Monthly Averages of Aerosol Properties A global comparison among Models , Satellite retrievals and AERONET ground data S.Kinn e J. Feichter U. Lohmann M. Schulz S. Ghan R. Easter M. Chin P. Ginoux T. Takemura S. Ghan I. Tegen D. Koch M. Herzog J. Penner G. Pitari W. Collins P. Rasch O. Torres I. Geogdzhayev M. Mishchenko L. Stowe A. Goulomb D. Tanre A. Chu Y. Kaufman B. Holben T. Eck A. Smirnov O. Dubovik I. Slutsker
Monthly Averages of Aerosol Properties. J. Feichter U. Lohmann M. Schulz S. Ghan R. Easter M. Chin P. Ginoux T. Takemura. S. Ghan I. Tegen D. Koch M. Herzog J. Penner G. Pitari W. Collins P. Rasch. B. Holben T. Eck A. Smirnov O. Dubovik I. Slutsker. O. Torres I. Geogdzhayev - PowerPoint PPT Presentation
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Monthly Averages of Aerosol Properties
A global comparison among
Models , Satellite retrievals and AERONET ground data
S.Kinne
J. FeichterU. LohmannM. SchulzS. GhanR. EasterM. ChinP. GinouxT. Takemura
S. GhanI. TegenD. KochM. HerzogJ. PennerG. PitariW. CollinsP. Rasch
O. TorresI. GeogdzhayevM. MishchenkoL. StoweA. GoulombD. TanreA. ChuY. Kaufman
B. HolbenT. EckA. SmirnovO. DubovikI. Slutsker
2
Climate Research and AEROSOL
AEROSOL has
one of the largest uncertainties in climate research
very low level of scientific understanding
even the sign of its climatic effect is in question
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Why these uncertainties
• Measurements ‘fail’ on global and long-temporal scales
• sparse sampling rate nature of measurements
• aerosol signals are often too small (natural variability?)
• no distinction between natural and anthropogenic contributions
• still needed to establish a framework for model-assumptions
• Modeling ‘works’ always
• but ... how well is aerosol characterized or processed ?
• but ... how well are feedbacks accounted for?
New Aerosol Climate Modeling
modeling of aerosol climatic impacts is done
- at coarse resolution (ca 30x30)
- in many individual steps
- individually by aerosol type
many processes
possibilities for errorsLets look at aerosol optical depth
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Aerosol Optical depth of visible light
• Cloud-free attenuation of visible sunlight due to
– Scattering (minus scattering on air-molecules) and
– Absorption (minus absorption by gases, mainly ozone)
• easily imaginable (clarity of the sun’s disk)
• measured from the ground (‘directly’ or ‘total-diffuse’)
• commonly retrieved from cloud-free satellite-scenes
• an integral parameter in forcing simulations (‘Step 3’)
• Comparisons based on monthly statistics focus on:– Strength
– Patterns
– Seasonality
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DATA
Name Method Simulation Authors
• MODIS (.44/.67/1.6/2.2m) refl (2001) Chu /Remer /Kaufman /Tanre
• AERONET (.44/.87m) attenu. (1994-2002) Holben /Eck /Smirnov– All data-sets are ‘normalized to .55m wavelength– Resolution of all data-sets is degraded to 10*10 horizontal resolution
– sulfate, organic carbon, black carbon, dust, sea-salt
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Models UL ULAQ GI GISS GO GOCART GR Grantour EC ECHAM4-old CC CCSR MI MIRAGE GO6 Gocart’96 GO7 Gocart’97 HA HadHam (no oc/ water) MP ECHAM4-new NC NCAR
Initial Impression
Similarities for patterns
Differencesin strength
- for yearly average (left)
- for season range (right)
no aerosol water
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Models
• variations due to simulations for different years are small compared to model-differences
• source aot-patterns agree better than source aot-amount
• transport / removal strength often differ
• different sensitivity to high relative hum (at higher latitudes)
• comparison to data have (are) improving models (eg. EC MP)
• some differences are better understood on component level
AERONET
no aerosol water
Relative Model Tendencies
• ECHAM4 strong bc-, du- seasonality, rh-sensitivity, low bc MEE
• GOCART strong oc-, bc- seasonality, strong su-, du -transport, ss
• MIRAGE strong su mass and transport, weak du, high lat. bias
• GISS low mass (except su), strong du MEE, strong su transport