ALBERT

Beschreibung: Total chlorophyll data satellite data is assimilated to MITgcm-REcoM in a global configuration for the year 2008 and 2009. The results of the simulation for Small Phytoplankton, Diatoms, Nutrients and Zooplankton for the 'assimilation run' and 'free run' are presented here. The correlation for Small Phytoplankton and Diatoms with respect to total chlorophyll are also included.

Beschreibung: Satellite data products are assimilated to MITgcm-REcoM coupled model for the year 2008 and 2009 in a global configuration. The model simulation uses PDAF an assimilation framework for data assimilation in a five day forecast-analysis cycle. Small Phytoplankton and diatoms were assimilated from SynsenPFT, OC-PFT and PhytoDOAS data while total chlorophyll from OC-CCI was assimilated into the coupled model. Simulation data without assimilation called as “Free run” is also available here.

Beschreibung: The coupled ocean circulation‐ecosystem model MITgcm‐REcoM2 is used to simulate biogeochemical variables in a global configuration. The ecosystem model REcoM2 simulates two phytoplankton groups, diatoms and small phytoplankton, using a quota formulation with variable carbon, nitrogen, and chlorophyll contents of the cells. To improve the simulation of the phytoplankton variables, chlorophyll‐a data from the European Space Agency Ocean‐Color Climate Change Initiative (OC‐CCI) for 2008 and 2009 are assimilated with an ensemble Kalman filter. Utilizing the multivariate cross covariances estimated by the model ensemble, the assimilation constrains all model variables describing the two phytoplankton groups. Evaluating the assimilation results against the satellite data product SynSenPFT shows an improvement of total chlorophyll and more importantly of individual phytoplankton groups. The assimilation improves both phytoplankton groups in the tropical and midlatitude regions, whereas the assimilation has a mixed response in the high‐latitude regions. Diatoms are most improved in the major ocean basins, whereas small phytoplankton show small deteriorations in the Southern Ocean. The improvement of diatoms is larger when the multivariate assimilation is computed using the ensemble‐estimated cross covariances between total chlorophyll and the phytoplankton groups than when the groups are updated so that their ratio to total chlorophyll is preserved. The comparison with in situ observations shows that the correlation of the simulated chlorophyll of both phytoplankton groups with these data is increased whereas the bias and error are decreased. Overall, the multivariate assimilation of total chlorophyll modifies the two phytoplankton groups separately, even though the sum of their individual chlorophyll concentrations represents the total chlorophyll.

Beschreibung: Phytoplankton functional‐type (PFT) data are assimilated into the global coupled ocean‐ecosystem model MITgcm‐REcoM2 for two years using a local ensemble Kalman filter. The ecosystem model has two PFTs: small phytoplankton (SP) and diatoms. Three different sets of satellite PFT data are assimilated: Ocean‐Color‐Phytoplankton Functional Type (OC‐PFT), Phytoplankton Differential Optical Absorption Spectroscopy (PhytoDOAS), and SYNergistic exploitation of hyper‐ and multi‐spectral precursor SENtinel measurements to determine Phytoplankton Functional Types (SynSenPFT), which is a synergistic product combining the independent PFT products OC‐PFT and PhytoDOAS. The effect of assimilating PFT data is compared with the assimilation of total chlorophyll data (TChla), which constrains both PFTs through multivariate assimilation. While the assimilation of TChla already improves both PFTs, the assimilation of PFT data further improves the representation of the phytoplankton community. The effect is particularly large for diatoms where, compared to the assimilation of TChla, the SynSenPFT assimilation results in 57% and 67% reduction of root‐mean‐square error and bias, respectively, while the correlation is increased from 0.45 to 0.54. For SP the assimilation of SynSenPFT data reduces the root‐mean‐square error and bias by 14% each and increases the correlation by 30%. The separate assimilation of the PFT data products OC‐PFT, SynSenPFT, and joint assimilation of OC‐PFT and PhytoDOAS data leads to similar results while the assimilation of PhytoDOAS data alone leads to deteriorated SP but improved diatoms. When both OC‐PFT and PhytoDOAS data are jointly assimilated, the representation of diatoms is improved compared to the assimilation of only OC‐PFT. The results show slightly lower errors than when the synergistic SynSenPFT data are assimilated, which shows that the assimilation successfully combines the separate data sources.

Beschreibung: Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: ScenarioMIP. These data include all datasets published for 'CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The model used in climate research named AWI-CM 1.1 MR, released in 2018, includes the components: atmos: ECHAM6.3.04p1 (T127L95 native atmosphere T127 gaussian grid; 384 x 192 longitude/latitude; 95 levels; top level 80 km), land: JSBACH 3.20, ocean: FESOM 1.4 (unstructured grid in the horizontal with 830305 wet nodes; 46 levels; top grid cell 0-5 m), seaIce: FESOM 1.4. The model was run by the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany (AWI) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km. Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions, and the results will undoubtedly be relied on by authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated at a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.

Beschreibung: MITgcm-REcoM is used to simulate biogeochemistry in a global ocean configuration. The Regulated Ecosystem Model REcoM simulates biogeochemical processes using two phytoplankton groups (small phytoplanktons and diatoms) in a quota-formulation using separate variables for carbon and chlorophyll. The model has been shown to produce realistic phytoplankton carbon concentrations with spatially constant parameters controlling the processes. To further improve the model representation we plan to estimate spatially varying parameters so that the biogeochemical processes can adapt to regional environmental conditions. The parameter estimation will be performed by assimilating ocean color data from OC-CCI using an ensemble filter provided by the Parallel Data Assimilation Framework (PDAF). We discuss the performance of the global model configuration with fixed parameters as well as data assimilation component and plans for the parameter estimation.

Beschreibung: MITgcm-REcoM is used to simulate biogeochemistry in a global ocean configuration. The Regulated Ecosystem Model REcoM simulates biogeochemical processes using two phytoplankton groups (small phytoplankton and diatoms) in a quota-formulation using separate variables for carbon and chlorophyll. The model has been shown to produce realistic phytoplankton concentrations with spatially constant parameters controlling the processes.To further improve the model representation we assimilate observational data from OC-CCI. The final goal is to estimate spatially varying parameters so that the biogeochemical processes can adapt to regional environmental conditions. As a first step, we assimilate chlorophyll-a data and assess the direct influence of the data assimilation on the concentrations of the biogeochemical model variables. The data assimilation is performed using an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF, http://pdaf.awi.de). We discuss the data assimilation component and the influence of the assimilation on the model fields.

Beschreibung: Data assimilation combines observational data with numerical simulation models. The methodology allos to improve the initialization of model predictions, determining model deficiencies, but also to enhance data sets by augmenting the data with dynamical information from numerical models simulating e.g. ocean physics or biogeochemistry. This combination can fill data gaps by an interpolation which accounts for the dynamical information provided by the numerical model. Further the observed information can be used to improve unobserved variables, and even fluxes. This is accomplished through the use of dynamically estimated cross-covariances between the observed and unobserved variables. The assimilation can result in data sets which, at the resolution of the model, exhibit smaller errors than using the observations or the model alone. I will discuss the method of ensemble-based data assimilation on the example of ocean-biogoechemical modeling with the assimilation of satellite ocean color data.