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Parameter Sensitivity of a Coupled Climate Model Estimated Through Data Assimilation Xueyuan Liu A. Köhl, D. Stammer CEN (Center für Erdsystemfurschung und Nachhaltigkeit) Hamburg University

Parameter Sensitivity of a Coupled Climate Model Estimated Through Data Assimilation Xueyuan Liu

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Parameter Sensitivity of a Coupled Climate Model Estimated Through Data Assimilation Xueyuan Liu A. Köhl, D. Stammer CEN (Center für Erdsystemfurschung und Nachhaltigkeit) Hamburg University. Decadal Prediction. The highlights of decadal climate predictions up to date: - PowerPoint PPT Presentation

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Page 1: Parameter Sensitivity of a Coupled Climate Model  Estimated Through Data  Assimilation Xueyuan Liu

Parameter Sensitivity of aCoupled Climate Model Estimated Through Data Assimilation

Xueyuan Liu A. Köhl, D. Stammer

CEN (Center für Erdsystemfurschung und Nachhaltigkeit) Hamburg University

Page 2: Parameter Sensitivity of a Coupled Climate Model  Estimated Through Data  Assimilation Xueyuan Liu

The highlights of decadal climate predictions up to date:

1)initialization 2)uncertainties

3) minimizing the influence of systematic model biases

4)measurements of the skill of hindcasts

Approach: A fully-coupled data assimilation was used to get the

optimal oceanic initial conditions and control variables(JAMSTEC).

Based on those, ensembles of hindcasts with implimentation of

greenhouse gas forcing are carried out every one year to assess how

the strategy works. A 20C run from 1913 shall also be done. Extra

ensembles might be necessary in order to give statistics. Ensembles

of decadal prediction is expected to give information on the climate

of the coming century.

Decadal Prediction

Page 3: Parameter Sensitivity of a Coupled Climate Model  Estimated Through Data  Assimilation Xueyuan Liu

Contribution of Control Variables

alphas=0 against alphas=climatologyhindcasts (alphas=0) against HadISST

The two patterns of mean bias of SST(1980-1989) are almost opposite. Despite the differences in the amplitude, we can come to a conclusion that control alphas contribute to the improvement of a hindcast.

Page 4: Parameter Sensitivity of a Coupled Climate Model  Estimated Through Data  Assimilation Xueyuan Liu

Annual-mean SST over 9 Years

Page 5: Parameter Sensitivity of a Coupled Climate Model  Estimated Through Data  Assimilation Xueyuan Liu

Titelmasterformat durch Klicken bearbeiten

21.04.23

Thanks for your attention!

Page 6: Parameter Sensitivity of a Coupled Climate Model  Estimated Through Data  Assimilation Xueyuan Liu

• Coupled Model---CFES (Coupled model for the Earth Simulator)

● T42L24 AFES (Atmospheric GCM for the Earth Simulator) for AGCM

● 1*1 degree, 45 vertical layers MOM3 for OGCM

● IARC (International Arctic Research Center) Sea-ice model

● MATSIRO (Minimal Advanced Treatments of Surface Interaction and Runoff)

Model for land

• Assimilation Method-----4D-VAR

K7 System from Japan Agency for Marine-Earth Science and Technology (JAMSTEC)

Assimilation period

forwardbackward

First guess field

Best guess trajectory

Obs

ObsObs

Page 7: Parameter Sensitivity of a Coupled Climate Model  Estimated Through Data  Assimilation Xueyuan Liu

Schematic view of the experimental configuration:

Improving Decadal Predictions

9 mon

9 mon

… …

1970 1980 1990 2000Spinup run by IAU

……First guess I.C.

First guess I.C. First guess I.C.

Assimilation Exp.by 4D-VAR

optimized

optimized

optimized optimized

First guess Exp.(Free run)

1.5 month

Jan 1980 Jan 1981 Jan 1982

Ensemble Exp.(each with 3 members-

shifted atmosphere)

10 yr

10 yr

10 yr

In all:1980-2007

Page 8: Parameter Sensitivity of a Coupled Climate Model  Estimated Through Data  Assimilation Xueyuan Liu

The research leading to these results has received funding from the European Union 7th Framework Programme (FP7 2007-2013), under grant agreement n.308299

NACLIM www.naclim.eu