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PPT – ATMOSPHERIC CHEMISTRY MODELS Daniel J. Jacob Harvard University http://www-as.harvard.edu/chemistry/trop PowerPoint presentation | free to download - id: 63a437-YWU1M

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ATMOSPHERIC CHEMISTRY MODELS Daniel J.

Jacob Harvard University http//www-as.harvard.edu

/chemistry/trop

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

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

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

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

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)

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

TURBULENT COMPONENT DOMINATES VERTICAL FLUX IN

LOWER ATMOSPHERE

Example CO2 flux observations at Harvard Forest,

Massachusetts

small large

vertical wind w

T

CO2

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

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

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

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

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)

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

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.

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

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

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

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

AIR MASS FACTOR (AMF) CONVERTS SLANT COLUMN WS

TO VERTICAL COLUMN W

Geometric AMF (AMFG) for non-scattering

atmosphere

q

EARTH SURFACE

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

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

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

GOME RETRIEVAL OF TROPOSPHERIC NO2 vs. GEOS-CHEM

SIMULATION (July 1996)

Martin et al. 2002

GEIA emissions scaled to 1996

FORMALDEHYDE COLUMNS FROM GOME July 1996 means

Palmer et al. 2001

BIOGENIC ISOPRENE IS THE MAIN SOURCE OF HCHO IN

U.S. IN SUMMER

MAPPING OF ISOPRENE EMISSIONS FOR JULY 1996 BY

SCALING OF GOME FORMALDEHYDE COLUMNS Palmer et

al., 2002

GOME

COMPARE TO

GEIA (IGAC inventory)

BEIS2

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

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

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