IFS-COMPO Pre-Cy49r1 is built on the previous operational cycle (Cy48r1) and contains 8 distinct aerosol types with multiple bins for size segregation, namely sea salt, desert dust, organic carbon, black carbon, SO4=, fine and coarse NO3-, NH4+ and Secondary Organic Aerosol. For pre-Cy49r1 updates have been made to the aerosol component of the model in the form of modifying both the description and properties of desert dust and sea-salt, the aging of Carbonaceous aerosol and an update to the aerosol optics by changing the assumptions used for the SO4= aerosol when referencing the lookup table for Mie scattering. This impacts the resident lifetimes, radiative properties and the long-range transport for each of the aerosol species. The modifications to the description of the aerosol optics have been shown to improve the simulation of Aerosol Optical Depth (AOD) and Ångström exponent when compared against regional observations (Rémy et al., 2024). The gas-phase chemistry, photolysis and dry deposition are identical to that used in Rémy et al. (2024) and as described in Williams et al. (2022). For brevity, and that this study is only concerned with soluble aerosols, we refer the reader to Rémy et al. (2024) for more explicit details related to the other aerosol types.

For pre-Cy49r1 there has been an integration of the aerosol and chemistry components in the code to make them more consistent, where both the sulfur and nitrogen cycles are now represented through the aerosol module (for particulate species) and the chemistry module (for gaseous species and aqueous SO4= production). The aerosol module also provides additional input to the chemistry module to better represent heterogeneous reactions (via Surface Area Density (SAD)) and the effects of aerosols on photolysis rates. The extent of gas–particle partitioning and conversion via heterogeneous reactions on dust and sea-salt particles are outlined in Rémy et al. (2019), as based on the work of Hauglustaine et al. (2014). In Cy48r1, the first version of the EQuilibrium Simplified Aerosol Model (EQSAM; Metzger et al., 2002) was implemented for the calculation of e.g. NH₄NO₃ concentrations. These parameterizations rely on meteorological data provided by IFS, as well as gaseous precursors such as HNO₃ and NH₃ from the chemistry module. The gas–particle partitioning scheme estimates compound production through the neutralization of HNO₃ by NH₃ left over after the neutralization of H₂SO₄. It also accounts for the formation of specific compounds from heterogeneous reactions of HNO₃ with calcite (found in dust aerosol) and sea-salt particles.

In IFS-COMPO pre-Cy49r1, EQSAM4Clim is used to estimate the gas/particle partitioning of the HNO₃-NO3- and NH₃-NH4+ couples and to provide an estimate of the aerosol pH. The pH of aqueous solutions, aquated aerosols and precipitation is now updated each time-step using the EQSAM4Clim approach accounting for additional cations (Ca²⁺, Mg²⁺, Na⁺, K⁺), anions (SO4=, HSO4-, NO3-, Cl⁻) and their solute interactions, whose methodology is comprehensively described in Metzger et al. (2002, 2016, 2024). This replaces the original estimate of the pH of the solution determined by summing the contributions from dissolved CO₂ and strong acids (HNO₃, HSO3-, H₂SO₄, NO3- and Methane Sulfonic Acid) which is buffered by dissolved NH₃. The contribution towards the pH of the solution of dissolved Formic and Acetic acid (HCOOH and CH₃COOH, respectively) are also now accounted for in pre-Cy49r1, which has been shown to contribute to the pH in cloud droplets (Shah et al., 2020). This impacts the phase-transfer, speciation, and subsequent aqueous-phase oxidation of SO₂ in cloud droplets (thus impacting SO4=). Also, the loss of gas-phase species such as e.g. H₂O₂ and the corresponding formation of SIA particles is affected. Note that both the original (Cy48r1) and updated (pre-Cy49r1) approaches account for the most dominant gaseous contributions towards the pH of the solution, namely SO₂, HNO₃ and NH_3. This means that differences imposed in cloud pH are naturally less than the associated changes in aerosol pH. The aqueous phase oxidation of SO₂ by transition metal ions (Fe²⁺, Mg²⁺) is not currently accounted for but considered a minor pathway.

Below cloud scavenging of gaseous precursors is also affected by the pH of solution (e.g. Seinfeld and Pandis, 2006). In Cy48r1 fixed values for cloud pH are used over land (pH = 5.0) and ocean (pH = 5.6), thus only providing limited variability with respect to regions affected by both high and low emissions. In pre-Cy49r1 this has been updated such that the calculator of the pH is coupled to resident trace gas and aerosol concentrations to improve consistency within IFS-COMPO and to provide variable scavenging rates dependent on tropospheric composition.

In Cy48r1, the wet deposition routines for aerosols and chemistry in IFS-COMPO are distinct, though both utilise a scheme adapted from Luo et al. (2019). To ensure a consistent approach between aerosol and trace gases wet deposition, and to simplify code maintenance, these separate implementations have been merged into a unified routine. This new routine now represents the wet deposition processes for both aerosols and chemical species and is called with either chemical or aerosol tracers as input. As with Cy48r1 and previous versions, the wet deposition routine in pre-Cy49r1 is executed twice: once for large-scale precipitation and once for convective precipitation. In the case of convective precipitation, the assumed precipitation fraction has been standardised to 0.05 (whereas in Cy48r1 a value of 0.1 was used for chemistry scavenging and 0.05 for aerosol scavenging).

Additional upgrades have been made for aerosol wet deposition as follows: (i) The aerosol activation parameterization of Verheggen et al. (2007) has been implemented. This parameterization estimates the fraction of aerosols that can be scavenged through in-cloud processes as a function of temperature. It is applied to mixed clouds, specifically for temperatures between the freezing point and 233 K. For temperatures above 0 °C, the consistency of the parameters determining the fraction of aerosols subject to in-cloud wet deposition with the results of the Verheggen parameterization has been verified. (ii) For below-cloud scavenging of aerosol species, the scavenging rates have been updated to reflect the particle size dependency more accurately as described by Croft et al. (2009). This update includes adjustments to the below-cloud scavenging parameters, which describe the efficiency with which aerosols are removed by rain and snow, depending on the species and the assumed size distribution.

