A concept for synergetic retrievals of self-consistent aerosol property climate data records

The influence of aerosols on climate is determined not only by their global distribution but also by their composition. However, a large fraction of the development efforts for satellite-based aerosol retrievals has focused on inverting the total column aerosol optical depth (AOD) from different single instruments. As the information content of single instruments is smaller than necessary to retrieve a comprehensive quantification of all parameters of the atmospheric aerosol, there is a need for using auxiliary information to fill this gap (e.g. pre-defined optical aerosol properties, climatological vertical profiles, ...). Retrievals inverting further aerosol parameters from one instrument (e.g. Fine Mode or Dust AOD) depend on the auxiliary assumptions and on the sensitivity of the available instrument channels to the various aerosol properties. This leads to inconsistencies even between AOD results and certainly for additional parameters inverted from different sensors. Examples of consequential inconsistencies are the step in the AOD Climate Data Record built from subsequent pieces from similar instruments but with opposing viewing directions in the “dual view radiometer” series (A)ATSR(-2) and SLSTR) or the combined use of retrieval results from thermal with UV-VIS instruments at the same wavelength (usually 550 nm). For both examples, synergetic retrievals hold the potential to reduce the dependence on assumed properties and thus improve consistency.

It can be argued that each new generation of satellite instruments offers new additional capabilities so that the most recent era of multi-angle, multi-spectral polarimeters provide significantly larger information content and are thus able to invert more aerosol parameters. However, the wealth of satellite-based aerosol climate-relevant time series dating back to early 1980s comes from much simpler instruments. Here I see the largest field of synergetic retrievals, ranging from combinations of AVHRR with TOMS over combinations of MODIS and MISR or AATSR / MERIS / SCIAMACHY (and IASI) and their successor instruments.

One alternative road to achieve this could be in data assimilation of single-sensor aerosol retrieval results. However, any inconsistency of those separate pieces ingested into an atmospheric model will create difficulties and, in addition, the effort for including several independent satellite aerosol products (error covariance matrices, bias corrections) has put a stringent limitation so far. Ultimately, the use of all available information from different sensors offers the perspective of “self-consistent” results.

Building on lessons from the decade of Aerosol_cci developments and my own early and simplistic synergetic retrievals (SYNAER with AATSR and SCIAMACHY), this work presents a conceptual framework for advancing synergetic aerosol retrieval methodologies. Key elements include multi-sensor information content analysis, strategies for addressing historical data gaps and practical challenges (e.g. cross-calibration, cross-channel correlations, colocations and differences in field of views). In combination with priorities for needed parameters and length and gaps in historic records, this paper aims to establish a roadmap for synergetic aerosol retrievals, paving the way for more robust and comprehensive, i.e. self-consistent climate data records.