GLOBAL MODELS OF ATMOSPHERIC COMPOSITION Daniel J Jacob

GLOBAL MODELS OF ATMOSPHERIC COMPOSITION Daniel J. Jacob Harvard University

HOW TO MODEL ATMOSPHERIC COMPOSITION? Solve continuity equation for chemical mixing ratios Ci(x, t) U = wind vector Eulerian form: Pi = local source Lightning of chemical i Lagrangian form: Transport Li = local sink Chemistry Aerosol microphysics Volcanoes Fires Human Land biosphere activity Ocean

EULERIAN MODELS PARTITION ATMOSPHERIC DOMAIN INTO GRIDBOXES This discretizes the continuity equation in space Solve continuity equation for individual gridboxes • Detailed chemical/aerosol models can presently afford -106 gridboxes • In global models, this implies a horizontal resolution of ~ 1 o (~100 km) in horizontal and ~ 1 km in vertical • Chemical Transport Models (CTMs) use external meteorological data as input • General Circulation Models (GCMs) compute their own meteorological fields

OPERATOR SPLITTING IN EULERIAN MODELS Reduces dimensionality of problem • Split the continuity equation into contributions from transport and local terms: … and integrate each process separately over discrete time steps: These operators can be split further: • split transport into 1 -D advective and turbulent transport for x, y, z (usually necessary) • split local into chemistry, emissions, deposition (usually not necessary)

SPLITTING THE TRANSPORT OPERATOR • Wind velocity U has turbulent fluctuations over time step Dt: Time-averaged component (resolved) Fluctuating component (stochastic) • Split transport into advection (mean wind) and turbulent components: advection turbulence (1 st-order closure) • Further split transport in x, y, and z to reduce dimensionality. In x direction: advection operator turbulent operator

SOLVING THE EULERIAN ADVECTION EQUATION • Equation is conservative: need to avoid diffusion or dispersion of features. Also need mass conservation, stability, positivity… • All schemes involve finite difference approximation of derivatives : order of approximation → accuracy of solution • Classic schemes: leapfrog, Lax-Wendroff, Crank -Nicholson, upwind, moments… • Stability requires Courant number u. Dt/Dx < 1 … limits size of time step • Addressing other requirements (e. g. , positivity) introduces non-linearity in advection scheme

VERTICAL TURBULENT TRANSPORT (BUOYANCY) • generally dominates over mean vertical advection • K-diffusion OK for dry convection in boundary layer (small eddies) • Deeper (wet) convection requires non-local convective parameterization Convective cloud (0. 1 -100 km) detrainment Model vertical levels updraft downdraft entrainment Model grid scale Wet 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.

LOCAL (CHEMISTRY) OPERATOR: solves ODE system for n interacting species For each species System is typically “stiff” (lifetimes range over many orders of magnitude) → implicit solution method is necessary. • Simplest method: backward Euler. Transform into system of n algebraic equations with n unknowns Solve e. g. , by Newton’s method. Backward Euler is stable, mass-conserving, flexible (can use other constraints such as steady-state, chemical family closure, etc… in lieu of DC/Dt ). But it is expensive. Most 3 -D models use higher-order implicit schemes such as the Gear method.

SPECIFIC ISSUES FOR AEROSOL CONCENTRATIONS • A given aerosol particle is characterized by its size, shape, phases, and chemical composition – large number of variables! • Measures of aerosol concentrations must be given in some integral form, by summing over all particles present in a given air volume that have a certain property • If evolution of the size distribution is not resolved, continuity equation for aerosol species can be applied in same way as for gases • Simulating the evolution of the aerosol size distribution requires inclusion of nucleation/growth/coagulation terms in Pi and Li, and size characterization either through size bins or moments. condensation nucleation coagulation Typical aerosol size distributions by volume

LAGRANGIAN APPROACH: TRACK TRANSPORT OF POINTS IN MODEL DOMAIN (NO GRID) • Transport large number of points with trajectories from input meteorological data base (U) + random turbulent component (U’) over time steps Dt • Points have mass but no volume • Determine local concentrations as the number of points within a given volume position to+Dt U’Dt position to UDt • Nonlinear chemistry requires Eulerian mapping at every time step (semi-Lagrangian) PROS over Eulerian models: • no Courant number restrictions • no numerical diffusion/dispersion • easily track air parcel histories • invertible with respect to time CONS: • need very large # points for statistics • inhomogeneous representation of domain • convection is poorly represented • nonlinear chemistry is problematic

