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ATMOSPHERIC CHEMISTRY MODELS Daniel J. Jacob Harvard University http://www-as.harvard.edu/chemistry/trop

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Title: ATMOSPHERIC CHEMISTRY MODELS Daniel J. Jacob Harvard University http://www-as.harvard.edu/chemistry/trop


1
ATMOSPHERIC CHEMISTRY MODELS Daniel J.
Jacob Harvard University http//www-as.harvard.edu
/chemistry/trop
2
OBJECTIVE OF ATMOSPHERIC CHEMISTRY MODELS
QUANTIFY THE CONCENTRATIONS AND FLUXES OF
ATMOSPHERIC SPECIES IN TIME AND SPACE
Lightning
MEASURES OF ATMOSPHERIC CONCENTRATIONS Number
density ni (x, t ) molecules cm-3 Mixing
ratio (mole fraction) Ci (x, t) mol/mol
physics chemistry biology
Human activity
Fires
Land biosphere
Volcanoes
Ocean
3
CONTINUITY EQUATION FOUNDATION OF ATMOSPHERIC
CHEMISTRY MODELS
advection
diffusion
chemistry, emissions, deposition
accumulation
temporal change in concentration in elemental
volume
Mass flux divergence in elemental volume (flux
in flux out) U wind vector D molecular
diffusion coefficient
Production and loss rates in elemental volume
  • Molecular diffusion is negligible relative to
    advection on scales gt 1 cm
  • Equation is given here in Eulerian form (fixed
    frame of reference) Lagrangian form (frame of
    reference moving with air) will be discussed later

4
CONTINUITY EQUATION CANNOT BE SOLVED EXACTLY
  • because transport is turbulent (stochastic
    high-frequency fluctuations)
  • because we do not have perfect information on
    transport (even time-averaged), emissions,
    chemistry, deposition
  • because the scales of variability range from
    10-3 to 107 m.
  • Solution requires a model simplified
    representation of complex system.

Design model make assumptions needed to
simplify problem (computational
resources, physical clarity)
Define problem of interest
Evaluate model with relevant observations
model development loop
Improve model, characterize its error
Apply model make hypotheses, predictions
5
DISCRETIZATION OF CONTINUITY EQUATION IN SPACE
PARTITION ATMOSPHERIC DOMAIN INTO GRIDBOXES
Solve continuity equation for individual gridboxes
  • Full chemistry/aerosol models can presently
    afford 105-106 gridboxes
  • In global models, this implies a horizontal
    resolution of 100-500 km in horizontal and 1 km
    in vertical

6
DISCRETIZATION OF THE CONTINUITY EQUATION IN
TIME OPERATOR SPLITTING
  • Split the continuity equation into contributions
    from different processes

and integrate each process separately over
discrete time steps
where
(similar forms for the other operators)
7
THE TRANSPORT OPERATOR parameterization of
turbulence
  • Consider 1-dimensional transport with wind u
  • Both u and ni have turbulent fluctuations over
    time interval Dt

Fluctuating component ltugt 0
Time-averaged component
  • Time-averaged flux ltunigt has a turbulent
    component

Mean advective flux
Turbulent flux (covariance of u and n)
  • Parameterize turbulent flux as diffusion process
    (diffusion coefficient K)

and replace in 3-D continuity equation. This is
1st-order closure for turbulence
8
TURBULENT COMPONENT DOMINATES VERTICAL FLUX IN
LOWER ATMOSPHERE
Example CO2 flux observations at Harvard Forest,
Massachusetts
small large
vertical wind w
T
CO2
9
THE TRANSPORT OPERATOR parameterization of
convection
Convective cloud (0.1-100 km)
Convection is subgrid scale in global models and
must be treated as a vertical mass exchange
separate from transport by grid-scale
winds. Need info on convective mass fluxes from
the model meteorological driver.
detrainment
Model vertical levels
downdraft
updraft
entrainment
Model grid scale
10
THE CHEMICAL OPERATOR consider system of n
interacting species
Solve system of n coupled ordinary differential
equations for species
System is typically stiff (lifetimes range over
many orders of magnitude) Aimplicit solution
method is necessary.
  • Simplest method backward Euler. Transform into
    system of m algebraic equations with m unknowns

Solve e.g. by Newtons method. Backward Euler is
stable, mass-conserving, flexible (can use other
constraints such as steady-state, chemical family
closure, etc in lieu of Dn/Dt). Unfortunately
it is expensive (inversion of nxn matrix at each
time step). Use it in 0-D calculations!
  • Most 3-D models use the Gear method, which is a
    higher-order implicit solution

