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Monthly and seasonal forecasts at ECMWF: operational plans and prospects from current research. Franco Molteni with M. Balmaseda, L. Ferranti, K. Mogensen, T. Palmer, T. Stockdale, F. Vitart European Centre for Medium-Range Weather Forecasts, Reading, U.K. Overview.

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slide1

Monthly and seasonal forecasts at ECMWF:

operational plans and prospects

from current research

Franco Molteni

withM. Balmaseda, L. Ferranti, K. Mogensen,

T. Palmer, T. Stockdale, F. Vitart

European Centre for Medium-Range Weather Forecasts, Reading, U.K.

overview
Overview
  • Results from ongoing experimentation on the development of Seasonal Forecast System-4

(caution: work in progress!!)

  • Prospects for the extension of the monthly forecast: progress with the simulation of the MJO and its teleconnections

Vitart and Molteni, MWR 2009; QJRMS 2010, under rev. ; Vitart GRL 2009.

  • Beyond System-4: what can we expect from the inclusion of a dynamical sea-ice model? Results from simulations for summer 2007 and 2008.

Balmaseda, Ferranti, Molteni, Palmer,QJRMS 2010, under rev.

ecmwf seasonal forecast system sys 3
ECMWF Seasonal forecast system (Sys-3)

IFS 31R1

1.1 deg.

62 levels

HOPE

~ 1. deg. lon

1./0.3 d. lat.

OASIS-2

TESSEL

Ens. Forecasts

Initial Con.

4-D variational d.a.

Gen. of

Perturb.

System-3

CGCM

Multivar. O.I.

ecmwf seasonal fc system 4 main features
ECMWF Seasonal Fc. System 4: main features
  • New ocean model : NEMO v. 3.0 + 3.1 coupling interface
    • ORCA-1 configuration (~1-deg. resol., ~0.3 lat. equatorial refinement)
    • 42 vertical levels, 20 levels with z < 300 m
  • Variational ocean data assimilation (NEMOVAR)
    • 3-D var with inner and outer loop
    • Collaboration with CERFACS, UK Met Office, INRIA
    • First re-analysis (1957-2009), no assim. of sea-level anomalies
    • Second re-analysis and real-time system including SLA
  • IFS model cycle: 36r3 or 36r4 (36r1 currently operational)
    • New physics package, including HTESSEL land-surface scheme, snow model (with EC-Earth), new land surface initialization
  • New formulation for prescribed sea-ice concentration
    • Sampling from most recent years
2010 time line
2010 time-line
  • Further tests of recent IFS cycles (1st/2nd quarter)
  • Set up real-time ocean analysis system (1st/2nd quarter)
  • Definition of final configuration (2nd quarter)
  • Start production of hindcasts (summer)
  • Validation of hindcasts and definition of operational products (late summer/fall)
  • Operational implementation (winter 2010/11)
ocean re analysis with nemo at ecmwf
Ocean Re-Analysis with NEMO at ECMWF
  • Using NEMO/NEMOVAR
  • Model configuration: ORCA1, smooth coastlines, closed Caspian Sea.
  • Forced by ERA40 (until 1989) + ERA Interim (after 1989)
  • Assimilates Temperature/Salinity from EN3
  • Strong relaxation to SST (OI_v2)
  • Online bias correction scheme
  • Preliminary ocean re-analysis 1957-2009
    • This used the EN3_v2a dataset, where the XBT were not corrected
  • First ensemble reanalysis 1957-2009
    • 5 ensemble members (perturbations to wind, initial conditions, observation coverage)
    • Corrected XBT
ocean re analysis with nemo
Ocean Re-Analysis with NEMO
  • Data assimilation helps the convergence of the solution
  • Periods of large differences between Assim and Control
    • 1970’s
    • Post 2000’s
an outstanding modelling issue tropical wind biases
An outstanding modelling issue: tropical wind biases

Biases in 850 hPa

streamfunction

(obs SST, DJF 1963-2006)

ERA40

31r1 (sys3) – ERA 36r1 - ERA

hindcasts with ifs 36r1 sst bias 1
Hindcasts with IFS 36r1: SST bias (1)

Start: 1 Nov.

