Further Insights on the Role of Accurate State Estimation in Coupled Model Parameter Estimation by a Simple Climate Model Study

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 parameters. The signal-to-noise ratio of error covariance between model states and initially perturbed parameters determinates directly the success of parameter estimation or not. With a conceptual climate model that couples the stochastic atmosphere and slow varying ocean, this study examines the sensitivity of the state-parameter covariance on the accuracy of estimated model states in different model components of a coupled system. Due to the interaction of multiple time scales, the fast varying "atmosphere" with the chaotic nature is the major source of state-parameter covariance uncertainties, and 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 in this study. This simple model study provides a guideline when real observations are used to optimize model parameter in a coupled general circulation model for improving climate analysis and predictions.

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

Nonlinear Processes in Geophysics ◽

10.5194/npg-24-125-2017 ◽

2017 ◽

Vol 24 (2) ◽

pp. 125-139 ◽

Cited By ~ 1

Author(s):

Xiaolin Yu ◽

Shaoqing Zhang ◽

Xiaopei Lin ◽

Mingkui Li

Keyword(s):

Parameter Estimation ◽

General Circulation ◽

Climate Model ◽

Coupled Model ◽

State Parameter ◽

Model Parameters ◽

Model Parameter ◽

Model Parameter Estimation ◽

Model Study ◽

The Impact

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 slow-varying ocean, this study examines the sensitivity of state–parameter 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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Review of “Further Insights on the Role of Accurate State Estimation in Coupled Model Parameter Estimation by a Simple Climate Model Study” by Yu et al.

10.5194/npg-2016-52-rc2 ◽

2016 ◽

Author(s):

Anonymous

Keyword(s):

Parameter Estimation ◽

State Estimation ◽

Climate Model ◽

Coupled Model ◽

Simple Climate Model ◽

Model Parameter ◽

Model Parameter Estimation ◽

Model Study

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A Study of Impacts of Coupled Model Initial Shocks and State–Parameter Optimization on Climate Predictions Using a Simple Pycnocline Prediction Model

Journal of Climate ◽

10.1175/jcli-d-10-05003.1 ◽

2011 ◽

Vol 24 (23) ◽

pp. 6210-6226 ◽

Cited By ~ 33

Author(s):

S. Zhang

Keyword(s):

Time Scales ◽

Parameter Optimization ◽

Time Scale ◽

Model Simulation ◽

Coupled Model ◽

State Parameter ◽

Model Parameters ◽

Forecast Errors ◽

Long Time ◽

The Impact

Abstract A skillful decadal prediction that foretells varying regional climate conditions over seasonal–interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climate-observing system tend to drift away from observed states toward the imperfect model climate because of the model biases arising from imperfect model equations, numeric schemes, and physical parameterizations, as well as the errors in the values of model parameters. Here, a simple coupled model that simulates the fundamental features of the real climate system and a “twin” experiment framework are designed to study the impact of initialization and parameter optimization on decadal predictions. One model simulation is treated as “truth” and sampled to produce “observations” that are assimilated into other simulations to produce observation-estimated states and parameters. The degree to which the model forecasts based on different estimates recover the truth is an assessment of the impact of coupled initial shocks and parameter optimization on climate predictions of interests. The results show that the coupled model initialization through coupled data assimilation in which all coupled model components are coherently adjusted by observations minimizes the initial coupling shocks that reduce the forecast errors on seasonal–interannual time scales. Model parameter optimization with observations effectively mitigates the model bias, thus constraining the model drift in long time-scale predictions. The coupled model state–parameter optimization greatly enhances the model predictability. While valid “atmospheric” forecasts are extended 5 times, the decadal predictability of the “deep ocean” is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time-scale predictions.

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Impact of Geographic-Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model

Monthly Weather Review ◽

10.1175/mwr-d-11-00298.1 ◽

2012 ◽

Vol 140 (12) ◽

pp. 3956-3971 ◽

Cited By ~ 20

Author(s):

Xinrong Wu ◽

Shaoqing Zhang ◽

Zhengyu Liu ◽

Anthony Rosati ◽

Thomas L. Delworth ◽

...

