Recent development of a supermodel - an interactive multi-model ensemble

2021 ◽  
Author(s):  
Shuo Wang ◽  
Francois Counillon ◽  
Shunya Koseki ◽  
Noel Keenlyside ◽  
Alok Kumar Gupta ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Covariance Matrix ◽  
Model Simulation ◽  
Optimal Interpolation ◽  
Model Ensemble ◽  
Sst Bias ◽  
Dynamical Regimes ◽  
The Individual ◽  
Spatially Varying ◽  
Earth System Models

<p>An interactive multi-model ensemble (named as supermodel) based on three state-of-the-art earth system models (i.e., NorESM, MPIESM and CESM) is developed. The models are synchronized every month by data assimilation. The data assimilation method used is the Ensemble Optimal Interpolation (EnOI) scheme, for which the covariance matrix is constructed from a historical ensemble. The assimilated data is a weighted combination of the monthly output sea surface temperature (SST) of these individual models, but the full ocean state is constrained by the covariance matrix. The synchronization of the models during the model simulation makes this approach different from the traditional multi-model ensemble approach in which model outputs are combined a-posteriori.</p><p>We compare the different approaches to estimate the supermodel weights: equal weights, spatially varying weights based on the minimisation of the bias. The performance of these supermodels is compared to that of the individual models, and multi-model ensemble for the period 1980 to 2006. SST synchronisation is achieved in most oceans and in dynamical regimes such as ENSO. The supermodel with spatially varying weights overperforms the supermodel with equal weights. It reduces the SST bias by over 30% compare to the multi-model ensemble. The temporal variability of the supermodel is slightly on the low side but improved compared to the multi-model ensemble. The simulations are being extended to 2100 to assess the simulation of climate variability and climate change. Performing prediction experiments with the supermodel is the main perspective in the next step.  </p>

A supermodel to enhance climate prediction

2020 ◽  
Author(s):  
Francois Counillon ◽  
Noel Keenlyside ◽  
Mao-Lin Shen ◽  
Shunya Koseki ◽  
Marion Devilliers ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Superior Performance ◽  
Optimal Interpolation ◽  
Surface Ocean ◽  
First Results ◽  
Dynamical Regimes ◽  
Using Data ◽  
The Individual ◽  
Earth System Models ◽  
Weighted Combination

<p>We present the first results from a supermodel constructed using three state-of-the-art earth system models: NorESM, CESM, MPIESM. A supermodel is an interactive ensemble in which models are optimally combined so that the systematic errors of the individual models compensate to achieve a model with superior performance. In the supermodel, the individual models are synchronized every month using data assimilation to handle the discrepancies of grid, resolution and variable representativity between the models. In particular, we assimilate a pseudo sea surface temperature (SST) that is computed as a weighted combination of the SST of the individual models. The synchronization of the models distinguishes this approach from the standard multi-model ensemble approach in which model outputs are combined a-posteriori. The data assimilation method used is the Ensemble Optimal Interpolation (EnOI) scheme, for which the covariance matrices are constructed from preindustrial control simulations of the individual models. The performances of a first version of the supermodel based on equal weights is compared to the individual models performances for the period 1980 to 2010. Synchronisation of the surface ocean is achieved in most places and dynamical regimes such as ENSO are occurring in phase. The biases of each model are reduced and the pathway of the Gulf Stream improved. The variability of the supermodel is not larger than in the super ensemble mean, but it is shown with an idealized model that the deflation is cause by a misconstruction of the pseudo observation and can be counteracted by perturbing them.  The Perspectives for performing predictions and climate change experiments with the supermodel method are presented and discussed.</p>

scholarly journals Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0

2021 ◽  
Vol 14 (5) ◽  
pp. 2635-2657
Author(s):  
Chao Sun ◽  
Li Liu ◽  
Ruizhe Li ◽  
Xinzhu Yu ◽  
Hao Yu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Data Exchange ◽  
Coupled Model ◽  
Earth System ◽  
Model Ensemble ◽  
Coupled Models ◽  
Weakly Coupled ◽  
Abstract Data ◽  
Global Communications ◽  
Earth System Models

