1. Recent advances in seasonal forecasting at the Met Office
Michael Vellinga and many colleagues at the Met Office Hadley Centre

2. Outline Background to seasonal forecasting at Met Office
The Met Office Seasonal prediction system GloSea4
Research activities: model development
Indian monsoon
ENSO teleconnections
Summary

3. Seasonal and decadal forecasting
Initialized near-term climate predictions:
Probabilistic forecast (ensemble prediction systems) designed to estimate risks, not a single deterministic outcome
Using the global coupled climate model HadGEM3: atmosphere, oceans, sea-ice, land-surface
Including information of current state of earth system (atmosphere, oceans, sea-ice, land-surface) to predict natural internal variability
Including information of climate forcings (greenhouse gases, aerosols, solar forcing, volcanic forcing) to predict long-term climate trends

4. GloSea4: Met Office’s new seasonal forecasting system
Started running operationally in September 2009
Met Office changed its seasonal forecasting system a lot - why?
Better link to HadGEM3 model development:
Development of improved model for S2D and regional climate
Traceable model science: use (essentially) same model science for NWP  seasonal decadal  climate applications
Glosea4 system more flexible: easy to upgrade
Perform operational hindcasts at same time as operational forecasts
Parallel suite for testing effect of proposed system changes
In short: frequent forecast updates, easy to implement changes

5. GloSea4: uses new model
HadGEM3_AO_r1.1 (version 1.1 of the Met Office Hadley Centre coupled climate model as in May 2009):
UM (Met Office Unified Model) atmosphere;
NEMO (Nucleus for European Modelling of the Ocean) ocean;
CICE (Los Alamos Sea-ice Model) sea-ice;
MOSES (Met Office Surface Exchange Scheme) land surface.
Model resolution
Atmosphere: N96 (approximately 120 km horizontal resolution in mid-latitudes) and 38 levels in the vertical.
Ocean: ORCA1 grid (1 degree ocean with 1/3 of a degree refinement between 20S and 20N) and 42 levels in the vertical.

6. GloSea4: new ensemble Ensemble prediction system:
Initial condition uncertainties:
no perturbations added to analysis, lagged approach is used.
Model uncertainties:
RP (Random Parameters) - aims to represent the structural uncertainty arising from subjective parameters in physical parameterizations.
SKEB2 (Stochastic Kinetic Energy Backscatter version2.0) - aims to represent the sub-grid scale uncertainty arising from advection and numerical dissipation.
Both schemes are currently used in the operational Met Office short- and medium-range ensemble systems (MOGREPS). Additional information can be found in Bowler et al. (2008) and Shutts (2005).
Atmospheric perturbations create good spread in ocean after 10 days

7. GloSea3 vs. GloSea4 GloSea3 (old) GloSea4 (new system) HadCM3
Model
HadCM3
(N48L19 – 1/3L40)
HadGEM3
(N96L38-ORCA1L42)
Initialization
Atmos/soil: ECMWF
Ocean: UM-ocean 3D-OI
Atmos/soil/sea-ice: Met Office
Ocean: NEMO 3D-OI
IC uncertainties
Wind stress and SST perturbations
added to a central analysis
Weekly lagged approach
Model uncertainties
None
Random parameters + stochastic
kinetic energy backscatter
Forecast ensemble
41-members (monthly bursts
from 1st )
~ 14 members per week
Hindcast ensemble
Run a priori off-line
15-members / 15-years
( ) ERA-40
Run in real-time
~3-members per week / ~14-years
( ) ERA-interim

8. GloSea4: a brief technical overview

9. GloSea4 ocean data assimilation
ocean data assimilation based on Optimal Interpolation (OI) scheme.
analysis done on model 1°x1° background state.
single covariance length scale (for spreading info)
N/S km N/S
E/W km -> to 1000km within 15 of equator
3DVAR upgrade (likely next year)
assimilate
SST
surface – ship data/buoys; hindcast uses ICOADS
satellite: forecast: GHRSST (AVHRR, AMSRE, AATSR, METOP)
hindcast: NOAA Pathfinder (AVHRR)
temperature/salinity profiles (Argo floats, TAO array, moorings, …); hindcast uses EN3 profiles
not assimilated currently: sea ice, altimeter data

10. GloSea4 ensemble setup Forecast
14 members initialised every week, on Monday
initialisation: Met Office NWP atmosphere analyses
Met Office optimal interpolation ocean analyses
Hindcast – used for calibration and skill assessment
14 years ( ); 3 members per start date (fixed calendar dates: 1st, 9th, 17th and 25th)
run in real time (i.e. shortly before forecast to facilitate model/system upgrades)
initialisation: ERA Interim reanalyses for atmosphere.
ocean states: Met Office optimal interpolation ocean analyses using observations from EN3 database

11. Example: monthly GloSea4 timetable
Tues
Wed
Thu
Fri
Sat
Sun 30 31 1 2 3 4 5
14 Forecasts
42 Hindcasts
(14-yr x 3-mem) 6 7 8 9 10 11 12
Latest forecast/ hindcasts are provided for free every month, for non-commercial use
Contact Met Office if interested
Maps (probability forecasts etc) on Met Office’s website 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3

