The GFDL Coupled Data Assimilation System Using …...improves the tropics. Middle to high latitude...

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S. Zhang, Tony Rosati and Matt Harrison GFDL/NOAA, Princeton University The GFDL Coupled Data Assimilation System Using a Parallelized Ensemble Filter for Climate Detection and Forecast Initialization

Transcript of The GFDL Coupled Data Assimilation System Using …...improves the tropics. Middle to high latitude...

Page 1: The GFDL Coupled Data Assimilation System Using …...improves the tropics. Middle to high latitude improvements were less dramatic, where uses of salinity observations become important.

S. Zhang, Tony Rosati and Matt Harrison GFDL/NOAA, Princeton University

The GFDL Coupled Data Assimilation System

Using a Parallelized Ensemble Filter

for Climate Detection and Forecast Initialization

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One side: facts -Climate model: inevitable drift from reality due to incomplete understanding of climate change and its modelingObservations: inevitable instrument and representation errors

The other side: we need -Analysis of climate variability (Carbon/heat uptake, circulation, …)Detection of climate changeObserving system evaluation/designForecast initialization (SI, decadal)Model evaluation, forecast verificationModel parameter estimation

Ocean Analyses Using Models and Data:

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OUTLINECan ocean data assimilation detect the signal of climate change? Are available observations sufficient? Idealized ‘twin’ experiments based on a real ocean obs network:− Climate detection by data assim: ‘to be’ or ‘not to be’?− Using 20th/21st century obs network: Observing system evaluation

A ‘super-parallelized’ ensemble filter with the GFDL’s fully-coupled GCM (CM2)− Importance of maintaining the T-S relationship in Oceanic Data Assimilation (ODA)− Importance of maintaining geostrophic balance in Atmospheric Data Assimilation

(ADA) Detection of climate changes using 20th/21st century ocean observational networks− ENSO variability− Multi-decadal variability of ocean heat content and salinity− Estimation of Atlantic meridional overturning circulation (MOC)

Impact of ODA’s initialization on SI forecasts− Vertical structure of forecasted NINO3, NINO3.4 and NINO4 temperature− Forecast skills in NINO3, NINO3.4 and NINO4− Case study for a strong ENSO warm event

Discussions and future directions

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Given ocean temperature observational networkCan we assess climate change in the 20th Century?

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(Levitus et al. 2005)

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Atmospheric model

AM2p12: 144x90x24

Sea-Icemodel

Land model

green-house-gas + natural aerosol radiative forcing

Ocean model

MOM4: 360x200x50

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North Atlantic Temp and Salt in CM2

:200m-1000m :200m-1000m

:1000m-5000m :1000m-5000m

S

S

T T

10.75 11.45 11.45

4.204.084.08 4.20

10.75

35.054

35.074

35.700

35.801 ICs

1861-19001901-19251926-1950

1951-19751976-2000

1860 control run historical run

Upper

Lower

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prior PDF

analysis PDFData

Assimilation(Filtering)

obs PDF

yobx

ax

4847644 844 76

tttt ttfdtd wxGxx ),(),( +=Deterministic (being modeled) Uncertain (stochastic)

Atmospheric internal variability

Ocean internal variability (model does not resolve)

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CM2 ensemble filter:

GlobalPE list(0:179)

Sub-PE listfor member-1

(0:29)

Sub-PE listfor member-2

(30:59)

Sub-PE listfor member-6

(150:179)

Coupled model domain decompo-

sition & run

Analysis dom-decmpon global PE list

Data transfer: mdlspace to ens space

Use obs updatingensemble members

Data transfer: ensspace to mdl space

Model ensemble organizer Ensemble filter

stop

׃

Coupled model domain decompo-

sition & run

Coupled model domain decompo-

sition & run

׃

Sgl model run Sgl ens mb run Multi- ens mbs run Assim w/o data run

PE grouping and domain decomposition

Identical test

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Atmospheric modeluo, vo, to, qo, pso

Ocean modelT,S,U,V

Sea-Icemodel

Land model

(τx,τy)(Qt,Qq)

(T,S)obs

GHG + NA radiative forcing

u, v, t, q, ps

ADA Component

ODA Component

(u,v)sobs,ηobs

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Importance of maintaining the

T-S relation

1) Isopycnal nature of water movements

Fact: over the 20th century, salinity observations are very sparse.