Keyword(s):

Parameter Estimation ◽

Surface Temperature ◽

Parameter Optimization ◽

General Circulation ◽

Coupled Model ◽

Model Parameters ◽

Estimation And Prediction ◽

Dependent Parameter ◽

The Impact ◽

Mean Square Errors

Abstract Because of the geographic dependence of model sensitivities and observing systems, allowing optimized parameter values to vary geographically may significantly enhance the signal in parameter estimation. Using an intermediate atmosphere–ocean–land coupled model, the impact of geographic dependence of model sensitivities on parameter optimization is explored within a twin-experiment framework. The coupled model consists of a 1-layer global barotropic atmosphere model, a 1.5-layer baroclinic ocean including a slab mixed layer with simulated upwelling by a streamfunction equation, and a simple land model. The assimilation model is biased by erroneously setting the values of all model parameters. The four most sensitive parameters identified by sensitivity studies are used to perform traditional single-value parameter estimation and new geographic-dependent parameter optimization. Results show that the new parameter optimization significantly improves the quality of state estimates compared to the traditional scheme, with reductions of root-mean-square errors as 41%, 23%, 62%, and 59% for the atmospheric streamfunction, the oceanic streamfunction, sea surface temperature, and land surface temperature, respectively. Consistently, the new parameter optimization greatly improves the model predictability as a result of the improvement of initial conditions and the enhancement of observational signals in optimized parameters. These results suggest that the proposed geographic-dependent parameter optimization scheme may provide a new perspective when a coupled general circulation model is used for climate estimation and prediction.

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A Study of Capturing AMOC Regime Transition through Observation-Constrained Model Parameters

10.5194/npg-2021-5 ◽

2021 ◽

Author(s):

Zhao Liu ◽

Shaoqing Zhang ◽

Yang Shen ◽

Yuping Guan ◽

Xiong Deng

Keyword(s):

Parameter Estimation ◽

General Circulation ◽

Multiple Equilibria ◽

Coupled Model ◽

Circulation Model ◽

Meridional Overturning Circulation ◽

Model Parameters ◽

Model Errors ◽

Model Study ◽

Regime Transitions

Abstract. The multiple equilibria are an outstanding characteristic of the Atlantic meridional overturning circulation (AMOC) that has important impacts on the Earth climate system appearing as regime transitions. The AMOC can be simulated in different models but the behavior deviates from the real world due to the existence of model errors. Here, we first combine a general AMOC model with an ensemble Kalman filter to form an ensemble coupled model data assimilation and parameter estimation (CDAPE) system, and derive the general methodology to capture the observed AMOC regime transitions through utilization of observational information. Then we apply this methodology designed within a twin experiment framework with a simple conceptual model that simulates the transition phenomenon of AMOC multiple equilibria, as well as a more physics-based MOC box model to reconstruct the observed AMOC multiple equilibria. The results show that the coupled model parameter estimation with observations can significantly mitigate the model deviations, thus capturing regime transitions of the AMOC. This simple model study serves as a guideline when a coupled general circulation model is used to incorporate observations to reconstruct the AMOC historical states and make multi-decadal climate predictions.

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Impact of the time scale of model sensitivity response on coupled model parameter estimation

Advances in Atmospheric Sciences ◽

10.1007/s00376-017-6272-6 ◽

2017 ◽

Vol 34 (11) ◽

pp. 1346-1357 ◽

Cited By ~ 2

Author(s):

Chang Liu ◽

Shaoqing Zhang ◽

Shan Li ◽

Zhengyu Liu

Keyword(s):

Parameter Estimation ◽

Time Scale ◽

Coupled Model ◽

Model Sensitivity ◽

Model Parameter ◽

Model Parameter Estimation

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Selection of catchment descriptors for the physical similarity approach. Part I: Theory.

