Reanalysis of NOAA H2 observations: implications for the H2 budget

Abstract

Hydrogen (${\mathrm{H}}_{\mathrm{2}}$) is a promising low-carbon alternative to fossil fuels for many applications. However, significant gaps in our understanding of the atmospheric ${\mathrm{H}}_{\mathrm{2}}$ budget limit our ability to predict the impacts of greater ${\mathrm{H}}_{\mathrm{2}}$ usage. Here we use NOAA H${}_{\mathrm{2}}$ dry air mole fraction observations from air samples collected from ground-based and ship platforms during 2010–2019 to evaluate the representation of H${}_{\mathrm{2}}$ in the NOAA GFDL-AM4.1 atmospheric chemistry-climate model. We find that the base model configuration captures the observed interhemispheric gradient well but underestimates the surface concentration of ${\mathrm{H}}_{\mathrm{2}}$ by about 10 ppb. Additionally, the model fails to reproduce the 1–2 ppb yr${}^{-\mathrm{1}}$ mean increase in surface H${}_{\mathrm{2}}$ observed at background stations. We show that the cause is most likely an underestimation of current anthropogenic emissions, including potential leakages from H${}_{\mathrm{2}}$-producing facilities. We also show that changes in soil moisture, soil temperature, and snow cover have most likely caused an increase in the magnitude of the soil sink, the most important removal mechanism for atmospheric ${\mathrm{H}}_{\mathrm{2}}$, especially in the Northern Hemisphere. However, there remains uncertainty due to fundamental gaps in our understanding of H${}_{\mathrm{2}}$ soil removal, such as the minimum moisture required for H${}_{\mathrm{2}}$ soil uptake, for which we performed extensive sensitivity analyses. Finally, we show that the observed meridional gradient of the ${\mathrm{H}}_{\mathrm{2}}$ mixing ratio and its seasonality can provide important constraints to test and refine parameterizations of the ${\mathrm{H}}_{\mathrm{2}}$ soil sink.

1 Introduction

Increased hydrogen (${\mathrm{H}}_{\mathrm{2}}$) usage has been proposed as a strategy to reduce the carbon intensity of many sectors of the economy that are difficult to electrify . The climate benefits of greater ${\mathrm{H}}_{\mathrm{2}}$ usage depend primarily on the ${\mathrm{H}}_{\mathrm{2}}$ production pathway. Current ${\mathrm{H}}_{\mathrm{2}}$ production is dominated by steam reforming of methane (${\mathrm{CH}}_{\mathrm{4}}$) in natural gas , a process that is very carbon intensive . Carbon capture can reduce ${\mathrm{CO}}_{\mathrm{2}}$ emissions associated with ${\mathrm{H}}_{\mathrm{2}}$ production. However, ${\mathrm{CH}}_{\mathrm{4}}$ leakage throughout the supply chain could offset much of the expected climate benefits of increased ${\mathrm{H}}_{\mathrm{2}}$ usage . Alternative production pathways such as renewable-based electrolytic ${\mathrm{H}}_{\mathrm{2}}$ can provide large and rapid reductions in radiative forcing , and considerable investments have been devoted to reducing their cost . Furthermore, evidence of significant concentrations of ${\mathrm{H}}_{\mathrm{2}}$ in surface and subsurface natural gases have spurred interest in the potential of naturally occurring ${\mathrm{H}}_{\mathrm{2}}$ as a new primary energy source .

${\mathrm{H}}_{\mathrm{2}}$ photooxidation in the atmosphere also tends to increase ${\mathrm{CH}}_{\mathrm{4}}$, ${\mathrm{O}}_{\mathrm{3}}$, and stratospheric water vapor, which results in indirect radiative forcing . recently calculated that ${\mathrm{H}}_{\mathrm{2}}$ has a global warming potential of $\simeq \mathrm{11.6}\pm \mathrm{2.8}$ and 37.3 $\pm$ 15.1 for a 100- and 20-year time horizon, respectively.

