# Presentation: 912292

## High resolution global-ocean

**and sea-ice data synthesis**
- Dimitris Menemenlis
- Jet Propulsion Laboratory
- Ninth Conference on Polar Meteorology and Oceanography
- May 28th - June 1st 2007, St. John's, Newfoundland, Canada
**Outline:**
- Preliminary Green’s function optimization (H. Zhang)
- Southern Ocean results (M. Schodlok)
- Arctic Ocean results (R. Kwok and A. Nguyen)
- Some early user applications
- Adjoint-based optimization (P. Heimbach & M. Mazloff)

## ECCO2:  High-Resolution Global-Ocean and Sea-Ice Data Synthesis

**ECCO2:  High-Resolution Global-Ocean and Sea-Ice Data Synthesis**

**MIT**
- Marshall, Heimbach, Hill Wunsch
**JPL**
- Fu, Kwok, Lee Menemenlis Zlotnicki
**GSFC**
- Rienecker Suarez
**ARC**
- Henze, Taft
**HARVARD**
- Tziperman
**GFDL**
- Adcroft
**ARGONNE**
- Hovland, Utke

- Objective: synthesis of global-ocean and sea-ice data that covers the full ocean depth and that permits eddies.
- Motivation: improved estimates and models of ocean carbon cycle, understand recent evolution of polar oceans, monitor time-evolving term balances within and between different components of Earth system, etc.

- Velocity (m/s)
- At 15 m depth

## List of CS510 integrations

**cube01       first CS510 integration**
**cube49       first optimization**
**code change**
- 01      02   sea ice dynamics and salt relaxation
- 03      05   use net shortwave over open water
- 06      09   horizontal viscosity
- 25      26   surface bulk formulae
- 26      27   horizontal viscosity
- 26      28   horizontal viscosity
- 31      37   C-grid sea ice with ice-ocean stress
- 38      40   downward shortwave over open water
- 43   treatment of snow and evaporation
- 48	50   snow advection
**ocean model parameters**
- 02      03   horizontal biharmonic viscosity
- 05      06   horizontal biharmonic viscosity
- 29      30   background vertical diffusivity
- 31      33   KPP Rc (PBL depth)
- 31      34   surface salinity relaxation
- 31      35   KPP Ro (shear instability)
- 50      51   bottom drag
- 50      52   open-water albedo
- 50      53   air-ocean drag

**sea ice model parameters**
- 09      11   sea-ice freezing temperature
- 12      13   sea-ice and snow albedos
- 13      15   sea ice strength
- 15      16   air-ocean, ice-ocean drag
- 15      18   air-ocean, air-ice, ice-ocean drag
- 16      19   air-ocean drag
- 22      23   reduced horizontal viscosity
- 22      24   limit air & ice surface temperature
- 24      25   vertical viscosity
- 29   sea ice strength
- 28	31   ice strength and air-ice-ocean drag
**initial conditions (IC)**
- 11      12   end of 11
- 16      20   WOA01 January
- 20      21   end of 20
- 19      22   end of 19
- 37      38   WGHC IC
- 49      48   Arctic sea-ice IC
**boundary conditions (BC)**
- 09      10   ERA40
- 38      39   air temperature increase
- 40      41   CORE surface BC
- 40      42   CORE winds
- 43      44   ECCO-GODAE surface BC
- 43      45   ECCO-GODAE winds
- 43      47   Arctic river runoff

## Green’s Function Approach

- (Stammer and Wunsch 1996; Menemenlis and Wunsch 1997; Menemenlis, Fukumori, and Lee 2005)

**GCM:*****	******	******	*****x**(*t*i+1)  =  *M* [**x**(*t*i), ****]
**Data:****	****	**** ****	*****y***o  =  *H* [**x**] + *******  =  ****G**** ***[****] + ****
**Cost function:****	***J  = *(****** - ******b)T **Q**-1 (****** - ******b) + ****** **T** R**-1** ******
**Linearization:****	**** *****y***o - *G**** ***[****b] = **G** (****** - ******b) + ****
- where **G** is a matrix whose columns are computed using a GCM sensitivity experiment for each parameter in vector ****.

