1. Do better aerosol forecasts improve weather forecasts? A regional modeling and assimilation study. Mariusz Pagowski Stuart McKeen Georg Grell Ming Hu NOAA/ESRL, Boulder, CO 1

2. Parallels between meteorology and air quality/chemical forecasting and data assimilation Scarcity of observations, especially in the vertical both for model verification and data assimilation and for aerosol species and sizes Over North America dense surface network of PM2.5 plus MODIS/VIIRS AOD around 18Z. Parallel in meteorology: given precipitable water and 2m QV provide vertical profile of water vapor, cloud water, rain water, cloud ice … Multiple state variables (in tens) and parameterizations have large uncertainties. Results very dependent on source emissions, error estimates in surface emissions 50-200% Parallel in meteorology: not unlike surface fluxes but much less controlled by physical environment (atmosphere, soil).

3. Effects of aerosol on weather/climate 3 Direct: Absorb/Scatter shortwave radiation; Radiate in long-wave range (negligible except for extreme conditions e.g. soot). Semi-direct: Through surface energy balance change surface temperature, wind, atmospheric stability. Indirect (hydrophilic aerosols): Increase number concentration -> decrease cloud drop size; Increase liquid water content & cloudiness, affect precipitation amount/rate. Incoming surface shortwave radiation is little sensitive to vertical distribution of aerosols so AOD is adequate for assessing magnitudes of direct and semi-direct effects.

4. Modeling period: June 1 – Aug 31, 2012, 9-hr forecasts 4 Meteorology 24km grid size, 40 levels YSU PBL NOAH soil Lateral boundary conditions from NMM Chemistry GOCART(prognostic 15 aerosol species) Lateral boundary conditions from MOZART or ECMWF’s IFS Forest fires inventory from NCAR Aerosol-radiation feedback on/off. Assimilation - 3D-Var Meteorology: prepbufr Chemistry: AIRNow surface PM2.5 and PM10, NASA NNR AOD (combines MODIS Aqua and Terra AOD 550 nm Level 2 with AERONET) 6hr cycle 24-hour average PM 2.5 concentrations for June 29, 2012 obtained from hourly analyses.

5. Aerosol observations 5 Surface PM2.5 Assimilation + evaluation AERONET UV: 340, 380 nm VIS: 440, 500, 675 nm IR: 870, 1020, 1640 nm Evaluation

6. Aerosol assimilation: verification statistics against surface PM2.5 June 1 – Aug 31, 2012 6 without (blue)/with (red) aerosol assimilation Significant improvement but forecasts quality deteriorates quickly.

7. Aerosol assimilation: verification statistics against surface PM2.5 June 1 – Aug 31, 2012 7 without/with aerosol assimilation bias correlation

8. 8 Aerosol assimilation: verification statistics against surface PM2.5 June 1 – Aug 31, 2012 without (blue)/with (red) AOD NNR Satellite pass over domain only within 18Z window; AOD NNR slightly better than AOD L2

9. 9 550nm AOD verification MOZART ECMWF’s IFS AnalysisAnalysis Fcst6hFcst6h

10. 10 AERONET AOD verification MOZARTECMWF’s IFS 440nm440nm 500nm500nm 870nm870nm Bias Correlation

11. 11 AOD verification τ= τ 0 (λ 0 /λ) α, α – Angstrom exponent, radius of particle increases -> α – decreases ECMWF: τ overestimated for longer wavelength -> probably to high concentration of large particles - dust MOZART: the opposite - τ underestimated for shorter wavelength e.g. too low concentration of black carbon Forecasts deteriorate on a timescale that is somewhat but not significantly longer than surface forecasts. AOD dependent on lateral boundary conditions. Assimilation of AOD has little effect on surface forecasts where network of surface observation is dense though it might improve surface forecasts if observations are sparse or missing. Assimilation of surface observations only little improves AOD. AOD can be “detached” from surface if aerosols at higher levels. Assimilation of different bands and other derived satellite products (e.g. AAOD) can be beneficial to improve speciation and size distribution of aerosols.

12. Meteorology – sanity checks 12 prior (6h) posterior

13. Meteorology – sanity checks 13 prior (6h) posterior

14. Meteorology – sanity checks 14 T2 Q2

15. Effects of aerosols on meteorology 15 T2Q2 stdev of difference with/without aerosol feedback

16. Effects of aerosols on meteorology 16 T2 Temperature

17. Effects of aerosols on meteorology 17 Q2 Water vapor mixing ratio

18. Conclusions 18 Only shortwave radiative effect considered – easiest to examine. Aerosol-microphysics interactions more complex, not likely to be well predicted because of poor aerosol forecasts plus large uncertainty in microphysics parameterizations. Impact of aerosols on meteorology can easily be demonstrated by modeling. The impact manifests itself most strongly through semi-direct effect. Despite better representation of aerosols as a consequence of the assimilation no positive effect on predicted meteorology under typical summer conditions over the Eastern USA. Limitations of regional models: Scarce satellite coverage; Strong influence of lateral boundaries of uncertain quality from global models on AOD; Global model more suitable for looking into aerosol impacts on weather/climate.