Seasonal Sea Ice Forecasting

Brief overview - challenges for YOPP

Matthieu Chevallier (CNRM/GAME, Météo France)With valuable help from and discussions with: Ed Blanchard-Wrigglesworth, Jonny Day, Virginie Guémas, François Massonnet.

YOPP Summit13-15 July 2015, WMO, Geneva

History - motivations

USERS

EXTREMES

LINKAGES

➡’Sea Ice Outlook’ (ARCUS/SEARCH, nowSIPN)

Arctic sea ice predictability

Coupled atmospheric-sea ice-ocean processes (dynamic/thermodynamic)Importance of information on sea ice thickness (melt ponds) ➙ observations?

thickness

concentration

SnowMeltponds

Blanchard et al., 2011Chevallier and Salas-Mélia, 2012Day et al., 2013Kapsch et al., 2014Schröder et al., 2014

•September sea ice extent: OK fromJuly, probably OK from May-June usingmelt ponds/radiative fluxes, potentiallyOK from March using ice thickness…

•March sea ice extent: OK fromJanuary, potentially OK earlier from icethickness, upper ocean…

Initial states:Initialize non-observable quantities (ice thickness)?Initialize the whole system (atmosphere, ocean, land surface…)?Sample initial state uncertainty (ensemble generation)?

How to build a sea ice forecasting system

MODEL

Initial state Forecast ObservationsObservations

Model:Statistical: long time series of observational data required, non-stationarity problemsPhysical: what complexity? Resolution? Uncertainty due to errors in model parameters?

Operational systemsHindcast: May 1 ➙ September (5 months)Mean September Arctic sea ice area

Do all these systems together provideuseful information?

➡YES!

Merryfield et al., 2013Chevallier and Guémas, 2015, in prep

Why are the predictions so different?-initialization?-model physics?

Guémas et al., 2014, QJRMS

➡Still unclear…

+ GFDL, UK Met Office…

Coupledmodels

Initialization issues

thickness

heatcontent

concentrationmixed layer heat content

SnowMeltpond

s

Courtesy F. Massonnet

➡ How to initialize the whole state with incomplete observations?➡ How about reanalyses?

Initialization issues

Average March sea ice thickness (2003-2007) in reanalysesChevallier et al., 2015, Clim DynGODAE OceanView/CLIVAR-GSOP Ocean ReAnalyses Intercomparison Project

Some systems assimilate concentration (some don’t…)

No thickness data assimilation

Differences in model, setups, datasets, DA methods…

Relevant diagnostics for end-users

+ Spatial patterns (ice edge) + Probabilistic information+ …?

Date (julian day) of ice clearance for May 1st 2008 hindcast with CNRM-CM5.1 (May 1st= day #120). Ensemble max and min.

Max MinDate of ice clearance/appearance

Sea

ice

area

ano

mal

iesRegional sea ice area Nov ➙ March forecasts

with CNRM-CM

Barents Sea

Recommendations for YOPPYOPP is a unique opportunity for better understanding the coupled processes at the ocean‐sea ice‐atmosphere‐land interfaces playing a role in sea ice predictability from hours to seasons and for improving our coupled forecasting systems and their uses.

Have sea ice models and diagnostics ready in all seasonal forecasting systems!

Coordinated sea ice forecasting experiments for all seasons Hindcast mode: systematic multi-model approach Sensitivity tests (initial conditions, new model parameterizations, ensemble generation) Dedicated experiments during YOPP using specific data Understanding the drivers of certain YOPP events

Use YOPP IOP to intensify links between modellers and observationalists and developcoupled data assimilation

Think beyond the September pan-Arctic sea ice extent (end-users diagnostics)!

Go for seasonal sea ice forecasting in the Southern Ocean!

Recommendations for YOPPYOPP is a unique opportunity for better understanding the coupled processes at the ocean‐sea ice‐atmosphere‐land interfaces playing a role in sea ice predictability from hours to seasons and for improving our coupled forecasting systems and their uses.

Have sea ice models and diagnostics ready in all seasonal forecasting systems! Météo-France subseasonal/seasonal forecasting systems

Coordinated sea ice forecasting experiments for all seasons Hindcast mode: systematic multi-model approach Sensitivity tests (initial conditions, new model parameterizations, ensemble generation) Dedicated experiments during YOPP using specific data Understanding the drivers of certain YOPP events

Joint effort Météo-France/IC3 (ANR), S2S, Sea Ice Prediction Network

Use YOPP IOP to intensify links between modellers and observationalists and developcoupled data assimilation

French ‘Chantier Arctique’: link with iAOOS, Copernicus Marine Service

Think beyond the September pan-Arctic sea ice extent (end-users diagnostics)! Sea Ice Prediction Network

Go for seasonal sea ice forecasting in the Southern Ocean!

Thank you for your attention

YOPP Summit13-15 July 2015, WMO, Geneva