The IFS-COMPO simulations used for evaluating the impact of IFS-COMPO atmospheric composition upgrades proposed for pre-Cy49r1 on tropospheric composition, precursor gases, particle distributions and wet deposition terms employ both the IFS cycles Cy48r1 and pre-Cy49r1. Here, pre-Cy49r1 denotes the IFS-COMPO Cy48r1 including updates of aerosol/chemistry modules which are now applied in IFS pre-Cy49r1. The meteorological component is the same between simulations and corresponds to Cy48r1. Still, marginal differences may occur between simulations due to a coupling of gases and aerosol with radiation, which is active by default. The simulations presented here are for the year 2018, with a one-month spin-up period. The vertical resolution uses 137 individual model levels and a horizontal resolution of TL511, corresponding to approx. 0.4° × 0.4° (with further details being given at https://confluence.ecmwf.int/display/UDOC/L137+model+level+definitions, last access: 25 November 2025). The experiments do not include the data assimilation of observations, meaning that although the changes shown will not be directly visible in the final operational forecast, they will influence the resulting skill scores for e.g. PM_2.5. Meteorology is initialised every 24 h based on ERA5 reanalysis data, i.e. IFS-COMPO is run in a cyclic forecast mode. A 15 min chemical time-step is used for solving a modified version of CB05 tropospheric chemistry (Williams et al., 2022), excluding active stratospheric chemistry for efficiency (with this study being focused on changes at the surface). Three-hourly 3D global output is used for the analysis and aggregated into weekly, monthly, and yearly mean values.

The details of the experiments are summarised in Table 1. The Cy48r1 reference simulation pertains to the 48R1 version of IFS-COMPO while the pre-Cy49r1 simulation is based on the version described in Rémy et al. (2024), which is Cy48r1 updated with new components for use in future versions of IFS-COMPO. The pre-Cy49r1_NOEQ4C simulation is identical to the pre-Cy49r1 simulation, except the EQSAM4Clim module (Metzger et al., 2024) is deactivated. For future reference, the experiment identities on the ECMWF Multiversion Asynchronous Replicated Storage system (MARS) are hylm (Cy48r1), i3bw (pre-Cy49r1_NOE4C) and i3ad (pre-Cy49r1). These three simulations use a configuration, and emissions as those used for the simulations presented in Rémy et al. (2024) for evaluating PM. We select the year 2018 to provide further evaluation which is complimentary to the results presented for 2019 in Rémy et al. (2024).

The anthropogenic emissions adopted in these configurations are taken from CAMS_GLOB_ANT v5.3 (Soulie et al., 2024), biogenic emissions taken from the CAMS_GLOB_BIO v3.1 dataset (Sindelarova et al., 2022; http://eccad.aeris-data.fr/, last access: 20 November 2025) and biomass burning emissions taken from GFAS v1.2 (Kaiser et al., 2012). All emissions are provided at 0.1×0.1 resolution at a monthly frequency, except for the biomass burning emissions which are prescribed daily. For biomass burning and anthropogenic emissions from specific sectors vertical profiles are used representing pyrogenic convection or industrial stack heights, with other emissions being applied in the lowest model level. A diurnal cycle is imposed for isoprene and biomass burning emissions to capture either photolytic activity or the tropical burning cycle. Volcanic outgassing of SO₂ is also included based on Andres and Kasgnoc (1998). For di-methyl sulphide (DMS) oceanic emissions are based on Kloster et al. (2006) i.e. not coupled to sea surface temperature which controls biogenic activity (Deschaseaux et al., 2019). Moreover, direct production of SO4= and HNO₃ in hot shipping exhausts is not accounted for (e.g. von Glasow et al., 2003).

Figure 1 shows the regional monthly mean distributions in surface SO₂ mixing ratios for July and December 2018 for Cy48r1, along with the relative percentage differences between Cy48r1, pre-Cy49r1_NOEQ4C and pre-Cy49r1. When comparing the spatial distributions across regions, Europe exhibits the lowest SO₂ mixing ratios in Cy48r1, where the region has undergone strong mitigation practices over the last decades (e.g. Vestreng et al., 2007). The maps for July show higher mixing ratios towards the east, with a significant contribution from shipping in the Mediterranean and the English Channel. For December, the highest mixing ratios occur over the continent, most notably in Germany and Poland. For the US, a stark east-west gradient exists as governed by the continental distribution in anthropogenic emissions, with higher emissions towards the East Coast, again with a seasonal signature. Maximal surface mixing ratios are 5–10 times higher than those simulated for Europe distributed over a much larger area. As expected, China exhibits the highest regional mixing ratios of between 10–20 ppb over the entire country, which is approximately 20 times higher than those simulated for Europe for both months shown.

When comparing Cy48r1 against pre-Cy49r1_NOE4C there are reductions in [SO₂(g)] at the surface for all regions of between 0 %–10 %, resulting in limited increases in SO42- production of a few percent due to changes other than those related to the application of EQSAM4Clim. This small increase in the SO42- production is reversed when applying the EQSAM4Clim pH methodology (Metzger et al., 2024), where the conversion efficacy of SO₂ is faster at a more alkalinic pH. Table 2 provides the global budget terms for SO₂(g), which shows that in addition to primary emission, approximately one third of SO₂ in the troposphere comes from the oxidation of DMS by the hydroxyl radical (OH), with DMS originating from biogenic activity in the oceans.

In Cy48r1, approximately 20 % of SO₂ is oxidised in the gas-phase and 43 % in the aqueous phase, with the remaining 37 % being lost to surface via dry and wet deposition. This increase in gas-phase production via OH is linked to changes imposed by differences in O₃-NO_x reaction cycles near anthropogenic source regions which results in a small increase in O₃ of a few percent (not shown). The corresponding values for pre-Cy49r1_NOE4C show changes in the order of a few percent across terms, increasing the global burden by of SO₂(g) by 1.4 % mostly in the lower troposphere. For pre-Cy49r1 the application of EQSAM4Clim pH in cloud droplets reduces both the uptake and oxidation of SO₂ by reducing aquated sulphite ([SO32-]_aq, pKa(HSO3-)=7.2) and an enhancement of the gas-phase oxidation due to increased OH, resulting in more gas-phase production of H₂SO4. This is subsequently scavenged into solution further increasing solution acidity (lowering pH values) in case of excess SO42- (insufficient cations to completely neutralise all SO42-).

Figure 2 shows a comparison of weekly [SO₂(g)] and [SO42-] surface composites as simulated in IFS-COMPO against weekly composites of measurements taken from the EMEP measurement network (top left panel). For SO₂(g) a consistent positive bias exists for the entire year across all IFS-COMPO simulations suggesting emission estimates which are high. Surface concentrations exhibit around 100 % positive bias, increasing to almost 200 % during wintertime. The variability in the observational means (grey shaded area) does indicate that the simulated increase in surface [SO₂(g)] during wintertime does occur at some of the measurement sites. The corresponding observational means of [SO42-] show that there is higher weekly variability during wintertime than summertime, with concentrations ranging typically between 1.0–2.0 µg m⁻³. This significant wintertime weekly variability is simulated well by IFS-COMPO across all simulations, where both Cy48r1 and pre-Cy49r1 exhibit significant positive biases of 0.5–1.0 µg m⁻³. Surprisingly, pre-Cy49r1_NOE4C has the lowest bias during wintertime of around 0.2–0.5 µg m⁻³. For pre-Cy49r1, there is an increase in surface [SO42-] by approximately 10 %–25 %, albeit with a significant negative bias of around 0.8–1.5 µg m⁻³. During summertime, the weekly variability in the observed weekly means is insignificant. All simulations capture this limited variability, with differences across simulations being around 0.1-0.2 µg m⁻³. Due to the primary source term of SO₂ being direct emissions, the high [SO₂(g)] simulated for Eastern Europe suggests a local overestimate (cf. Fig. 1).