We need a methodology to maintain T-S relationship where only T is observed.

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2) Pentadal Salinity Obs

Importance of maintaining the T-S relation

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Idealized twin experiments: perfect model/observationsAssimilation Model: CM2 with GHG +NA radiative forcingObservations: Project Jan., 1991 to Dec., 1991 CM2.0 IPCC historical run monthly mean temp/salt onto ocean XBT network, plus a white noise [NT(0,0.5) and NS(0,0.1)]Initialize the model from arbitrary initial conditions (01/01/95)

Test cases: 1) Tobs -> T (T2T): Only allow temperature obs to correct temperature2) Tobs -> T,S (T2TS): Based on 1), using cov(T,S) to correct salinity also 3) Tobs,Sobs -> T,S (TS2TS): Using (T,S) obs and cov(T,S) to correct T & S

Question: How does the filter converge to the truth?

Importance of maintaining the T-S relation3) test cases : using 1991 month mean data

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Atmospheric model

u, v, t, q, ps

Ocean modelT,S

U,V

Sea-Icemodel

Land model

(τx,τy)

Tobs,Sobs

(Qt,Qq)

GHG and NA radiative forcing

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T2T

T2TS TS2TS

CTL

Ocean temperature errors at equator

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T2TS TS2TS

T2TCTL

Ocean salinity errors at the equator

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T, S corrections and T-S covariance at y=0 (1991 annual mean)

Temp correction

Salinity correction

T-S covariance

TSTST

Δ=Δ 2

),cov(δ

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T2T

T2TS TS2TS

CTL

Vertical motion errors at the equator

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T2T

T2TS TS2TS

CTL

Under current errors at the equator

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t2tt2tsts2ts

truth

Integral of meridional heat/salinity transport in zonal-depth

salt

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ADA Impact on Climate data assimilation:Importance of maintaining geostrophic balance

• Monthly mean atmospheric data plus a white noise as obs

• Daily atmospheric analysis:– Assimilate winds only– Assimilating atmospheric temperature only– Assimilating atmospheric winds and

temperature

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Control (without any data constraint)

Atmospheric Zonal Wind Errors (vertical average)

Ocean Data Assimilation (ODA) Only

ODA and Atmosphere Data Assimilation(ODA+ADA, wind only)

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Control (without any data constraint)

Atmospheric Temperature Errors (vertical average)

Ocean Data Assimilation (ODA) Only

ODA and Atmosphere Data Assimilation(ODA+ADA, T only)

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Atmospheric U & T rms errors

U rms

T rms

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Atmosphere Vertical Motion (Walker Cell) Errors at Tropics (20oS – 20oN average)

Control (without any data constraint)

Ocean Data Assimilation (ODA) Only

Ocean and Atmosphere Data Assimilation(ODA+ADA)

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Zonal Wind Stress Errors at Ocean Surface Exerted by The Atmosphere Bottom

Control (without any data constraint)

Ocean Data Assimilation (ODA) Only

Ocean and Atmosphere Data Assimilation(ODA+ADA)

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Summary on CDA tests:

Estimating a temporally-evolving joint-distribution requires:− Multi-variate analysis scheme maintains physical

balances among state variableT-S relationship in ODAGeostrophic balance in ADA

− Adjustment ensemble filter maintains the properties of high order moments of error statistics (nonlinear evolution of errors) mostly

An adjustment ensemble filter maintains the above two properties as much as possible

Accessing Climate Changes Using Coupled Model & Data Solves a Temporally-Evolving Joint-Distribution

of Climate State Variables

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Idealized twin experiments:Truth: 20th Century climate simulation forced by time-varying green-house-gas radiation (IPCC historical run)Observations: Projecting the IPCC historical run temperature onto 20th

century ocean temperature observational network (XBT, CDT, MBT, OSD, …), plus an N(0,0.5) white noise

Assimilation Model: CM2 control run – Fixed-year (1860) GHGNA forcing

B-grid atmospheric model, 144x90x24 and Land model (LM2.0)Version 4 of module ocean model (MOM4), 360x200x50 and Sea Ice Simulator (SIS)

Initialize the model from arbitrary initial conditions (75 years ago, for instance)

Question: How much can we retrieve signals of climate change, both variability and trend, given the 20th century temperature observational network?

