AERONET in the context of

aerosol remote sensing from space

and

aerosol global modeling

Stefan KinneMPI-Meteorology, Hamburg Germany

Overview

• AERONET statistics – a sampler

• AERONET and satellite data• Validation of Satellite Data ()• Evaluation or Regional Representation ()

• AERONET and global modeling• Evaluation of Model Simulation ()• Data on composition and vertical profile ()

• state in global modeling and AeroCom

AERONET statisticsmonthly average properties

• a sampler for three sites:• GSFC (near Washington DC) ‘urban’ • Mongu (Zambia) ‘biomass’ [Jul-Nov]• Cape Verde (west of Sahara) ‘dust’

– measured properties (aot, alfa)– derived properties (absorption, size)– value-added properties (forcing, lidar ratio)

• locally – aerosol can be defined well

urban

absorption: 10*aot*(1-0)lidar ratio: for spheres too small for non-spheresToA Forcing: clr-sky [W/m2]

biomass

absorption: 10*aot*(1-0)lidar ratio: for spheres too small for non-spheresToA Forcing: clr-sky [W/m2]

70%PDFvalue

30%PDFvalue

dust

absorption: 10*aot*(1-0)lidar ratio: for spheres too small for non-spheresToA Forcing: clr-sky [W/m2]

the consistency among data allows combination for global assessment

this example:seasonal avg.

for aerosol absorption

[ * (1-0)]

interesting…

… AERONET indicates more absorption by aerosol over Europe than over east- US

US Europe

AERONET and

other aerosol data-sets

• Aeronet data can ‘help’ ()

• Aeronet data can ‘learn’ ()

– examples are introduced next

AERONET satellite data

satellite aot data (aerosol optical thickness or depth)

• what is available ? what is best?

Satellite Advantage Disadvantage---------------------------------------------------------------------------------------------------------AVHRR historic record calibration, not over landTOMS historic record 50km pixel, height or

absorption neededMODIS small pixel failure over desertsMISR altitude info temporally sparePOLDER short record, land: less

sensitive to large sizesSEAWIFS not over land, no IR channelsGOES or high temporal lack of detail with broad MSG

resolution bands, land limitations

aot - global yearly averages

with all data available

normalized by model to offset sampling biases

00.050.1

0.150.2

0.25

aot [at 550nm]

AERONET"best" MO/MIMO/TOMODISMISRTOMSAVHRRPOLDER

00.050.1

0.150.2

0.25

aot [at 550nm]

MODISMISRTOMSAVHRRPOLDER

AERONET haslower aots than satellite retrievals a clear-sky bias?

comparisons or annual patternMo: MODIS composites:Mi: MISR 12:Mo,MiTo: TOMS 13:Mo,ToAv: AVHRR Po: POLDER

Ae:Aeronet

difficult to depict a best global retrievalcomposite needed

a MODIS (ocean) MISR (land) combination seems promising … …but differences to AERONET still exist

local comparisons to AERONET

• choice : MI / MO

still …

generally larger than AERONET, particular in urban regions

…but seasonal data show biomass burning aots are too low

seasonal comparisons at AERONET

first impressions

• MODIS best choice over the oceans … but too low in dust outflow regions (high aot clouds)

• MISR most complete land cover … while biased high over oceans

• MODIS (ocean) / MISR (land) combination the ‘best’ satellite product is generally larger than AERONET … but too low during biomass burning

open issues:• are AERONET aot smaller due to a clear-sky bias?• what can be said about the quality of retrievals of

low aot in remote regions (of no AERONET sites?)• is it ‘fair’ to compare point data with regional data?

satellite data AERONET

• use spatial information of satellite data– to relate local measurement detail to • coarse gridded data-sets• coarse resolution data in global modeling

• how ?– compare averages for different scales• agreement … indicates a ‘useful’ site • bias: ‘useful’ site after a bias adjustment• highly variable (season/years) : leave off

comparison … unless secondary data exist

“scaling”• Comparison of

– 300*300km data– 100*100km data– 10*10km data

• GSFC (urban)– 20% above the

regional average

• Mongu (biomass)– good match for the

biomass season (Jul-Nov)

at the bottom are AERONET-MODIS comparisons (2001) note: MODIS statis- tics are very poor!

