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AbstractAn ensemble Kalman filter reanalysis has been archived in the Research Data Archive at the National Center for Atmospheric Research.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.

A New CAM6+DART Reanalysis with Surface Forcing from CAM6 to Other CESM Models

2021 ◽  
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

Abstract An ensemble Kalman filter reanalysis has been archived in the Research Data Archive at the National Center for Atmospheric Research.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, ∼ 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 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 Tropical climate variability in the Community Earth System Model: Data Assimilation Research Testbed

2019 ◽  
Vol 54 (1-2) ◽  
pp. 793-806 ◽  
Author(s):  
Jonathan Eliashiv ◽  
Aneesh C. Subramanian ◽  
Arthur J. Miller
Keyword(s):  
Data Assimilation ◽  
Climate Variability ◽  
Tropical Climate ◽  
Earth System Model ◽  
The Other ◽  
System Model ◽  
Earth System ◽  
Ocean Atmosphere ◽  
Community Earth System Model ◽  
Free Running

AbstractA new prototype coupled ocean–atmosphere Ensemble Kalman Filter reanalysis product, the Community Earth System Model using the Data Assimilation Research Testbed (CESM-DART), is studied by comparing its tropical climate variability to other reanalysis products, available observations, and a free-running version of the model. The results reveal that CESM-DART produces fields that are comparable in overall performance with those of four other uncoupled and coupled reanalyses. The clearest signature of differences in CESM-DART is in the analysis of the Madden–Julian Oscillation (MJO) and other tropical atmospheric waves. MJO energy is enhanced over the free-running CESM as well as compared to the other products, suggesting the importance of the surface flux coupling at the ocean–atmosphere interface in organizing convective activity. In addition, high-frequency Kelvin waves in CESM-DART are reduced in amplitude compared to the free-running CESM run and the other products, again supportive of the oceanic coupling playing a role in this difference. CESM-DART also exhibits a relatively low bias in the mean tropical precipitation field and mean sensible heat flux field. Conclusive evidence of the importance of coupling on data assimilation performance will require additional detailed direct comparisons with identically formulated, uncoupled data assimilation runs.

scholarly journals Implementation of the Community Earth System Model (CESM) version 1.2.1 as a new base model into version 2.50 of the MESSy framework

2016 ◽  
Vol 9 (1) ◽  
pp. 125-135 ◽  
Author(s):  
A. J. G. Baumgaertner ◽  
P. Jöckel ◽  
A. Kerkweg ◽  
R. Sander ◽  
H. Tost
Keyword(s):  
Atmospheric Chemistry ◽  
Atmospheric Model ◽  
The United States ◽  
Earth System Model ◽  
System Model ◽  
Climate Modelling ◽  
Earth System ◽  
Spectral Transform ◽  
Community Earth System Model ◽  
Component Models

Abstract. The Community Earth System Model (CESM1), maintained by the United States National Centre for Atmospheric Research (NCAR) is connected with the Modular Earth Submodel System (MESSy). For the MESSy user community, this offers many new possibilities. The option to use the Community Atmosphere Model (CAM) atmospheric dynamical cores, especially the state-of-the-art spectral element (SE) core, as an alternative to the ECHAM5 spectral transform dynamical core will provide scientific and computational advances for atmospheric chemistry and climate modelling with MESSy. The well-established finite volume core from CESM1(CAM) is also made available. This offers the possibility to compare three different atmospheric dynamical cores within MESSy. Additionally, the CESM1 land, river, sea ice, glaciers and ocean component models can be used in CESM1/MESSy simulations, allowing the use of MESSy as a comprehensive Earth system model (ESM). For CESM1/MESSy set-ups, the MESSy process and diagnostic submodels for atmospheric physics and chemistry are used together with one of the CESM1(CAM) dynamical cores; the generic (infrastructure) submodels support the atmospheric model component. The other CESM1 component models, as well as the coupling between them, use the original CESM1 infrastructure code and libraries; moreover, in future developments these can also be replaced by the MESSy framework. Here, we describe the structure and capabilities of CESM1/MESSy, document the code changes in CESM1 and MESSy, and introduce several simulations as example applications of the system. The Supplements provide further comparisons with the ECHAM5/MESSy atmospheric chemistry (EMAC) model and document the technical aspects of the connection in detail.

