MiKlip – PastLand A-8: Summary

Principal Investigator: Stefan Hagemann (MPI-M)

Project partner:

MPI-M (Tobias Stacke, Alexander Löw, Christian Reick)

FastOpt (Thomas Kaminski)




Predictions of the global climate depend on both the model's initial state and the anticipated change in aerosols and greenhouse gases; for decadal predictions anthropogenic climate change and natural variability are expected to be equally important. A close representation of the observed climate state in global coupled climate models is therefore crucial for (the initialization of) decadal predictions. However, predictability beyond two weeks is essentially influenced by time scales which are longer than typical scales of weather phenomena. These slow components which affect seasonal to decadal predictability of the Earth system are beside the oceans, glaciers and sea ice, the moisture content of the soil, snow cover and the terrestrial biosphere.


MiKlip PastLand aims at a comprehensive, combined state and parameter estimation of a climate model land surface scheme using simultaneously observations for different land surface variables. Focus is hereby given on a most realistic model parameter optimization at the model grid scale to improve the model predictive skills for seasonal to decadal predictions. A flexible observational framework will be built up that utilizes existing land surface observations obtained by remote sensing from satellites. Investigations will be based on the JSBACH land surface scheme, which is part of the MPI-M coupled ECHAM/MPI-OM/JSBACH Earth system model. Model predictive skills will be verified using coupled and uncoupled climate model simulations in hindcasting applications and independent observational data.


The variational state and parameter estimation for JSBACH will be carried out in offline mode, i.e. the model is driven with 'observed' atmospheric forcing. The project nevertheless addresses the initialization for the coupled model by analyzing and correcting potential biases in the exchange fluxes with the atmosphere that may be caused by the offline approach. The development and operation of a variational assimilation system for JSBACH in offline mode already constitutes a scientific and technological challenge and is an important first step towards a variational initialization of the entire MiKlip-model.



-        Identify regions where the memory of the land surface has an impact on the climate and on which time scales (from seasons to decades) this impact is noticeable.

-        Exploit the potential of new satellite observations (e.g. SMOS, SENTINEL-2) for Earth System research and an improved estimate of the land surface state.

-        Assessing the impact of observational datasets and initialization procedures on seasonal to decadal climate predictions.

-        Development and assessment of a combined optimum model and state estimation tool which can be used for the initialization of seasonal to decadal climate predictions.

-        Evaluation of observation uncertainties on model initialization and prognostic skills.