Background & Summary

Emissions of air pollutants from surface natural processes are an essential component of the Earth system, with strong impacts on air quality, climate and ecosystems. In particular, soil emissions of nitrogen oxides (soil NOx) contributed ~50% of global NOx emissions in preindustrial times and currently contribute ~15%, and are a major source of the NOx budget outside of cities1,2,3. Sea salt aerosols (SSAs) are a key player in the climate and chemistry of the marine atmosphere, and dominate the top-of-atmosphere clear sky radiative effect over the oceans4,5,6. They are also an important source of halogens, provide large surface area for heterogeneous reactions, and affect ozone, nitrogen, bromine chemistry and many other pollutants7,8,9,10,11,12. Biogenic non-methane volatile organic compounds (BVOCs), among which the most abundant species are isoprene and monoterpenes, are the dominant contributor to the global VOCs flux into the atmosphere13,14,15. BVOCs affect the production of near-surface ozone in urban and surrounding areas16,17, alter the atmospheric oxidative capability and methane lifetime on regional and global scales18,19, and are important precursors of carbon dioxide14,20.

Natural emissions are nonlinearly dependent on meteorological factors such as temperature, radiation, humidity, and winds2,6,14,21. For soil NOx and BVOCs, the properties of soils (e.g., water content, organics contents, microbes, and the amount of fertilizer applied) and vegetation (e.g., type, density and physiology) are also critical2,14,15,22. Especially for the global domain, these natural emissions are typically estimated through parameterization, due to inadequate mechanistic knowledge about emission processes as well as concerns about the computational costs needed to fully resolve such processes. Parameterizations are typically nonlinear – meaning that the horizontal resolution of inputted meteorological and other variables has an important influence on the calculated emission totals and spatial distributions. Parameterizations are typically embedded in three-dimensional (3-D) chemical transport models (CTMs), climate-chemistry models and earth system models to calculate natural emissions online, and are thus sensitive to model resolution. Resolution-dependent emissions are a major factor affecting the accuracy of 3-D models23,24,25,26,27.

Moreover, historical records of global high-resolution (≤50 km) natural emissions, shown in Tables 1–3, are relatively small, hindering the understanding of variations in global emission totals, spatial distributions and their air quality and climate impacts.

Table 1 Comparison with previous studies for soil NOx emissions.
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Table 2 Comparison with previous studies for sea salt emissions.
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Table 3 Comparison with previous studies for biogenic isoprene emissions.
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Here we use the Harvard-NASA Emissions Component28 (HEMCO) to produce monthly global emissions of soil NOx, SSAs, and BVOCs at different resolutions. These emissions are calculated at 0.5° lat. × 0.625° lon. for 1980–2017 using the MERRA-2 assimilated meteorology and at three resolutions (0.25° lat. × 0.3125° lon., 2° lat. × 2.5° lon., and 4° lat. × 5° lon.) for 2014–2017 using GEOS-FP. The datasets will be continuously updated and published. The datasets can be used to study the effects of these natural emissions on air quality, climate, and the carbon cycle, as well as the effects of horizontal resolution on emissions estimates. The datasets can be downloaded freely through Peking University Atmospheric Chemistry & Modeling Group (http://www.phy.pku.edu.cn/~acm/acmProduct.php#NATURAL-EMISSION) and Figshare29.

Methods

HEMCO

The HEMCO28 is a software package to compute pollutant emissions at user-defined resolutions. HEMCO can be run in a standalone mode or coupled to a 3-D model like GEOS-Chem. Here we use HEMCO version 2.1 at the standalone mode to calculate natural emissions based on different meteorological, ancillary variables, and nonlinear parameterizations.

