Core Theme 5: Technological Advancements for Improved near-

realtime data transmission and Coupled Ocean-Atmosphere Data

Assimilation

WP 5.2 Development of coupled ocean-atmosphere assimilation

capabilities

Lead: Detlef Stammer (UHAM)

Participants: UHAM, MPG-M, ECMWF, KNMI

WP 5.2: Development of coupled ocean-atmosphere assimilation capabilities

Objectives :

• Improve initialization of coupled models using

ocean syntheses and evaluate the improved

skill of those coupled models

• Building of coupled assimilation capabilities that

ultimately will allow to constrain coupled model

directly through climate observations.

Individual Tasks

Coupling MITgcm to Planet Simulator and testing. Done

Running forecast experiments. Done

Preparation of observational atmospheric test data set. Done

Developing and testing variational data assimilation system around the Planet Simulator.

Done

Planet Simulator assimilation. Done

Integration of coupled components. Done

Coupled MITgcm-Planet Simulator assimilation Ongoing

Initialization Techniques GOALS & TASKS & METHODS

GOALS• to improve climate forecasts through better initialization procedures

and by improving uncertain model parameters• To evaluate predictability in coupled ocean-atmosphere models

TASKS• Testing initialization procedures:

1. full state optimization, diagnostic of mean drift2. drift correction by employing flux correction3. anomaly initialization

THE COUPLED MODEL combines two GCM:• ocean model – MITgcm (Massachusetts Institute of Technology, Cambridge)• atmospheric model - UCLAgcm(University of California, Los Angeles)

DATA• GECCO synthesis (1952-2001)• HadI SST • Levitus climatology

TEST EXPERIMENTS• control run, integration over 50 year Initial state = 50 yrs after spinup from Levitus (January)• initializing with absolute values (ensemble: 5 members, 10 year)Initial state = GECCO (January 1997)For evaluation: anomalies are computed with respect to 50 yrs GECCO climatology• anomaly coupling scheme (ensemble: 5 members, 10 year)Initial state = Model climatology + [GECCO (January 1997) – GECCO mean]For evaluation: anomalies are computed with respect to the climatology of yrs 20-50 of the coupled run • flux correction

Initialization Techniques TEST EXPERIMENTS

Ocean Model MITgcm

• Domain80°N:80°S;

360×244×46• Resolution

1°× 1° (± 80°: ± 30°);

1°× 1/3° (± 30°: 0°)• 46 vertical layers

Atmospheric Model

UCLAgcm• Domain

90°N:90°S; 140×91×30

• Resolution2.5°× 2°

• 30 vertical layers

MOCPredictions

FSI

AI

FCI

Skill Scores:

SST

FSIAIFCI

MOC

Correlation RMSE

THOR Coupled Model: pleTHORa

• MITgcm: ocean only configurations,

with and without seaice, from

ECCO/GECCO;

• PlanetSimulator: an Earth System

Model of Interme-diate Complexity

built around an atmospheric dynami-

cal core based on the Hoskins and

Simmons (1975) multispectral layer

model.

WP 5.2: pleTHORa

• coupling: replacement of seaice- and

ocean- compartments of the

PlanetSimulator by MITogcm plus seaice

• configuration: coarse resolution setup

with an atmosphere on a T21 grid and 5

sigma levels, and the MITogcm on a 5.625º

grid having the North Pole shifted to

Greenland, using 15 vertical levels

• testing: coupled system with single CPU

on a notebook, performance is approx. 30

model years/day

Model configuration:

1. Atmosphere with T21L10 resolution: all ice processes (thermodynamic sea ice model, snow on sea ice allowed, skin temperature

computed) land processes on (except “biome” module) no fresh water correction all moisture processes off (evaporation, large scale precipitation, convective precipitation and dry

convective adjustment are switched off)

2. Ocean: global domain with 4 degree uniform lat/lon resolution and 15 depth levels

Identical Twin Experiment Test

Control Variables: Scalar perturbation applied only to the atmospheric parameters

Data: only ocean temperature and salinity data.

Assimilation window: 7 days

Adjoint of pleTHORa

Ten process parameters in the atmosphere :tfrc(1): time scale for linear drag, top leveltfrc(2): time scale for linear drag, level 2tdissd: diffusion time scale for divergencetdissz: diffusion time scale for vorticitytdisst: diffusion time scale for temperaturetpofmt: tuning of long wave radiation schemevdiff_lamm: constant for vert. diffusion and surface fluxesvdiff_b: constant for vertical diffusion and surface fluxesvdiff_c: constant for vertical diffusion and surface fluxes

vdiff_d: constant for vertical diffusion and surface fluxes

Improving Coupled Model through parameter optimization

Model Used: Planet Simulator (PlaSim-T21)

Data used: Simulated observations from the model.

Results:

Sensitivity of the model cost function with number of integration days. The cost function becomes noisier as integration time increases

GF approach: Using a set of 9 control parameters the Cost function can be reduced for the model integrated upto 7 days.

GF works for linear models and can’t be used for longer period runs with PlaSim.

SPSA approach: Using a set of 2 control parameters. Cost function can be reduced for longer model runs. (figure attached for 30 day run)

Future Work: Make use of real observations (ERA-40 data).

1 Day 30 Days

Sensitivity of the cost function to perturbation in model parameters for (a) 1 day integration (b) 30 days integration. The x- axis denotes percentage of perturbation applied to each parameter. Y axis denotes model cost function

Control Parameters UsedTime scale for linear drag (tfrc1,2)Diffusion time scale for vorticity (tdissz)Diffusion time scale for divergence (tdissd)Diffusion time scale for temperature (tdisst)Tuning parameters for vertical diffusivity and surface fluxes (vdiff_b, vdiff_c, vdiff_d)Tuning of longwave radiation scheme (tpofmt)absorption coefficient for h 2O continuum (th2oc)tuning of cloud albedo range 1 (tswr1)

PlaSim’s Sensitivity

For more days the model’s behavior is non linear

The graphs show the plot of cost function for PlaSim integrated for 30 days. Control parameters in this case were TH2OC and TSWR1.X axis represents the number of iterations. There is ~ 60% reduction in Cost function

Green’s Function approach works for shorter time scales (up to 7 days) during which the model’s behavior is nearly linear

The graphs show the plot of cost function, parameter norm and gradient norm on the y axis w.r.t. number of iterations on the xaxis. Green function plot is on Logarithmic scale. The model was run for 7 days using 9 control parameters.

SPSA approach works even when the model behavior is non-linear

Green’s function approach is not suitable for longer time scale due to non-linearity of the model.

Deliverables

THOR is a project financed by the European Commission through the 7th

Framework Programme for Research, Theme 6 Environment, Grant agreement

212643 http://ec.europa.eu/index_en.htm