J.-F. Müller and T. Stavrakou IASB-BIRA Avenue Circulaire 3, 1180 Brussels NCAR/ACD seminar, Dec Multi-year inversion of emissions using the adjoint model technique

Emission inversion: methodology Inversion of NOx emissions The MEGAN-ECMWF dataset HCHO production from pyrogenic NMVOCs Inversion of NMVOC emissions Conclusions and perspectives Outline

Inversion methodology and setup Prior emission distributions : base functions (one per grid cell, category and month)‏ f j are the emission parameters, which minimize the cost function: Optimized emissions : anthropogenic biomass burning biogenic

Correlations for anthropogenic emission errors E n,k = total emission of country n in subcategory k (=1,… 7)‏ σ En,k = standard error for this country/subcategory Φ i = total flux emitted in grid cell I E n,k = total emission of country n in subcategory k (=1,… 7)‏ σ En,k = standard error for this country/subcategory Φ i = total flux emitted in grid cell I = fraction of flux Φ i due to country n and subcategory k A nm = 1 when n=m, = 0 if n and m belong to different large regions (Western Europe, Eastern Europe, FSU, etc.)‏ C ij nm = 1 when i=j, <1 otherwise Subcategories for NOx: 1. Road transport 2. Power generation 3. Fossil fuel use in industry 4. Biofuel (residential)‏ 5. Cement 6. Non-road land transport 7. Other Basic assumption: errors on emissions from different subcategories, or from different large regions of the world are uncorrelated. + Temporal correlations

Correlations for pyrogenic and BVOC emission errors + Temporal correlations Plant types considered in the MEGAN model for isoprene: 1. Needleleaf evergreen 2. Needleleaf deciduous 3. Broadleaf deciduous 4. Broadleaf evergreen 5. Shrub 6. Grass 7. Crops Basic assumptions: errors on biogenic emissions decrease exponentially with geographical distance; and the errors on emissions from different vegetation types are uncorrelated. d ij = geographical distance between grid cells i and j decorrelation lengths : 500 km for pyrogenic / 3000 km for biogenic x i n : fraction of the flux Φ i emitted by vegetation type n (forest/savanna for pyrogenic, 7 plant functional types for biogenic VOC emissions)‏ d ij = geographical distance between grid cells i and j decorrelation lengths : 500 km for pyrogenic / 3000 km for biogenic x i n : fraction of the flux Φ i emitted by vegetation type n (forest/savanna for pyrogenic, 7 plant functional types for biogenic VOC emissions)‏

Gradient of the cost function Calculation of new parameters f with a descent algorithm Minimum of J(f) ? Observations Forward CTM Integration from t 0 to t Transport Chemistry Cost function J(f)‏ Adjoint model Integration from t to t 0 Adjoint transport Adjoint chemistry Adjoint cost function Checkpointing Control variables f yes no Optimized control parameters Minimizing the cost > 20 iterations needed to decrease the norm of the gradient by factor of ~100

70 chemical compounds, 5°x5° resolution, 40 σ-p levels ( Stavrakou and Müller, JGR, 2006 )‏ Revised chemical mechanism for isoprene and biomass burning NMVOCs, based on box model calculations using the MCMv3 Monthly wind fields from ECMWF, impact of wind variability represented as horizontal diffusion Daily ECMWF fields for convective fluxes, PBL mixing, cloud fields, T and H 2 O Biomass burning emissions : GFED versions 1 and 2 (Van der Werf et al., 2003, 2006)‏ Two main modes: (A) with or (B) without diurnal cycle calculations Mode B (Δt=1 day) uses info. on diurnal profiles of chemical species calculated in mode A (Δt=20 min) to correct the kinetic rate constants and photorates Inverse modeling: only in mode B (emission updates not expected to affect the diurnal behavior of chemical compounds)‏ 10-year simulations, plus spin-up The IMAGES (v2) model

10-year inversion of NOx emissions based on GOME/SCIAMACHY NO 2 columns (TEMIS dataset)‏  Use averaging kernels 10-year inversion of biogenic and pyrogenic NMVOC emissions based on GOME/SCIAMACHY HCHO columns (new dataset developed by I. De Smedt and M. Van Roozendael, IASB-BIRA)  the HCHO retrievals use HCHO vertical profile shapes from IMAGES model In both cases, the observations are monthly averaged accounting for the actual sampling times of the observations at each location Optimisations

Results: NOx Optimized / prior emission ratio for anthropogenic NOx (here, July 2000)‏ Inferred anthropogenic emission trend , %/year

Global results Units : Tg N/yr Anthropogenic Soils Lightning Biomass burning Negative trend in both anthropogenic and natural NOx emissions  indication of a temporal drift in the observations?

