Stefano Ciavatta, Ricardo Torres, Stephane Saux-Picart, Icarus Allen

The PML data assimilation system For the North East Atlantic and ocean colour

OPEC, Dartington, 11-12 December 2012

Outline

• Main features of the assimilation system

• Preliminary results: twin experiment

• Computational requirements

• What’s next

NEODAAS-RSG

Data MODIS chlorophyll L4 biogeoch.

WCO RSG-NEODAAS

ERSEM-POLCOMS

Lewis and Allen, 2007

Model

DA •Localized Ensemble Kalman Filter (log-transformation, 100 members)

•MODIS 5-day composites of chlorophyll •Weekly assimilation (year 2006)

Main features of the DA system

Root mean square error vs satellite chlorophyll Model Assimilation

RM

SE

(µg/

l)

Improved yearly and seasonal simulation of carbon-cycle variables

Other 11 time series, Other 3 skill metrics… ok, but not short term (ME)

Total particulate carbon at L4

RMSE=144 mgC/m-3

RMSE=133 mgCm-3

∆RMSE = -7.7%

Model Assimilation L4 data

Time

Ciavatta et al., J. Geophys. Res., 2011

Main features of the DA system

Main features of the DA system

Model: • POLCOMS-ERSEM • 49 state variables (including carbonate system) + 1 passive tracer • Benthic module: on

Satellite data: • GlobColour chlorophyll from 1997 to 2010. • Data errors are available pixel x pixel as percentage standard deviation • Resolution: 4 km, reprocessed to 9 km grid • Daily product are used to compute 5 days composites centred on the assimilation date • Assimilation frequency: monthly

Assimilation: Ensemble Kalman filter [Evensen, 2003] 100 members Log-transformation of states and observations Local analysis. Radius variable in space as a function of the bathimetry: - depth < 50 m : radius = 25000 m (14%) - 50 m < depth < 2000 m: radius = 50000 m (51%) - depth > 2000m : radius = 100000 m (35%)

Depth

m

Main features of the DA system

Nutrients: N1p, N3n, N4n, N5s Phytoplankton types: Chl1, Chl2, Chl3, Chl4, P1c, P1n, P1p, P1s, P2c, P2n, P2p, P3c, P3n, P3p, P4c, P4n, P4p Zooplankton types (only C): Z4c, Z5c, Z6c, Bacteria (only C): B1c , Detritus: R1c, R1n, R1p, R2c, R4c, R4n, R4p, R6c, R6n, R6p, R6s, R8c Carbonate sys: O3c, bioalk

Main features of the DA system (2/3) Analysed” 39 out of 49 variables (max is 41):

Ciavatta et al., JGR, 2011

Hyper-parameters: Model error (forecast time): Gaussian pseudo-random perturbation of the input irradiance values (stand dev: 20% of the irradiance value) Model error (analysis time): pseudo-random Gaussian perturbations of the 3D fields [Evensen, 2003] of all the analysed variables (stand dev: 10% of the values of the variables) Observational error: pseudo-random Gaussian perturbations of the 2D fields [Evensen, 2003] of the total chlorophyll data (stand dev: XX% of the chl values)

Main features of the DA system

Twin experiment (“year 2000”)

Nitrate

Reference Analysis Forecast

Tot. chlorophyll (assimilated)

mg m

-3 m

mol m

-3

Spatial distributions (April)

Chl

orop

hyll

[mg

m-3

] N

itrat

e [m

mol

m-3

]

Months

Reference Forecast

Analysis

Ocean (OC) North Sea (NS) L4

Twin experiment (“year 2000”)

OC

NS L4

Note: different

scales

Year evolutions

Chlorophyll [mg m-3]

Nitrate [mmol m-3]

Concentration

Ocean (OC) North Sea (NS)

Dep

th (l

evel

) D

epth

(lev

el)

Twin experiment (“year 2000”)

Reference Forecast

Analysis OC

NS L4

L4

Note: different

scales

Vertical profiles

Technical notes – computational requirements

Forecast step: - 1 member x 1 node = 32 cpus - 100 members x 100 nodes: 3200 cpus - Walltime x 1 month (100 members in parallel) = 02:10 hours - Estimated time x 1 year = about 26 hours (without queuing)

Analysis step: - 100 members, 39 variables : requires about 30 GB RAM - uses 5 nodes (mppwidth=160 cpus): - Walltime x 1 analysis = 01:20 hours - Estimated time x 1 year = about 16 hours (without queuing)

1 year ∼ 42 hours

What’s next

• Investigate further the system features (twin exp)

• Set-up outputs for skill assessment

• Assimilation of real GlobColor data !

Thank you