Soil NOx emissions

Inside HEMCO, the algorithm for above-canopy soil NOx emissions (soil NOx) follows Hudman, et al.2, with the efficiency of loss to canopy depending on vegetation type and density. Based on soil chamber and field measurements, soil NOx varies greatly with climate and edaphic conditions, and are most strongly correlated with N-availability, temperature, precipitation patterns, and fertilizer management practices21,22. In the Hudman, et al.2 algorithm, soil NOx emissions flux is a complex function of biological and meteorological drivers:

$${S}_{N{O}_{x}}={A{\prime} }_{biome}({N}_{avail})\times f\left(T\right)\times g\left(\theta \right)\times P({l}_{dry},t)$$
(1)
$${A{\prime} }_{biome}={A}_{w,biome}+{N}_{avail}\times \bar{\mathrm{E}}$$
(2)
$${N}_{avail}\left(t\right)={N}_{avail}\left(0\right){e}^{-\frac{t}{\tau }}+F\times \tau \times \left(1-{e}^{-\frac{t}{\tau }}\right)$$
(3)
$$f\left(T\right)\times g\left(\theta \right)={e}^{0.103T}\times a\theta {e}^{-b{\theta }^{2}}$$
(4)
$$P\left({l}_{dry},t\right)=\left[13.01\,{\rm{ln}}({l}_{dry})-53.6\right]\times {e}^{-ct}$$
(5)

\({A{\prime} }_{biome}\), representing the biome-dependent emission factors of N in the soil, is a function of \({N}_{avail}\) and the \({A}_{w,biome}\) coefficients. \({A}_{w,biome}\) is the wet biome-dependent emission factors updated based on estimates from Steinkamp and Lawrence30. \(\bar{\mathrm{E}}\) is the mean emission rate of fertilizer, and is treated identically to the natural pool of N.

\({N}_{avail}\), representing the sum of fertilizer N and deposited N, is the mass of available nitrogen in the soil. F is the fertilizer application rate and τ is a decay lifetime, which is chosen as 4 months based on measurements within the top 10 cm of soil31,32. Although atmospheric deposition also contributes to the available nitrogen in soils (about ~5% globally, based on Hudman, et al.2), this amount can only be calculated through 3-D model simulations and is thus not accounted for here.

\(f(T)\times g(\theta )\) represents the combination of the soil temperature (T) and soil moisture dependence of soil NOx. The temperature dependence of soil NOx is an exponential dependence on temperature between 0 °C and 30 °C (constant at T > 30 °C), where 0.103 is the weighted average of temperature dependencies for several biomes. The parameterization for soil moisture is a Poisson function scaling, where θ (water-filled pore space) is defined as the ratio of the volumetric soil moisture content to the porosity33.

\(P({l}_{dry},t)\) represents the pulsed soil NOx, which occur when very dry soil is wetted resulting in a reactivation of water-stressed bacteria. The parameterization, following Yan, et al.34 is derived from four field studies relating pulsed emissions to the length of the antecedent dry period35,36,37,38. The rate constant c reflects the rise/fall time of the pulse (c = 0.068 h−1). The value of ldry is the antecedent dry period in hours.

Emissions of sea salt aerosols

Parametrization of sea salt emissions in HEMCO is modified from Jaeglé, et al.6. It considers two categories of SSAs based on their radii. The radius of accumulation mode sea salt aerosol (SALA) ranges from 0.01 to 0.5 μm, while that for coarse mode sea salt aerosol (SALC) ranges from 0.5 to 8 μm.

Parametrization of sea salt aerosols emissions includes both a wind speed and a sea surface temperature (SST) dependence. The SSAs emission flux density function dE/dr80 is formulated as follows:

$$\frac{{\rm{dE}}}{{\rm{d}}{r}_{80}}=\left(0.3+0.1T-0.0076{T}^{2}+0.00021{T}^{3}\right)\times 1.373{u}_{10m}^{3.41}{r}_{80}^{-A}\times \left(1+0.057{r}_{80}^{3.45}\right)\times 1{0}^{1.607{e}^{-{B}^{2}}}$$
(6)

The SST dependence \(\left(0.3+0.1T-0.0076{T}^{2}+0.00021{T}^{3}\right)\) was derived based on a comparison of the GEOS-Chem sea salt simulation with SALC mass concentration observations obtained from six cruises conducted by the National Oceanic and Atmospheric Administration Pacific Marine Environmental Laboratory6.

