numerical diffusion in sectional aerosol modells stefan kinne, mpi-m, hamburg stefan.kinne@zmaw.de...

Numerical diffusion in sectional aerosol

modells

Stefan Kinne, MPI-M, Hamburg

stefan.kinne@zmaw.de

DATA in global modeling

aerosol climatologies&

impact of clouds

MODELING needs DATA

data to initialize modeling

data to evaluate modeling

INPUT MODEL OUTPUT

DATADATA

MODELING needs DATA

data to initialize modeling AEROSOL REPRESENTATION

data to evaluate modeling

INPUT MODEL OUTPUT

EM

DATA

aerosol – complexity to modeling

aerosol (‘small atmos.particles’)

many sources short lifetime diff. magnitudes in size changing over time

aerosol cloudsaerosol chemistryaerosol biosphereaerosol aerosol

ocean

desertindustry cities

volcano forest

rapidatmospheric

‘cycling’

highly variablein space and time !

modeling shortcut needs for radiative transfer simulation

single scattering properties at all model spect.bands aerosol optical depth attentuation (scatter +absorption) single scattering albedo scattered fraction asymmetry-factor scattering behavior

concept improve ensemble average ‘ssp’ monthly fields

from global modeling* with quality local stats *** median of 20 global models (with detailed aerosol

modules) participating in AeroCom excercises **AERONET: global sun-/sky- photometer network

extend data spectrally with ‘smart’ assumptions samples at 0.55m (visible) and 11.2m (IR-window)

adopt vertical distribution from global modeling

aerosol opt. properties AOD aerosol optical depth annual fields SSA single scattering albedo (…of monthly data) ASY asymmetry-factor

h h h h

natural and anthropogenic previous fields are based on yr 2000 emissions

AOD can be split into those of coarse sizes (> 1m) and those of accumulation mode sizes (< 1m) assuming a bi-modal size-distribution shape use the AOD spectral dependence (by pre-defining a fine

mode Angstrom parameter as function of low cloud cover)

coarse mode AOD is assumed to be all natural no anthropogenic IR effect (anthropogenic dust neglected) distinction between SEASALT and DUST via visible SSA

accumulation mode AOD is partly natural and partly anthropogenic AOD fraction estimates are derived from comparisons of

simulationed accumulation mode AODs with yr1750 and yr 2000 emissions (AeroCom excercises)

annual fields ofmonthly data

summary what these data can do for you

simple method to include aerosol in simulations not just amount … but also size and absorption monthly (seasonal) variations are considered typical environmental conditions are considered separation into natural and anthrop. components

what these data can NOT do no interaction with simulated dynamics

humidity, clouds … no response to unusual emissions

surface wind speed anomaly scaling ?

where to get the data contact stefan.kinne@zmaw.de anonymous ftp ftp-projects.zmaw.de

MODELING needs DATA

data to initialize modeling

data to evaluate modeling CLOUD IMPACT on broadband radiative fluxes

INPUT MODEL OUTPUT

DATA

model - validationtesting the impact (on the radiative budget) of CLOUDS

major impact, highly variable the main modulators of climate

how well are clouds simulated in ECHAM5 ?

no atmosphere

validation approach

global modeling is ‘tuned’ to the ToA impact

how well is the surface impact simulated? reductions to the solar down flux (opt.depth info) increases to the IR down flux (altitude/cover info)

‘participants’ SRB / ISCCP cloud climatology products (1984-2004)

(cloud data based on satellite observations)

cloud climatologies applied in RT modeling TOVS, HIRS, MODIS, ISCCP

IPCC (1980-2000) (20 models … including ECHAM5)

focus: (monthly) statistics of 1984-1995 average

ECHAM5 - IPCC

Sdt solar dn all-sky flux at top-of-atmosphere Sut solar up all-sky flux at top-of-atmosphere Sds solar dn all-sky flux at surface Lds longwave dn all-sky flux at surface

ECHAM5 - IPCC

cloud effect = ‘all-sky flux’ minus ‘clear-sky flux’ on surface fluxes

solar (shortwave) dn all-sky flux at surface ’Sds’ minus ’sds’ IR (longwave) dn all-sky flux at surface ’Lds’ minus ‘lds’

solar IR

‘data-tied’ Cloud Effect References

SRB surface radiation budget (GEWEX)

ISCCP intern. satellite cloud climatology project

NO certain reference !

all-sky all-sky

all-sky

SRB ECHAM5ISCCP

12 year average (1984 -1995)

ECHAM5 solar diff. to SRB

IR monthly diff. to SRB

initial assessment deviations of cloud-effect at surface

SOLAR info on cloud optical depth more negative more cloud opt. depth / cover

IR info on altitude of lower clouds more negative higher clouds or less opt.depth /cover

MPI has overall higher cloud optical depth esp. May-August

higher opt. depth: at high-latitudes in (NH) summer lower opt. depth: off-coastal stratus, ITCZ,

Asia

overall higher altitude / lower fract of low clouds e.g.: less re- radiation to surface in (sub-) tropics despite more re- radiation to surf. at high latitudes

final thoughts

useful data are collected on an opportunity basis e.g. http://disc.sci.gsfc.nasa.gov/techlab/giovanni/

near-term focus on Calipso / A-train data clues for parameterization in global modeling

data quality must be explored (are data useful ?)

e.g. are the satellite cloud climatology products of SRB and ISCCP consistent ?

support by institute and MPG is appreciated !

EXTRAS

cloud effect - solar dn ECHAM5

cloud effect - IR dn ECHAM5

LOGO 1

COSMOS

LOGO 2

CO MO

S

LOGO 3

COS MOS