Abstract. Data assimilation (DA) provides initial states of model runs by combining observational information and models. Ensemble-based DA methods that depend on the ensemble run of a model have been widely used. In response to the development of seamless prediction based on coupled models or even Earth system models, coupled DA is now in the mainstream of DA development. In this paper, we focus on the technical challenges in developing a coupled ensemble DA system, especially how to conveniently achieve efficient interaction between the ensemble of the coupled model and the DA methods. We first propose a new DA framework, DAFCC1 (Data Assimilation Framework based on C-Coupler2.0, version 1), for weakly coupled ensemble DA, which enables users to conveniently integrate a DA method into a model as a procedure that can be directly called by the model ensemble. DAFCC1 automatically and efficiently handles data exchanges between the model ensemble members and the DA method without global communications and does not require users to develop extra code for implementing the data exchange functionality. Based on DAFCC1, we then develop an example weakly coupled ensemble DA system by combining an ensemble DA system and a regional atmosphere–ocean–wave coupled model. This example DA system and our evaluations demonstrate the correctness of DAFCC1 in developing a weakly coupled ensemble DA system and the effectiveness in accelerating an offline DA system that uses disk files as the interfaces for the data exchange functionality.

scholarly journals Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic

2021 ◽  
Vol 11 (4) ◽  
pp. 1959
Author(s):  
Kyung M. Han ◽  
Chang H. Jung ◽  
Rae-Seol Park ◽  
Soon-Young Park ◽  
Sojin Lee ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Model Simulation ◽  
Interpolation Method ◽  
Correlation Coefficients ◽  
The Arctic ◽  
Optimal Interpolation ◽  
Accurate Information ◽  
In Situ Observation ◽  
Particulate Matters ◽  
Pm10 And Pm2.5

In this study, more accurate information on the levels of aerosol optical depth (AOD) was calculated from the assimilation of the modeled AOD based on the optimal interpolation method. Additionally, more realistic levels of surface particulate matters over the Arctic were estimated using the assimilated AOD based on the linear relationship between the particulate matters and AODs. In comparison to the MODIS observation, the assimilated AOD was much improved compared with the modeled AOD (e.g., increase in correlation coefficients from −0.15–0.26 to 0.17–0.76 over the Arctic). The newly inferred monthly averages of PM10 and PM2.5 for April–September 2008 were 2.18–3.70 μg m−3 and 0.85–1.68 μg m−3 over the Arctic, respectively. These corresponded to an increase of 140–180%, compared with the modeled PMs. In comparison to in-situ observation, the inferred PMs showed better performances than those from the simulations, particularly at Hyytiala station. Therefore, combining the model simulation and data assimilation provided more accurate concentrations of AOD, PM10, and PM2.5 than those only calculated from the model simulations.

scholarly journals Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0

2020 ◽  
Author(s):  
Chao Sun ◽  
Li Liu ◽  
Ruizhe Li ◽  
Xinzhu Yu ◽  
Hao Yu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Coupled Model ◽  
Parallel Execution ◽  
Earth System ◽  
Model Ensemble ◽  
Coupled Models ◽  
Weakly Coupled ◽  
Abstract Data ◽  
Processor Cores ◽  
Earth System Models

Abstract. Data assimilation (DA) provides better initial states of model runs by combining observational information and models. Ensemble-based DA methods that depend on the ensemble run of a model have been widely used. In response to the development of seamless prediction based on coupled models or even earth system models, coupled DA is now in the mainstream of DA development. In this paper, we focus on the technical challenges in developing a coupled ensemble DA system, which have not been satisfactorily addressed to date. We first propose a new DA framework DAFCC1 (Data Assimilation Framework based on C-Coupler2.0, version 1) for weakly coupled ensemble DA, which enables users to conveniently integrate a DA method into a model as a procedure that can be directly called by the model. DAFCC1 automatically and efficiently handles data exchanges between the model ensemble members and the DA method, and enables the DA method to utilize more processor cores in parallel execution. Based on DAFCC1, we then develop a sample weakly coupled ensemble DA system by combining the ensemble DA system GSI/EnKF and the coupled model FIO-AOW. This sample DA system and our evaluations demonstrate the effectiveness of DAFCC1 in both developing a weakly coupled ensemble DA system and accelerating the DA system.

scholarly journals An Adjoint-Based Adaptive Ensemble Kalman Filter

2013 ◽  
Vol 141 (10) ◽  
pp. 3343-3359 ◽  
Author(s):  
Hajoon Song ◽  
Ibrahim Hoteit ◽  
Bruce D. Cornuelle ◽  
Xiaodong Luo ◽  
Aneesh C. Subramanian
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Covariance Matrix ◽  
Ensemble Kalman Filter ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Optimal Interpolation ◽  
Adjoint Model ◽  
Estimation Errors ◽  
New Members