Emissions over the Far East increase by 70% over the decade  ~1/3 of the total anthropogenic source in 2006 North American and European emissions decrease by ~30% and ~10%, respectively Temporal evolution of anthropogenic emissions

-3.2Houston area -4.8Indiana/Ohio -4.1New York area Annual trend (%/yr)‏ Regions Anthropogenic emission trend

Total inferred emissions over the Far East Prior inversion with all data inversion with summertime data

6.6Wuhan area, China 9.5Jinan area, China 13.6Shanghai area, China 11.8Beijing area, China Annual trend (%/yr)‏ Regions Trend in anthropogenic emissions over Far East only “summertime” data Inferred trend by the standard inversion is generally higher using all data

Change in OH and O 3 surface mixing ratios due to the change in NOx emission – over the Far East up to 45% increase over Shanghai July increase of ~18% over Shanghai January reduction ~ 10% in Beijing, Shanghai -70% over Beijing, -40% over Shanghai Δ [ OH] (%)‏ Δ [ O 3 ] (%)‏ January July

Over the US January slight increase in east.US decrease up to 20-30% July increase by up to 25% decrease of 7-14% in east. US Δ [ OH] (%)‏ Δ [ O 3 ] (%)‏ JanuaryJuly

Chemical feedbacks: impact of anthropogenic emission trends on the NO 2 column chemical lifetime JULY JANUARY (%)‏ up to 35% increase in N-E US (OH decrease)‏ up to 35% decrease in E. China (OH increase)‏ up to 20% increase in E. US (less hetero- genous removal)‏ up to 35% decrease in E. China (more hetero- genous removal)‏ Chemical feedbacks are generally negative, attenuating the impact of NOx emission changes

= emission rate in standard conditions, = response functions to radiation and temperature at leaf level = dependence to leaf age = dependence to soil moisture stress LAI = Leaf Area Index LAI from MODIS ECMWF analyses provide: canopy top values of downward solar radiation, temperature, wind, and humidity + cloudiness + soil moisture in 4 layers values inside the canopy require a multi-layer canopy model (MOHYCAN)‏ (Wallens, 2004; Müller et al., 2007)‏ ε The MEGAN-ECMWF isoprene emission dataset

Very similar to distribution obtained by Guenther et al. (ACP2006) using NCEP meteorological data, except over arid areas Results: isoprene emissions in 2003

Large reduction over arid areas Global annual emissions reduced by > 20% Impact of soil moisture stress

Global annual emission is ~30% than in Guenther et al. (1995, 2006)‏ Large interannual variability (20% difference between extreme years)‏ Zonally-averaged isoprene emissions

Isoprene emissions : trends induced by climate change? Global source increases from 350 to 415 Tg/year over 30 years (+19%) !

Biomass burning emissions of NMVOCs based on emission factors from Andreae and Merlet, GBC, 2001

HCHO Production from biomass burning "Ultimate" production (after several months)‏ Short-term production (after 1 day)‏ Based on HCHO yields calculated with the MCMv3 at 1 ppbv NOx Which NMVOCs contribute to the HCHO columns observed from satellites?

Inversion results: Biogenic and pyrogenic VOCs Biogenic emission ratio for July 1997 when GFEDv1 is usedfactor of 2 decrease over the Eastern U.S. when GFEDv2 is used when GEOS-Chem isoprene mechanism is used Large increase in Southern Africa, esp. over shrubland Soil moisture activity factor in MEGAN

Comparison with aircraft campaigns

Improving biomass burning inventories? Prior, GFEDv2 Prior, GFEDv1 Optimized, GFEDv2 Optimized, GFEDv1 Bad timing and amplitude in GFEDv1, optimization fails Strong overestimation in GFEDv2 Strong underestimation in GFEDv2, optimization wrongly increases biogenic emissions to compensate

Improving biomass burning inventories? Using GFEDv1 Using GFEDv2 Optimized/prior emission ratio for year 1997

Both the chemical observations and the prior information on the emissions (distributions, errors) determine the optimization results Ideally, emission models should be coupled to the CTM and incorporated in the optimization system; but even then, characterization of the error co(variances) remain difficult Anthropogenic emission trends can be determined from 10-year NO 2 dataset – caution is needed due to the indication of temporal drifts in the data Chemical feedbacks are important in NOx optimization studies The NOx optimization would benefit from an increased horizontal resolution in the IMAGES CTM Biogenic NMVOC emissions determined from the HCHO retrievals developed at IASB-BIRA (De Smedt &Van Roozendael) are generally lower than previously estimated based on another HCHO retrieval and on the GEOS-Chem model – most of the difference is apparently related to the retrievals Intercomparisons of the HCHO retrievals are clearly needed! Biomass burning inventories can be evaluated and even improved based on HCHO retrievals Conclusions