GLOBAL MODELS OF ATMOSPHERIC COMPOSITION Daniel J. Jacob Harvard University

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

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

OPERATOR SPLITTING IN EULERIAN MODELS … and integrate each process separately over discrete time steps: Split the continuity equation into contributions from transport and local terms: 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) Reduces dimensionality of problem

SPLITTING THE TRANSPORT OPERATOR U Wind velocity U has turbulent fluctuations over time step  t : Time-averaged component (resolved) Fluctuating component (stochastic) Further split transport in x, y, and z to reduce dimensionality. In x direction: Split transport into advection (mean wind) and turbulent components: advection turbulence (1 st -order closure) 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  t/  x < 1 … limits size of time step Addressing other requirements (e.g., positivity) introduces non-linearity in advection scheme

VERTICAL TURBULENT TRANSPORT (BUOYANCY) Convective cloud ( km) Model grid scale Model vertical levels updraft entrainment downdraft detrainment 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. 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

LOCAL (CHEMISTRY) OPERATOR: solves ODE system for n interacting 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  C  t )  But it is expensive. Most 3-D models use higher-order implicit schemes such as the Gear method. For each species

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 P i and L i, and size characterization either through size bins or moments. Typical aerosol size distributions by volume nucleation condensation coagulation

LAGRANGIAN APPROACH: TRACK TRANSPORT OF POINTS IN MODEL DOMAIN (NO GRID) UtUt U’  t Transport large number of points with trajectories from input meteorological data base (U) + random turbulent component (U’) over time steps  t Points have mass but no volume Determine local concentrations as the number of points within a given volume 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 position t o position t o +  t

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 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 S. California fire plumes, Oct

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 o x1 o to 4 o x5 o, 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; NO x = NO + NO 2 Sources: combustion, soils, lightning Volatile organic compounds (VOCs) Methane Sources: wetlands, livestock, natural gas… Non-methane VOCs (NMVOCs) Sources: vegetation, combustion Carbon monoxide (CO) Sources: combustion, VOC oxidation Tropospheric ozone precursors

RADICAL CYCLE CONTROLLING TROPOSPHERIC OH AND OZONE CONCENTRATIONS O3O3 O2O2 h O3O3 OHHO 2 h, H 2 O Deposition NO H2O2H2O2 CO, VOCs NO 2 h STRATOSPHERE TROPOSPHERE 8-18 km SURFACE GEOS-Chem simulation for tropospheric ozone includes 120 coupled species to describe HO x -NO x -VOC-aerosol chemistry global sources/sinks in Tg y

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

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

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

GEOS-Chem GLOBAL BUDGET OF TROPOSPHERIC OZONE O3O3 O2O2 h O3O3 OHHO 2 h, H 2 O Deposition NO H2O2H2O2 CO, VOC NO 2 h STRATOSPHERE TROPOSPHERE 8-18 km Chem prod in troposphere, Tg y Chem loss in troposphere, Tg y Transport from stratosphere, Tg y Deposition, Tg y Burden, Tg Lifetime, days Present-day Preindustrial

IPCC RADIATIVE FORCING ESTIMATE FOR TROPOSPHERIC OZONE (0.35 W m -2 ) RELIES ON GLOBAL MODELS Preindustrial ozone models } Observations at mountain sites in Europe [Marenco et al., 1994] …but these underestimate the observed rise in ozone over the 20 th century

RADIATIVE FORCING BY TROPOSPHERIC OZONE COULD THUS BE MUCH LARGER THAN IPCC VALUE Standard model:  F = 0.44 W m -2 “Adjusted” model (lightning and soil NOx decreased, biogenic hydrocarbons increased):  F = 0.80 W m -2 Global simulation of late 19 th century ozone observations [Mickley et al., 2001]

IMPLICATION OF RISING BACKGROUND FOR MEETING AIR QUALITY STANDARDS ppbv Europe AQS (seasonal) U.S. AQS (8-h avg.) U.S. AQS (1-h avg.) Preindustrial ozone background Present-day ozone background at northern midlatitudes Europe AQS (8-h avg.) 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 (1)Standard simulation; include all sources (2) Set U.S. or N. American anthropogenic emissions to zero  infer policy-relevant background (3) Set global anthropogenic emissions to zero  estimate natural background Difference between (1) and (2)  regional pollution Difference between (2) and (3)  background enhancement from hemispheric pollution This background cannot be directly observed, must be estimated from models Because chemistry is strongly nonlinear, sensitivity simulations are necessary

Summer 1995 afternoon (1-5 p.m.) ozone in surface air over the U.S. Observations r = 0.66, bias=5 ppbv GEOS-CHEM standard simulation Fiore et al. [2002]

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

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 ppbv Probability ppbv -1 CASTNet sites GEOS-Chem Model at CASTNet Natural 18±5 ppbv GEOS-Chem PRB 26±7 ppbv GEOS-Chem PRB 29±9 ppbv MOZART-2 Fiore et al., JGR 2003

EFFECT OF CLIMATE CHANGE ON U.S. OZONE POLLUTION climate Wu et al. [2007] Run GEOS-Chem driven by GISS GCM for present vs climate 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

CONSTRAINING NO x AND REACTIVE VOC EMISSIONS WITH NO 2 AND FORMALDEHYDE (HCHO) MEASUREMENTS FROM SPACE Emission NO h (420 nm) O 3, RO 2 NO 2 HNO 3 1 day NITROGEN OXIDES (NO x ) VOLATILE ORGANIC COMPOUNDS (VOC) Emission VOC OH HCHO h (340 nm) hours CO hours BOUNDARY LAYER ~ 2 km Tropospheric NO 2 column ~ E NOx Tropospheric HCHO column ~ E VOC Deposition GOME: 320x40 km 2 SCIAMACHY: 60x30 km 2 OMI: 24x13 km 2

TOP-DOWN CONSTRAINTS ON NO x EMISSION INVENTORIES FROM OMI NO 2 DATA INTERPRETED WITH GEOS-Chem Tropospheric NO 2 (March 2006) OMI observations GEOS-Chem with EPA 1999 emissions OMI – GEOS-Chem difference 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 Boersma et al. [2007]

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]