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 drift 2. drift correction by employing flux correction 3. 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 ( ) 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 of the coupled run flux correction Initialization Techniques TEST EXPERIMENTS Ocean Model MITgcm Domain 80°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 Resolution 2.5°× 2° 30 vertical layers

MOC Predictions FSI AI FCI

Skill Scores: SST FSI AI FCI 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 level tfrc(2): time scale for linear drag, level 2 tdissd: diffusion time scale for divergence tdissz: diffusion time scale for vorticity tdisst: diffusion time scale for temperature tpofmt: tuning of long wave radiation scheme vdiff_lamm: constant for vert. diffusion and surface fluxes vdiff_b: constant for vertical diffusion and surface fluxes vdiff_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 Used Time 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 2 O 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