The sea salt source function \(\left(1.373{u}_{10m}^{3.41}{r}_{80}^{-A}\times \left(1+0.057{r}_{80}^{3.45}\right)\times 1{0}^{1.607{e}^{-{B}^{2}}}\right)\) is based on Gong39. r80 is the particle radius at RH = 80% (with r80 ~ 2rdry), and u10m is the 10-meter wind speed. \({\rm{A}}=4.7{(1+\Theta {r}_{80})}^{-0.017{r}_{80}^{-1.44}}\), and \({\rm{B}}=\left[0.433-{{\rm{\log }}}_{10}\left({r}_{80}\right)\right]/0.433\). The adjustable parameter Θ controls the shape of the size distribution for submicron aerosols, and the value we use is 30 according to Gong39.

Biogenic VOC emissions

Inside HEMCO, BVOCs emissions are computed by the Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1)14. MEGAN2.1 includes two major components: calculation of landscape average emission factors, and algorithms describing emission responses to variations in environmental conditions. The emissions (Fi) of species i is the product of the two components summed over all vegetation types:

$${F}_{i}={\gamma }_{i}\sum {\varepsilon }_{i,j}{\chi }_{j}$$
(7)
$${\gamma }_{i}=CL{\gamma }_{p,i}{\gamma }_{T,i}{\gamma }_{A,i}{\gamma }_{S,i}{\gamma }_{C,i}$$
(8)

\({\varepsilon }_{i,j}\) is the average emission factor of species i for vegetation type j at standard conditions (leaf temperature = 297 K; air temperature = 303 K; the photosynthetic photon flux density averaged over the past 24 h is equal to 200 μmol m−2s−1 for sun leaves and 50 μmol m−2s−1 for shade leaves), and χj is the fractional grid box areal coverage for the same vegetation type. The emission factor accounts for the estimated in-canopy deposition flux so that Fi represents the net above-canopy flux.

The emission activity factor (γi) reflects the emission response to environmental drivers. The canopy environment coefficient (C) is assigned a value that results in γ = 1 for the standard conditions and is dependent on the canopy environment model being used. A detailed description of the model parameterizations for light (γp), temperature (γT), leaf age (γA), soil moisture (γS), leaf area index (L) and CO2 inhibition (γC) can be obtained from Guenther14.

Our BVOCs emission dataset includes isoprene (ISOP, the most abundant species), acetone (ACET), acetaldehyde (ALD2), ethene (C2H4), ethanol (EOH), propene (PRPE), lumped monoterpenes (MTPA, sum of α pinene, β pinene, sabinene and carene), other monoterpenes (MTPO, sum of myrcene, ocimene and other monoterpenes), limonene (LIMO), and sesquiterpenes (SESQ, sum of farnesene, β caryoph and other sesquiterpenes).

LAI data for calculation of soil NOx and BVOCs emissions

Vegetation composition is principal information needed to estimate BVOCs and soil NOx emissions14,15. The density of vegetation is represented in the parametrizations by leaf area index (LAI), which is defined as the amount of leaf area per unit surface of the ground (m2m−2). We use monthly MODIS-derived LAI with gap filling and smoothing described by Yuan, et al.40 (here after referred to as Yuan LAI). For 2005–2017, we use year-specific Yuan LAI data. For years prior to 2005, we use the LAI values in 2005 due to lack of year-specific data. This would introduce certain uncertainty for these earlier years. We did a test to fix LAI to a certain year, and the effect on global emissions is relatively small (within 5%). Therefore, our extrapolation of LAI data before 2005 does not significantly affect the emissions time series.