Significant uncertainties regarding the overall budget of ${\mathrm{H}}_{\mathrm{2}}$ remain. ${\mathrm{H}}_{\mathrm{2}}$ sources include both emissions and photochemical production from the oxidation of volatile organic compounds (VOCs). Estimates for the overall source of atmospheric ${\mathrm{H}}_{\mathrm{2}}$ range from $\simeq$ 70 to 110 Tg yr${}^{-\mathrm{1}}$, a large spread primarily associated with the magnitude of the ${\mathrm{H}}_{\mathrm{2}}$ photochemical sources . In recent work it has also been argued that current estimates of ${\mathrm{H}}_{\mathrm{2}}$ sources need to be revised upward to account for geologic ${\mathrm{H}}_{\mathrm{2}}$ seepage . These uncertainties in the nature and magnitude of ${\mathrm{H}}_{\mathrm{2}}$ sources have proved challenging to reduce, in part because of commensurate uncertainties in ${\mathrm{H}}_{\mathrm{2}}$ sinks. The atmospheric oxidation of ${\mathrm{H}}_{\mathrm{2}}$ by OH is well understood but is estimated to account for less than one third of the overall atmospheric sink . The most important removal pathway is the consumption of ${\mathrm{H}}_{\mathrm{2}}$ by high-affinity hydrogen-oxidizing bacteria (HA-HOB), a class of bacteria that have been identified in many different soils . Several parameterizations of the ${\mathrm{H}}_{\mathrm{2}}$ soil sink have been developed that aim at capturing the observed sensitivity of ${\mathrm{H}}_{\mathrm{2}}$ soil removal to soil temperature, soil moisture, and ecosystem and soil type . However, observational constraints on ${\mathrm{H}}_{\mathrm{2}}$ soil removal remain very limited and this process is challenging to represent in global models .

Here, we leverage the recently completed recalibration of H${}_{\mathrm{2}}$ measurements collected by the NOAA Global Monitoring Laboratory to perform a comprehensive evaluation of the simulation of ${\mathrm{H}}_{\mathrm{2}}$ in the Geophysical Dynamics Laboratory (GFDL) AM4.1 model . The NOAA monitoring network provides additional spatial coverage that complements other existing networks (AGAGE , CSIRO ) and offers a unique opportunity to evaluate the skill of the model in capturing changes in ${\mathrm{H}}_{\mathrm{2}}$ atmospheric concentration since 2010. This period is especially important in gaining a quantitative understanding of the present-day H${}_{\mathrm{2}}$ budget, also given that recent ${\mathrm{H}}_{\mathrm{2}}$ observations at Mace Head show both an increase in ${\mathrm{H}}_{\mathrm{2}}$ concentration and in its soil removal rate. The study is organized as follows: we first describe and evaluate the representation of ${\mathrm{H}}_{\mathrm{2}}$ in the GFDL-AM4.1 global chemistry-climate model, focusing on changes in ${\mathrm{H}}_{\mathrm{2}}$ over the 2010–2019 period. We then assess the sensitivity of the ${\mathrm{H}}_{\mathrm{2}}$ simulations to uncertainties in the ${\mathrm{H}}_{\mathrm{2}}$ budget focusing on the representation of anthropogenic ${\mathrm{H}}_{\mathrm{2}}$ emissions and soil removal.

2 Methods

2.1 Observations

The NOAA Global Monitoring Laboratory (GML) provides long-term monitoring of long-lived greenhouse gases and other trace species. The NOAA GML Global Cooperative Air Sampling Network is a partnership between GML and many outside organizations and individual volunteers to collect discrete air samples approximately weekly from 60$+$ globally distributed sites . These sites are often situated so as to collect air representative of large regional air masses. Priorities are placed on sites where opportunities exist for local support which can be maintained over long (decadal) timescales. The discrete air samples are collected weekly in pairs of 2 L glass flasks and are returned to GML for measurements of multiple species on central measurement systems thus providing a high level of consistency across the globally distributed network.

GML measurements of H${}_{\mathrm{2}}$ in the discrete air samples began in the late 1980s as an opportunistic measurement associated with the analytical technique then used for measuring atmospheric carbon monoxide (CO). To facilitate these H${}_{\mathrm{2}}$ measurements, NOAA/GML developed an in-house H${}_{\mathrm{2}}$-in-air reference scale based on a few gravimetric standards (the latest iteration named “H${}_{\mathrm{2}}$-X1996”). This reference scale was not stable over time and introduced significant time-dependent measurement errors. GML recently converted part of the historical H${}_{\mathrm{2}}$ measurement records to the H${}_{\mathrm{2}}$ calibration scale recommended by the World Meteorological Organization (WMO/MPI H${}_{\mathrm{2}}$-X2009) maintained by the Max Planck Institute (MPI) in Jena, Germany . Measurements since approximately 2010 have been reprocessed onto the MPI scale to remove the biases inherent in the NOAA X1996 scale . NOAA-reprocessed H${}_{\mathrm{2}}$ data since 2010 are consistent to within 1–2 ppbv on an annual basis for the same air measurements with CSIRO and the MPI-BGC . However, earlier NOAA data that remain on the obsolete NOAA X1996 scale are known to be biased relative to the later NOAA data and to other monitoring programs.