1989/2008

Verify: Dec-Feb

System 3

IFS 36r1 T159/L91

+ NEMO

hindcasts with ifs 36r1 sst bias 2
Hindcasts with IFS 36r1: SST bias (2)

Start: 1 May

1989/2008

Verify: Jun-Aug

System 3

IFS 36r1 + NEMO

hindcasts with ifs 36r1 sst bias 3
Hindcasts with IFS 36r1: SST bias (3)

Start: 1 May

1989/2008

Verify: Sep-Nov

System 3

IFS 36r1 + NEMO

hindcasts with ifs 36r1 bias in z500
Hindcasts with IFS 36r1: bias in Z500

Start: 1 Nov.

1989/2008

Verify: Dec-Feb

System 3

IFS 36r1 + NEMO

impact of vertical res ens mean acc sst
Impact of vertical res.: Ens-mean ACC, SST

36r1 T159/L62 + NEMO 36r1 T159/L91+ NEMO

impact of horizontal res ens mean acc sst
Impact of horizontal res.: Ens-mean ACC, SST

36r1 T255/L91 + NEMO 36r1 T159/L91+ NEMO

hindcasts with ifs 36r1 ens mean acc z500
Hindcasts with IFS 36r1: Ens-mean ACC, Z500

Start: 1 Nov.

1989/2008

Verify: Dec-Feb

System 3

IFS 36r1 + NEMO

hindcasts with ifs 36r1 ens mean acc t 2m
Hindcasts with IFS 36r1: Ens-mean ACC, T_2m

Start: 1 Nov.

1989/2008

Verify: Dec-Feb

System 3

IFS 36r1 + NEMO

hindcasts with ifs 36r1 ens mean acc t 2m18
Hindcasts with IFS 36r1: Ens-mean ACC, T_2m

Start: 1 May

1989/2008

Verify: Jun-Aug

System 3

IFS 36r1 + NEMO

work to do and outstanding issues
Work to do and outstanding issues

NEMOVAR

  • Inclusion of altimeter data
  • Implementation of real-time suite

Coupled model

  • Further tests at higher horizontal resolution (T255)
  • Bias in tropical winds:
    • Wait for a “better” IFS cycle (how better?)
    • Test flux correction for wind stress/heat fluxes (as a diagnostic tool; an option for Sys-4 ?)
slide20

Coupled forecast at TL159

Initial condition

Day 32

EPS Integration at T399

Coupled forecast at TL255

Initial condition

Day 32

Day 10

Heat flux, Wind stress, P-E

Ocean only integration

Unified EPS/monthly forecasts at ECMWF

Original “stand-alone” monthly system:

32-day EPS/monthly system since March 2008:

Further resolution upgrade to T639/T319 on 26 January 2010

slide21
June monsoon rainfall over India: EPS-monthly (from 15 May): ACC = 0.57Sys-3 from 1 May /1 June (avail. on 15): ACC = 0.29/0.50

46-day exp. from the 15thof each month, 1989-2008

mjo impact on jja precipitation in 46 day eps
MJO impact on JJA precipitation in 46-day EPS

EPS ERA-In

Wheeler-

Hendon 2004

Phase 2-3

Phase 4-5

Phase 6-7

Phase 8-1

impact of mjo on forecast reliability
Impact of MJO on forecast reliability

T_850 > upper tercile,

fc. day 19-25

Blue line: no MJO in IC

Red line: MJO in IC

conclusions 1
Conclusions (1)
  • In terms of modelling/analysis infrastructure, the development of a new seasonal forecast system based on NEMO/ NEMOVAR is close to completion.
  • The tropical wind biases arising on seasonal and longer timescales in recent IFS cycles lead to a significant cold bias in the tropical Pacific SST; in turn, this causes a reduction in predictability of west-Pacific SST and associated teleconnections.
  • Performance in the tropical Indian and Atlantic oceans is comparable or moderately better than System-3; biases in the Southern Ocean SST and NH winter circulation are substantially reduced.
  • Recent changes in physical parametrizations have, on the other hand, substantially improved the simulation of tropical intra-seasonal variability (e.g. MJO) on weekly/monthly time-scales and beyond.
  • The improved MJO simulation leads to increased predictive skill in both the tropics and extratropics on the 15-45 day range. Forecast reliability in the NH extratr. is strongly dependent on MJO conditions
arctic sea ice variability
Arctic sea-ice variability
  • The summers of 2007-2008 have seen unprecedented anomalies in the Arctic ice extension
  • The ECMWF Seasonal Forecast system does not represent interannual variations of the sea-ice. Would the SF over Europe improve if Arctic sea-ice anomalies were predicted?