Implementing irrigation techniques in CESM2

2020 ◽  
Author(s):  
Yi Yao ◽  
Sean Swenson ◽  
Dave Lawrence ◽  
Wim Thiery
Keyword(s):  
Land Surface ◽  
Surface Fluxes ◽  
System Model ◽  
Earth System ◽  
Irrigation Scheme ◽  
Irrigation Systems ◽  
Community Land Model ◽  
Community Earth System Model ◽  
Induced Changes ◽  
Earth System Models

<p>Several recent studies have highlighted the importance of irrigation-induced changes in climate. Earth system models are a common tool to address this question, and to this end, irrigation is increasingly being represented in their land surface modules. Despite this evolution, currently, none of them considers different irrigation techniques. Here we develop and test a new parameterization that represents irrigation activities in the Community Land Model version 5 (CLM5) and considers three main irrigation techniques (surface, sprinkler and drip irrigation). Using global maps of the areas equipped by different irrigation systems, we will employ version 2 of the Community Earth System Model (CESM2) and its improved irrigation representation to detect the impacts of irrigation on climate. Two control experiments are designed, one with the new irrigation scheme and another with the original one. We will conduct an evaluation by comparing the simulated results against observed surface fluxes and meteorological variables. Subsequently, the differences between the experiments will be analyzed to quantify the impacts of irrigation on climate. We anticipate that our results will uncover whether considering different irrigation schemes is of value for exploring irrigate-induced impacts on climate.</p>

scholarly journals An Ensemble Kalman Filter for Numerical Weather Prediction Based on Variational Data Assimilation: VarEnKF

2017 ◽  
Vol 145 (2) ◽  
pp. 617-635 ◽  
Author(s):  
Mark Buehner ◽  
Ron McTaggart-Cowan ◽  
Sylvain Heilliette
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Variational Approach ◽  
Variational Data Assimilation ◽  
Ensemble Prediction ◽  
Deterministic System ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Ensemble Mean

Several NWP centers currently employ a variational data assimilation approach for initializing deterministic forecasts and a separate ensemble Kalman filter (EnKF) system both for initializing ensemble forecasts and for providing ensemble background error covariances for the deterministic system. This study describes a new approach for performing the data assimilation step within a perturbed-observation EnKF. In this approach, called VarEnKF, the analysis increment is computed with a variational data assimilation approach both for the ensemble mean and for all of the ensemble perturbations. To obtain a computationally efficient algorithm, a much simpler configuration is used for the ensemble perturbations, whereas the configuration used for the ensemble mean is similar to that used for the deterministic system. Numerous practical benefits may be realized by using a variational approach for both deterministic and ensemble prediction, including improved efficiency for the development and maintenance of the computer code. Also, the use of essentially the same data assimilation algorithm would likely reduce the amount of numerical experimentation required when making system changes, since their impacts in the two systems would be very similar. The variational approach enables the use of hybrid background error covariances and may also allow the assimilation of a larger volume of observations. Preliminary tests with the Canadian global 256-member system produced significantly improved ensemble forecasts with VarEnKF as compared with the current EnKF and at a comparable computational cost. These improvements resulted entirely from changes to the ensemble mean analysis increment calculation. Moreover, because each ensemble perturbation is updated independently, VarEnKF scales perfectly up to a very large number of processors.

scholarly journals Implementation of the Community Earth System Model (CESM1, version 1.2.1) as a new basemodel into version 2.50 of the MESSy framework

2015 ◽  
Vol 8 (8) ◽  
pp. 6523-6550
Author(s):  
A. J. G. Baumgaertner ◽  
P. Jöckel ◽  
A. Kerkweg ◽  
R. Sander ◽  
H. Tost
Keyword(s):  
Surface Pressure ◽  
Atmospheric Chemistry ◽  
The United States ◽  
Earth System Model ◽  
System Model ◽  
Climate Modelling ◽  
Earth System ◽  
Dynamical Core ◽  
Community Earth System Model ◽  
Component Models