LAGRANGIAN RECEPTOR-ORIENTED MODELING Run Lagrangian model backward from receptor location, with points released at receptor location only • Efficient cost-effective quantification of source influence distribution on receptor (“footprint”) • Enables inversion of source influences by the adjoint method (backward model is the adjoint of the Lagrangian forward model)

EMBEDDING LAGRANGIAN PLUMES IN EULERIAN MODELS Release puffs from point sources and transport them along trajectories, allowing them to gradually dilute by turbulent mixing (“Gaussian plume”) until they reach the Eulerian grid size at which point they mix into the gridbox S. California fire plumes, Oct. 25 2004 • Advantages: resolve subgrid ‘hot spots’ and associated nonlinear processes (chemistry, aerosol growth) within plume • Difference with Lagrangian approach is that (1) puff has volume as well as mass (2) turbulence is deterministic (Gaussian spread) rather than stochastic

GEOS-Chem GLOBAL 3 -D CHEMICAL TRANSPORT MODEL • Solves 3 -D continuity equations on global Eulerian grid using NASA Goddard Earth Observing System (GEOS) assimilated meteorological data (1985 -present) or GISS GCM output (paleo and future climate) • Horizontal resolution 1 ox 1 o to 4 ox 5 o, 48 -72 vertical layers • Used by ~30 groups around the world for wide range of atmospheric composition problems: aerosols, oxidants, carbon, mercury, isotopes… Illustrate here with Harvard work on tropospheric ozone

OZONE: “GOOD UP HIGH, BAD NEARBY” Nitrogen oxide radicals; NOx = NO + NO 2 Sources: combustion, soils, lightning Tropospheric ozone precursors Volatile organic compounds (VOCs) Methane Sources: wetlands, livestock, natural gas… Non-methane VOCs (NMVOCs) Sources: vegetation, combustion Carbon monoxide (CO) Sources: combustion, VOC oxidation

RADICAL CYCLE CONTROLLING TROPOSPHERIC OH AND OZONE CONCENTRATIONS GEOS-Chem simulation for tropospheric ozone includes 120 coupled species to describe HOx-NOx-VOC-aerosol chemistry O 2 hn O 3 STRATOSPHERE 8 -18 km TROPOSPHERE 400 global sources/sinks in Tg y-1 4300 O 3 hn, H 2 O 4000 Deposition NO 2 hn NO OH 700 HO 2 CO, VOCs SURFACE H 2 O 2

GLOBAL DISTRIBUTION OF TROPOSPHERIC OZONE Climatology of observed ozone at 400 h. Pa in July from ozonesondes and MOZAIC aircraft (circles) and corresponding GEOSChem model results for 1997 (contours). GEOS-Chem tropospheric ozone columns for July 1997. Li et al. , JGR [2001]

COMPARISON TO TES SATELLITE OBSERVATIONS IN MIDDLE TROPOSPHERE (July 2005) averaging kernels Zhang et al. [2006]

TES ozone and CO observations in July 2005 at 618 h. Pa North America Asia TES observations of ozone-CO correlations test GEOS-Chem simulation of Zhang et al. , 2006 ozone continental outflow

GEOS-Chem GLOBAL BUDGET OF TROPOSPHERIC OZONE Present-day Preindustrial O 2 Chem prod in troposphere, Tg y-1 Transport from stratosphere, Tg y-1 hn O 3 Burden, Tg STRATOSPHERE 8 -18 km 4300 1600 400 360 230 Chem loss in troposphere, Tg y-1 Deposition, Tg y-1 Lifetime, days 4000 1600 700 400 28 42 TROPOSPHERE O 3 hn, H 2 O NO 2 hn NO OH Deposition HO 2 CO, VOC H 2 O 2

IPCC RADIATIVE FORCING ESTIMATE FOR TROPOSPHERIC OZONE (0. 35 W m-2) RELIES ON GLOBAL MODELS …but these underestimate the observed rise in ozone over the 20 th century Preindustrial ozone models } Observations at mountain sites in Europe [Marenco et al. , 1994]

RADIATIVE FORCING BY TROPOSPHERIC OZONE COULD THUS BE MUCH LARGER THAN IPCC VALUE Global simulation of late 19 th century ozone observations [Mickley et al. , 2001] Standard model: DF = 0. 44 W m-2 “Adjusted” model (lightning and soil NOx decreased, biogenic hydrocarbons increased): DF = 0. 80 W m-2

