Igor Kamenkovich

Modeling in SOCCOM: State Estimate, Metrics and OSSEs

SOCCOM goals

Theme 1: Observations

• to develop a new observing system for carbon, nutrients, and oxygen• deploy a large array (~200) of profiling floats with biogeochemical sensors• complement by shipboard measurements, instrument and sensor development• carry out data analysis and state estimateTheme 2: Modeling• to accelerate the process of reducing the uncertainty in climate projections • to improve our understanding of the uptake of carbon and heat • to improve our ability to project the role of winds, buoyancy and stratification in

determining the impacts of climate change on the oceansTheme 3: Broader Impacts• to promote understanding of climate science • to collaborate with educators and media professionals to inform policymakers and

the public about the challenges of climate change and its impacts on marine life in the context of the Southern Ocean

SOCCOM BGC-ARGO Float Data

Carbon System

AlgorithmsSouthern Ocean State

Estimation (SOSE)

Observing System Simulation Experiments

(OSSE)

Assessment ofGlobal Climate and Earth System Models

(CM4/CM2.6/CMIP5/SOMIP)

The SOCCOM float program informs several of the modeling projects and the modeling projects are helping with the planning, design and quality control of the float deployments and data.

The SOCCOM Modeling Plan

B-SOSE (Matt Mazloff and Ariane Verdy) BiogeochemicalSouthern Ocean

StateEstimate

B-SOSE

B-SOSE goals and role in SOCCOM

Setup:

• Multi-scale optimization most efficient. Start at 1/3 with 52 levels.

• Biogeochemical – ice – ocean Southern Hemisphere model is Mercator projection poleward of 30oS, then telescopes to equator.

pCO2

pH

Fe

ALK

DIC

light temperature

scavenging

sediments

dust

remineralization

air-sea flux air-sea flux

phytoplankton community production

carbon system

chl-aBlargeBsmall DOP

O2

NO3PO4

DON

Biogeochemistry with Light, Iron, Nutrients and Gases (BLING) version 2.

State estimate is being derived with MITgcm-ECCO machinery: Closed budgets

SOSESOSE

B-SOSE Iteration 45:RMS fit to obs: 19.2 μmol/kg

December 2008 oxygen at 500 m. Argo observations shown with filled circles.

World Ocean Atlas 2009 climatology. RMS fit to obs: 24.8 μmol/kg

B-SOSE 23% more consistent with obs.

SOSE

WOA09

SOSE: ProgressSOSE: Progress

Comparison with gridded products

Comparison with in situ observations

Comparison with gridded productsBiogeochemical state

Physical state

Takahashi mean CO2 flux

-10

-10

0

10

0

SOSE Iter45 2008-2009 CO2 flux10

Extensive validation documentation being made available at http://sose.ucsd.edu/bsose_valid.html

Air-sea CO2 flux with LDEO (Takahashi) climatology

(i) to assist in interpretation of data and development of new analysis techniques;

(ii) to provide guidance on the optimal design of an observing system.

OSSE Goals

2. Reconstruct gridded model fields, using the multi-scale objective mapping (Gray and Riser 2015)

3. Analyze reconstruction errors (RErr) – the weighted difference between the reconstructed and actual model fields – as a quantitative metric for the reconstruction skill

OSSEs: MethodologyOSSEs: Methodology

1. Sample model-simulated fields in the same ways the real observational array samples the real ocean use global 1/12o data-assimilating HYCOM model to simulate

trajectories of Argo and SOCCOM profiling floats; use actual Argo trajectories to validate the HYCOM model and

reduce uncertainty in conclusions; use these trajectories to “sample” BGC tracers from GFDL

CM2.6 simulations

Area‐averaged RErr for the annual‐mean O2 as a function of the number of floats. Trajectories are chosen randomly from the full set; the annual‐mean fields arereconstructed from 5 years of synthetic data 

OSSEs: Dependence on the number of floatsOSSEs: Dependence on the number of floats

• Study dependence of RErr on the number of profiles• Results: Large increase in RErr for < 150-200 floats (consistent with

Majkut et al. 2014)

• Run an ensemble of simulations for each available SOCCOM station (ensemble members differ by deployment date/year, ~96 in total)

• Where will the floats go? • How sensitive is the reconstruction skill (RErr) to the uncertainty in

trajectories?