11
DEPOSITION PROCESSES dry deposition
  • Dry deposition describes uptake at Earths
    surface by chemical reaction, absorption, or
    collision (aerosols)

concentration in lowest model level
Deposition flux Vd n1
deposition velocity (cm s-1)
  • Use resistance-in-series model for dry
    deposition Vd 1 / (Ra Rc)

Lowest model level (z1)
n1
Aerodynamic resistance to turbulent transport
Ra z/K (units s cm-1)
no
SURFACE
Surface resistance Rc to uptake
12
DEPOSITION PROCESSES wet deposition
  • Soluble gases (KH gt 104 M atm-1), aerosols are
    efficiently scavenged by clouds and precipitation

nucleation
diffusion (gases, aerosols)
impaction
large and small aerosol particles
  • Our ability to model wet scavenging is limited
    mostly by the quality of the precipitation data
  • where it rains
  • subgrid extent of precipitation, wet convection
  • Also need better understanding of ice processes

13
Eulerian research models use assemblages of boxes
exchanging mass to resolve spatial structure
LAGRANGIAN vs. EULERIAN MODELING APPROACHES
Lagrangian research models use assemblages of
traveling puffs not exchanging mass, and sum over
all puff trajectories to resolve spatial structure
ni(x,toDt)
ni(x,to)
14
HOW CAN WE USE ATMOSPHERIC OBSERVATIONS TO
IMPROVE MODELS?
ISSUES
  • Observed variables (e.g., concentrations) may be
    different from the state variables for which we
    want to improve our knowledge (e.g., emissions)
  • Observations may not be in the right places, or
    may be subject to errors that reduce the
    information they contain.
  • Trivial example let us improve our estimate of
    variable x by making a direct measurement
  • Before we make the measurement, we have an a
    priori estimate
  • xa sa for its value
  • The measurement indicates a value xm sm
  • What is our best estimate of x after the
    measurement? Minimize a cost function

(least-squares)
Our new best estimate is
with error
15
INVERSE MODELING GENERALIZATION OF CONCEPT
  • Consider a state vector x, observation vector y
    which is linear function of x
  • K is a Jacobian matrix from our atmospheric
    chemistry model (termed the forward model). If
    model is not linear, linearize it about a ref.
    point
  • e is the observational error vector
  • Let Sa, Se be the error covariance matrices on
    the a priori xa and on the observations y then
    the best estimate of x after the observations is

with error covariance matrix
Chemical data assimilation follows the same
principle with x y (optimize gridded field of y
from observations of y). Method is then called
Kalman filter. In advanced data assimilation,
one wishes to optimize y(to) from multiple
observations at t to, toDt in a
non-linear model this requires local
linearization at t with a tangent linear (or
adjoint) model.
16
SOME APPLICATIONS USING THE GEOS-CHEM GLOBAL 3-D
MODEL OF TROPOSPHERIC CHEMISTRY (http//www-as.har
vard.edu/chemistry/trop/geos)
  • Meteorological input fields from NASA/DAO
    assimilated data, 1988-present 1ox1o to 4ox5o
    horizontal resolution, 20-48 vertical levels
  • Ozone-NOx-CO-hydrocarbon chemistry, aerosols,
    CH4, CO2 up to 80 interacting species depending
    on application
  • Applied to a wide range of problems, e.g.,
  • Testing of atmospheric transport with chemical
    tracers
  • Long-range transport of pollution
  • Support of aircraft missions
  • Satellite retrievals
  • Inversion of sources

17
METHYL IODIDE TRACER OF MARINE CONVECTION IN
GLOBAL ATMOSPHERIC MODELS Loss by photolysis
(4 days), relatively uniform ocean source,
large aircraft data base D.R. Blake, UCI
Observations Model (GEOS-CHEM)
MCI 0.40 (obs) 0.22 (mod)
MCI 0.16 (obs) 0.14 (mod)
  • Define Marine Convection Index (MCI) as ratio of
    upper tropospheric (8-12 km)
  • to lower tropospheric (0-2.5 km) CH3I
    concentrations
  • MCI over Pacific ranges from 0.11 (Easter Island
    dry season) to 0.40 (observations over tropical
    Pacific)
  • GEOS-CHEM reproduces observed MCI with little
    global bias (11) but poor correlation (r2
    0.15, n11)