IMPLICATION OF RISING BACKGROUND FOR MEETING AIR QUALITY STANDARDS Europe AQS (8 -h avg. ) Europe AQS (seasonal) 0 Preindustrial ozone background 20 40 U. S. AQS (8 -h avg. ) 60 80 U. S. AQS (1 -h avg. ) 100 120 ppbv Present-day ozone background at northern midlatitudes Shutting down N. American anthropogenic emissions in GEOS-Chem reduces frequency of European exceedances of 55 ppbv standard by 20%

The U. S. EPA defines a “policy-relevant background” (PRB) as the ozone concentration that would be present in U. S. surface air in the absence of N. American anthropogenic emissions • This background cannot be directly observed, must be estimated from models • Because chemistry is strongly nonlinear, sensitivity simulations are necessary (1) Standard simulation; include all sources (2) Set U. S. or N. American anthropogenic emissions to zero a infer policy-relevant background (3) Set global anthropogenic emissions to zero a estimate natural background Difference between (1) and (2) a regional pollution Difference between (2) and (3) a background enhancement from hemispheric pollution

Summer 1995 afternoon (1 -5 p. m. ) ozone in surface air over the U. S. Observations GEOS-CHEM standard simulation Fiore et al. [2002] r = 0. 66, bias=5 ppbv

* Examine a clean site: Voyageurs National Park, Minnesota (May-June 2001) CASTNet observations Regional Model D = pollution Background Hemispheric D = + Natural O 3 level pollution X Stratospheric } } High-O 3 events: dominated by regional pollution; minor stratospheric influence (~2 ppbv) regional pollution hemispheric pollution Fiore et al. [2003] Background: 15 -36 ppbv Natural level: 9 -23 ppbv Stratosphere: < 7 ppbv

Probability ppbv-1 Compiling daily afternoon (1 -5 p. m. mean) surface ozone from all CASTNet rural sites for March-October 2001: Policy-relevant background ozone is typically 20 -35 ppbv Natural 18± 5 ppbv GEOS-Chem PRB 26± 7 ppbv GEOS-Chem Fiore et al. , JGR 2003 PRB 29± 9 ppbv MOZART-2 CASTNet sites GEOS-Chem Model at CASTNet

EFFECT OF 2000 -2050 CLIMATE CHANGE ON U. S. OZONE POLLUTION Run GEOS-Chem driven by GISS GCM for present vs. 2050 climate 2000 2050 climate - 2000 • Climate change decreases the background ozone because higher water vapor increases ozone loss; • but it aggravates ozone pollution episodes due to less ventilation (fewer mid-latitudes cyclones), faster chemistry, higher biogenic VOC emissions Wu et al. [2007]

CONSTRAINING NOx AND REACTIVE VOC EMISSIONS WITH NO 2 AND FORMALDEHYDE (HCHO) MEASUREMENTS FROM SPACE GOME: 320 x 40 km 2 SCIAMACHY: 60 x 30 km 2 OMI: 24 x 13 km 2 Tropospheric NO 2 column ~ ENOx Tropospheric HCHO column ~ EVOC ~ 2 km BOUNDARY LAYER hn (420 nm) NO hn (340 nm) HCHO OH CO hours NO 2 O 3, RO 2 1 day Emission NITROGEN OXIDES (NOx) VOC HNO 3 Deposition Emission VOLATILE ORGANIC COMPOUNDS (VOC)

TOP-DOWN CONSTRAINTS ON NOx EMISSION INVENTORIES FROM OMI NO 2 DATA INTERPRETED WITH GEOS-Chem Tropospheric NO 2 (March 2006) Boersma et al. [2007] OMI – GEOS-Chem difference OMI observations GEOS-Chem with EPA 1999 emissions Fitting OMI NO 2 with GEOS-Chem requires • 25% decrease in power plant emissions • 30% increase in vehicle emissions relative to EPA 1999 official inventory

FORMALDEHYDE COLUMNS FROM OMI (Jun-Aug 2006): high values are due to biogenic isoprene (main reactive VOC) OMI GEOS-Chem model w/best prior (MEGAN) biogenic VOC emissions MEGAN emission hot spots not substantiated by the OMI data Millet et al. [2007]