Abstract. The Community Earth System Model (CESM1), maintained by the United States National Centre for Atmospheric Research (NCAR) is connected with the Modular Earth Submodel System (MESSy). For the MESSy user community, this offers many new possibilities. The option to use the CESM1(CAM) atmospheric dynamical cores, especially the spectral element (SE) core, as an alternative to the ECHAM5 spectral transform dynamical core will provide scientific and computational advances for atmospheric chemistry and climate modelling with MESSy. The SE dynamical core does not require polar filters since the grid is quasi-uniform. By advecting the surface pressure rather then the logarithm of surface pressure the SE core locally conserves energy and mass. Furthermore, it has the possibility to scale to up to 105 compute cores, which is useful for current and future computing architectures. The well-established finite volume core from CESM1(CAM) is also made available. This offers the possibility to compare three different atmospheric dynamical cores within MESSy. Additionally, the CESM1 land, river, sea ice, glaciers and ocean component models can be used in CESM1/MESSy simulations, allowing to use MESSy as a comprehensive Earth System Model. For CESM1/MESSy setups, the MESSy process and diagnostic submodels for atmospheric physics and chemistry are used together with one of the CESM1(CAM) dynamical cores; the generic (infrastructure) submodels support the atmospheric model component. The other CESM1 component models as well as the coupling between them use the original CESM1 infrastructure code and libraries, although in future developments these can also be replaced by the MESSy framework. Here, we describe the structure and capabilities of CESM1/MESSy, document the code changes in CESM1 and MESSy, and introduce several simulations as example applications of the system. The Supplements provide further comparisons with the ECHAM5/MESSy atmospheric chemistry (EMAC) model and document the technical aspects of the connection in detail.

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.

Parameter estimation for ocean biogeochemical component in a global model using Ensemble Kalman Filter: a twin experiment

2021 ◽  
Author(s):  
Tarkeshwar Singh ◽  
Francois Counillon ◽  
Jerry F. Tjiputra ◽  
Mohamad El Gharamti
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Parameter Values

<p>Ocean biogeochemical (BGC) models utilize a large number of poorly-constrained global parameters to mimic unresolved processes and reproduce the observed complex spatio-temporal patterns. Large model errors stem primarily from inaccuracies in these parameters whose optimal values can vary both in space and time. This study aims to demonstrate the ability of ensemble data assimilation (DA) methods to provide high-quality and improved BGC parameters within an Earth system model in idealized twin experiment framework.  We use the Norwegian Climate Prediction Model (NorCPM), which combines the Norwegian Earth System Model with the Dual-One-Step ahead smoothing-based Ensemble Kalman Filter (DOSA-EnKF). The work follows on Gharamti et al. (2017) that successfully demonstrates the approach for one-dimensional idealized ocean BGC models. We aim to estimate five spatially varying BGC parameters by assimilating Salinity and Temperature hydrographic profiles and surface BGC (Phytoplankton, Nitrate, Phosphorous, Silicate, and Oxygen) observations in a strongly coupled DA framework – i.e., jointly updating ocean and BGC state-parameters during the assimilation. The method converges quickly (less than a year), largely reducing the errors in the BGC parameters and eventually it is shown to perform nearly as well as that of the system with true parameter values. Optimal parameter values can also be recovered by assimilating climatological BGC observations and challenging sparse observational networks. The findings of this study demonstrate the applicability of the approach for tuning the system in a real framework.</p><p> </p><p><strong>References</strong>:</p><p>Gharamti, M. E., Tjiputra, J., Bethke, I., Samuelsen, A., Skjelvan, I., Bentsen, M., & Bertino, L. (2017). Ensemble data assimilation for ocean biogeochemical state and parameter estimation at different sites. Ocean Modelling, 112, 65-89.</p>

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