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Towards Improving Coupled Climate ModelUsing EnKF Parameter Optimization
Towards Improving Coupled Climate ModelUsing EnKF Parameter Optimization
Zhengyu Liu1, Shaoqing Zhang2, Yun Liu1
R. Jacob3, Xinrong Wu4, Xuefeng Zhang4, Feiyu Lu1
1. Univ. Wisconsin-Madison2. GFDL/NOAA, USA3. ANL/DOE4. NMDIS/SOA, China
Motivation: Motivation: CoupledCoupled Model Biases Model Biases
SST (shading), Rainfall (contour)
Pre
p
Latitude
Lin, 2007, JC
Improve a complex climate model directly in the coupled mode?(after the tuning of each component model)
ObjectiveObjective
Model biases
Structure biases Parameter biases
Parameter Estimation using Data Assimilation:
4D Var: adjoint in CGCM
EnKF: forward modeling, practical for a complex system
Meso-scale weather model Objective: improve forecasting error, Approach: time-marching EnKF on synoptic obs (Anderson, 2001) Aksoy et al., 2006a (perfect model) Aksoy et al, 2006b (perfect model) Tong and Xue, 2007 (perfect model) Hu et al., 2010 (real data) …….
Climate model Objective: reduce climatology bias for projection Approach: Iterative EnKF on climatological obs (Annan et al., 2004) Annan et al., 2005: AGCM (reanalysis atmosphere) Edwards and March, 2005: OGCM (perfect model, ocean obs) Ridgewell et al., 2007: Marine BGC model (real data, ocean obs)
EnKF Parameter OptimizationEnKF Parameter Optimization
Anderson, J., 2001: Lorenz Model: Variable Augmentation
and prediction
EnKF Parameter Optimization for Coupled ModelsEnKF Parameter Optimization for Coupled Models
Potential Issues
•Different time scales: fast processes, slow processes, coupled processes
•Biases in climatology, climate variability, “climate noise” e.g: tropical bias, ENSO, NAO,.. atmos. synoptics
•Biases in single component (before coupling) vs. coupled system
•Atmosphere obs + Ocean obs + (other system component obs)
• Great spatial variation: low vs high lat? ocean vs land? ….
…..
EnKF Parameter Optimization for Coupled ModelsEnKF Parameter Optimization for Coupled Models
Outline
• A conceptual coupled model study (perfect model)Zhang S., Liu, Z., A. Rosati and T. Delworthy, 2012. A study of enhancive parameter correction with coupled data assimilation for climate simulation and prediction using a simple coupled model. Tellus, A first try!
• A CGCM study (perfect model)Liu Y., Z. Liu, S. Zhang, X. Rong, R. Jacob, S. Wu and F. Lu, 2014: Ensemble-based
parameter estimation in a coupled GCM using the adaptive spatial average method. J. Climate (in press) First Success in CGCM!
• A intermediate coupled model study (“biased physics”)Zhang X., S. Zhang, Z. Liu, X. Wu and G. Han, 2014: Parameter optimization in an
intermediate coupled climate model with biased physics. J. Climate (in rev) Further challenges…
All use EAKF (Anderson, J., 2001)
A Conceptual Model Study (“Perfect Model”)
Obs Frq: different in A (1 day) and O (4 day)
N=20
Initial para error ~10%
Fixed low threshold of para ensemble spread (as in Askoy et al. 2006)
Correlation cut-off
Atmos
Ocean(Om=10)
Step 1: State estimation to quasi-equilibriumStep 2: Simultaneous state-para estimationZhang S. et al., 2011, Tellus
Sp
in-u
p
Multi-parameter Estimation
Zhang S. et al., 2011, Tellus
Fast Ocean Atmosphere Model (FOAM) (Jacob, 2007)Atmosphere: CCM2 R15 +CCM3 PhysicsOcean: POP-like 2.4ox1.2ox24-levels
Obs. Err = 10% Std(CTRL)SST Obs: 1 monthly (gridded)Atmosphere Obs: T, U, V, 12 hrly (gridded)
N=30 membersOcean/Atmosphere coupling covariance not usedLocalization: atmos: 1000 km, ocn:500 kmParameters: Solar Penetration Depth (SPD) + Other parameters
A CGCM Study (“Perfect Model”)
Liu Y., PH.D thesisLiu Y. et al., 2014a,b, J. Clim
Parameter Sensitivity
a) SST: ann. climatology sensitivity
c) SST: 1 month sensitivity (Dec)
b) SST: 1 month sensitivity (June)
a) SST: ann. climatology sensitivitya) D SST: ann. SPD 20m - 17m
b) <SST, SST>
c) <SST, SST>
Liu Y. et al., 2014, J. Clim
1 month sensitivityEnsemble spread: June
Climatology sensitivity
1 month sensitivity,Ensemble spreadDec
Estimation of SPD
Liu Y. et al., 2014, J. Clim
Year
Assimilation of monthly SST obs only
Adaptive Spatial Average Scheme (ASA)
Liu Y. et al., 2014, J. Clim
For a global uniform parameter
Spatial updating (localization):
GPO (Wu et al. 2012)
SA (Spatial Average): Askoy et al., 2006
ASA (Adaptive Spatial Average): Liu Y. et al., 2014
Spatial Variation of Parameter Sensitivity
a) SST: ann. climatology sensitivity
c) SST: 1 month sensitivity (Dec)
b) SST: 1 month sensitivity (June)
a) SST: ann. climatology sensitivitya) D SST: ann. SPD 20m - 17m
b) <SST, SST>
c) <SST, SST>
Liu Y. et al., 2014, J. Clim
1 month sensitivityEnsemble spread: June
Climatology sensitivity
1 month sensitivity,Ensemble spreadDec
ASA: Quality of Estimation and Ensemble Spread
ASA (Adaptive Spatial Average):
A good estimate~ small posterior ensemble spread σ(β)
β=SPDx=SST
Liu Y. et al., 2014, J. Clim
σ(SPD)
rmse(SPD)
Obs = truth Obs = truth + error
rmse
(SP
D)
rmse
(SP
D)
σ(SPD) σ(SPD)
SP
D
SP
D
% average grids % average grids
Summary
Different time scales…?
Spatial and temporal (stochastic) variation ?
• Preliminary results encouraging
• Parameter optimization seems feasible for CGCMs,
but
• Real world parameter optimization most challenging!
Flux adjustment?
Physical mechanism ? (breeding mode?)
Earth system model?