Soil and Water Research ◽

10.17221/22/2012-swr ◽

2013 ◽

Vol 8 (No. 3) ◽

pp. 133-140 ◽

Cited By ~ 1

Author(s):

M. Heřmanovský ◽

P. Pech

Keyword(s):

Parameter Estimation ◽

Model Parameters ◽

Trial And Error ◽

Physical Similarity ◽

Model Parameter ◽

Model Parameter Estimation ◽

Trial And Error Method ◽

Homogeneous Regions ◽

Error Method ◽

Selection Of

This paper focuses on a description of the method used for the identification of optimal catchment descriptors for the physical similarity approach consisting of a scheme for the identification of optimal catchment descriptors and the procedure for finding hydrologically homogeneous regions using inverse clustering. Andrews’ curves are used as the basis for homogeneity checking. The identification of an optimum catchment descriptor is based on the assumption that the addition of an optimal catchment descriptor to a predefined set of catchment descriptors improves the accuracy of model parameter estimation within a set of tested catchments. Two criteria are proposed for the selection of optimal catchment descriptors – a criterion evaluating estimates of model parameters on the basis of different potentially optimal groups of catchment descriptors, MIN, and a criterion evaluating the improvement in model parameter estimation after the addition of a potentially optimal catchment descriptor into the group of preliminarily identified optimal catchment descriptors, MAX. The proposed method provides an alternative to the trial-and-error method for the identification of optimal catchment descriptors.

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Cole-Cole Model Parameter Estimation from Multi-frequency Complex Resistivity Spectrum Based on the Artificial Neural Network

Journal of Environmental and Engineering Geophysics ◽

10.32389/jeeg20-054 ◽

2021 ◽

Vol 26 (1) ◽

pp. 71-77

Author(s):

Weiqiang Liu ◽

Rujun Chen ◽

Liangyong Yang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Parameter Estimation ◽

Model Parameters ◽

Near Surface ◽

Complex Resistivity ◽

Model Parameter ◽

Model Parameter Estimation ◽

Artificial Neural ◽

Cole Model

In near surface electrical exploration, it is often necessary to estimate the Cole-Cole model parameters according to the measured multi-frequency complex resistivity spectrum of ore and rock samples in advance. Parameter estimation is a nonlinear optimization problem, and the common method is least square fitting. The disadvantage of this method is that it relies on initial value and the result is unstable when data is confronted with noise interference. To further improve the accuracy of parameter estimation, this paper applied artificial neural network (ANN) method to the Cole-Cole model estimation. Firstly, a large number of forward models are generated as samples to train the neural network and when the data fitting error is lower than the error threshold, the training ends. The trained neural network is directly used to efficiently estimate the parameters of vast amounts of new data. The efficiency of the artificial neural network is analyzed by using simulated and measured spectral induced polarization data. The results show that artificial neural network method has a faster computing speed and higher accuracy in Cole-Cole model parameter estimation.

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Predicted small catchment responses to heavy rainfalls with SEGMO and two sets of model parameters

Annals of Warsaw University of Life Sciences – SGGW Land Reclamation ◽

10.2478/sggw-2014-0017 ◽

2014 ◽

Vol 46 (3) ◽

pp. 205-220 ◽

Cited By ~ 2

Author(s):

Adam Krajewski ◽

Hyosang Lee ◽

Leszek Hejduk ◽

Kazimierz Banasik

Keyword(s):

Parameter Estimation ◽

Model Parameters ◽

Rainfall Runoff ◽

Small Catchment ◽

Model Parameter ◽

Model Parameter Estimation ◽

Design Storm ◽

Catchment Characteristics ◽

Catchment Responses ◽

Heavy Rainfalls

Abstract Predicted small catchment responses to heavy rainfalls with SEGMO and two sets of model parameters. The study tests the ability of hydrological part of SEGMO (SedimentGraph Model), i.e. lumped parametric rainfall-runoff procedure of SEGMO to simulate design storm runoff in a Korean catchment. The aim of the investigation is to predict responses of small catchment of the Jeungpyeong river, located in central part of South Korea, with the area of 133.6 km2, to 100-year rainfall events, applying SEGMO and using two parallel approaches for model parameter estimation. The fi rst approach is based on catchment characteristics and USDA-SCS procedures, which is suitable for ungauged basins, and the other one is based on rainfall-runoff measurements. The way of estimation of model parameters has been demonstrated. Finally, the model outputs are compared. The difference in largest peak discharges obtained from SEGMO with the two sets of model parameters, i.e. when estimated on the base of catchment characteristics and USDA-SCS procedures, and on the base of rainfall-runoff measurements were relatively small, approaching 37%. This investigation can be seen as checking the uncertainties in model parameter estimation and their infl uence on fl ood fl ows.

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