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Coordinator: D. Stammer

Overarching Questions

The primary focus of RA-A is on questions of cause and effect of past climate variability and how current trends of the climate system, mediated by fluxes of water, energy, carbon and nutrients among its components, can best be monitored from the existing suboptimal data basis. Answers are required for testing hypotheses about processes involved in past and ongoing changes, such as GHG-forcing, regional modifications due to aerosols, volcanic eruptions or feedbacks in land use or ice extension. Answers are likewise required as a reference against which future changes (observed or predicted by models) can be interpreted.

Goals of RA-A

To answer the above questions, RA-A will provide a comprehensive and detailed description of past variability, and will perform a robust analysis of ongoing changes and their mechanisms by explicitly accounting for uncertainties propagating from individual data sets to derived conclusions about causal relationships and impacts. Goals of this effort are accordingly:

• To develop a comprehensive integrated climate system data base, required to cover past changes over as long a period as possible. This will be done through an optimal exploitation of in situ data and extensive use of all available satellite data. As part of this effort, new proxy data sets will be developed, suitable to describe important, but not directly observable, climate variability. Furthermore, our algorithmic understanding of the relation between satellite data and climate variables will be expanded. Also considered, where useful, will be palaeo-data existing in partner institutions.
• To perform a robust and detailed description of past variability and ongoing change that will explicitly account for uncertainties in the data and inferences drawn from them. We will develop procedures to solve non-stationary and inhomogeneous estimation problems, globally and on a regional scale, required to infer climate variability from incomplete and noisy data sets and to advance our understanding of the underlying processes.
• To develop capabilities to perform data assimilation into coupled climate and Earth system models. We will develop statistical approaches suitable for estimating uncertainties in climate diagnostics that results from analysis, assimilation and modelling results.
• To perform global and regional analyses of cycles of water, energy and matter through the climate system, as well as their roles in changing ecosystems, impacting resources (e.g., water resources), and shaping climate risks (e.g., sea level rise).

Because the problem of estimating and understanding climate and Earth system variations is intrinsically coupled, the analysis and synthesis (assimilation) of climate data and their uncertainties needs to be performed in the framework of coupled models, too. Many aspects of the analyses will be global, but a specific focus will be on the Atlantic sector (including the Arctic), the European Shelf Seas and northern and western Europe. Enhanced efforts will also be put on permafrost regions because of their large climate change potential. Time scales that will be addressed, reach from short-time scale variations involved in regional small-scale processes, to decadal and centennial time scales of processes occurring on spatial scales of continents, ocean basis, or globally.

Methods

• Methodically the research in RA-A will involve the optimal use and expansion of an integrated climate system data base for the purpose of climate studies, by expanding our use of proxy information (including those available in forestry data banks), through the re-processing of the existing satellite and in situ data with respect to improved understanding of data-process relations (algorithms), and the development of new understanding linking available observations to important climate parameters and indices not currently available. The resulting data base will cover the land surface and soil, the atmosphere, the ocean, and the cryosphere including anthropogenic parameters, e.g., land use and greenhouse gas emissions.
• Because observations are always incomplete, models must be used to interpret the observed variations of the climate state in terms of processes, to estimate uncertainty limits, and to attribute/interpret observed variability in terms of natural climate signals or in terms of anthropogenic changes. In return, new types of data (e.g. satellite flux measurements, temporally and/or spatially highly resolved newly available data fields, radioactive noble gases as tracers) will provide new opportunities for the testing of models and the improvements of parameterisations used in them.
• While many model errors can be remedied through improvements of model physics and improved boundary conditions, some errors will always remain, originating from random errors in the initialisation and the forcing. It will therefore be necessary to constrain fully coupled, dynamical models of the climate system with all available climate observations by merging them with in situ and satellite data through the method of statistical inference (data assimilation).This will also involve the use of data assimilation to obtain a best possible description of the past climate variability and to create best possible initial conditions for climate forecast studies performed under RA-B. Improving coupled models through data assimilation will provide new insight into the interactions between different components of the climate system, and will establish a long-sought link between climate observing systems and climate change assessment studies.
• Based on those data and supporting model approaches, an analysis of past variability in the climate system will be performed, present changes will be monitored and, by relating them to earlier changes, will be used to test the hypothesis of accelerated climate change. Changes of the circulation and sea level in the Atlantic circulation will be related to changes in the MOC and to changes of climate over Europe. More generally, hypotheses on climate variability and feedback mechanism will be tested. An analysis of ongoing ecosystem changes will be performed and related to ecosystem-climate interactions.
• Based on the climate data base, climate indices will be provided on a regular basis, including drought and dust/pollution indices, indices to characterise sea ice coverage, in the ocean circulation, land use as well as urban air quality

 

Relation to other RA's

RA-A will contribute to the overarching themes of this CoE by describing the climate state and its uncertainties over the past century, by putting existing (anthropogenic) variations into the context of past (natural) changes and by diagnosing the prime factors responsible for those changes.
Reaching the goals of RA-A requires an intense cooperation across all RA’s.  The outcome of RA-A will support the studies of predictability (RA-B) feedbacks (RA-C) and impacts (RA-D). In particular, RA-A will produce initial conditions for RA-B. RA-A and RA-B jointly will produce boundary conditions for the regional studies performed under RA-D. The interpretation of past observations in terms of processes strongly depends on the understanding of feedback mechanisms diagnosed under RA-C. The work planned here would be a pre-requisite for testing and demonstrating decadal forecast capabilities of the climate system.
RA-A will contribute to the public interface of this CoE by providing a best possible integrated Earth system data base, by providing on a regular basis estimates of climate indices and their uncertainties and by providing new approaches for coupled data assimilation and data analysis.

Participating Scientists

1. UniHH: A. Brandt; J.U.Ganzhorn (BioZentrum Grindel), K. Emeis (Institute for Biogeochemistry and Marine Chemistry), J. Oßenbrügge (Institute of Geography), E.-M. Pfeiffer (Institute of Soil Science), M. St John, A. Temming (Institute of Hydrobiology and Fishery Science), B. Bruemmer, K. Fraedrich, F. Lunkeit, M. Claussen, M. Schatzmann, H. Schlünzen (Meteorological Institute), M. Hort, T. Dahm, D. Gajewski (Institute for Geophysics), L. Kaleschke, A. Koehl, D. Quadfasel, D. Stammer (Institute for Oceanography), M. Funke (Institute for Macroeconomics and Economic Policy), M. Kalinowski (ZNF), M. Köhl (Centre for Forestry and Forest Products)
2. MPI-M: J. Jungclaus; J. Marotzke; E. Meier-Reimer; E. Roeckner; J.-S. von Storch; H. Feichter; R. Schnur.
3. GKSS: H. von Storch; R. Weisse, H., E. Zorita