalies SST anomalies SST increase Bjerknes feedback NOAA/NCEP/CPC Monthly Ocean Briefing

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