MODIS

AERONET

needed scaling activities

• for different spatial domains a data-base of simultaneous satellite retrievals over AERONET sites is needed

• satellite requirements:• small (~1km) pixel retrievals at regional coverage• sufficient data (for seasonal /annual dependence)• coverage of all AERONET sites (incl. desert sites)

MODIS and MISR data are a start … although their smallest pixels size at 10.0 and 17.6 km is too large to represent ‘truly’ local characteristics

AERONET global modeling

• pick 20 sites (well spread globally)• compare

• aerosol optical depth• sub-micron sized aerosol optical depth• aerosol mass (note AERONET: wet mass, Models: dry mass)• sub-micron sized mass• refractive index, imaginary part• …

• identify large disagreements• identify poor concepts • eliminate poor concepts (or poor models)

white rings: AERONET aot smaller than model average

black rings: AERONET aot larger than model average

color-coded compositional fractions as predicted by global models at selected AERONET sites

aerosol optical depth comparisons

beyond annual aot averages

• differences among (15) models and to AERONET are better understood on a monthly basis

– test seasonality major ‘enhancements’• MARCH dust (Africa and Asia)• JUNE urban aerosol (NH)• SEPTEMBER biomass burning (SH)• DECEMBER biomass burning (trop. Africa)

– identify monthly outliers in modeling

aerosol optical depth in March

aerosol optical depth in June

aerosol optical depth in September

aerosol optical depth in December

aerosol optical depth comparisons for selected month

anthropogenic= smaller sizes = SU + OC + BC

optical depth comparisons for selected month for submicron size aerosol

aerosol mass comparisons (Models: dry, AERONET: wet)

AERONET over-estimates expectedin humid conditions

aerosol mass comparisons for selected months (Models: dry, Aeronet: wet)

mass comparisons for submicron size aerosol (Models: dry, AERONET: wet)

Anthropogenic= smaller sizes ?= SU + OC + BC ?

aerosol refractive index imaginary part (absorption) (Models: dry, AERONET: wet)

modeling and AeroCom

• AeroCom http://nansen.ipsl.jussieu.fr/Aerocom

– validate against data!• surface concentrations (IMPROVE, EMEP, GAW)• surface remote sensing (AERONET, EARLINET)• remote sensing from space (MODIS, MISR)

– 14 groups participate so far• A: ‘best as you can’ – simulation• B: yr 2000 simulation with prescribed emissions• C: yr 2000 simulation with pre-industrial emissions

– to address anthropogenic ‘forcing’

global modeling AERONET

compositional is the dominant component captured?detail is the seasonality captured?

is the anthropogenic fraction correct?

• Aeronet • component combined totals• vertical integrated column properties

• Modeling• detail on compositional mixture• detail on vertical distribution

…still modeling though

3 examples of AERONET sites dominated by a particular type

dust biomass (carbon) urban (sulfate)

Aerosol modules agree better on total aot than in terms of aot composition

AERONET can help identify unusual model behavior

Summary

• large differences in component aerosol modules in global modeling

• annual averages hide larger seasonal differences • global averages hide larger regional difference • component totals hide larger diff. among compo.• integrated properties (e.g. forcing) hide larger

differences on a sub-level basis (e.g. mass)

• AERONET provide constraints to models• for aot (and locally even for component aots)• for other aerosol properties (e.g. size, mass,

absorption) only at lower certainty

Message

• anthropogenic impact of aerosol on climate needs to be better quantified (reduce uncertainties)

• uncertainties in aerosol forcing (the end product in

modeling) do not represent ‘actual’ uncertainties• model differences at intermediate processing

steps and on different scales are much larger

• AERONET can provide needed constraints … …especially in conjunction with space data and value-added modeling (e.g. inversions)

Outlook

• tighter and stronger links (via selective sampling) between AERONET and remote sensing from space are needed

• to extend detail on size and composition to remote sensing from space• to identify sites with regional representation

needed for evaluations in global modeling

• complementary information on vertical distribution is highly desirable

... and we keep our fingers crossed for the A-train

extras

AERONET statistics

statistics for1998-2001

absorption = 10* aot*(1-0)