In the right-hand panels of Fig. 2 a similar comparison is made against weekly observational composites of surface [SO₂(g)] and [SO42-] from the CASTNET measurement network. As for Europe, no seasonal cycle exists in the observations of either surface [SO₂(g)] or [SO42-] with typical weekly mean values of around 0.7–1.0 µg m⁻³. For the wintertime, lower weekly MB occur for both pre-Cy49r1_NOE4C and pre-Cy49r1 as compared with Cy48r1, where the weekly variability is inverse of that seen in the observations. During the summertime much larger positive biases occur, reaching between 200 %–300 % across all IFS-COMPO simulations. Simulated [SO42-] exhibits a strong seasonal cycle despite no such increase in the simulated [SO₂(g)]. Although some small differences in the SO₂(g) weekly values occur between simulations, there is no improvement in pre-Cy49r1 with respect to the weekly MB. The positive summertime MB for SO₂ implies that there is either an overestimate in the regional source terms or an underestimate in the depositional loss term (cf. [SO42-] below)

Figure 3 shows the yearly means of surface [SO42-] for Cy48r1 and pre-Cy49r1 for Europe (top panels), the US (middle panels) and Southeast Asia (bottom panels). The changes in surface [SO42-] here are somewhat unaffected by the changes in PM_2.5 due to EQSAM4Clim that are shown in Rémy et al. (2024) for 2019, due to the dominant aqueous-phase production term (albeit with small increases due to the additional contribution from organic acids). One main difference for SO_x than either NH_x or NO_x, is that the gas-particle partitioning is dependent on cloud pH, dissolved O₃ and hydrogen peroxide (H₂O₂), where SO42- production is irreversible.

For Europe, a sharp north-south gradient exists imposed by the variability in H₂SO₄ production between seasons, cloud cover for the wet production term and the distribution of the primary point sources for SO₂ emissions, although mitigation measures remove the increase in the emitted flux during the cold winter months associated with domestic heating (Veheggen et al., 2007). Simulated concentrations in Cy48r1 are lower in Scandinavia as compared with e.g. France, which results in a low bias of around 1 µg m⁻³ in e.g. Finland and around the Baltic, here related to missing shipping emissions of SO₂, which quickly converts to SO42 in the plume (Celik et al., 2020). For the other sites in Europe agreement is better with the low bias decreasing to approx. 0.5 µg m⁻³. One outlier exists for the most easterly station, which exhibits a significant high bias of 1.5 µg m⁻³. Comparing pre-Cy49r1 shows increases in the simulated surface [SO42-] of between 0.2–0.4 µg m⁻³, which leads to an improved bias. Only small improvements are made to the correlation coefficient due to identical emission estimates being used and EQSAM4Clim affecting SO_x the least, i.e., only indirectly through changes in pH.

For the US, the CASTNET observations show an east-west continental gradient in surface [SO42-] exists as determined by the distribution in the primary SO₂ emissions and transport (cf. Fig. 1). A significant transport component exists for SO42-, resulting in surface [SO42-] in the Marine Boundary Layer between 1.0–2.5 µg m⁻³, where transport dominates local surface [SO42-] produced from DMS oxidation (Simpson et al., 2014). For pre-Cy49r1 there is a reduction in surface [SO42-] at continental scale, with a decrease in the yearly MB from 0.67 to 0.20 µg m⁻³, with a corresponding increase in the correlation coefficient to 0.43, albeit remaining only rather weakly correlated. For the West of the US a positive MB is introduced for the rural background in pre-Cy49r1 of 0.5–0.7 µg m⁻³, with a contribution being transported from the east. Hence, reductions in the yearly MB primarily stem from improved agreement at Eastern US monitoring sites. That a positive MB of approx. 1–1.5 µg m⁻³ exists in the yearly mean values around Kentucky/Tennessee suggests that the local SO₂ emission estimates are too high (see Discussion in Sect. 5).

For Southeast Asia, the scarcity of sampling sites in the EANET network results in a less robust evaluation. Many sampling sites are located at coasts rather than inland, thus the influence of changes at coastal regions has an influence on the regional statistics. Higher primary SO₂ emissions occur on the land. Therefore, any positive MB near source regions is not included in the statistics; the results shown here for surface SO42- should be considered lower limits. The long-range transport of SO42- in Asia has been shown to somewhat neutralise national SO₂ mitigation measures taken in e.g. Taiwan and South Korea. This originates from changing trends in SO₂ emission from mainland China as captured by EANET measurement sites (Chang et al., 2022). For Cy48r1 the yearly mean statistics show an exceptionally low MB and a good correlation coefficient of 0.75. For pre-Cy49r1 there is a significant degradation, where the MB increases to 0.48 µg m⁻³ showing a trend in the performance for SO_x that is like the US. Notably, the more remote sampling stations (e.g. oceanic) exhibit regional negative biases (approx. -0.7 µg m⁻³) whereas those situated near Mongolia and South Korea agree well with low MB values. For Thailand and Vietnam there are typically large MB values, suggesting regional SO₂ emission estimates are too high. Unfortunately, there are no in-situ measurements available for better quantification. The correlation coefficient degrades in pre-Cy49r1 compared to Cy48r1 towards 0.66. Overall, the improvements are mixed for the SO₂ - SO42- couple and much less pronounced compared to the other SIA.

The regional distribution of surface NH₃ for 2018 in the three chosen global regions and the resulting changes for pre-Cy49R1_NOE4C and pre-Cy49r1 are shown in Fig. 4. Although there is a declining trend in European regional NH₃ emissions (Tichý et al., 2023), a strong seasonal cycle exists in Cy48r1. Maximal mixing ratios are situated around Benelux and northern Italy, with local differences of 8–20 ppb between July and December across regions. The CAMS_GLOB_ANT v5.3 (Soulie et al., 2024) emission inventory has recently been validated for NH₃ against top-down estimates providing confidence in the quality of the estimates for Europe (Ding et al., 2024). For the US, a similar seasonal signature exists especially for the northwest and southeast associated with agricultural emissions (Wang et al., 2023), with background mixing ratios of between 0.5–2.0 ppb remaining constant. For China, whose NH₃ emissions have increased over the last decades (Liu et al., 2019; Chen et al., 2023), surface mixing ratios of between 5–20 ppb occur for July for large areas, again associated with agricultural practices. Likewise, high mixing ratios are found around Bangladesh (> 20 ppb). For December, mixing ratios are typically an order of magnitude lower, except for the Southwest, which again exhibits high mixing ratios (> 20 ppb). Measurements of NH_x over the ocean are rare, thus the substantial increase which is simulated in pre-Cy49r1 cannot be evaluated. Nevertheless, estimates range from 0.1–4.2 ppb depending on season and location (Sharma et al., 2012) indicating that Cy48r1 has a significant negative bias which is somewhat improved in pre-Cy49r1.