Here, we only consider ground stations from the NOAA Cooperative Air Sampling Network with at least 96 distinct monthly observations over the 2010–2019 period (80 % coverage, Fig. S1 in the Supplement). Ship-based observations are binned in 4° $\times$ 4° regions and we only consider regions with at least 40 observations.

2.2 Model setup

We use the GFDL Atmospheric Chemistry Model AM4.1 . For all configurations, the model is run from 2004 to 2019. Monthly sea surface temperature and sea ice concentration are from and . Horizontal winds are nudged to 6-hourly horizontal winds from the National Center for Environmental Prediction . The model output is sampled at the time and location of the air sampling. To better quantify the drivers of the ${\mathrm{H}}_{\mathrm{2}}$ distribution and trend, we tag ${\mathrm{H}}_{\mathrm{2}}$ associated with anthropogenic, marine, soil, and biomass burning direct ${\mathrm{H}}_{\mathrm{2}}$ emissions and with ${\mathrm{H}}_{\mathrm{2}}$ produced by the oxidation of VOCs.

2.2.1 BASE simulation

AM4.1 includes a detailed representation of ${\mathrm{H}}_{\mathrm{2}}$ , which is briefly summarized here. This configuration will be referred to as “BASE” (Table ) hereafter. ${\mathrm{H}}_{\mathrm{2}}$ sources include both direct emissions from anthropogenic and natural sources as well as photochemical production. Anthropogenic emissions of ${\mathrm{H}}_{\mathrm{2}}$ ($\simeq$ 13 Tg yr${}^{-\mathrm{1}}$ over the 2010–2019 period) are estimated from CO emissions in the Community Emissions Data System (CEDS) v20210421 using time-invariant sector-specific H${}_{\mathrm{2}}$ $:$ CO emission ratios (Table S1 in the Supplement). The transportation and residential sectors are the largest contributors to anthropogenic ${\mathrm{H}}_{\mathrm{2}}$ emissions (Fig. S2). Biomass burning emissions ($\simeq$ 8 Tg yr${}^{-\mathrm{1}}$) are estimated using the Global Fire Emissions Database (GFED4s, ) with emission factors from and . Marine (6 Tg yr${}^{-\mathrm{1}}$) and terrestrial (3 Tg yr${}^{-\mathrm{1}}$) sources of ${\mathrm{H}}_{\mathrm{2}}$ are prescribed as a monthly climatology and distributed spatially (Fig. S3) based on the soil and marine CO emission patterns in the Precursors of Ozone and their Effects in the Troposphere inventory . The BASE emission inventory does not include geological sources of ${\mathrm{H}}_{\mathrm{2}}$.

The production of ${\mathrm{H}}_{\mathrm{2}}$ associated with ${\mathrm{CH}}_{\mathrm{2}}\mathrm{O}$ photolysis is calculated interactively using FAST-JX version 7.1, as described by . Formaldehyde sources are dominated by the oxidation of VOCs from anthropogenic , biomass burning , and natural origins. Biogenic emissions of VOCs are prescribed as a monthly climatology , except for isoprene and terpenes, of which emissions are calculated interactively using the Model of Emissions of Gases and Aerosols from Nature . Surface CH${}_{\mathrm{4}}$ is prescribed as a monthly latitudinal profile from observations up to 2014 and from the SSP1-2.6 scenario after 2015 . We select this scenario as it tracks well the observed global CH${}_{\mathrm{4}}$ surface mixing ratio from the World Meteorological Organization Global Atmospheric Watch greenhouse gases observational network . To characterize the contribution of different VOC emissions to the photochemical production of ${\mathrm{H}}_{\mathrm{2}}$, we perform a set of sensitivity experiments in which we perturb the emission of a given VOC by 10 % and quantify the response of ${\mathrm{H}}_{\mathrm{2}}$ production. For ${\mathrm{CH}}_{\mathrm{4}}$ oxidation, we directly track the different oxidation pathways that result in ${\mathrm{H}}_{\mathrm{2}}$ production. The molar yield of ${\mathrm{H}}_{\mathrm{2}}$ from ${\mathrm{CH}}_{\mathrm{4}}$, isoprene, methanol, and terpene are estimated to be 0.38, 0.56, 0.21, and 0.70 mol mol${}^{-\mathrm{1}}$, respectively. These yields are broadly similar to estimates derived by (0.37, 0.54, 0.19, and 0.71, respectively) but are lower than estimates derived from a box model (0.38, 0.83, 0.38, and 0.85, respectively for NO${}_x$ $=$ 160 pptv; ), which may reflect the impact of wet and dry deposition. In particular, Fig. S4 shows that the simulated yield of ${\mathrm{H}}_{\mathrm{2}}$ from CH${}_{\mathrm{4}}$ oxidation is lowest in the tropics, where most ${\mathrm{CH}}_{\mathrm{4}}$ is oxidized, as a greater fraction of ${\mathrm{CH}}_{\mathrm{2}}\mathrm{O}$ is oxidized by OH in this region than at high latitudes.