Images from the National Snow and Ice Data Center: http://www.nsidc.org/sotc/sea_ice.html

sensitivity exp on response to sea ice anomalies
Sensitivity exp. on response to sea-ice anomalies

1 May - 30Sep 2007 & 2008, 40-m. ensembles

  • A1: Sys3 AGCM with prescribed (obs.) SST, observed sea-ice concentration
  • A2: Sys3 AGCM with prescribed (obs.) SST, climatological sea-ice concentration
  • C1: Sys3 CGCM, predicted SST, observed sea-ice concentration
  • C2: Sys3 CGCM, predicted SST, climatological sea-ice concent.
  • P1: Sys3 CGCM with prescribed SST in NW Atlantic only, observed sea-ice concentration
  • P2: Sys3 CGCM with prescribed SST in NW Atlantic only, climatological sea-ice concentration

AGCM response : A1 – A2

CGCM response : C1 - C2, P1 – P2

impact on 500 hpa geop height in agcm

Z500 JA 2007: Obs-Clim Ice

Z500 JA 2008: Obs-Clim Ice

Observed Anomalies

Impact on 500 hPa geop. height in AGCM

2007

2008

Atmos model

(uncoupled)

how is the response in coupled model experiments

Z500 JA 2007: Obs-Clim Ice

Z500 JA 2008: Obs-Clim Ice

Z sensitivity: Obs-Clim JA 2008

Z sensitivity: Obs-Clim JA 2008

Coupled

model

How is the response in coupled-model experiments?

2007

2008

Atmos model

(uncoupled)

Exp B: Coupled integrations with climatological and Observed ice extension. 2007 & 2008.

impact of sst bias in the gulf stream region on z 500

Z500: Uncoupled - Coupled

Z500: Partial Coupling - Coupled

SST bias in the Gulf Stream region may explain a large part of the coupled-model systematic errors over the North Atlantic Sector

Impact of SST bias in the Gulf Stream region on Z_500

Differences in mean 500hPa height in July-August

impact on the response to the 2008 arctic ice anomaly

Z sensitivity: UNCOUPLED

Z sensitivity: Prescribed Gulf Stream

Z sensitivity: COUPLED

Correcting the SST bias in the Gulf Stream region changes the atmospheric response to the prescribed sea-ice anomaly

Impact on the response to the 2008 Arctic ice anomaly
is the response to sea ice dependent on the real sst
Is the response to sea-ice dependent on the “real” SST?
  • Variable SST ensembles with AGCM:
    • 5 member ensembles, initialized in May 1987-2006 (20 years)
    • Performed with 2007 and climatological sea-ice concentration
    • 1000 40-member sub-ensembles with variable SST generated by randomly selecting 2 out of 5 ensemble members for each year
    • 1000 40-member-mean response to 2007 sea-ice anomaly with variable SST are obtained
  • The 40-member response with 2007 SST can be compared with the population of 1000 responses with variable SST: does it belong to the same population?
    • Yes: the SST of the same year (2007) does not induce a statistically significant difference in the response to sea-ice
    • No: the response to sea-ice anomaly is significantly dependent on the SST of the same year
is the response to sea ice dependent on the real sst37
Is the response to sea-ice dependent on the “real” SST?
  • Project the Z500 responses on EOFs of monthly means in Jul-Aug
  • Compute PDF of variable-SST response in PC1-PC2 plane

The PC1 response with the 2007 SST (blue cross) has just a 2.3 % probability of belonging to the variable-SST response distribution

conclusions 2
Conclusions (2)
  • Experiments indicate that the observed Arctic ice anomalies in summers 2007 and 2008 had a significant impact on the atmospheric circulation over the North Atlantic sector.
  • The incorrect representation of the Gulf Stream in the coupled model is partly responsible for the biases in the atmospheric circulation over the North Atlantic sector in the coupled model. The SST bias in the Gulf Stream region also affects the response of the atmosphere to anomalous sea-ice concentration.
  • In general, the response of the atmosphere to the sea-ice anomaly depends on the SST state. The sign of the response with actual, observed SST may be opposite to the average response with climatological or randomly-selected SST.
  • A posteriori bias correction of model biases is inadequate in the presence of a non-linear response to sea-ice anomaly; a better simulation of western boundary currents is needed in coupled models.