Data Records

Our datasets contain 4 data records for monthly global gridded emissions. Each record contains monthly emission data for soil NOx, 2 SSAs species (SALA and SALC), and 10 BVOCs species (ISOP, ACET, ALD2, C2H4, EOH, PRPE, MTPA, MTPO, LIMO, and SESQ). Our data were constructed in nc file format which can be read by many tools like IDL, MatLab, and so on. Of these,

  • One is from 1980 to 2017 based on MERRA-2 at 0.5° lat. × 0.625° lon. [Available in Hongjian and Jintai29, File ‘MERRA-2_05 × 0625_monthly_1980–2017.nc’];

  • One is from 2014 to 2017 based on GEOS-FP at 0.25° lat. × 0.3125° lon. [Available in Hongjian and Jintai29, File ‘GEOS-FP_025 × 03125_monthly_2014–2017.nc’];

  • One is from 2014 to 2017 based on GEOS-FP at 2° lat. × 2.5° lon. [Available in Hongjian and Jintai29, File GEOS-FP_2 × 25_monthly_2014–2017.nc’];

  • One is from 2014 to 2017 based on GEOS-FP at 4° lat. × 5° lon. [Available in Hongjian and Jintai29, File ‘GEOS-FP_4 × 5_monthly_2014–2017.nc’];

Table 4 presents the global annual total emissions of soil NOx, SSAs, and BVOCs over 1980–2017 derived from MERRA-2 at 0.5° lat. × 0.625° lon. Averaged over all years, global total soil NOx emissions amounts to 9.5 TgN/yr. Global total SSAs reaches 3,560 Tg/yr, as contributed by SALC (98.4%) and SALA (1.6%). Global total BVOCs emission reaches 563 TgC/yr, as contributed by emissions of ISOP (61.2%), ACET (4.4%), ALD2 (1.6%), C2H4 (3.4%), EOH (1.6%), PRPE (3.0%), MTPA (13.5%), MTPO (6.4%), LIMO (1.5%), and SESQ (3.4%).

Table 4 Global annual total emissions of soil NOx, SSAs, and BVOCs (with standard deviation) over 1980–2017 derived based on MERRA-2 at 0.5° lat. × 0.625° lon.
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Figure 1a–d shows the MERRA-2 based spatial distribution of soil NOx emissions at 0.5° lat. × 0.625° lon. in January, April, July, and October averaged over 1980–2017. High values of soil NOx emissions move between the two hemispheres as a result of the seasonal variation of temperature. Spatially, the highest emissions occur over regions with intensive agricultural activities, e.g., the Ganges River Basin of India and the North China Plain. Figure 1e further shows the temporal profile of global monthly total emissions (blue line) and annual total emissions (red line), which indicates the strong seasonality (with a July to January ratio of 2.5) and interannual variability (with maximum values in the early 2000s).

Fig. 1
figure 1

Spatial distribution of soil NOx emissions in January (a), April (b), July (c), and October (d), temporal profile of global monthly total emissions (blue line in e), and temporal profile of global annual total emissions (red line in e) over 1980–2017 derived based on MERRA-2 at 0.5° lat. × 0.625° lon.

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Figure 2a–d represents the spatial distribution of total SSAs (sum of SALA and SALC) emissions in different seasons, and Fig. 2e shows the temporal profile of the respective global total emissions. SSAs are the largest over the North Atlantic in January and over the Indian Ocean in July. Emissions are also strong over the Southern Ocean. The temporal variation of global total SSAs emissions is characterized by lower values in the 1980s and 1990s than in later years (with a difference by about 10%), and by a modest seasonality (within 15%).

Fig. 2
figure 2

Spatial distribution of SSAs (sum of SALA and SALC) emissions in January (a), April (b), July (c), and October (d), temporal profile of global monthly total emissions (blue line in e), and temporal profile of global annual total emissions (red line in e) over 1980–2017 derived based on MERRA-2 at 0.5° lat. × 0.625° lon.

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Figure 3 shows the spatial and temporal distributions of total BVOCs emissions (sum of ISOP, ACET, ALD2, C2H4, EOH, PRPE, MTPA, MTPO, LIMO, and SESQ). The total BVOCs exhibits strong seasonality and cross-hemispheric seasonal migration (Fig. 3a–d) because of changes in radiation and temperature. The highest emissions occur over the Amazon, Southeast Asia, Southeast United States, and Central Africa. The global total emission also exhibits a large seasonality, with a July to January ratio of 1.3, due to variation of LAI, especially in the Northern Hemisphere. The interannual variation is modest (within 20%) (Fig. 3e).

