The Global Aerosol Synthesis and Science Project (GASSP): Using a Comprehensive Synthesis of Aerosol Observations and Statistical Modelling to Constrain Model Uncertainty
Abstract:Over the past few decades there has been enormous investment in atmospheric aerosol measurements across the globe. However, ultimately only a small fraction of these measurements are used to test and improve models. GASSP aims to bring together as much aerosol measurement data as possible in combination with a novel application of statistical methods to test and improve atmospheric model processes and improve our understanding of global aerosol and climate.
Presently, we have synthesised a vast array of diverse aerosol measurements from aircraft, ground stations and ships, combining campaign and long-term measurements conducted over the past two decades. These data include in-situ measurements of cloud condensation nuclei and aerosol particle number concentrations, sizes and chemical composition. By combining different aerosol measurements we can ensure that the model skill is consistent across a range of aerosol properties in a range of environments. We will present spatial maps and time series of these data, identifying key regions where gaps currently exist in the dataset and where future contribution from the measurement community will be most crucial.
We have also performed a sensitivity analysis of the output from a global aerosol model, which has identified the important sources of parameter uncertainty in all model grid cells throughout a single year. Cluster analysis of this data shows which model uncertainties can be constrained by observations in any particular global region during the year. Similarities and distinctions between clusters allows us to identify how observations made around the globe have the potential to constrain the global aerosol model and identify which model uncertainties will remain irreducible with the current suite of observations. As a first step we have used synthetic observations to constrain the model uncertainties and quantify the potential of real observations for model constraint. We then use these results to target real observations from the GASSP database that will provide the greatest constraint on model uncertainty.
GASSP is foreseen as an ongoing effort to continually improve global models by synthesising the latest observations and we welcome continued contributions from the aerosol measurement community to our ever growing database.