Climate change detection for the last quarter of 20th century?

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Atmospheric model

Ocean modelT,S,U,V

Sea-Icemodel

Land model

(τx,τy)

Tobs

1860 GHG and NA radiative forcing

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Temp errors over top 500m

Global Rms error

CTL Averaged errors

ODA Averaged errors

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1) Temperature at NINO3.4

CTL

ODA

Truth

Truth ODA CTL spread bounds

25yr climate detection: ENSO variability

ODA spread bounds CTL

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2) Zonal wind stress (τx) at tropical Pacific (5S-5N average)

CTL

ODA

Truth

truth ODA CTL

25yr climate detection: ENSO variability

ODA spread bounds CTL spread bounds

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Truth

CTL

Assim

CTL spreadbounds

Assimspreadbounds

1) Top 500 m Ocean Heat Content (Averaged Temperature) AnomaliesHow much can we retrieve the trend of climate change?

ARCTIC

INDIAN INDIAN INDIAN

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2) Top 500 m ocean heat content 10yr tendency(2000 – 1990)

How much can we retrieve the trend of climate change?

Truth

ODA

ODA - Truth

Contour interval = 0.2 oC

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3) Sea surface height10yr tendency(2000 – 1990)

How much can we retrieve the trend of climate change?

Truth

ODA

ODA - Truth

Contour interval = 0.02 (m)

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Challenges:1) North Atlantic Meridional Overturning

Truth ODA

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Global Rms error

CTL Averaged errors

ODA Averaged errors

2) Salinity errors over top 500m

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Truth

CTL

Assim

CTL ens

Assim ens

Challenge: 2) Top 500 m Salt Anomalies

INDIAN INDIAN INDIAN

ARCTIC

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Challenge: 3) Salt 10yr tendency(2000-1990)

Truth

ODA

ODA - Truth

Contour interval = 0.05 (PSU)

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Argo and the Salinity Budget

Historical Salinity ProfilesHistorical Salinity Profiles

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2005 June Argo network

Temperature

Salinity

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Estimate of Atlantic Meridional Overturning

Circulation (MOC)

Based on 2005 Argo network:− Only using top 500 m ocean temperature

measurements− Only using top 500 m ocean temperature and salinity

measurements− Using Argo measurements (down to 2000 m deep for

temperature and salinity)

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① THC’s heat/salt transport

② boundary forcing from atmosphere

③fresh water forcing from ice & runoff

④Topography processing

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Truth: Historical radiative forcing run from 1861-2000, initializing the model from 300-yr spinupusing 1860 radiative forcing

Truth

Control

Control: Historical radiative forcing run from 1861-2000, initializing the model from 380-yr spinupusing 1860 radiative forcing

25-yr (76-00) mean

Page 46: The GFDL Coupled Data Assimilation System Using …...improves the tropics. Middle to high latitude improvements were less dramatic, where uses of salinity observations become important.