Table 4 provides the global budget terms for all three simulations, showing the significant increase in deposition terms and tropospheric burden for NH₃(g). For pre-Cy49r1, the improved gas/particle partitioning from EQSAM4Clim reduces the particle phase concentrations of the semi-volatile aerosol species which leads to an increase in the respective gas phase concentrations, also affecting aerosol pH. This determines the solubility of NH₃(g), also contributing to its reduced conversion into NH4+ (see Table 4, approx. 44 % reduction), which is an effect amplified by the inclusion of mineral cations (i.e. Ca²⁺, Na⁺, K⁺, Mg²⁺). The tropospheric lifetime of NH₃(g) more than doubles in pre-Cy49r1 allowing more transport from strong source regions, in line with changes in the tropospheric burden. Both the associated loss due to dry and wet deposition increases (37 % and 51 %, respectively), due to lower NH4+ particle production (see Sect. 4.2).

Figure 5 shows comparisons between weekly observational composites from EMEP of [NH₃(g)] against those extracted from the various IFS-COMPO simulations for 2018. The observational composite shows that there is a skewed seasonal cycle exhibiting a maximum in April/May from agricultural activity, with wintertime values being around 0.4–0.5 µg m⁻³ increasing to 0.8–1.5 µg m⁻³ during spring and summertime. This seasonal variability is captured across all simulations with a high correlation with a Pearsons's R value between 0.71–0.73. For wintertime there is limited variability and an associated low weekly bias of around 0.1 µg m⁻³. For summertime, there is a weekly bias of between 0.5–1.0 µg m⁻³ in Cy48r1 (yearly MB of approximately 0.5 µg m⁻³), with the MB almost doubling for pre-Cy49r1 (with a yearly MB of approximately 0.7 µg m⁻³.

The bottom left panel of Fig. 5 shows the corresponding comparisons of [NH4+] in Europe using the weekly observational means from the EMEP network. There is a reversed seasonality for [NH4+] with higher [NH4+] during wintertime due to the colder temperatures decreasing the volatility of the particles (e.g. Tang et al., 2021). Moreover, for Cy48r1 there is clearly a large positive bias of [NH4+] with only minor changes introduced due the wet scavenging in pre-Cy49r1_NOE4C. Wintertime biases are reduced in pre-Cy49r1 especially for weeks which exhibit peaks in the observational means which IFS-COMPO can capture.

Similar comparisons are shown for the US in the bottom right panel of Fig. 5 using weekly composites assembled from the CASTNET data. In contrast to the seasonal cycle for [NH₃(g)], which peaks during April/May, there is no corresponding seasonal cycle in the weekly [NH4+] values peaks, being consistent between 0.4–0.5 µg m⁻³ during summertime. This indicates that a saturation occurs with respect to NH4+ particle formation which is linked to availability of HNO₃(g) (cf. see Sect. 4.3. below). Again, significant biases exist in Cy48r1 and pre-Cy49r1_NOE4C, reaching around 1.0 µg m⁻³ during summertime (which is 200 % of the summertime observational values). Applying EQSAM4Clim essentially removes this positive MB in pre-Cy49r1, resulting in good agreement for the entire year.

Figure 6 shows the distribution of the corresponding yearly mean [NH4+] for the three chosen regions for both Cy48r1 and pre-Cy49r1 during 2018, with the associated regional yearly mean statistics being given in Table 5. The location of the measurement sites is shown in each panel, with the respective yearly mean values within each circle. Significant decrease in the conversion rate of NH₃ in pre-Cy49r1 results in lower [NH4+]. This is a result of the improved gas/aerosol partitioning and the subsequent increase in aerosol pH when applying EQSAM4Clim (see Table 4; Rémy et al., 2024). More direct depositional loss to the surface for NH₃(g) occurs in pre-Cy49r1 due to increased residence time. There is a wide variability in the simulated aerosol pH between regions, with Europe exhibiting aerosol pH values of around 3–4, whereas the southern US and northern China exhibit aerosol pH values in the range 2–3 (Pan et al., 2024; Rémy et al., 2024) which indirectly affects the temporal variability in NH4+ production. Once formed, regional transport contributes to the continental distribution of NH4+ away from strong source regions (e.g. Simpson et al., 2010; Renner and Wolke, 2010; Du et al., 2020).

For Europe (top panels), most observational yearly mean values are between 0.2–1.2 µg m⁻³ which are exceeded by > 50 % in Cy48r1. In pre-Cy49r1 the reductions in the yearly mean [NH4+] are of the order of 0.5–1.0 µg m⁻³ and result in low yearly mean [NH4+] values for e.g. Spain/UK, whilst reducing maximal concentrations by approx. 50 % in northern Italy. This subsequently contributes to a reduction in the cumulative PM_2.5 biases for this region as shown in Rémy et al. (2024) for 2019. The associated MB values in Table 5 show a significant reduction in the bias (> 80 %) along with an increase in the correlation coefficient, although the simulated NH4+ distribution is still only moderately correlated (r=0.62). Unfortunately, the lack of available measurements means no quantification of the performance of IFS-COMPO around the mediterranean can be shown. It should be noted that with a more realistic distribution and seasonal variability in NH₃(g) emissions (e.g. Shephard et al., 2011; Dammers et al., 2019), the [NH4+] distributions shown here will not be affected as other SIA species determine the NH₃ – NH4+ gas/aerosol partitioning.

For the US (middle panels) similar decreases in yearly mean [NH4+] values occur in pre-Cy49r1 as compared to Cy48r1, with very low concentrations of between 0.1–0.4 µg m⁻³ for the west of the US, which shows less bias when compared against the observational mean values. This causes a reduction in the yearly mean regional bias of around 0.7 µg m⁻³ as shown in Table 5. A gradient exists in the aerosol pH from EQSAM4Clim ranging from yearly mean values of pH = 3.0 towards the northwest of the US and becoming more acidic towards the southwest US with yearly mean values of pH = 2.0 (Rémy et al., 2024). This reduces the transfer of NH₃(g), thus moderating NH4+ production (cf. Table 5). For the northeast of the US with high NO_x emissions, there are reductions of between 0.5–1.0 µg m⁻³. For the South-western US, with high [NH₃(g)] from agriculture (cf. Fig. 5) there are reductions of between 0.3–1.0 µg m⁻³. There is a degradation in the correlation coefficient exhibiting a moderate yearly mean correlation with significant overestimates for the South-western US as shown for the comparisons of [NH₃(g)] at selected sites in Fig. 5.

