A-Coordination2 is concerned with the coordination of Module A and with performing research within the module. The main objectives of the coordination activity is to ensure close collaborations and linkages between different WPs within Module A, and between Module A and other modules, especially Module D, thereby assuring the immediate relevance of results obtained in Module A for the development of a final MiKlip prediction system. The main goals of A-Coordination2 research activities are:
The objectives of A-Coordination2 are subdivided into three WPs:
WP1.1: Estimating initial conditions and developing a mode-initialization method.
WP1.2: Testing the flux correction method.
WP1.3: Developing a coupled data assimilation approach.
WP1.1: Creating initial conditions through the global GECCO approach will continue, while taking into account all available ocean and sea ice information. Testing a new climate mode initialization method is subdivided into the following tasks: estimating climate modes for MPI-ESM using empirical orthogonal function analysis; filtering out components from the synthesis/reanalysis that do not correspond to climate modes represented by MPI-ESM which may thus lead to unbalanced states; carrying out the assimilation run to produce the initial conditions for the initialized decadal hindcasts that will be performed and compared with the hindcasts from Module D.
WP1.2: For testing the full state initialization with a momentum and buoyancy flux correction scheme, we plan the following: construction of flux correction terms from repeating the historical run with nudging of SST, SSS to the values from reanalyses, following the strategy described by Polkova et al. (2014a,b); carrying out the assimilation run and the initialized hindcasts with employing the diagnosed flux correction terms. Finally, we plan to analyze the predictive skill and make a comparison with the skill for the Prototype hindcasts (GECCO2, full field initialization) using MiKlip Central Evaluation System (CES).
WP1.3: We will perform assimilation experiments with the CESAM model to test the efficiency of the approach described by Abarbanel et al. (2010) to improve model parameters by assimilating data in coupled climate models. The objective is twofold; first, we will test how the improved consistency of the model climate with the data and with model-consistent initial conditions influences the forecast skill of a coupled climate model on various space and time scales. Second, we aim at producing a coupled synthesis that can provide model-consistent initial conditions for decadal predictions. Although the synthesis may be at first build on a simpler and coarser resolution climate model than what is currently state-of-the-art and used as the MiKlip prediction system, hindcasts initialized with this optimized model fields will provide insight into how model-consistent initialization affects the forecast skill.
The following deliverables are planned for A-Coordination2:
WP1.1: Updates of GECCO2 are being performed on a regular basis to provide initial conditions for MiKlip II. The updates of GECCO2 are available through http://icdc.zmaw.de/1/daten/reanalysis-ocean/gecco2.html. For mode-initialization method, we have been testing different methods to derive the representative climate modes from the MPI-ESM including different normalization, weighting and truncation approaches.
WP1.3: The coupled data assimilation model that we are using is the coupled adjoint system CESAM (CEN Earth System Assimilation Model, https://www.cen.uni-hamburg.de/en/research/cen-models/cesam.html). Various experiments were performed with different choices of control parameters and different model configurations: low resolution (4 degrees resolution and 15 depth levels in the ocean, and T21 and 10 levels in the atmosphere) and medium resolution (1 degree resolution and 23 levels in the ocean, and T42 and 10 levels in the atmosphere), forward 500yrs long integrations of the climate model and are used for: (i) evaluation of CESAM and the quality of the coupled model forward runs (this analysis focuses on understanding how parameterization of some processes in the atmosphere and ocean affect the simulated mean-state climate and climate variability), and (ii) generation of initial conditions for sensitivities studies of near-surface air temperature over Europe to changes in sea surface temperature in different regions of global ocean (e.g., the North Atlantic Ocean).
Institut für Meereskunde,Universität Hamburg
Prof. Dr. Detlef Stammer
+49 40 42838-5052
Marini, C. | I. Polkova, A. Köhl, and D. Stammer
Polkova, I. | A. Köhl, and D. Stammer
Polkova, I. | A. Köhl, and D. Stammer
Blessing, S. | T. Kaminski, F. Lunkeit, I. Matei, R. Giering, A. Köhl, M. Scholze, P. Herrmann, K. Fraedrich, and D. Stammer