Atmospheric model

Ocean modelT,S,U,V

Sea-Icemodel

Land model

(τx,τy)

Tobs,Sobs

1860 GHG and NA radiative forcing

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13-point running average of Max(ψ) in (40n:70n)TruthControl

ODA (500m) T+Cov(T,S)ODA (500m) T,S +Cov(T,S)

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25-yr Time Mean of the Atlantic MOC

Truth

ODA (500m)T,S + Cov(T,S)

ODA (500m)T + Cov(T,S)

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RMS errors of top 2000 m norh Atlantic(30n:70N)

Temperature (oC)

Salinity (psu)

Control

ODA (500m)T+Cov(T,S)

ODA (500m)T,S + Cov(T,S)

ODA (2000m)T,S + Cov(T,S)

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13-point running average of Max(ψ) in (40n,70n)

ODA (500m) T+Cov(T,S)

ODA (500m) T,S +Cov(T,S)

ODA (2000m) T,S +Cov(T,S)

Control

Truth

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25-yr Time Mean of the Atlantic MOC

Truth

ODA (2000m)T,S + Cov(T,S)

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Timeseries of regionally-averaged errorsover north Atlantic (30n:70n)

water flux heat flux

control ODA (500m) T+Cov(T,S)ODA (2000m) T,S+Cov(T,S)ODA (500m) T+Cov(T,S)

τx τy

Page 53: The GFDL Coupled Data Assimilation System Using …...improves the tropics. Middle to high latitude improvements were less dramatic, where uses of salinity observations become important.

Due to uses of cross-covariance between physical variables, while assimilating obs info the ensemble filter in GFDL’s ODA system, to some degree, is able to maintain physical balances in climate evolution, depending on ensemble size, availability of obs ...

Given the 20th century ocean temperature observational network, the ODA process in GFDL’s CDA system can retrieve the ocean heat content on basin scales after a few year assimilation spin-up.

Based on the temperature observational network, uses of T-S covariance greatly improves the tropics. Middle to high latitude improvements were less dramatic, where uses of salinity observations become important.

Based on 2005 Argo network and perfect model framework, the GFDL’sensemble CDA system is able to reproduce the large time scale (decadal) trend of the Atlantic MOC by assimilating both ocean temperature and salinity.

The variability of the Atlantic MOC is associated with large-scale THC’s heat/salt transport, sea surface forcing from atmosphere, fresh water forcing from ice and runoff and their interaction with the NA topography as well. Thus, atmospheric data constraint in the next phase of the CDA experiments would improve the estimate of interannual timescale variability of the Atlantic MOC.

Summary for Climate detection

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10 forecast cases:− Vertical structure of NINO3.4, NINO3 and NINO4

temperature− Forecast skills of SST of NINO4, NINO3.4 and

NINO3− A case study

Impact of ODA’s initialization for fully-coupled GCM on SI forecasts

Page 55: The GFDL Coupled Data Assimilation System Using …...improves the tropics. Middle to high latitude improvements were less dramatic, where uses of salinity observations become important.

Vertical structure of forecasted Nino3 temperature anomaly

FCST upper/lower bound

TRUTH FCST ENS MEANCTL

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TRUTH FCST ENS MEANCTL

FCST upper/lower bound

Vertical structure of forecasted Nino3.4 temperature anomaly

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FCST upper/lower bound

TRUTH FCST ENS MEANCTL

Vertical structure of forecasted Nino4 temperature anomaly

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SST’s forecast skillNINO4 NINO3.4 NINO3

RM

SE( oC)

Anom

aly Corr

Persistent anomaly forecast Coupled model forecast

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A case study: 1982

Initialize the coupled model from:

ODA atmos and ocean

Perfect ocean and ODAatmos

Perfect atmos and ODAocean

NINO3.4 temperature

Persistent forecast

RMSEs of 4 forecasts

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The ensemble mean of forecasts initialized from ODA can catch up the warming/cooling trend with a weak amplitude for warming events within one-year forecastForecast spread increases very rapidly, which implies the atmosphere needs data constrained ICs Skills of forecasts initialized from ODA are much higher than persistent forecasts in the second half yearOne case study shows based on the oceanic initial conditions from ODA, the improvement of atmospheric initial conditions is very important for the first half year forecast while oceanic initial conditions govern larger timescale signal

Summary for SI forecast experiments

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Idealized experiments to uncover issues and advance the understanding

Atmospheric constraint improves the surface forcing of ocean to refine the estimate of Atlantic MOC and improve SI forecastsAltimetry assimilation (TOPEX,JASON,GRACE)