## Preliminary Green’s function optimization (H. Zhang)

**Data:**
- WOCE Global Hydrographic Climatology (WGCH).
**Optimized parameters:**
- Horizontal viscosity, vertical viscosity, vertical diffusivity, albedos,
- KPP critical Richardson numbers, sea-ice model parameters,
- initial T/S conditions, and surface boundary conditions.
**For example,**
- Optimized initial conditions **= 1.28** WGCH **+ 0.66** WOA01 **- 0.89** 5-yr-spin-up
-                            **+ 0.24** 11-yr-spin-up **- 0.04** 23-yr-spin-up **- 0.25** 35-yr-spin-up
- Optimized winds **= 0.70** CORE **+ 0.23** ERA40 **+ 0.07** NCEP
- Optimized air temperature **= 0.49** CORE **+ 0.23** ERA40 + **0.28** NCEP + **0.24****°****C**
**Cost function reduction:**
- Optimal linear combination:** ****74%** for temperature and **68%** for salinity.
- Optimized integration: **74%** fro temperature and **50%** for salinity.

## 0-700-m temperature difference from WGHC (oC)

**0-700-m temperature difference from WGHC (oC)**

## Antarctic sea ice extent (M. Schodlok)

**Antarctic sea ice extent (M. Schodlok)**

**Baseline**
**Optimized**

## Weddel Sea TS diagram (M. Schodlok)

**WOCE Global Hydrographic Climatology**
**Baseline**
**Optimized**

## ACC Transport across Drake Passage (M. Schodlok)

**ACC Transport across Drake Passage (M. Schodlok)**

**Baseline**
**Optimized**

**134****±0.8 Sv**
**Sloyan and Rintoul, 2001**

## Arctic sea ice advection & deformation from RGPS (R. Kwok)

**RGPS**
**November**
**1997**
**Baseline**

**RGPS**
**April**
**1998**
**Optimized**

## Ice export from Fram Strait (A. Nguyen)

**Baseline                Optimized**

## Summary of preliminary Green’s function optimization

- 1992

- 2002

- 2005

**140Sv**

**170Sv**

- Drake Passage Transport

**Baseline**

**Optimized**

- A Green’s function approach is being used to fit a high-resolution configuration of the MITgcm to satellite and in-situ ocean and sea ice data.  Top panel shows temperature difference in top 700 m between WGHC climatology and a 1992-2002 baseline integration driven by NCEP.  Bottom panel shows temperature difference from WGHC for an integration whose initial conditions, surface boundary conditions, and internal model parameters have been calibrated using 30 forward sensitivity integrations (H. Zhang).

- This first optimization improves Southern Ocean circulation and stratification (M. Schodlok) ...

- ... but it degrades Arctic Ocean sea ice distribution and freshwater budget (R. Kwok and A. Nguyen).

-        RGPS                     RGPS                  Baseline              Optimized
-  November 1997          April 1998              April 1998             April 1998

- Optimized minus  Baseline
- time-mean wind difference

## 22-23 January 2007 ECCO2 meeting: Early User Applications

**Subtropical mode water (Maze, MIT)**
**Eddy propagation velocity (Fu, JPL)**
**GRACE data constraints (Zlotnicki, JPL)**
**Errors estimates (Forget, MIT)**
**Eddy parameterizations (Ferreira, MIT)**
**Arctic freshwater budget (Condron, WHOI)**
**Arctic sea ice budget (Kwok, JPL)**
**Sea ice data/model comparison (Nguyen, JPL)**
**Carbon cycle modeling (Manizza, MIT)**
**Eddy variability in Indian Ocean (Lee, JPL)**
**Darwin project (Hill, MIT)**
**Southern Ocean (Schodlok, JPL)**
**MITgcm assimilation efforts (Cornuelle, SIO)**
**Adjoint assimilation efforts (Edwards, UCSC )**

## Adjoint-based optimization (P. Heimbach, MIT)

- Iteration 0 sea-ice concentration
- cost relative to microwave data

- Iteration 27 sea-ice concentration
- cost relative to microwave data

## Iteration 4: Cost vs observed sea surface height anomaly

- SSH cost change:
- Iteration 4 - Iteration 0

**Eddy-Permitting Southern Ocean State Estimate Using the Adjoint Method (M. Mazloff, MIT)**

## http://ecco2.org/

- ECCO2 project aims to produce increasingly accurate syntheses of all available global-scale ocean and sea-ice data at resolutions that start to resolve ocean eddies and other narrow current systems.
- A first public ECCO2 ocean state estimate, obtained using model Green’s functions, is scheduled for this summer.
- A number of early user projects have started using pre-release ECCO2 solutions.
- Adjoint state estimation in the presence of vigorous eddying circulation has been successfully demonstrated.