For Southeast Asia (bottom panels), the simulated yearly mean [NH4+] over land are typically much higher than those simulated for either Europe or the US, with maximal values of between 7.0–9.0 µg m⁻³ for eastern China, in-spite of the similar surface mixing ratios in NH₃(g) between Europe and China as shown in Fig. 5. This difference is predominantly driven by the relatively high HNO₃(g) in China due to the more polluted chemical regime (the availability of O₃(g), NO₂(g) and OH(g) determining gas-phase HNO₃(g) production). Applying EQSAM4Clim in pre-Cy49r1 results in significant decreases of between 1–2 µg m⁻³ [NH4+] as associated with the highest yearly mean [NH4+] > 6.0 µg m⁻³. This reduces the associated yearly mean regional bias by 0.4 µg m⁻³, with a corresponding reduction in the correlation, due to less transport. Again, the lack of sampling sites in the region with high primary NH₃(g) emissions skews the associated yearly mean biases. For the more remote locations (e.g. Mongolia/South China Sea) low values of < 0.5 µg m⁻³ are captured well in both Cy48r1 and pre-Cy49r1.

Figure 7 shows the monthly mean distributions and mixing ratios of HNO₃(g) for July and December 2018 for the three chosen global regions for Cy48r1, along with the relative differences when compared with pre-Cy49r1_NOE4C and pre-Cy49r1. The corresponding global chemical budget terms for HNO₃(g) during 2018 are provided in Table 6. No direct emission of HNO₃ occurs in IFS-COMPO, which is often prescribed in global chemistry transport models to represent e.g. chemistry in the plumes of ships (e.g. Vinken et al., 2011), with the main source being the oxidation of NO₂ by OH in the gas-phase as shown in Table 6. This production term increases in pre-Cy49r1 by approx. 14 % because of the enhancements in OH via changes in O₃ (not shown). For heterogeneous conversion, the cumulative HNO₃ production term is approximately 50 % that of the gas-phase production term, remaining relatively constant between simulations. There is a shift between fine mode NO3- (NH₄NO₃) and coarse mode NO3- (CaNO₃ / NaNO₃), strengthening the link between NH4+ and NO3- in pre-Cy49r1. Both dry and wet loss terms increase significantly due to the increase in the availability of HNO₃(g), which reduces the fraction converted to NO3-. The temporal variability of HNO₃(g) is influenced by the magnitude and extent of regional NO_x emissions, photochemical activity (via OH formation), gas/aerosol partitioning (where particles with high SO4= content having an associated low NO3- content) and scavenging in clouds and aerosols.

For Europe very low surface mixing ratios occur over land for both months shown in Cy48r1 (< 0.1 ppb), which is surprising considering that the Benelux has been shown to have high NO_x levels (van der A, 2024) therefore likely to have correspondingly high HNO₃(g) mixing ratios. Higher mixing ratios of between 0.25–0.5 ppb occur around the Coasts and the Mediterranean originating from direct shipping emissions of NO₂. This can lead to elevated NO3- concentration due to uptake of HNO₃(g) on sea salt, which might be too high as the current formulation of EQSAM4Clim only assumes thermodynamic equilibrium which is not dynamically limited. Here, an aerosol dynamical model refers to a microphysical framework that prognoses particle number/size distributions (via condensational growth, coagulation, nucleation and inter-mode transfer), thus enabling kinetically limited gas-to-particle uptake and condensation processes beyond the instantaneous thermodynamic equilibrium assumed by EQSAM4Clim (Metzger et al., 2018). A coupling with a dynamical aerosol model is foreseen for future versions of IFS-COMPO. In contrast, the application of EQSAM4Clim in pre-Cy49r1 results in large increases in surface HNO₃(g) at continental scale during July. For December, the relative differences in pre-Cy49R1 compared to Cy48r1 show increases in HNO₃(g) at latitudes below 60° N, together with decreases of between 25 %–75 % at latitudes higher than 60° N, i.e. at lower temperatures, and under a relatively low NO_x environment.

For the US, the highest HNO₃(g) mixing ratios in Cy48r1 occur for the eastern states and California (1–2 ppb), with much lower values in the more remote central US (0.1–0.2 ppb), with a strong seasonal cycle in maximal values peaking in July. Comparing relative differences between simulations shows a significant increase of surface HNO₃(g) in pre-Cy49r1 (between 0.1–6 ppb) at continental scale for both months, with the largest increases occurring in the northern States. In contrast to Europe no significant decreases in [HNO₃(g)] occur for any location or season.

Finally, in comparison to the other regions the surface [HNO₃(g)] are highest for Southeast Asia. Maximal mixing ratios of 4–5 ppb occur towards the eastern coast (July) and central regions (cf. December). Comparing the relative differences between simulations shows significant increases of between 50 %–5000 %, apart from the more remote regions to the north where NO_x emissions are lower. As for Europe, a strong seasonality can be seen with decreases above 30° N occurring regardless of the NO_x regime. As shown for NH₃ (cf. Fig. 4), there are significant increases in HNO₃ over the ocean for both months shown, associated with lower [NO3-] (as shown by the cumulative reduction in global conversion by 50 %).

Figure 8 shows the resulting changes in surface [HNO₃(g)] across simulations for Europe when compared against weekly means from the EMEP measurement sites (top left panel). The location of these sites is shown in Fig. A1 in the Appendix, where the location of the sampling sites results in a significant bias in the evaluation towards northern Europe. No obvious seasonal cycle is evident in the observational weekly means, with concentrations in the range of 0.3–0.7 µg m⁻³, although many sites are located away from strong NO_x sources. Both Cy48r1 and pre-Cy49r1_NOE4C exhibit negative weekly biases resulting in under-estimations of around 100 % in concentrations during the summertime, although the weekly variability is captured to a degree. There is a reduction in the bias of around 0.2–0.4 µg m⁻³ in pre-Cy49r1_NOE4C due to changes in the gas-phase production term. (cf. Table 8). For pre-Cy49r1 there is a significant excess of [HNO₃(g)] due to enhanced production and less transfer into the particulate phase, despite an increase in the cumulative deposition terms. Such changes are associated with relatively low [HNO₃(g)] IFS-COMPO values of around < 0.1 ppb.

