Further work is now underway to relate all the measurement types in GASSP to their effectiveness at constraining the modeled radiative forcing.
This is an important discovery of the GASSP project--that even a tightly constrained global aerosol model can still generate a wide range of aerosol radiative forcings even though the change in aerosols directly causes the forcing, and we have assumed that the uncertainty in the simulated forcing comes only from the aerosol component of the model.
The extensive GASSP aerosol database combined with the perturbed parameter ensembles provides an optimum way to detect such structural errors.
GASSP set out to understand and reduce uncertainty in model simulations of aerosol radiative forcing caused by the uncertainties in the aerosol component of the model.
GASSP has created a harmonized dataset of nearly a quarter of a century of aerosol in situ measurements comprising over 46,000 measurement hours from aircraft and ships and from over 300 surface sites (Fig.
Aerosol measurements are highly diverse and difficult to harmonize (the GASSP database includes 70 measured aerosol variables).
Analysis in GASSP shows that much of the uncertainty in radiative forcing stems from the properties of pristine aerosol environments (Carslaw et al.
2012), but equally important is data harmonization to reduce the very large number of data formats that have proliferated (see "The GASSP aerosol measurement database" section).
GASSP has shown that we can define representative "uncertainty environments" (Fig.
A similar effort to GASSP dedicated to collecting and harmonizing cloud microphysical properties, such as droplet and ice crystal number concentrations, would be very valuable.
A logical next step is to extend the statistical approach used in GASSP to multiple models and thereby merge efforts on parametric and structural uncertainty (Shiogama et al.
GASSP was funded by the Natural Environment Research Council (NERC) under Grants NE/J024252/1, NE/J022624/1, and NE/J023515/1; ACID-PRUF under Grants NE/I020059/1 and NE/I020148/1; the European Union BACCHUS project under Grant 603445-BACCHUS; ACTRIS under Grants 262254 and 654109; and by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund.