department of meteorology – university of reading ...ben/blocking2016/talks/rodwell.pdf6-8 april...

Diagnostics of the prediction and maintenance

of Euro-Atlantic blocking

Mark Rodwell, Laura Ferranti,

Linus Magnusson

European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, RG2 9AX, UK © ECMWF March 3, 2016 mark.rodwell@ecmwf.int

Workshop on Atmospheric Blocking

6-8 April 2016, University of Reading

Difficulties in predicting blocking

European Centre for Medium-Range Weather Forecasts mark.rodwell@ecmwf.int 2

Animation of ‘bust’ forecast

Animation of forecast started at 0 UTC on 10 April 2011

Potential Vorticity on 320K

Animation of ‘bust’ forecast

Animation of forecast started at 0 UTC on 10 April 2011

Block forms in observations, but not in forecast

It is difficult, by day 6, to disentangle model error from the natural growth

of initial condition uncertainty (chaos)

Animation of ensemble forecast: Initial peturbations

Animation of ensemble forecast

Ensemble forecasting (flow evolution to day-6)

Occasional ‘busts’ in forecast performance

Spatial Anomaly Correlation Coefficient for 500 hPa geopotential height in [12.5oW –42.5oE, 35oN–75oN]. Date is forecast start

Rodwell et al, 2013, BAMS

European Z500 skill at day 6

Bust around 10 April 2011

• Initial condition error?

• Model error?

• Reduced predictability?

Verifying conditions composited over many bust forecasts

Composite of 584 busts in ERA Interim forecast prior to 24 June 2010

Rex-type block

Unit = m Bold colours = statistical significance at 5% level

500 hPa geopotential height (Z500) anomaly

Regimes

Regimes based on clustering of daily anomalies for 29 cold seasons (1980-2008)

Ferranti et al. 2015, QJRMS

500 hPa geopotential

Blocking: More poorly predicted and not persistent enough

From Laura Ferranti

Day 0 Day 1 Day 5 Day 7 Day 10

100/100 70/70 44/52 36/47 29/41

NAO+ European Blocking

Skill in predicting regime projection

Blocking persistence: ECMWF model/analysis

1 day worse than for

NAO+ at CRPS=0.5

Ensemble evolution in phase space

Initial date: 22 September 2015 0UTC

In presentation to ECMWF Scientific Advisor Committee, 2015

Analysis

ENS member

• Nice way to summarise ENS in

two dimensions

• Transition to blocking well-

predicted 4 days ahead

• Blocking projection perpetuates to

day 10, but spread increases

pean B

lock →

- ← NAO → +

“Ferranti - Magnusson diagram”

(similar to phase-space diagram of MJO)

Blocking onset and forecast ‘reliability’

12European Centre for Medium-Range Weather Forecasts Mark J Rodwell

Composite initial conditions of bust forecasts

There is an initial flow regime: “Rockies

trough” with high CAPE ahead

Conducive to the formation of

mesoscale convective events (MCS)

Remarkable that we can identify any

significant initial conditions 6 days

ahead of the busts – this must be due to

the large composite (584 events) used

Other bust causes not so geographically

fixed and are not highlighted by this

composite-mean

‘CAPE’ = Convective Available Potential

Energy

Bold = 5% significance

Spread-error for Trough/CAPE composite (MCS)

• Following conditions conducive to MCS development, enhanced errors and spread propagate east towards Europe → ‘Busts’

• Note: -ve residuals occur in non-trough/CAPE situation too.

• +ve residual at D+5 is not significant (Chaos? → use bigger sample or shorter leadtime? But analysis uncertainty at D+1?)

