1. The Met Office GloSea5 System
Description and Latest Results
Jeff Knight and many other colleagues, Met Office Hadley Centre
13th Session of the Forum on Regional Climate Monitoring, Assessment and Prediction for Asia (FOCRAII), 24th April 2017

2. The Met Office Global Seasonal Forecast System: GloSea5

3. GloSea5 Met Office Global Seasonal forecast system, version 5
Model: HadGEM3 GC2 (updated Feb 2015)
Resolution: Atmos, N216 L85 (~60km); Ocean: 0.25° L75
Initialisation: Daily, NWP state + NEMOVAR 0.25°
Ensembles: Stochastic physics + lagged initialisation
Forecasts: 2 per day -> 42 members,
Hindcasts: 3 per 4 times/month -> 12 members, 1996 – 2009
Products: to-decadal/gpc-outlooks
The Korea Meteorological Administration (KMA) produce long-range forecast ensembles with a very similar system
MacLachlan et al. 2014, Scaife et al. 2014
© Crown copyright Met Office

4. Global Coupled modelling configuration
6.0
6.0
5.0
6.0
CICE
The Los Alamos Sea Ice Model
N216 (~60km)
ORCA025

5. Initialisation of the system
Forecast (initialised daily):
- Atmosphere & land surf *: Met Office NWP analysis (4d-Var)
- Ocean & sea-ice: NEMOVAR (3d-Var joint system for ocean, med-range, monthly and seasonal)
14-year hindcast ( ):
- Atmosphere & land surf *: ERA-interim
- Ocean & sea-ice: NEMOVAR
- Fixed start dates of 1st, 9th, 17th, 25th of each month
- 3 members per start date
* Soil moisture set to climatological average

6. GloSea5 forecast schedule
Seasonal Forecast:
- 2 members run each day.
- Seasonal forecast updated weekly by combining members from last 3 weeks (i.e. 42 members)
Monthly Forecast:
- 2 additional members run each day.
- Monthly Forecast updated daily by combining members from last 7 days (i.e. 28 members)
Hindcast (for monthly-seasonal):
14 year hindcast run in real time ( 42 members run each week = 14 years x 3 members)

7. A month in the life ….
MacLachlan et al, 2014

8. Products Met Office website:
sonal-to-decadal/gpc-outlooks
Lead Centre for Long-Range Forecast Multi- Model Ensemble (hosted by Korea Meteorological Administration):

9. Forecast maps

10. Timeseries (plume diagrams)

11. Skill scores

12. New developments in GloSea5

13. New developments Aug 2016: Mar 2017: May 2017:
Increased hindcast length
( )  ( )
14 years  23 years
Mar 2017:
Increased hindcast size
3 members  7 members, 4 times per month
Effective hindcast size 12  28
May 2017:
Initialised soil moisture

14. Impact of larger ensemble
February PMSL

15. Initialised Soil Moisture
Use Japanese Reanalysis (JRA) from the Japan Meteorological Agency:
drive our land-surface model JULES in reanalysis mode
update in near-real time as JRA is available in near-real time (2-day delay)

16. The 2015-16 El Niño: impacts in UK and China

17. ENSO, as we saw it in May 2015
HadISST
monitoring

18. ENSO forecasts in 2015 Slight warming at start of 2015
From MAR
Slight warming at start of 2015
Signals grew during year
Very clear by Autumn
From MAY
From JUL
From SEP
From NOV

19. Winter 2015/16: a near record El Niño
2015/16 Forecast
2015/16
1997/8
1982/3
Very clear signals for a near record event
Remote but not irrelevant
Similar to 1982/83….return to this later
Scaife et al, ASL, 2017

20. Europe: Dec 2015

21. North Atlantic Oscillation
(single most important factor for UK winters and responds to many drivers)

22. Seasonal forecasting of the NAO
Mild, wet
and stormy
Retrospective and real time forecasts from November
- NAO
Cold, snowy
and still
Ensemble
Mean
Observations
Ensemble
Member
Our original tests are shown in orange and indicate a correlation skill of 62%
More ensemble members => more skill and ~0.8 may be possible
So far so good with real time forecasts...
Scaife et al 2014, Dunstone et al 2016

23. December 2015 Very clear signals for a westerly winter from October
From November
Observations
Very clear signals for a westerly winter from October
Good agreement with subsequent observations
Early warning of winter flooding
Scaife et al, ASL, 2016

