AeroCom marine intercomparison

1. AeroCom marine intercomparison Eimear Dunne Finnish Meteorological Institute First MAGIC Science Workshop BNL, 6th May 2014

2. AeroCom marine intercomparison Eimear Dunne Finnish Meteorological Institute First MAGIC Science Workshop BNL, 6th May 2014

3. Outline • Marine CCN project • AeroCom marine intercomparison • Which models? • What data? • Polite request for any and all available marine data

5. Marine CCN project • Finnish Meteorological Institute project, funded by the Academy of Finland • PI: Hannele Korhonen Purpose: To improve our understanding of current and future sources of CCN in the marine atmosphere • Primary model: GLOMAP (University of Leeds) I will be giving a presentation on Thursday at 11am

6. AeroCom • “Aerosol Comparisons between Observations and Models” • Aerosol modellers submit simulations from their models with prescribed emissions etc. • Their performance is then compared against each other and against observations • As part of Marine CCN, I am coordinating a miniature AeroComintercomparison focusing on marine regions

7. Sectional versus modal models • Sectional models can capture the shape of the size distribution more accurately • Modal models are much less computationally intensive

8. Models included in the comparison • GLOMAP-mode • University of Leeds • Modal aerosol scheme in TOMCAT chemical transport model • HadGEM • Hadley Centre / UK Met Office • GLOMAP-mode implemented in HadGEM climate model • ECHAM-SALSA • University of Hamburg • Sectional aerosol scheme implemented in ECHAM climate model • CAM-Oslo • Norwegian Meteorological Institute • Sectional aerosol scheme implemented in CAM atmosphere model

9. Challenges from global models • Large grid box size means that in-situ measurements may be very different from the grid box average value in the model • This can be an especially difficult issue when a grid box contains both continental and marine regions • Global models cannot resolve sub-grid-scale processes and will use parameterisations to represent cloud-scale processes etc. • Some of these processes (like wet removal) are extremely important to aerosol concentrations, but aren’t explicitly resolved

10. Challenges from AeroCom • Data provided to the AeroCom database are monthly mean values for e.g. mode radius, number concentration etc. • Advantage: gets rid of a lot of noise • Disadvantage: if we’re looking at daily values of shipboard measurements, and the ship moves between grid-boxes… • Just got to do the best we can

11. What kind of data are we looking for? • Literally any marine-based aerosol measurements • The more remote, the more relevant to the project • But we are still interested in coastal measurements! • Already have several offers of Arctic/Antarctic measurements, so lower latitudes would be especially welcome • Aircraft are great, too – as long as they’re over the ocean! • We always use data within relevant data policy • Always interested in collaboration if you would like to compare observations with models

12. MAGIC data • MAGIC is exactly what we’re looking for – long-term, high-quality aerosol measurements in marine regions • We will try to fit the model investigation to whatever data are made available • Because the models are global, we’ll be using a grid-box average • The inclusion of statistics from long-term MAGIC measurements will improve the comparison • Things we are particularly interested in: • Size distributions • Compositions • AODs at different wavelengths

13. Data Please? Eimear.Dunne@fmi.fi

14. Discussion