WP4 Integration between the scales

WP4 integrates the scales and aims at deriving descriptive proxy variables for the feedback mechanisms and essential climate variables from the space-borne Earth Observation data.

Task 4.1 Up/downscaling of the processes

Development effort is mobilized to bridge the gap between aerosol-cloud processes at the microphysical level and sub-grid scale effects of aerosols and clouds in ESMs, including existing and new aerosol and cloud-activation sub-grid schemes. The observed and simulated aerosols and clouds will be analyzed to evaluate the sub-grid scale process representations performance, providing information on the uncertainties related to the closure parameters of cloud-aerosol parameterizations. As a common denominator, aerosol data assimilation is developed to properly interface observations and models. Improved modeling of ecosystem productivity and up-scaling from individual tree/stand to regional scale will be contributing to better understanding of feedbacks mediated through land use changes (Valentine & Mäkelä 2012).

Task 4.2 Derivation of proxy variables

The task will derive descriptive proxy variables for the feedback mechanisms and essential climate variables from the space-borne EO data with a global view. This activity enables us to generalize the data from the state-of-the-art measurement networks, remote sensing and modeling frameworks for assessing the role of the feedback mechanisms both in different future climate scenarios over decadal and centennial scales and in the past changes over millennia scales. In order to extract the full information content of the past and future measurements, latest data analysis methods incl. Extended Kalman Filter, will be implemented to "re-analyze" the field ATM Research and Action plan 2014-2019 Page 11 measurements. We comprehensively integrate the EO from the world-wide network of in-situ and active and passive remote sensing stations. With this re-analysis data we will provide microphysical state estimates with appropriate error bars, opening new opportunities to retrospective studies of existing data. We also improve the retrieval algorithms for atmospheric state from the satellites (Kulmala et al. 2011b).

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