24. Early vs late winter and an analogue...
Early winter
Nov-Dec
Late winter
Jan-Feb
2015/16
1982/83
Toniazzo and Scaife,
GRL, 2006
Fereday et al,
Clim. Dyn., 2008
Remarkable similarity with 1982/3 case
Remarkable similarity in late and early winter to other strong El Nino events
Scaife et al, ASL, 2017

25. Forecasts of the polar vortex
Late Winter 2015/16
Strong polar vortex
Weak polar vortex
Forecasts of the polar vortex
NSIDC
Very strong in December, weak towards late winter
=> low pressure and a mild, wet and stormy start to winter
Scaife et al, ASL, 2017

26. Stratospheric conditions: winter 2015/16
A sudden warming finally happened in early March (consistent with the cold dry start to spring)
Later than the most likely time in the forecasts but within the spread of forecasts from Autumn
Scaife et al, ASL, 2017

27. Winter 2015/16: November Forecast
December showed a very clear signal for wet
Circulation implied increased storm risk
Dec-Feb showed similar signal overall but a switch to colder in late winter
Allowed real time warnings to:
DEFRA, Cabinet Office and DfT

28. Winter 2015/16 Good agreement with subsequent observations
From November
Observations
December Temperature
December Rainfall
Good agreement with subsequent observations
Early warning of December flooding
Driven by ENSO + few others
Scaife et al, ASL, 2017

29. China:
summer 2016

30. Skilful predictions of Yangtze rainfall and river flow
Collaborative work with Chaofan Li and Riyu Lu (IAP), Jianglong Li (BCC-CMA)
Work under the Climate Science for Services Partnership (CSSP) between the UK and China (CMA/IAP)
Modest but significant grid
point skill
Useful regional average
Skill (r = 0.55)
30 hindcast members - real time predictions have
~42 members
This is an underestimate of
actual forecast skill
Li et al 2016

31. Larger signals than in previous forecast systems
There is a larger ensemble mean signal in GloSea5 forecasts
More variability is actually predictable
Of course we hope to increase this in future forecast systems.
Li et al 2016

32. Improved tropical teleconnections
Yangtze is part of a banded structure of tropical rainfall
Extends down to the deep tropics
Poleward, moisture bearing winds occur to the south
Li et al 2016

33. ENSO is a big part of this
El Nino
El Nino
Xie et al 2009
Several mechanisms:
IO memory
Seasonal – ENSO
interaction
Stuecker et al 2015

34. Precipitation forecasts in 2016
From MARCH
From MAY
El Niño over by summer 2016
Very similar banded structure (Wet/Dry/Wet)
Signals for wet in SE China

35. Real time forecast with China Met Administration (CMA)
Philip Bett (Met Office) in collaboration with Chaofan Li (IAP) and Peiqun Zhang (CMA)
Li et al, ERL, 2016
Useful regional average skill (r = 0.55)
Real time service tested
Wuhan flooding

36. Summer 2016 Yangtze Forecast Verification
May-Jun-Jul
precip was above-average
and forecast was good

37. Summer 2016 Yangtze Forecast Verification
Jun-Jul-Aug
Precip was near-average
Early forecasts were for wetter-than- average but the final forecast was close to average

38. Teleconnections of La Niña

39. This winter was more La Niña-like

40. Yangtze river summer precipitation following winter El Nino / La Nina
Stephen Hardiman (Met Office) CSSP collaborative work with Chaofan Li (IAP) and Bo Lu (CMA)
El Nino
La Nina
0.24 mm/day
-0.02 mm/day
GPCP
0.28 mm/day
0.07 mm/day
GloSea5
Yangtze river basin experiences anomalously wet summer following winter El Nino
No signal in Yangtze summer precipitation following winter La Nina (response is NOT LINEAR)
Response following both El Nino and La Nina is well simulated by GloSea5

41. El Nino
La Nina
Following winter El Nino, a strong anti-cyclone in the NWP drives northward, moisture bearing winds over the SCS.
There is no signal in MSLP or meridional wind following a winter La Nina.
Understanding why is the subject of further work (and may shed light on which mechanisms are important)!

