A study of impact of the geographic dependence of observing system on parameter estimation with an intermediate coupled model

Wu, Xinrong; Zhang, Shaoqing; Liu, Zhengyu; Rosati, Anthony; Delworth, Thomas L.

doi:10.1007/s00382-012-1385-1

Cited by 29 publications

(43 citation statements)

References 26 publications

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“…Wu et al (2012) further introduced a geographic dependent parameter optimization (GPO) scheme to increase the signal-to-noise ratio of the background error covariance in parameter estimation, and examined the impact of this new scheme on climate estimation and prediction using an intermediate coupled model within a perfect model framework (Wu et al, 2013). Recently, Zhang et al (2013b) investigated the impact of parameter estimation on climate estimation and prediction in an intermediate coupled model with biased physics within a biased twin experiment framework, which indicates that the adverse impact of biased physical schemes in a coupled model on climate estimation and prediction can be compensated partly by optimizing the most sensitive parameters employed in the physical schemes.…”

Section: G-j Han Et Al: Mitigation Of Model Biases Through Parametmentioning

confidence: 99%

Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model

Han

Zhang

et al. 2014

Nonlin. Processes Geophys.

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Abstract. Imperfect dynamical core is an important source of model biases that adversely impact on the model simulation and predictability of a coupled system. With a simple pycnocline prediction model, in this study, we show the mitigation of model biases through parameter optimization when the assimilation model consists of a "biased" timedifferencing. Here, the "biased" time-differencing is defined by a different time-differencing scheme from the "truth" model that is used to produce "observations", which generates different mean values, climatology and variability of the assimilation model from the "truth" model. A series of assimilation experiments is performed to explore the impact of parameter optimization on model bias mitigation and climate estimation, as well as the role of different media parameter estimations. While the stochastic "physics" implemented by perturbing parameters can enhance the ensemble spread significantly and improve the representation of the model ensemble, signal-enhanced parameter estimation is able to mitigate the model biases on mean values and climatology, thus further improving the accuracy of estimated climate states, especially for the low-frequency signals. In addition, in a multiple timescale coupled system, parameters pertinent to low-frequency components have more impact on climate signals. Results also suggest that deep ocean observations may be indispensable for improving the accuracy of climate estimation, especially for low-frequency signals.

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Section: G-j Han Et Al: Mitigation Of Model Biases Through Parametmentioning

confidence: 99%

Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model

Han

Zhang

et al. 2014

Nonlin. Processes Geophys.

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show abstract

“…Parameter optimization, which includes the model parameters into control 20 variables, is a promising way to partly compensate for the bias of the values of the model parameters and improve the climate predictability(e.g. Zhang, 2011a,b;Zhang et al, 2012Zhang et al, ,2013bWu et al, 2012Wu et al, ,2013Liu et al 2014a,b;Han et al, 2014;).…”

Section: Introductionmentioning

confidence: 99%

“…And the larger the characteristic variability time scale is, the larger the corresponding OTW is. The model parameters are lack of direct observations and prognostic equations, parameter optimization completely relies on the covariance between a parameter and the 15 model state (e.g., Zhang, 2011a,b;Zhang et al, 2012;Wu et al 2012Wu et al ,2013Han et al, 2014;Liu et al 2014a,b). Thus the observational time window (OTW) of the model state in each media of the coupled climate system will do some impact on the quality of parameter optimization and climate prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Discuss., doi:10.5194/npg-2015Discuss., doi:10.5194/npg- -76, 2016 Evensen, 1994Evensen, , 2007Anderson, 2001;Hamill et al, 2001;Zhang, 2011a,b;Zhang et al, 2012;Wu et al, 2012Wu et al, ,2013Han et al, 2014;Liu et 5 a., 2014a,b;). And the ensemble-evaluated covariance between the model states and model parameters can also be used to estimate the model parameters (e.g., Anderson, 2001;Annan and Hargreaves, 2004;Annan et al, 2005;Askoy et al, 2006a,b;Evensen, 2007;Hansen and Penland, 2007;Kondrashov et al, 2008;Tong and Xue, 2008b;Yang and Delsole, 2009;Delsole and Yang, 2010;Zhang, 2010;Zhang 2011a,b;Wu et al, 2012Wu et al, ,2013Han et al, 2014;Liu at al., 2014a,b;Zhang et al, 2015;). In this 10 study, the authors employ the DAEPC algorithm (Zhang et al, 2012), which is based on the standard ensemble adjustment Kalman filter (EAKF, e.g., Anderson 2001;2003;Zhang and Anderson, 2003;Zhang et al, 2007), to implement the coupled state estimation and adaptive parameter optimization.…”

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confidence: 99%

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Impact of Optimal Observational Time Window on Parameter Optimization and Climate Prediction: Simulation with a Simple Climate Model

Zhao

Deng

Yang

2016

Preprint

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Abstract. Usually, an optimal time window (OTW) centred at the assimilation time to collect measured data for an assimilation cycle, can greatly improve the CDA analysis skill. Here, with a simple coupled 10 model, we study the impact of optimal OTWs on the quality of parameter optimization and climate prediction. Results show that the optimal OTWs of valid atmosphere or ocean observations exist for the parameter being estimated and incorporating the parameter optimization will do some impact on the optimal OTWs for the state estimation. And using the optimal OTWs can enhance the predictability both of the atmosphere and ocean. 15

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“…In particular, one traditionally determines the values of model parameters by experience or a trial procedure which heuristically provides a reasonable estimate but usually is not optimal for the coupled model. Recently, with the aid of information estimation (filtering) theory (e.g., Jazwinski, 1970), research on optimization of coupled model parameters based on instantaneous observational information has grown quickly (e.g., Wu et al, 2013;Liu et al, 2014a, b;Li et al, 2016). Traditional data assimilation that only uses observations to estimate model states (i.e., state estimation) becomes both state estimation (SE) and parameter estimation (also called optimization) (PE) with observations.…”

Section: Introductionmentioning

confidence: 99%

Insights on the role of accurate state estimation in coupled model parameter estimation by a conceptual climate model study

Zhang

Lin

et al. 2017

Nonlin. Processes Geophys.

Self Cite

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Abstract. The uncertainties in values of coupled model parameters are an important source of model bias that causes model climate drift. The values can be calibrated by a parameter estimation procedure that projects observational information onto model parameters. The signal-to-noise ratio of error covariance between the model state and the parameter being estimated directly determines whether the parameter estimation succeeds or not. With a conceptual climate model that couples the stochastic atmosphere and slowvarying ocean, this study examines the sensitivity of stateparameter covariance on the accuracy of estimated model states in different model components of a coupled system. Due to the interaction of multiple timescales, the fast-varying "atmosphere" with a chaotic nature is the major source of the inaccuracy of estimated state-parameter covariance. Thus, enhancing the estimation accuracy of atmospheric states is very important for the success of coupled model parameter estimation, especially for the parameters in the air-sea interaction processes. The impact of chaotic-to-periodic ratio in state variability on parameter estimation is also discussed. This simple model study provides a guideline when real observations are used to optimize model parameters in a coupled general circulation model for improving climate analysis and predictions.

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A study of impact of the geographic dependence of observing system on parameter estimation with an intermediate coupled model

Cited by 29 publications

References 26 publications

Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model

Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model

Impact of Optimal Observational Time Window on Parameter Optimization and Climate Prediction: Simulation with a Simple Climate Model

Insights on the role of accurate state estimation in coupled model parameter estimation by a conceptual climate model study

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