Fig. 3
figure 3

Spatial distribution of total BVOCs (sum of ISOP, ACET, ALD2, C2H4, EOH, PRPE, MTPA, MTPO, LIMO, and SESQ) emissions in January (a), April (b), July (c), and October (d), temporal profile of global monthly total emissions (blue line in e), and temporal profile of global annual total emissions (red line in e) over 1980–2017 derived based on MERRA-2 at 0.5° lat. × 0.625° lon.

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The parameterized nonlinear relationships between emissions and controlling factors means that the horizontal resolution of inputted meteorological and other variables has important influences on the calculated emission magnitudes and spatial distributions.

Table 5 presents the global annual total emissions of soil NOx, SSAs and BVOCs derived based on GEOS-FP at different resolutions (4° lat. × 5° lon., 2° lat. × 2.5° lon., and 0.25° lat. × 0.3125° lon.) over 2014–2017. The resolution dependence of emission magnitude is evident especially for soil NOx and SSAs, that is, a higher resolution results in greater global emission totals. The global total SSAs emission increases from 3,157 Tg/yr to 3,239 Tg/yr (by 2.6%) and to 3,860 Tg/yr (by 22.3%) as the resolution changes from the coarsest to the finest. This increase is primarily because emissions are parameterized as a function of wind speed to the 3.41-th power. For soil NOx, the global total increases from 7.1 TgN/yr to 7.5 TgN/yr (by 5.6%) and to 8.8 TgN/yr (by 23.9%) as the resolution increases. This is mainly because the parameterized NOx emission is convex functions of temperature and soil moisture. For BVOCs, the resolution dependence of the global total emission is weaker, i.e., within 5% for ISOP and within 10% for other species. The magnitude of horizontal resolution dependence for BVOCs here is similar to that of temporal resolution dependence shown by Ashworth, et al.41 who showed that using monthly mean inputted data instead of hourly data would reduce the global ISOP emission total by 7%.

Table 5 Global annual total emissions of soil NOx, SSAs, and BVOCs over 2014–2017 derived based on GEOS-FP at three resolutions. The percentage values represent the relative changes from emissions at 4° lat. × 5° lon.
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Figure 4 shows the temporal profile of monthly global total emissions of soil NOx (a), sea salt (b), and biogenic VOCs (c) over 2014–2017 derived based on GEOS-FP at 4°lat. × 5° lon. (gray line), 2° lat. × 2.5° lon. (blue line), and 0.25° lat. × 0.3125° lon. (red line). Although the total emissions of soil NOx and sea salt increase with the horizontal resolution, the interannual and seasonal variability and trends are similar at different horizontal resolutions.

Fig. 4
figure 4

Temporal profiles of monthly global total emissions of soil NOx (a), sea salt (b) and BVOCs (c) over 2014–2017 derived based on GEOS-FP at different resolutions.

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Figure 5 shows the 2014–2017 average spatial distributions of annual emissions of soil NOx, SSAs (SALA + SALC), and total BVOCs (summed over all species) estimated at different resolutions based on GEOS-FP. Figures 6–8 further show the respective spatial distributions of emission differences and percentage differences from 0.25° lat. × 0.3125° lon. to coarser resolutions. As the resolution increases, fine scale patterns of emissions become much more evident, which has important implications for air quality simulations. For soil NOx, northern India and North China, which are major source regions, exhibit the largest resolution dependence for absolute emission differences (Fig. 6a,b). The percentage difference is most evident along the coasts where a fine resolution (0.25° lat. × 0.3125° lon.) resolves the land-ocean contrast much better than coarser resolutions do (Fig. 6c,d). For sea salt emissions, the major source regions at high latitudes of both hemispheres exhibit a large resolution dependence (Fig. 7). For BVOCs, tropical regions have the largest resolution dependence in terms of absolute difference (Fig. 8a,b), while the coastal and low-emission regions exhibit the largest resolution dependence in terms of percentage difference (Fig. 8c,d).

Fig. 5
figure 5

Spatial distributions of annual emissions of soil NOx (first column, kgN/m2), SSAs (SALA + SALC, second column, kg/m2), and total BVOCs (summed over all species, last column, kgC/m2) over 2014–2017 derived based on GEOS-FP at different resolutions. The rectangles in (a) show the regions whose regional emission totals are shown in Fig. 9.

