Climate Forecasting Unit Arctic Sea Ice Predictability and Prediction on Seasonal-to- Decadal Timescale Virginie Guemas, Edward Blanchard-Wrigglesworth,

Climate Forecasting Unit

Arctic Sea Ice Predictability and Prediction on Seasonal-to-

Decadal Timescale

Virginie Guemas, Edward Blanchard-Wrigglesworth, Matthieu Chevallier, Michel Déqué, Francisco Doblas-Reyes, Neven S Fuckar, Francois Massonnet, Danila

Volpi, David Salas y Mélia


I - Arctic sea ice predictability sourcesPersistence

Sea Ice Thickness Sea Ice Thickness

Guemas et al (2014) QJRMS


I - Arctic sea ice predictability sources

Chevallier and Salas-Melia (2012) Journal of Climate

Ice thickness preconditioning

Thick ice = best predictor of September extent, thin ice good predictor for March

extent

Chevallier and Salas y Mélia, JCLIM, 2012

Total volume

Total area

Area of ice h > 0.9m

Area of ice h > 1.5m Area of ice h < 1.5m

Total volume

Total area

Area of ice h < 0.9m


I - Arctic sea ice predictability sourcesAdvection



I - Arctic sea ice predictability sourcesOcean heat transport


Correlation detrended annual-mean ORAS4 60N heat transport and sea ice concentrationAtlantic OHT lag -3 Pacific OHT lag -3

-0.8 -0.6 -0.4 -0.2 0.2 0.4 0.6 0.8


I - Arctic sea ice predictability sourcesReemergence

Blanchard-Wrigglesworth al (2011) Journal of Climate

Mel

tin

g s

easo

nG

row

ing

season

Local sea ice area anomalies associated with SST anomalies

Local sea ice area anomalies with sea ice thickness anomalies

SST persistence

Local SST anomalies impact sea ice area

anomalies

Local sea ice thickness anomalies impact sea

ice area anomalies

SIT persistence


II – Sea Ice Initialization

Guemas et al (2014) Climate Dynamics

NEMO3.2 ocean model + LIM2 sea ice model

Forcings : 1958-2006 DFS4.3 or 1979-2013 ERA-interim

Nudging : T and S toward ORAS4, timescales = 360 days below 800m, and 10 days above except in the mixed layer, except at the equator (1°S-1°N), SST & SSS restoring (-40W/m2, -150 mm/day/psu)

Wind perturbations + 5-member ORAS4 - - - > 5 members for sea ice reconstruction

5 member sea ice reconstruction for 1958-present consistent with ocean and atmosphere states used for initialization


Reconstruction IceSat

Too much ice in central Arctic, too few in the Chukchi and East Siberian Seas

2003-2007 October-November Arctic sea ice thickness




March and September Arctic sea ice

Sea ice area Sea ice volume

DFS4.3 + Nudging

ERAint + Nudging

UCL

HadISST NSIDC

DFS4.3 Free

ERAint + Nudging

DFS4.3 Free

UCL

PIOMAS

DFS4.3 + Nudging

Bias but reasonable agreement in terms of interannual variability

Th

ou

san

ds

km3

Mil

lio

ns

km2




II – Sea Ice InitializationAnomaly versus Full Field Initialization

EC-Earth2.3, 5 members, start dates every 2 years from 1960 to 2004

NOINI : historical simulation

FFI : Full-field initialization from ORAS4 + ERA

OSI-AI : Ocean and sea ice anomaly initialization with corrections to ensure consistency

rho-OSI-wAI : Ocean and sea ice weighted anomaly initialization to account for the different model and observed amplitudes of variability + (density, temperature) Instead of (temperature, salinity) anomaly initialisation

[email protected]


RMSE sea ice area. Ref: Guemas et al (2014)

RMSE sea ice volume. Ref: Guemas et al (2014)

NOINI NOINIFFI

FFI

OSI-AIOS

I-AI

rho-OSI-wAI rho-OSI-wAI

II – Sea Ice InitializationAnomaly versus Full Field Initialization

[email protected]

Lower forecast error when using ocean and sea ice anomaly initialization, even lower with weighted anomalies


Chevallier and Guemas (2014) in preparation

III – Multi-model Sea Ice Prediction

CNRM-CM5.1 and EC-Earth2.3

Seasonal forecasts initialized every 1st November and every 1st May from 1990 to 2008

5 month long

Initialized from ERA-interim for the atmosphere + in-home sea ice reconstructions + ORAS4 (Ec-Earth2.3) or ocean reconstruction (CNRM-CM5.1) for the ocean

5 members using initial perturbations applied to all components for EC-Earth2.3, only the atmosphere for CNRM-CM5




Summer prediction

Winter prediction

Bering

Okh

otsk

Greenland

Barents

Kara

Baffin

Hudson




Total Arctic Baffin Bay

Linearly Detrended Anomaly Correlation for Sea Ice Extent (SIE)

EC-Earth 2.3CNRM-CM5.1EC-Earth 2.3 + CNRM-CM 5.1

Predictability until March Similar performance between CNRM-CM5.1

and EC-Earth 2.3

95% confidence interval

correlation




Greenland Sea Barents and Kara Seas



Predictability for the first month only in Greenland, until March in Barents and Kara Better performance from CNRM-

CM5.1


correlation




Okhotsk Sea Bering Sea



Better initialisation in Okhotsk Sea with CNRM-CM5: ocean ? Multi-model allows to make the most of individual performance


correlation


Conclusions Sources of sea ice predictability: persistence, ice

thickness preconditioning, advection, ocean heat transport, reemergence

Initialization: 1. Need to make the most of the growing sea ice observations (thickness, melt pond, ice drift) 2. Need for ensembles consistent with the atmospheric and oceanic state

3. Need for further investigation of anomaly initialisation benefits

Prediction: winter predictability until March in the Baffin Bay, Kara, Barents and Okhotsk seas

Documents

Climate Forecasting Unit Arctic Sea Ice Predictability and Prediction on Seasonal-to- Decadal Timescale Virginie Guemas, Edward Blanchard-Wrigglesworth,