Rodwell et al., 2015, Report to SAC

95% confident

Not significant

54 cases 200 hPa geopotential

Error2 Ensemble Variance Residual

Error2 = EnsVar + Residual

Reliability [Residual]=0

EDA reliability budget: Trough/CAPE composite

• Residual highlights MCS, and suggests lack of background variance. (Obs uncertainty changes 2nd-order)

• MCS uncertainty (existence, intensity, location) not well reflected in Jetstream uncertainty (with downstream consequences)

• Budget useful to diagnose biases, modelling of observation error and representation of model uncertainty (including

stochastically-formulated process parametrizations)

Depar2 = Bias2 + EnsVar + ObsUnc2 + Residual

Reliability [Residual]=0

Relative to aircraft observations of zonal wind 200hPa (±15)54 cases Rodwell et al., 2015, Report to SAC

EDA = “Ensemble of Data Assimilations”

MCS – Jetstream interaction (composite)

• Increments emphasize model systematic error: MCS does not interact enough with Jetstream

• Also need to strengthen stochastic physics to increase background variance?

PhysicsPhysics + analysis increment

u=25ms-1

Jetstream

Met3D: Marc Rautenhaus

MCS – Jetstream interaction (composite)

• Increments emphasize model systematic error: MCS does not interact enough with Jetstream

• Also need to strengthen stochastic physics to increase background variance?

PhysicsPhysics + analysis increment

u=25ms-1

Jetstream MCS

Met3D: Marc Rautenhaus

Maintenance of blocking

Initial process tendencies and analysis increment (DJF 2016)

Dynamical and physical process tendencies integrated over the 12h background forecast of the data assimilation (EDA cntl)

Analysis increments

suggest model warms

over land a little too

(note different contour

interval to tendencies)

Initial process tendencies and analysis increment (Blocked)

Composite over three blocked periods: Dec 4-9, Dec 26-28, Jan 26-28 (24 analysis cycles)

Cloud forcing (net latent

heating associated with

microphysics) highlights

Warm Conveyor Belt (WCB)

Note negative dynamical

tendency in this region

Initial process tendencies and analysis increment (Blocked-DJF)

Composite over three blocked periods: Dec 4-9, Dec 26-28, Jan 26-28 (24 analysis cycles) minus DJF 2015/16

Convective forcing

unchanged relative to full

season mean

Increments suggest WCB

cloud forcing too weak?

(can probably discount effect

on increments of

compositing on observed

blocking)

Barotropic vorticity equation in upper troposphere (analysis)

Rossby Wave Source and advection by rotational flow smoothed, averaged over composite and integrated 100 – 300 hPa

Divergence associated with

Down-stream advection (+ve)

and β-effect (-ve)

Deficiencies in perpetuating blocking associated

with slight weakness of WCB heating?

Rossby wave source: -𝛻. 𝑣𝜒𝜁

Vorticity advection by rotational flow: -𝑣𝜓. 𝛻𝜁

DJF 2016 Blocked

DJF 2016 Blocked Blocked-DJF

Blocked-DJF

Onset of blocking

• Difficult to predict beyond a few days

… associated with ‘busts’

• Diabatic processes important

… produce large-amplitude waves that break to form a block

… associated with instabilities that decrease predictability

• We may under-represent uncertainty

… due to systematic errors or deficiencies in stochastic physics

Maintenance of blocking

• Warm conveyor belts important for vorticity forcing that stabilises block

• We may have too weak cloud forcing in the WCBs

Summary

Extra slides

Skill in predicting from regimes

Skill in predicting from a given regime. October – March, 2007 – 2012. 95% confidence based on bootstrapping

Ferranti et al. 2015, QJRMS

European (and Atlantic) blocking is more

poorly predicted than NAO

Reliability in ensemble forecasting

(Cross-terms on squaring have zero expectation. EnsVar is scaled variance to account for finite ensemble-size)

Error2 = EnsVar + Residual

The importance of

reliability is the motivation

for using ‘proper’ scores

(such as the Brier Score or

CRPS).

Reliability (at all leadtimes)

should reduce ‘jumpiness’

of ensemble forecasts

Adapted from Rodwell et al., 2015, QJRMS

Reliability in ensemble data assimilation

Adapted from Rodwell et al., 2015, QJRMS

Depar2 = Bias2 + EnsVar + ObsUnc2 + Residual

department of meteorology – university of reading ...ben/blocking2016/talks/rodwell.pdf6-8 april...

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