Abstract A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.

scholarly journals A new CAM6 + DART reanalysis with surface forcing from CAM6 to other CESM models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin Raeder ◽  
Timothy J. Hoar ◽  
Mohamad El Gharamti ◽  
Benjamin K. Johnson ◽  
Nancy Collins ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
High Frequency ◽  
Training Dataset ◽  
Earth System ◽  
Large Dataset ◽  
Ensemble Mean ◽  
Community Earth System Model ◽  
Component Models ◽  
Earth System Models

AbstractAn ensemble Kalman filter reanalysis has been archived in the Research Data Archive at the National Center for Atmospheric Research. It used a CAM6 configuration of the Community Earth System Model (CESM), several million observations per day, and the Data Assimilation Research Testbed (DART). The data saved from this global, $$\sim 1^\circ $$ ∼ 1 ∘ resolution, 80 member ensemble span 2011–2019. They include ensembles of: sub-daily, real world, atmospheric forcing for use by all of the nonatmospheric models of CESM; weekly, CAM6, restart file sets; 6 hourly, prior hindcast estimates of the assimilated observations; 6 hourly, land model, plant growth variables, and 6 hourly, ensemble mean, gridded, atmospheric analyses. This data can be used for hindcast studies and data assimilation using component models of CESM; CAM6, CLM5, CICE5, POP2. MOM6, MOSART, and CISM; and non-CESM Earth system models. This large dataset (~ 120 Tb) has a unique combination of a large ensemble, high frequency, and multiyear time span, which provides opportunities for robust statistical analysis and use as a machine learning training dataset.

scholarly journals Multivariate Error Covariance Estimates by Monte Carlo Simulation for Assimilation Studies in the Pacific Ocean

2005 ◽  
Vol 133 (8) ◽  
pp. 2310-2334 ◽  
Author(s):  
Anna Borovikov ◽  
Michele M. Rienecker ◽  
Christian L. Keppenne ◽  
Gregory C. Johnson
Keyword(s):  
Monte Carlo ◽  
Data Assimilation ◽  
Forecast Error ◽  
Independent Set ◽  
Velocity Fields ◽  
Optimal Interpolation ◽  
Model Errors ◽  
Error Statistics ◽  
Practical Applications ◽  
Monte Carlo Techniques

Abstract One of the most difficult aspects of ocean-state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model–observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross covariances between different model variables used. Here a comparison is made between a univariate optimal interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature profiles. In the UOI case only temperature is updated using a Gaussian covariance function. In the MvOI, salinity, zonal, and meridional velocities as well as temperature are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimate of the forecast error statistics is made by Monte Carlo techniques from an ensemble of model forecasts. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross covariances between the fields of different physical variables constituting the model-state vector, at the same time incorporating the model’s dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere–Ocean array have been assimilated in this study. To investigate the efficacy of the multivariate scheme, two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity, and temperature. For reference, a control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when the multivariate correction is used, as is evident from the analyses of the rms differences between these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating water masses with properties close to the observed, while the UOI fails to maintain the temperature and salinity structure.

Balanced Ocean-Data Assimilation near the Equator

2002 ◽  
Vol 32 (9) ◽  
pp. 2509-2519 ◽  
Author(s):  
Gerrit Burgers ◽  
Magdalena A. Balmaseda ◽  
Femke C. Vossepoel ◽  
Geert Jan van Oldenborgh ◽  
Peter Jan van Leeuwen
Keyword(s):  
Data Assimilation ◽  
Shallow Water ◽  
General Circulation ◽  
General Circulation Models ◽  
Density Field ◽  
Water Model ◽  
Optimal Interpolation ◽  
Shallow Water Model ◽  
Ocean Data Assimilation ◽  
Zonal Velocity

Abstract The question is addressed whether using unbalanced updates in ocean-data assimilation schemes for seasonal forecasting systems can result in a relatively poor simulation of zonal currents. An assimilation scheme, where temperature observations are used for updating only the density field, is compared to a scheme where updates of density field and zonal velocities are related by geostrophic balance. This is done for an equatorial linear shallow-water model. It is found that equatorial zonal velocities can be detoriated if velocity is not updated in the assimilation procedure. Adding balanced updates to the zonal velocity is shown to be a simple remedy for the shallow-water model. Next, optimal interpolation (OI) schemes with balanced updates of the zonal velocity are implemented in two ocean general circulation models. First tests indicate a beneficial impact on equatorial upper-ocean zonal currents.

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