Figure 8 also shows comparisons of weekly [HNO₃(g)] from the CASTNET measurement network in the US (top right panel). As for Europe, both the concentrations and seasonal variability in the observations is low, with typical weekly concentrations being approximately 0.5 µg m⁻³. The homogeneous distribution of measurement sites in the US means the evaluation presented does not have any significant regional bias. That the observed weekly mean concentrations are rather constant is surprising considering that variability in the gas-phase chemical production term involves OH. However, changes in RH, temperature and NH₃(g) across seasons also contribute to the seasonal lifetime. In contrast to Europe, both Cy48r1 and pre-Cy49r1_NOE4C show good agreement against the measurements with weekly biases of the order of 0.2–0.25 µg m⁻³. However, for pre-Cy49r1 a large positive bias is introduced directly from the use of EQSAM4Clim due to a limitation in the ability for HNO₃ to condense on particle surfaces. This means that condensed HNO₃ does not contribute to NH₄NO₃ formation (which requires a coupling of EQSAM4Clim with an aerosol dynamical model) as proposed for future cycles of IFS-COMPO. It also shows that although the cumulative global dry and wet deposition terms in pre-Cy49r1 have increased markedly compared to Cy48r1 (cf. Table 6), fluxes are not large enough to compensate for the reduced aerosol formation.

Figure 8 also shows the corresponding changes in surface [NO3-] for Europe (bottom left panel). The observational composites show that [NO3-] has concentrations almost twice those of the corresponding [HNO₃(g)] during wintertime. For summertime there is almost an equal split in the phase partitioning between precursor and SIA. Unlike for HNO₃(g), a shallow concave seasonal cycle exists in the weekly observational composites, related to seasonal differences in temperatures and lifetime (Tang et al., 2021). Both Cy48r1 and pre-Cy49r1_NOE4C fail to capture the correct seasonality and exhibit higher concentrations during summertime, resulting in substantial positive biases of 1–2 µg m⁻³. Considering the associated biases in [HNO₃(g)] shows that the HNO₃-NO3- partitioning is not captured well. For pre-Cy49r1 the description of the seasonal cycle is significantly improved due to the inclusion of EQSAM4Clim, resulting in much lower biases of < 0.5 µg m⁻³ throughout the year, also pointing to the importance of a better representation of gas/particle partitioning. The corresponding changes in [NO3-] in the simulations are evaluated against weekly composites from the CASTNET measurement network are shown in the bottom right panel of Fig. 8. A similar seasonal cycle exists as for Europe with similar concentrations. For Cy48r1 and pre-Cy49r1_NOE4C an inverse seasonal variability occurs in [NO3-] as compared with the observational weekly means, resulting in significant positive biases of around 1.0–1.5 µg m⁻³. For pre-Cy49r1 biases decrease by an order of magnitude and the seasonal variability is improved markedly, again showing improved particle distribution when using EQSAM4Clim.

In Fig. 9 we show the corresponding regional distributions of the yearly mean [NO3-] for Cy48r1 and pre-Cy49r1 during 2018 for the three chosen global regions, where the associated changes in the regional yearly mean statistics are provided in Table 7. Some commonality exists between the changes shown for yearly mean [NH4+] and yearly mean [NO3-], due to the speciation of the SIA involved. The cumulative sums of the smaller NO3- particle (fine mode NO3- in Table 6 with the form NH₄(NO3)) and the larger NO3- particle (coarse mode NO3- in Table 6 with the form of CaNO₃ and NaNO₃) are both included in the plots. Therefore, the changes evaluated here are a combination of changes to both the fine and coarse mode NO3-, rather than the changes associated with individual particle sizes. In contrast to the reduced N analysis provided above, which is impacted more directly due to changes in the fine mode NO3-, with [NO3-] also indirectly affected by the coarse mode assumptions through the effect of cations on the neutralisation level that subsequently controls the gas/aerosol equilibrium partitioning. The change in the partitioning for HNO₃ shown above results in an associated reduction in the fraction of NO_x held in particulate form due to e.g. a higher dry deposition component.

For Europe, the simulated yearly mean [NO3-] in Cy48r1 generally ranges between 0.2–2.0 µg m⁻³ over Scandinavia/Spain and the surrounding seas and between 2.0–6.3 µg m⁻³ over northwestern/central Europe and the Mediterranean, becoming lower towards the northeast and southwest. The highest European NO_x emissions occur around the south-east of the UK, Benelux, the Ruhr, and Po valleys (e.g. Liu et al., 2021; van der A., 2024). This, and the rather homogeneous distribution within central Europe shows that a significant degree of transport occurs once NO3- particles are formed. No such continental gradient in the yearly mean [NO3-] exists in the observational mean values indicating an overestimate in IFS-COMPO. Nevertheless, in pre-Cy49r1 decreases in [NO3-] of between 2–4 µg m⁻³ occur for the Baltic states/France/Germany and over the Mediterranean Sea (from relatively high shipping NO_x emissions) compared to Cy48r1. This results in much better agreement with the yearly mean observed values for the individual measurement stations. The yearly regional MB is reduced by ≅ 90 %, decreasing to 0.1 µg m⁻³ in pre-Cy49r1 with an associated increase in the correlation coefficient due to lower transport of [NO3-] out of the main source region. A large impact occurs due to the acidification of sea salt aerosols under high NO_x emission from dense shipping lanes, which can be seen by similar [NO3-] reductions over the sea, though difficult to evaluate due to the lack of sufficient in-situ measurements.

For the US, there is a similar impact on [NO3-] as shown for Europe, where the high yearly MB in [NO3-] decreases significantly (94 %) from Cy48r1 to pre-Cy49r1 (bottom right panel of Fig. 8). For Cy48r1, [NO3-] typically ranges between 2–4 µg m⁻³ with medium-range transport resulting in appreciable concentrations over the surrounding oceans. There is surprisingly little variability in the observed yearly mean [NO3-] despite the large difference in the resident [HNO₃(g)] across different states of the US related to the distribution of the NO_x emissions (Fig. 7; Goldberg et al., 2021). Only in the southwest, around California, the yearly mean [NO3-] > 2.0 µg m⁻³, shows typical yearly mean [NO3-] being ≤ 1.0 µg m⁻³ in pre-Cy49r1 for most of the US. This implies that the cations used as input for EQSAM4Clim imposes a limit concerning the phase-transfer of HNO₃(g) into more acidic aerosols via neutralisation of the anions by cations in the particle phase.