Real data (profiles+satellite data) to reconstruct the history of climate changes in the 20th

century, and continuing to …New century for estimating climate states and initializing the SI/decadal forecasts

Ongoing experiments for climate detection and forecasts at GFDL

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Real data – Model biases: more source information + multi-model ‘super’-ensemble

Altimetry data; Equatorial current measurements from drifting buoymulti-model ‘super-’ensemble filter (Current: atmospheric B-grid and Finite-Volume cores; Soon: mom4+isopycnal ocean model)

Impact of more coupling variable adjustments on ocean statesAdjustment of heat/water fluxes using ocean observations within mixing layer Direct constraint on atmospheric boundary by ocean surface observationsDirect constraint on synoptic scales from the atmospheric data (using reanalysis, for instance), specially for SI forecasts?

How to increase the observation constraint when ensemble spread is very small (deep water, for instance)?

A hybrid filter combining stationary/non-stationary error statistics? A 4-dimensional variational ensemble filtering algorithm in which B = B0 + B '(t), possible?

Impact of ensemble size on the accuracy of error covariance estimate24-member system is running on NASA Columbia cluster (max PEs:2048)Daily atmospheric disturbance as ensemble perturbation

Challenges

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Atmospheric modelsuo, vo, to, qo, pso

Ocean model (MOM4)T,S,U,V

Sea-Icemodel

Land model

τx,τy

(T,S)obs

(Qt,Qq)

GHGNA Radiative Forcings

B-grid differencing dynamical

core

Finite-Volume dynamical

core

GFDL’s Ensemble Data Assimilation System Using Multi- Coupled Climate Models

ADA Component

ODA Component

Page 64: The GFDL Coupled Data Assimilation System Using …...improves the tropics. Middle to high latitude improvements were less dramatic, where uses of salinity observations become important.

Improve climate variability analysis (Carbon/heat uptake, circulation, …)Improve forecast (SI, decadal/multi-decadal) by improving initializationDetection of climate changeAnalysis estimate of variables in sea ice and land model (ice mass, run-off etc., for instance)Observing system evaluation/designModel evaluation/verification for improving modelingModel parameter estimation

Long Term Efforts …

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Balaji for supports on domain decomposition and communication, computer resources on ColumbiaJeff Anderson for helpful discussions on filtering, and early discussions on filtering parallelizationAndrew for persistent helps on visualization and generous discussions on physical oceanographyFengrong, Mike, Hyun-Chul for helps on cm2’s configurations; Zhi, Hans for helps on NCO and mppnccombine utilities; Jianjun and Jian for helpful discussionsAll GFDL staff for efforts on climate modeling

Thanks to …


Upper ocean salinity stratification in the Tropics as derived from N 2 (the buoyancy frequency) Dr Christophe Maes Laboratoire de Physique des Océans -

Upper ocean salinity stratification in the Tropics as derived from N 2 (the buoyancy frequency) Dr Christophe Maes Laboratoire de Physique des Océans -

Climate Modeling at GFDL - NASAGeophysical Fluid Dynamics Laboratory Princeton, New Jersey Climate Modeling at GFDL Leo Donner GFDL/NOAA, Princeton University CERES Science Team, Princeton,

Climate Modeling at GFDL - NASAGeophysical Fluid Dynamics Laboratory Princeton, New Jersey Climate Modeling at GFDL Leo Donner GFDL/NOAA, Princeton University CERES Science Team, Princeton,

Report on Salinity Assessment and Salinity Management Plan

Report on Salinity Assessment and Salinity Management Plan

Absolute Salinity, “Density Salinity” and the Reference-Composition Salinity … · 2011-01-30 · D. G. Wright et al.: Absolute Salinity, Density Salinity, and Reference Salinity

Absolute Salinity, “Density Salinity” and the Reference-Composition Salinity … · 2011-01-30 · D. G. Wright et al.: Absolute Salinity, Density Salinity, and Reference Salinity

Use of the GFDL Vortex Tracker

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