42. ENSO in the previous winter has large influence on Yangtze summer rainfall What are the mechanisms??
Xie et al 2009
Stuecker et al 2015
Explain all mechanisms whilst on this slide... :
Positive feedback from NE trade winds, getting stronger on Eastward flank (cold SST) and weaker on Westward flank (warm SST)
Indian Ocean warms following El Nino, and keeps memory of warm SSTs longer. This produces Kelvin waves which anchor the AAC.
Thus see a double peak in warm T in SCS (in winter and summer but not so much in spring)
Alternatively, El Nino driven variability in NWP interacts non-linearly (i.e. constructive interference) with annual cycle of fields in that region – see peaks in the power spectra of the NWPAC (AAC) streamfunction at frequencies of 1-fe and 1+fe. i.e. this interaction acts to maintain the AAC.
All the above seems to be about feedbacks between waves, circulation, and winds...
Several possible mechanisms:
Seasonal – ENSO interaction
Local interactions in NWP
IO memory of warm SSTs
All lead to NWPAC, and thus poleward winds in SCS in JJA...
May help to maintain NWP-AC
Xie et al 2016

43. Evaluating the Risk of Extremes

44. South east England flooding
In south east England January 2014 saw the greatest monthly rainfall total on record
Could it have been even worse?

45. Monthly rainfall totals
In a given winter, there is an 8% risk of a month wetter than has been previously observed in south east England
Thompson et al. submitted to Nature Communications

46. Hot summers in the Yangtze basin
Vikki Thompson (Met Office) CSSP collaborative work with Hongli Ren and Bo Lu (CMA)
Health impacts, with greater effect
in urban areas
Increases consumption of electricity
and water
Can lead to forest fires and crop
losses
Heat related mortalities in China
have increased in recent decades
What is the risk of a hotter summer month than has been seen?

47. Temperature data 40x more data available from model than observations
Observations from WATCH Forcing ERA-interim Data (WFDEI):
- Weedon et al. 2014
- Reanalysis data bias corrected with CRU TS3.1
- July and August monthly air temperatures
- 35 x 2 = 70 months
(years x months)
Using Met Office decadal prediction system:
- 40 realisations
- 35 x 40 x 2 = 2800 months
(start dates x realisations x months)
40x more data available from model than observations

48. Model fidelity
Model and observations
distributions appear similar

49. Model fidelity We resample the model 1000x and
compare to the observations
Model and observations
distributions appear similar
All measures must agree to 95% level

50. Only these regions pass the model fidelity tests for all measures
Choosing the region
Pass
Fail
Only these regions pass the model fidelity tests for all measures

51. Yangtze Climatology Monthly air temperature,
black = observations, red = model

52. Yangtze Climatology Only July or August show record high temperatures
Monthly air temperature,
black = observations, red = model

53. Monthly temperatures Monthly air temperature for July and August
black = observations, red = model

54. 83 unprecedented extremes in the model
Monthly temperatures
83 unprecedented extremes in the model
In a given summer, there is a 6% risk of a hotter month than has yet been observed
Monthly air temperature for July and August
black = observations, red = model

55. Risk of an extreme in China
6% chance of a record
each summer
1% chance each summer of July or August being 0.8 °C warmer than the current record

56. Monthly temperatures Top 10 August events Monthly air temperature,
black = observations, red = model

57. 2m air temperature standardised anomaly for top 10 August months
Global temperatures
2m air temperature standardised anomaly for top 10 August months

58. 2m air temperature standardised anomaly for top 10 August months
Global temperatures
2m air temperature standardised anomaly for top 10 August months
Hot Yangtze – more than two standard deviations above normal

59. Standardised precipitation anomaly for top 10 August months

60. Standardised precipitation anomaly for top 10 August months
High rainfall over India,

61. Standardised precipitation anomaly for top 10 August months
High rainfall over India, low rainfall over Yangtze basin

62. Conclusions
The Met Office produces routine long-range forecasts using GloSea5 prediction system
Outputs are available from the Met Office website and the Lead Centre for Long-Range Forecast Multi-Model Ensemble
New developments are improving skill estimation and bias correction
Research on various topics for European and global predictability is bearing fruit
A number of topics on East Asian climate are being jointly tackled with collaborators at CMA and IAP under the UK-China Climate Science for Services Partnership (CSSP)

63. Thank You! Any Questions?

64. Skill for winter NAO is robust
Winter (DJF) NAO
r=0.53
GloSea5 skill level holds for:
A longer (35 year) time series – using decadal version
Similar skill for 1st and 2nd half of the period (Corr = / 0.57)
New model physics
Decadal system DePreSys3;
November starts; hindcast =
Skill found with GloSea is robust – replicated over a longer period and with different model version
Good news for NH extratropics!
Nick Dunstone

65. Skill depends on ensemble size
Forecast skill increases with ensemble size
Occurs in other regional predictions
Rate of growth depends on signal to noise ratio
Li et al 2016, Scaife et al 2014