Bell et al. 2002
18
LONG-RANGE TRANSPORT OF POLLUTION SURFACE OZONE
ENHANCEMENTS CAUSED BY ANTHROPOGENIC EMISSIONS
FROM DIFFERENT CONTINENTS
GEOS-CHEM model, July 1997
North America
Europe
Asia
Li et al. 2001
Li et al. 2002
19
COLUMN MEASUREMENT OF AN ABSORBING GAS USING
SOLAR BACKSCATTER
absorption
Backscattered intensity IB
l1
l2
wavelength
l1, l2
Slant optical depth
ATMOSPHERE
Slant column
Scattering by Earth surface and by atmosphere
EARTH SURFACE
20
AIR MASS FACTOR (AMF) CONVERTS SLANT COLUMN WS
TO VERTICAL COLUMN W
Geometric AMF (AMFG) for non-scattering
atmosphere
q
EARTH SURFACE
21
IN SCATTERING ATMOSPHERE, AMF CALCULATION REQUIRES
MODEL INFORMATION ON THE SHAPE OF THE VERTICAL
PROFILE
RADIATIVE TRANSFER MODEL
ATMOSPHERIC CHEMISTRY MODEL
z
IB
Io
dt(z)
EARTH SURFACE
number density n(z)
Scattering weight
Shape factor
Palmer et al. 2001
22
ATMOSPHERIC COLUMNS OF NO2 AND FORMALDEHYDE
(HCHO) MEASURED BY SOLAR BACKSCATTER FROM
GOME ALLOW MAPPING OF NOx AND HYDROCARBON
EMISSIONS
but model info is needed for the vertical
distributions of NO2 and HCHO
GOME SATELLITE INSTRUMENT
Tropospheric NO2 column ENOx Tropospheric HCHO
column ENMHC
2 km
hn (420 nm)
hn (340 nm)
BOUNDARY LAYER
NO2
NO
HCHO
OH
CO
hours
O3, RO2
hours
NMHC
1 day
HNO3
Emission
Emission
Deposition
NITROGEN OXIDES (NOx)
NON-METHANE HYDROCARBONS
23
CAN WE USE GOME TO ESTIMATE NOx EMISSIONS? TEST
IN U.S. WHERE GOOD A PRIORI EXISTS
Comparison of GOME retrieval (July 1996) to
GEOS-CHEM model fields using EPA emission
inventory for NOx
GOME
GEOS-CHEM (EPA emissions)
BIAS 3 R 0.79
Martin et al. 2002
24
GOME RETRIEVAL OF TROPOSPHERIC NO2 vs. GEOS-CHEM
SIMULATION (July 1996)
Martin et al. 2002
GEIA emissions scaled to 1996
25
FORMALDEHYDE COLUMNS FROM GOME July 1996 means
Palmer et al. 2001
BIOGENIC ISOPRENE IS THE MAIN SOURCE OF HCHO IN
U.S. IN SUMMER
26
MAPPING OF ISOPRENE EMISSIONS FOR JULY 1996 BY
SCALING OF GOME FORMALDEHYDE COLUMNS Palmer et
al., 2002
GOME
COMPARE TO
GEIA (IGAC inventory)
BEIS2
27
PROGRESS IN ATMOSPHERIC CHEMISTRY
REQUIRES INTEGRATION OF MEASUREMENTS AND MODELS
SATELLITE OBSERVATIONS Global and continuous but
few species, low resolution
Source/sink inventories
3-D CHEMICAL TRACER MODELS
SURFACE OBSERVATIONS high resolution but
spatially limited
Assimilated meteorological data
AIRCRAFT OBSERVATIONS High resolution, targeted
flights provide critical snapshots for model
testing
Chemical and aerosol processes
QUANTITATIVE PREDICTIONS
28
NASA TRACE-P aircraft mission over western
Pacific(Mar-Apr 2001)
Satellite data in near-real time MOPITT TOMS SEAW
IFS AVHRR LIS
Stratospheric intrusions
FLIGHT PLANNING
Long-range transport from Europe, N. America,
Africa
3D chemical model forecasts - ECHAM -
GEOS-CHEM - Iowa/Kyushu - Meso-NH -LaRC/U.
Wisconsin
ASIAN OUTFLOW
Boundary layer chemical/aerosol processing
DC-8
P-3
PACIFIC
  • Emissions
  • Fossil fuel
  • Biomass burning
  • Biosphere, dust

ASIA
PACIFIC
29
FURTHER READING
  • Jacob, D.J., Introduction to Atmospheric
    Chemistry, Princeton University Press, 1999
  • Basic treatment of model design
  • Brasseur, G.P. et al. (eds), Atmospheric
    Chemistry and Global Change, Oxford University
    Press, 1999
  • Chap. 12 (Modeling) is concise and excellent
  • Seinfeld, J.H., and S. Pandis, Atmospheric
    Chemistry and Physics, Wiley, 1996
  • Excellent insights into modeling principles
  • Jacobson, M.Z., Fundamentals of Atmospheric
    Modeling, Cambridge University Press, 1999
  • Excellent presentations of modeling techniques
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