Weighted RErr in the annual mean surface O2 from 42 simulated floats (2014‐2016 deployments, simulated until year 2020). Red dots show profile locations

OSSEs: SOCCOM deployment planning (In Progress) OSSEs: SOCCOM deployment planning (In Progress)

Southern Ocean Model Intercomparison Project

Joellen Russell, Ron Stouffer, Mike Winton, Steve Griffies, Gokhan Danabasoglu, Matt England, Stephanie Downes, Ricardo Farnetti

OVERALL GOAL: SOMIP is primarily focused on the CMIP6 scientific question “How does the Earth System respond to forcing?” with the aim of reducing uncertainties in climate projections by defining the role of the oceans in climate with regards to the Southern Ocean

OBJECTIVES: • to understand the causes of differences in the model responses • to compare models to observations • to increase our understanding of the important processes influencing

model response

The wind perturbations are the zonal and annual mean of the zonal wind perturbations applied as part of FAFMIP

• start from the end of the preindustrial control simulation; • apply strong poleward increase in the wind stress; • assess the ability of wind forcing to both mix surface properties

downward and bring interior properties to the surface;• assess the momentum balance in the Antarctic Circumpolar Current

especially with respect to eddies (simulated or parameterized); • assess upwelling of Circumpolar Deep Waters along the Antarctic coast

that has been hypothesized to lead to changes in the ice shelves

Southern Ocean Model Intercomparison Project

In the freshwater perturbation experiments, we will impose a standard size perturbation equivalent to an anomalous freshwater input of 0.1 Sv applied as:

• uniform anomalies around Antarctica, • more realistic ice-melt scenarios where locations and amounts are

based on the existing patterns of melt and flow, or• via icebergs as a freshwater delivery mechanism:

We will address the basic questions of wind vs. stratification

Southern Ocean Model Intercomparison Project

Metrics for the Evaluation of the Southern Ocean in Earth System Models

Goal: to develop observationally-based data/model metrics for the consistent evaluation of modeling efforts by Southern Ocean and Antarctic scientists

• Result of the CLIVAR Working Group on Heat and Carbon Uptake in the Southern Ocean• Each member proposed his/her metrics (see the next Table)• The CMIP5 analysis was based on these recommendations (where possible)

Person Affiliation Area of Interest Metric(s)

Cecilia Bitz U. Washington Role of Sea Ice in Climate Sea Ice Extent/Volume/Seasonality

Raffaele Ferrari MIT Ocean Turbulence Eddy Kinetic Energy; Eddy‐induced diffusivities and heat transport/uptake

Sarah Gille UCSD/SIO Air/Sea Exchange Mixed‐layer depth; Heat Content (400m)Non‐solubility pCO2 variance

Robert Hallberg NOAA/GFDL Ocean Dynamics Water mass properties  (upper 2000m and abyssal);Age tracer distribution; Drake Passage transport

Ken Johnson MBARI Chemical Sensors/Biogeochemical Cycles

Seasonal cycle of nitrate

Igor Kamenkovich U. Miami Mesoscale Eddies/ Role of SO in global MOC

Stratification at the northern flank of the SO; Eddy‐induced diffusivities

Irina Marinov U. Pennsylvania Carbon Cycle/Ecology Oxygen, Temperature, SalinityPrecipitation; Background nutrients

Matt Mazloff UCSD/SIO State Estimates Mean dynamic topography;Temperature transport through the Drake Passage

Joellen Russell U. Arizona Role of Ocean in Climate Strength and position of SO Westerly WindsArea of deep‐water outcrop; Depth of AAIW isopycnal

Jorge Sarmiento Princeton U. Biogeochemical Cycles Fractional uptake of heat and carbon by the SO

Kevin Speer Florida State U. Large‐Scale Circulation Stratification north and south of ACC (esp. SAMW)Mean flow/shear in SE Pacific; tracer spreading rates

Lynne Talley UCSD/SIO Physical Oceanography Repeat hydrography inventories

Rik Wanninkhof NOAA/AOML Inorganic Carbon Cycle Aragonite saturation state

Year 2 Progress

• Surface (0‐100m) concentrations of dissolved inorganic carbon (DIC) from the observations (GLODAP) and the model simulations. All model figures cover the simulated years 1986‐2005 from the HISTORICAL forcing scenario (from Russell and Kamenkovich 2015)

Year 2 Progress

• Annual mean surface flux of carbon (gC/m2/yr) from (a) observations (2009 Takahashi dataset) and (b‐f) model simulations,1986‐2005 from the HISTORICAL forcing scenario. Red shading indicates degassing from

• the ocean into the atmosphere, while blue shading indicates uptake by the ocean(from Russell and Kamenkovich 2015)

• Observational and modeling activities are closely linked with each other.• Models are used to

• assimilate and interpret data (SOSE);• assist in interpretation and design of the observing system (OSSE),

and in quality control of the data;• identify gaps in our understanding of processes governing ocean

response to climate change (CMIP, SOMIP intercomparison);• identify observational metrics for model evaluations/improvement

• Observations• inform models on the real-ocean processes;• monitor changes in the ocean state;• provide information for model validation and improvement

• This synthesis is essential to the success of SOCCOM!

Summary and Conclusions