12. Model development activities
Indian monsoon

13. Model development
Met Office Hadley Centre models have generally produced a comparatively good simulation of the Asian summer monsoon
The climate version HadCM3, used in the old seasonal forecasting system GloSea3, shows some skill in representing monsoon climatology and variability.
The monsoon precipitation deteriorated in the next generation climate version, HadGEM1 (used in IPCC AR4).
Focussed work involving monsoon experts from the Met Office, University of Reading, IITM (Pune) and NCMRWF (Delhi), using the suite of MetUM configurations covering a range of spatial and temporal scales, has improved understanding of the errors.
HadGEM2 (used in IPCC AR5) shows some improvement and HadGEM3 (currently under development) shows further improvement.
The current operational seasonal forecasting system (GloSea4) is built using the first prototype release of HadGEM3.
HadGEM3 is still under development and the monsoon remains an area of intense focus.

14. HadGEM3 model climatology: Seasonal cycle of AIR
Diagram shows monthly
mean observed ( ) seasonal
cycle (black) and IQR (grey)
with model values overlaid: HadGEM3 and HadGEM2
Index: intermonthly standard
deviation of curves
(values on side)
Seasonal cycle of precipitation in HadGEM3 is significantly improved over HadGEM2
Late onset (linked to cold SSTs) in HadGEM3, but still better than HadGEM2
There is a compensation of errors: dry over peninsular India, wet over Himalayan foothills
Courtesy of Andrew Turner, Univ. of Reading

15. HadGEM3 model climatology: monsoon precipitation
HadGEM3 – GPCP Obs
Long-term systematic model error (since HadGEM1): dry over Indian subcontinent, wet over the W eq Indian Ocean and Himalayan foothills
Improvements in systematic error in HadGEM3 compared to HadGEM1 and HadGEM2 (CAPE time-scale increase), but N India worse

16. HadGEM3 teleconnection: Monsoon (JJAS) AIR rainfall - SST
Models’ teleconnections lie within observational variability over the last century
HadGEM3-AO prototypes
Observations
Observations
Observations

17. ENSO teleconnections in GloSea4 hindcasts: El Niño - La Niña
JJA (for h/c in May)
DJF (for h/c in November)

18. Summary

19. Seasonal and decadal forecasting at Met Office: Summary
The model used for seasonal forecasting is essentially the same as that for climate prediction and weather prediction
Our latest seasonal forecasting system has been designed to be very flexible and fully integrated within our model development strategy
Global seasonal forecast is updated every week, on Met Office website every month [examples in my talk tomorrow]
Contact if you wish to receive ‘packaged’ GloSea4 seasonal forecasts
The Met Office Hadley centre has now a clear focus on seasonal-decadal: We expect to make substantial progress in the next few years

20. Questions and Answers

21. GloSea3/HadCM3
JJA rainfall climatology from 8 seasonal forecasting systems for the period 1987 to 2001.
“UKMO” represents GloSea3, based on climate version HadCM3.
Model climatology compares well with observations.

22. Climate version HadGEM1
New generation climate model including:
semi-Lagrangian dynamics;
increased vertical resolution in atmosphere (38 vs 19 levels);
increased horizontal resolution in atmosphere (1.875° x 1.25°; ~140 km at mid-latitudes);
increased horizontal resolution in ocean (1.0° x (1.0° increasing smoothly from 30°N/S to 0.33° at equator));
improvements to physical parametrisations (surface, boundary layer, clouds, convection, gravity wave drag, river routing, aerosols).
This resulted in improved representation of physical processes, although removal of some compensating errors resulted in some detrimental impacts on the simulation.

23. Proto-HadGEM3 (Autumn 2009)
GPCP observations
HadGEM3 prototype
20-year JJAS climatology from proto-HadGEM3-A model.
Improvement to HadGEM2 mainly due to an increased CAPE closure timescale for convection.
ERA40 Reanalyses
ERA40 Reanalyses
HadGEM3 prototype
© Crown copyright Met Office

24. HadGEM2-AO 20-year JJAS climatology from HadGEM2-AO coupled model.
Improvement mainly due to the inclusion of an adaptive detrainment parametrisation for deep convection.

25. Climatology: monsoon precipitation
Long-term systematic model error (since HadGEM1): dry over Indian subcontinent, wet over the W eq Indian
Ocean and Himalayan foothills
Mainly improvements in systematic error compared to HadGEM2 (CAPE time-scale increase), but N India worse

26. Anomaly Correlations – Nino (August start dates)
Lead month
Lead month
GloSea3
GloSea4

27. Nino 3.4 SST August starts RMSE/Spread BSS spread GloSea3 (15 members)
Lead month
Lead month
spread
GloSea3 (15 members)
GloSea4 (4-6 members)

28. GloSea4 : Problems adding perturbations to ICs
Pert. Members
CTRL
(Bowler, Arribas and Mylne, MWR)