Multivariate Data Assimilation of Multivariate Data Assimilation of Carbon Cycle Using Local Carbon Cycle Using Local Ensemble Transform Kalman Ensemble Transform Kalman FilterFilter

1Ji-Sun Kang, 1Eugenia Kalnay, 2Junjie Liu, 2Inez Fung, and 1Ning Zeng

1University of Maryland, College Park, Maryland2University of California, Berkeley, California

IntroductionIntroduction

Anthropogenic emission of CO2 is increasing

Atmospheric CO2 variability is relevant to climate Carbon Sink/Source on the globe: surface fluxes Estimate surface CO2 fluxes using Local

Ensemble Transform Kalman Filter technique

http://svs.gsfc.nasa.gov/vis/a000000/a003300/a003308/index.html http://earthobservatory.nasa.gov/Library/CarbonCycle/carbon_cycle4.html

SPEEDY-C & VEGAS with SLandSPEEDY-C & VEGAS with SLand

SPEEDY (Molteni, 2003) AGCM with T30 resolution (96X48) and 7 layers in vertical Prognostic variables: U, V, T, q, Ps Adapted for 6-hr assimilation cycle by Dr. Miyoshi Added one additional prognostic variable, atmospheric CO2

: SPEEDY-C

SPEEDY-C coupled with vegetation and soil model

Terrestrial carbon model VEGAS (Zeng, 2005) Physical land surface model SLand (Zeng et al., 2000a) Time-varying fluxes of surface CO2 over land

SPEEDY-C Nature, including only fossil fuel emission (6 PgC/yr) Forecast

Simulated Observations U, V, T, q, Ps: rawinsonde distribution (9% coverage)

Observation error: 1m/s for U & V, 1K for T, 0.1g/kg for q, 1hPa for Ps

Atmospheric CO2 concentration: every other grid (25%) Observation error: 1ppmv

NO observation of CO2 fluxes (CF) 20 ensemble members, 8% of multiplicative

inflation for all the dynamical variables Initial condition for CF

Close to zero-mean fields NO a-priori information

1) Perfect Model Simulation1) Perfect Model Simulation

Three Types of Data Three Types of Data AssimilationAssimilation Univariate Data Assimilation [Uncoupled]

Background errors of (CO2, CF) have NO effect on errors of other atmospheric variables

Multivariate Data Assimilation Errors of all variables are coupled

One-way Multivariate Data Assimilation

U, V, T, q, Ps CO2, CF

U, V, T, q, Ps,CO2, CF

U, V, T, q, Ps U, V

U, V, T, q, Ps CO2, CF

U, V, CO2, CF

RMS ErrorRMS Error

Two-month analysis

Univariate DA Diverges

Multivariate DA Better than

Univariate DA

One-way multivariate DA

Best performance for CO2 variables

01JAN 01MAR 01JAN 01MAR

01JAN 01MAR 01JAN 01MAR

01JAN 01MAR 01JAN 01MAR

Results: Perfect Model Results: Perfect Model SimulationSimulation

One-way Multivariate DA

Truth

Multivariate DA

Uncoupled DA

[*10-9kg/m2/s]

: Fossil fuel fluxes

2) Imperfect Model:2) Imperfect Model: Nature=coupled vegetation Nature=coupled vegetation modelmodel SPEEDY-C: forecast model SPEEDY-C coupled with VEGAS and SLand:

nature Surface CO2 fluxes over land from the coupled model

+ Prescribed monthly mean of ocean CO2 fluxes (Takahashi et al., 2002)

One-way multivariate DA with 10% of multiplicative inflation

Initial condition for CF Adding small random

perturbation to randomly chosen 20 CF from nature

With model bias, EnKF does not work (climatology of forecast model: significantly different from the nature)

Bias CorrectionBias Correction

Nature: SPEEDY-C & VEGAS Forecast: SPEEDY-C Restart from the nature every six hour Average the departure of 6-hour forecast for

two months Similar to Danforth et al., 2007

Nature

Forecast

ResultsResults

>24

<0.9

No Bias Correction Bias Correction

Remarkable improvement in the analysis

Especially for the atmospheric variables and atmospheric CO2 which have observations

CF analysis diverges for two months

InflationInflation

No Bias Correction Bias Correction Bias Correction +

Different Inflations for CO2 and CF

Large inflation (150%) for CO2

Small inflation (5%) for CF

Excellent Result

Atmospheric COAtmospheric CO22

with Bias Correction

with Bias Correction+diff_infl

Analysis of atmospheric CO2 after two months

Bias correction experiment has better result in assimilating atmospheric CO2

Bias correction + Different inflation experiment still performs well with atmospheric CO2

Surface COSurface CO22 Fluxes Fluxes

with Bias Correction

with Bias Correction+diff_infl

Analysis of atmospheric CO2 after two months

Bias correction experiment has divergence in the analysis of surface CO2 fluxes

Bias correction + Different inflation experiment (large inflation for CO2, small inflation for CF) works very well in estimating CF under the imperfect model simulation

SummarySummary

SPEEDY-C coupled with VEGAS and SLand Simulated transport of atmospheric carbon dioxide

and calculated time-varying fluxes over land in a simple but realistic global model

Three types of data assimilation examined Multivariate data assimilation is much more effective

in estimating surface CO2 fluxes, even in the absence of observations or prior estimation of surface fluxes

With bias correction and different inflations for CO2 variables, we could get improvement in the analysis of surface CO2 fluxes

Adaptive inflation and observation error (Li, 2008)

The EndThe End

Thank you for your attention