Global Aerosol Synthesis and Science Project (GASSP) to reduce the uncertainty in aerosol radiative forcing

The motivation for this project is that aerosols have persistently been assessed by the IPCC as the largest uncertainty in the radiative forcing of climate over the industrial period. This means that our ability to understand temperature changes over the industrial period is hampered by very poorly constrained aerosol processes in models. The main uncertainty is due to the effect that aerosols have on clouds - the so-called aerosol indirect effect by which anthropogenic aerosols make clouds more reflective. In the IPCC assessment, the range of predictions of the aerosol indirect forcing lies between -0.4 to -1.8 Wm-2, a far larger range than associated with CO2 forcing (1.6-1.9 Wm-2). Thus, to improve our understanding of climate change, we need to reduce the uncertainty in the aerosol indirect effect.

The controlling factor in the indirect effect is the concentration in the atmosphere of "cloud condensation nuclei" (CCN). CCN are a subset of the aerosol particles in the atmosphere, typically larger than 50 nm diameter and sufficiently water soluble to form cloud drops. Only recently, global models have been developed that are able to explicitly simulate CCN concentrations. This opens up the possibility of reducing model uncertainty by exploiting extensive measurements of CCN that have been made over many years.

We propose to undertake the first ever comprehensive synthesis of global CCN and related aerosol observations within the UK aerosol-chemistry-climate model. The overall aim is to reduce uncertainty in the indirect effect by constraining modern aerosol as much as possible based on present observing systems and models. We will reduce the uncertainty by producing a global model of CCN with well defined uncertainties that are constrained by worldwide observations. We will then use the "calibrated" aerosol model to quantify the indirect radiative forcing and its uncertainty. We will also use the new and better model to understand the sources of CCN in different environments, and thereby the factors that will drive future changes in the concentration. As a spin-off of the project we will also be able to use the model and data to identify the regions or environments in which new measurements would have the greatest impact on reducing the uncertainty further.

An important new aspect of the project will be the use of new uncertainty information about the global model. In most similar studies it has been possible to run the model only a few times. However, in reality the model has a wide uncertainty range due to the very large number of uncertain processes in the model. In this project we will use new information that tells us how the model behaves under all possible assumptions of uncertainty. From this collection of model runs we will be able to identify the best possible model in all parts of the world. This procedure is known as "calibration", and it has not been attempted before for a complex global model. With this approach we can be sure the model is as close to observations of CCN as can presently be achieved.

GASSP is very well placed to have a direct impact on national and international climate and weather prediction centres, and therefore climate policy and the IPCC. The key to our engagement is that we are proposing to synthesise a vast number of existing measurements to constrain and improve aerosol forcing in the UK's climate model HadGEM. The unique compilation of harmonised observational data, synthesised to suitable formats / statistics, will then be available for wider community use and directly contribute to key international activities, such as the International Aerosol Intercomparison Project (AeroCom, http://aerocom.met.no/). Our engagement activities are a direct extension to our well-established collaborations.

Specifically in this project, our user engagement will include:

European Centre for Medium-Range Weather Forecasts (ECMWF): Our impact on the ECMWF will be through the EU Global Monitoring for Environment and Security MACC-II project (http://www.gmes-atmosphere.eu/). MACC combines state-of-the-art atmospheric modelling with Earth observation data to provide information services covering European Air Quality, Global Atmospheric Composition, Climate, and UV and Solar Energy. As part of MACC-I Leeds incorporated the GLOMAP aerosol model (the same as in HadGEM) in the ECMWF forecast model to enable future aerosol microphysics data assimilation and operational forecasts of aerosols and air quality. MACC makes a direct connection to the public and environmental agencies. The GASSP project will have an impact on the ECMWF by providing a comprehensive microphysics dataset to evaluate their model. Three months of the project has been set aside for this work at the end of the project (see Pathways to Impact).

Met Office: Engagement with the Met Office will continue through our ongoing NCAS-funded collaboration (UKCA). The data synthesis will provide a level of model evaluation superior to that of any other model, and will lead to improvement of the UK's climate modelling capability. Met Office staff will be invited to the GASSP data workshop in month 26. The workshop will produce a community publication on the observational needs for reducing the uncertainties in aerosol radiative forcing. This paper will have a long-term impact on Met Office model development.

The international Aerosol Model Intercomparison Project (AeroCom): AeroCom brings together the majority of global models in the world and its results have provided significant input to the IPCC concerning aerosol forcing of climate. In GASSP we will produce extensive harmonised datasets and new evaluation methodologies that can be taken up immediately by AeroCom. In our Pathways to Impact activity we are proposing to hold a session at an AeroCom workshop to discuss how our new knowledge on model uncertainty can be exploited by AeroCom. Through this activity, GASSP can have an impact on model evaluation and future development internationally.