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Fig. 6
figure 6

Spatial distributions of annual soil NOx emissions differences between 0.25° lat. × 0.3125° lon. and coarser resolutions over 2014–2017 based on GEOS-FP. Case_0.25 represents emissions at 0.25° lat. × 0.3125° lon. Case_4 represents emissions re-gridded from 4° lat. × 5° lon. to 0.25° lat. × 0.3125° lon. Case_2 represents emissions re-gridded from 2° lat. × 2.5° lon. to 0.25° lat. × 0.3125° lon. Percentage differences are calculated as 2 * (Case_0.25-Case_4)/(Case_0.25 + Case_4)*100% and 2*(Case_0.25-Case_2)/(Case_0.25 + Case_2)*100%.

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Fig. 7
figure 7

Similar to Fig. 6 but for total sea salt emissions.

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Fig. 8
figure 8

Similar to Fig. 6 but for total BVOCs emissions.

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Figure 9 further shows the resolution dependence of calculated regional annual emission totals over eight major regions. Compared to results for global total emissions in Table 5, the resolution dependence of emission magnitude in some regions is more evident. For Southeast Asia, soil NOx emissions total at 0.25° lat. × 0.3125° lon. is higher than that at 4° lat. × 5° lon. by 38%. Similar results are shown for Europe (38% higher) and Australia (37% higher). For sea salt, emissions for North Hemisphere Africa and Southeast Asia increase by 38% and 30%, respectively, from 4° lat. × 5° lon. to 0.25° lat. × 0.3125° lon. The resolution dependence of regional emissions is smaller for BVOCs (within 10% for all regions) than for soil NOx and SSAs.

Fig. 9
figure 9

2014–2017 average annual total emissions of soil NOx (a), SSAs (SALA + SALC, b), and BVOCs (summed over all species, c) in eight regions: North America, South America, Europe, Northern Hemisphere Africa, Southern Hemisphere Africa, Russia, Southeast Asia and Australia. See Fig. 5 for regional definitions. Data are derived based on GEOS-FP at different resolutions.

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More figures and tables are available from Peking University Atmospheric Chemistry & Modeling Group, including global and regional monthly totals and spatial distributions from 1980 to 2017 derived based on MERRA-2 at 0.5° lat. × 0.625° lon., as well as respective results from 2014 to 2017 derived based on GEOS-FP at 0.25° lat. × 0.3125° lon., 2° lat. × 2.5° lon., and 4° lat. × 5° lon.

Technical Validation

Uncertainty

A major source of uncertainty in our calculated emission data is the use of parameterization as an approximate of the complex processes involved in the emissions of these species2,6,14. Parameterization is also sensitive to errors in the inputted meteorological and ancillary data23,26.

The parameterization of soil NOx emissions includes a continuous dependence on soil moisture and temperature, a representation of biogeochemistry that induces pulsing of the emissions following dry spells, and a detailed spatial and temporal representation of N-inputs both from chemical/manure fertilizer and atmospheric N-deposition (not included here). Our sensitivity test for 2017 at GEOS-FP 4° lat. × 5° lon. shows that a 1 °C increase in temperature would lead to 5.2% increase in the calculated global total emission, a 10% increase in soil moisture would lead to 15.8% decrease in emission, and a 10% increase in LAI would lead to 1% decrease in emission. Sensitivity tests for other resolutions show similar results.

For sea salt emissions, the strong power law relationship with wind speed and the polynomial relationship with SST mean that errors in wind speed and SST have a significant impact on calculated sea salt emission. Based on our sensitivity test for 2017 at GEOS-FP 4° lat. × 5° lon., a 10% increase in wind speed would lead to 38.4% increase in the calculated global emission total, and a 1 °C increase in SST would lead to 6.7% increase in emission. This is consistent with the evident dependence of calculated emissions to the horizontal resolution of inputted meteorological data. By comparison, an increase in the shape parameter by 10% (from constant 30 to 33) would lead to 0.1% increase in the calculated emissions.