Figure 10 shows the regional distribution and values of yearly wet S deposition for Europe, the US, and Southeast Asia in both Cy48r1 and pre-Cy49r1 during 2018. To allow a direct comparison across regions we use a colour scale which covers values larger than 500 mgS m⁻² yr⁻¹ in Southeast Asia. The global budget terms for SO42- are presented in Table 8 and show that, despite the global burden increasing by one third, only very small increases of a few percent occur in the yearly wet SO42- totals (Rémy et al., 2024), with associated decreases in the dry deposition term. The significant increase in the tropospheric SO42- lifetime means more remains in the aerosol phase and will increase both AOD and the degree of scattering in IFS-COMPO as shown for AOD comparisons in Rémy et al. (2024). The most significant change is in the direct gas-phase production term of H₂SO₄(g), where increases in [SO₂(g)] subsequently increase the total mass scavenged into aqueous cloud droplets. This results in an extent of acidification (slowing in-situ oxidation; cf. Table 2), which is buffered by the increased phase transfer of NH₃(g) (cf. Table 4). These changes are the result of updates to the scavenging approach rather than the application of EQSAM4Clim. Although there is a 16 % reduction in the total global SO₂(aq) wet deposition (cf. Table 2), this is compensated for by increases in [SO42-(aq)] which results in an increase in the cumulative wet S deposition yearly totals.

For Europe (top panels), the changes between model simulations are like the changes in the SO₂(g) and SO42- particle concentrations discussed in Sect. 4. Compared against the yearly mean observational values from EMEP, which range from 100–500 mgS m⁻² yr⁻¹, there is an underestimation in Cy48r1 of approximately 100 mgS m⁻² yr⁻¹ for northwest Europe. For the observational means in Ireland, Poland, northern Spain and on the Iberian Peninsula negative MB are enhanced to around 300 mgS m⁻² yr⁻¹. This indicates missing local source terms in the global emission inventory provided as monthly mean fluxes. For other regions, the agreement is satisfactory capturing the observed deposition gradient from Germany into Austria/northern Italy. For pre-Cy49r1, strong similarity exists for Benelux, Denmark, and Italy, where negative biases of around 50–100 mgS m⁻² yr⁻¹ occur. A significant negative yearly MB exists in Europe, which decreases by around 10 % in pre-Cy49r1 (cf. Table 9) with a marginal increase in the correlation. Considering the associated positive MB for aerosol SO42- shown during summertime (cf. Fig. 2), there is an indication that not enough phase transfer occurs, thus negative MB values for the yearly deposition totals, and the large observational means at selected stations influencing the regional yearly mean.

For the US (middle panels) there is a stark contrast to the changes shown for Europe. Analysis of the yearly observational yearly mean values from CASTNET shows that a gradient exists in the wet deposition yearly totals from east to west, similar to that for SO₂(g) and the location of primary SO₂ emission sources (cf. Fig. 1). Maximal values of 300–400 mgS m⁻² yr⁻¹ occur towards the east coast where a positive MB occurs for [SO42-] (cf. Fig. 3). For Cy48r1, this gradient in yearly wet deposition is captured to a large degree, albeit with significant positive biases of > 100 mgS m⁻² yr⁻¹ with a maximal range of 700–900 mgS m⁻² yr⁻¹. There is a significant yearly wet deposition of SO_x in the Atlantic (250–400 mgS m⁻² yr⁻¹) due to the local oxidation of DMS (released from the ocean) and the long-range transport of SO₂(g) / SO42- from the anthropogenic source regions. In pre-Cy49r1 the area of maximal wet S deposition increases around, e.g., New York state extending south to Texas resulting in an increase in the positive yearly MB for the US by nearly 40 % (to 190 mgS m⁻² yr⁻¹). This contrasts with the significant improvement in the yearly MB for [SO42-] given in Table 3, which provides the continental mean rather than statistics for the east coast. The changes are determined by changes in the scavenging of SO42- particles into the aqueous phase, with the application of EQSAM4Clim having a minor influence (Table 8).

Finally for Southeast Asia (bottom panels), the magnitude of EANET yearly wet depositional totals show that more than twice the amount of S deposition occurs as measured in either Europe or the US over an identical period and a much wider area, reaching 1200–1300 mgS m⁻² yr⁻¹ in central and eastern China (not shown). The spatial distribution of stations shows that a negative gradient exists between deposition totals in China and those extending towards Myanmar (west) and Japan (east) (2000–2200 mgS m⁻² yr⁻¹; not shown). This indicates the importance of the transport component of SO42- when considering the low regional SO₂(g) precursor mixing ratios around the equator (cf. Fig. 1), and that the primary sources are typically infrequent volcanic eruptions for the region between 5° N–5° S (Fioletov et al., 2020) that typically inject the SO₂ above the boundary layer (thus with a limited impact on the surface values). Towards the coast of eastern China and Japan, the observations show yearly totals of 250–350 mgS m⁻² yr⁻¹, in contrast to high values near the middle of China. Such local scale variability is not captured using the current emission inventory that is employed, where the positive MB increases significantly in pre-Cy49r1 using the same observational stations and emission estimates (cf. Table 3). The extent of high wet deposition values reaches hundreds of kilometres from the coast far away from prescribed emission sources. The regional yearly MB improves markedly to 8.7 mgS m⁻² yr⁻¹ in pre-Cy49r1 despite increases in the MB and decreases in the Pearson's R value for [SO42-] (Table 3).

In Fig. 11 we show the corresponding changes in the total yearly mean wet deposition of reduced N for both Cy48r1 and pre-Cy49r1 during 2018 for the three global regions. The location of the sampling stations is identical to those shown above for the wet S deposition evaluation. In Table 8, the global chemical budget terms for NH4+ show that for pre-Cy49r1_NOE4C there is an increase the tropospheric burden by one third (as for SO42-, with (NH4)2SO₄ being a dominant SIA, Seinfeld and Pandis, 2006). This is subsequently reversed when applying EQSAM4Clim in pre-Cy49r1 This results in significant decreases in both the total yearly global dry and wet deposition terms for reduced N (> 50 % from Table 8).

For Europe (top panels), where high summertime NH₃(g) mixing ratios are simulated (cf. Fig. 4 in Sect. 4), the EMEP observational yearly wet deposition totals show that peak values exist in the Balkans, Germany, Austria and northern Italy (Po valley), and that local regional variability exists in e.g. France (ranging between 250–350 mgN m⁻² yr⁻¹). For regions with low NH₃ emissions, such as Scandinavia and the Iberian Peninsula, wet deposition totals generally have a lower range, of between 50–200 mgN m⁻² yr⁻¹. For Cy48r1, the high resident surface mixing ratios of NH₃(g) (5–15 ppb; Fig. 1) results in a relatively high NH_x yearly total wet deposition values of between 350–500 mgN m⁻² yr⁻¹ for northwest and central Europe at a country-wide scale (e.g. the Netherlands and Belgium). Measured yearly mean values from EMEP are typically exceeded, which results in a yearly MB of 61 mgN m⁻² yr⁻¹ in Cy48r1, albeit with a high correlation of 0.69 (cf. Table 8), reflecting the positive local MB shown for [NH4+] shown in Fig. 6. Comparing the yearly mean temporal distribution simulated in pre-Cy49r1 shows a significant reduction in the area with maximal values (> 450 mgN m⁻² yr⁻¹), being limited to northern Italy only. The reduction in [NH4+] (cf. Table 4) reduces the yearly MB in wet deposition by nearly 60 %, without any notable degradation in the correlation coefficient. Thus, the application of EQSAM4Clim significantly improves the simulation of reduced N wet deposition in IFS-COMPO for Europe.

For the US (middle panels), the AMoN observations show that there is a similar east-west gradient in the total reduced N wet deposition totals as that for NH₃(g) surface mixing ratios and [NH4+] distributions (cf. Figs. 4 and 6, respectively). The range in observed total wet deposition values is between 30–400 mgN m⁻² yr⁻¹ showing that, in the absence of local NH₃ emission sources, that levels of deposition are low (lower than that observed for Europe). Comparing the temporal distribution of reduced N wet deposition in Cy48r1 shows that the continental gradient is captured, although maximal values which occur towards central US are not seen in the measurements (> 100 % MB) being influenced by the high local NH₃ emission flux. For the east coast, where most primary NH₃ sources are located, there is generally an overestimation in the wet deposition simulated in Cy48r1. Compared to Europe, the yearly MB for the US is low at 9 mgN m⁻² yr⁻¹ in Cy48r1. This is a result of the large positive MB towards the east being moderated by negative in other parts of the US. There is an improvement in the MB in pre-Cy49r1, albeit with a degradation in the Pearsons' R to 0.72.

For Southeast Asia, the EANET observational yearly mean wet deposition totals show that, similar to that shown for S deposition, much higher values occur than for the other two regions presented. The yearly deposition values range between 200–2400 mgN m⁻² yr⁻¹ (not shown), with the highest measured values occurring more inland and away from coastal regions. The simulated temporal distribution in wet reduced N deposition does capture the variability across individual stations quite well across a wide area (cf. from China to Japan). In Cy48r1, the yearly MB is 12 mgN m⁻² yr⁻¹ on high yearly totals resulting in it being the lowest for all the regions, with a high correlation coefficient of 0.75. For pre-Cy49r1, there is a larger negative MB (although still relatively small compared to the large totals), in-spite of the lower (positive) MB simulated for the [NH4+] comparisons as compared to Cy48r1 (cf. Table 5), again with a modest degradation in the correlation.

Finally, in Fig. 12 the corresponding changes in the total yearly mean wet deposition of oxidised N for both Cy48r1 and pre-Cy49r1 during 2018 are shown. The global chemical budget terms provided in Table 6 reveal that there is an increase in the gas-phase production term for HNO₃, with a relatively constant heterogeneous conversion term for N₂O₅ when summed over the various reactive surfaces. Once formed, a significant fraction of HNO₃ is directly scavenged into aqueous cloud droplets and deposited to the surface as wet (acidic) deposition (cf. Rémy et al., 2024). However, the large biases shown for HNO₃(g) do reveal a limit to the loss via wet scavenging and deposition, with an excess remaining in the gas phase, which impacts the results shown in this section. Note that the particulate NO3- takes various chemical forms in IFS-COMPO (Ca(NO3)2, NaNO₃, KNO₃, NH₄NO₃), therefore there is only partial commonality between the changes shown for NH4+ and NO3-. With the application of EQSAM4Clim in pre-Cy49r1, the surface concentration and burden of NH4+ decreases strongly, while the gas phase concentration of HNO₃ increases (cf. Tables 4 and 6; Figs. 5 and 8).

For Europe (top panels), the observational yearly total wet deposition values from EMEP show that there is a rather constrained range in the deposition of total oxidised N deposition (150–275 mgN m⁻² yr⁻¹) apart from a few outliers such as on the southern coast of Ireland which have values > 500 mgN m⁻² yr⁻¹. In general, there are increases in the yearly wet deposition of [NO3-] between Cy48r1 and pre-Cy49r1, which increases the regional background totals improving agreement in e.g. Germany, which significantly reduces the yearly MB (cf. Table 9). In contrast to oxidized S, pre-Cy49r1_NOE4C simulates a reduction in the yearly wet deposition totals, for oxidized N which is further decreased for the coarse mode by applying EQSAM4Clim. The increase in [HNO₃(g)] means a larger fraction of oxidized N remains in the gas-phase thus reducing [NO3-] and improving the MB against observations as shown in Sect. 4. This subsequently leads to an improvement in performance for the regional yearly wet deposition totals. However, the large significant MB introduced for [HNO₃(g)] shows that another loss term is necessary for fully describe the budget of oxidized N across phases. For the US (middle panels), higher values of oxidised N deposition occur towards the east coast as defined by the distribution of NO_x emissions (Fig. 12). The range in the observational total wet deposition values from CASTNET is between 50–500 mgN m⁻² yr⁻¹, exhibiting a strong longitudinal gradient. Although this gradient is captured rather well, there is typically an overestimation of between 100–200 mgN m⁻² yr⁻¹ for e.g. New York state and the surrounding regions in Cy48r1. For the Western US, observations provide the range of between 0–100 mgN m⁻² yr⁻¹, with positive model biases of 100 mgN m⁻² yr⁻¹ for the northwestern states in both model versions. For the southern US, Cy48r1 exhibits overestimates of 50–70 mgN m⁻² yr⁻¹, which decreases noticeably in pre-Cy49r1. There is a large positive MB in Cy48r1 of 130 mgN m⁻² yr⁻¹ which is improved in pre-Cy49r1 decreasing by 23 %. Again, the Pearsons R value remains relatively unaffected showing the governing influence of the spatial distribution of the main point sources.

For Southeast Asia (bottom panels), the range in the EANET observational total wet deposition values is between 50–800 mgN m⁻² yr⁻¹ (not shown), with the highest values of > 2000 mgN m⁻² yr⁻¹ occurring on the Malaysian Coast. For northern and central China, total wet deposition values of up to 500 mgN m⁻² yr⁻¹ occur, covering a wide area including the similar to oxidised S. For Cy48r1, simulated values exceed observed values by 100 %–400 %, with a lack of spatial variability across the domain. Much better agreement occurs for Myanmar, Cambodia and Thailand related to much lower NO_x emissions. Comparing Cy48r1 and pre-Cy49r1 shows that a marked decrease in the MB of 31 % occurs between cycles, although the MB is at least twice that simulated for the other continents.