The parameterization of BVOCs emissions involves meteorological (temperature, solar radiation, humidity, wind speed and soil moisture), land cover data (LAI and PFT fractions) and the PFT-specific average emission factor at standard conditions. According to Guenther, et al.14, uncertainties associated with the global annual emissions of several compounds (isoprene, acetone and acetaldehyde) are about a factor of two while estimates of uncertainties are a factor of three or higher for other compounds here. The average emission factor is the largest contributor to the uncertainty of estimated emission. Uncertainties in land cover and meteorological variables are also important. Wang, et al.42 showed that an average bias of about 2 °C in temperature is associated with an error in isoprene emissions by ~23% in the Pearl River Delta of China. The error in LAI and its extrapolation to 1980–2004 leads to an additional uncertainty in calculated emissions. Our sensitivity test for 2017 at GEOS-FP 4° lat. × 5° lon. shows that a 1 °C increase in temperature would lead to 12.9% increase in the calculated global total BVOCs, and a 10% increase in LAI would lead to 4.6% increase in emission.

Comparison with existing emission estimates

Comparisons with existing emission estimates are mainly for our results derived based on MERRA-2, which contain much longer data records than those based on GEOS-FP.

As shown in Table 1, global total above-canopy soil NOx emissions are estimated at 3.3–10 TgN/yr in previous bottom-up studies and at 7.9–16.8 TgN/yr for satellite-based top-down estimates. The Yienger and Levy II43 bottom-up algorithm used in many CTM simulations results in global soil NOx emissions of 3.3–7.7 TgN/yr depending on parameters used. Updating upon Yienger and Levy II43, Steinkamp and Lawrence30 use a new biome type land-cover map and improved emission factors, resulting in an estimate of 8.6 TgN/yr. Hudman, et al.2 further includes a more physical parameterization that takes into account the pulsing, soil moisture and temperature dependence. Based on Hudman, et al.2 parameterization and MERRA-2 meteorological data, our calculated soil NOx emissions are 9.5 TgN/yr averaged over 1980–2017, within the (wide) range of values in previous bottom-up and top-down estimates.

Table 2 shows that our global sea salt aerosols emission total (3,560 Tg/yr, based on MERRA-2 for 1980–2017) is in lower end of previous estimates (3,140–10,800 Tg/yr), but is in the middle of the range presented in the IPCC Fifth Assessment Synthesis Report (1,400–6,800 Tg/yr)44. It is reduced from the estimate (4,300Tg, based on GEOS-4 winds for 2003) by Jaeglé, et al.6 by 20%. The decrease is due to the difference in meteorological field data (especially for winds) and some recent updates of sea salt simulation (http://wiki.seas.harvard.edu/geos-chem/index.php/Sea_salt_aerosols#Recent_Updates_to_sea_salt_simulation). In particular, Jaeglé, et al.6 included one accumulation bin (0.01–0.5 μm) and two coarse mode bins (0.5–4 μm; 4–10 μm), whereas we use one accumulation bin (0.1–0.5 μm) and one coarse bin (0.5–8 μm) here.

The existing estimates of total BVOCs range from 200 to 1,000 TgC/yr depending on the meteorological and vegetation datasets used15,45, and our estimate (563 TgC/yr averaged over 1980–2017 based on MERRA-2) is within this range. Table 3 further compares our estimate of global isoprene emission total with others. Our global isoprene emission total (330–345 TgC/yr) is at the lower end of previous bottom-up estimates (303–529 TgC/yr), although it is consistent with those used in various versions of GEOS-Chem (http://wiki.seas.harvard.edu/geos-chem/index.php/Benchmark/GEOS-Chem_12.5.0#GEOS-Chem_Classic_1-month_benchmark). Our isoprene emission total is larger than a recent top-down estimate based on formaldehyde measurements from the Ozone Monitoring Instrument (240 TgC/yr for 2005–2013)17, but lower than an earlier top-down estimate (566 TgC/yr for 1996–1997)46. The tropical ecosystems are a crucial factor affecting these estimates17.