Figure 1

Global source of H${}_{\mathrm{2}}$ (a) and changes in the magnitude of H${}_{\mathrm{2}}$ sources over the 2010–2019 period (b). For clarity, the green line denotes the combined change in ${\mathrm{H}}_{\mathrm{2}}$ emissions and photochemical production from natural sources (marine and soil emissions $+$ BVOCs photooxidation).

[Figure omitted. See PDF]

Overall, we find that ${\mathrm{CH}}_{\mathrm{4}}$ oxidation is the largest photochemical source of ${\mathrm{H}}_{\mathrm{2}}$ ($\simeq$ 27 Tg yr${}^{-\mathrm{1}}$). The oxidation of biogenic VOCs (BVOCs) accounts for the majority of the remaining photochemical source of ${\mathrm{H}}_{\mathrm{2}}$ ($\simeq \mathrm{14}$ Tg yr${}^{-\mathrm{1}}$) primarily from isoprene (8 Tg yr${}^{-\mathrm{1}}$), methanol (3 Tg yr${}^{-\mathrm{1}}$), and terpene (1.5 Tg yr${}^{-\mathrm{1}}$). The oxidation of VOCs from anthropogenic and biomass burning origin produces $\simeq$ 3 Tg yr${}^{-\mathrm{1}}$ of ${\mathrm{H}}_{\mathrm{2}}$. Our estimates are in good agreement with previous estimates : CH${}_{\mathrm{4}}$ ($\mathrm{23}\pm \mathrm{8}$ Tg yr${}^{-\mathrm{1}}$), isoprene ($\mathrm{9}\pm \mathrm{6}$ Tg yr${}^{-\mathrm{1}}$), biomass burning and anthropogenic VOCs (3 Tg yr${}^{-\mathrm{1}}$). This similarity can be attributed to the similar yield of ${\mathrm{H}}_{\mathrm{2}}$ from ${\mathrm{CH}}_{\mathrm{2}}\mathrm{O}$ (0.4 mol mol${}^{-\mathrm{1}}$ compared to 0.37; ). More work is needed to better characterize the temperature and pressure sensitivity of ${\mathrm{CH}}_{\mathrm{2}}\mathrm{O}$ photolysis quantum yields .

Figure a summarizes the simulated sources of ${\mathrm{H}}_{\mathrm{2}}$ associated with photochemical production and direct emissions in the BASE run. Over the 2010–2019 period, the average global simulated source of H${}_{\mathrm{2}}$ is 74 $\pm$ 1 Tg yr${}^{-\mathrm{1}}$, with 60 % from photochemical production. Anthropogenic activities are estimated to account for $\simeq$ 40 % of the overall ${\mathrm{H}}_{\mathrm{2}}$, primarily from CH${}_{\mathrm{4}}$ oxidation. Note that we assume that 50 % of the photochemical production of ${\mathrm{H}}_{\mathrm{2}}$ from ${\mathrm{CH}}_{\mathrm{4}}$ oxidation is anthropogenic based on the detailed bottom–up inventory of ${\mathrm{CH}}_{\mathrm{4}}$ sources . Top–down estimates suggest a higher contribution of anthropogenic sources ($\simeq$ 60 %; ), which would further increase the fraction of ${\mathrm{H}}_{\mathrm{2}}$ associated with anthropogenic activities. Figure b shows that the simulated total source of H${}_{\mathrm{2}}$ changes little over the 2010–2019 period. The simulated annual photochemical source of ${\mathrm{H}}_{\mathrm{2}}$ (excluding non-methane VOCs from biomass burning and anthropogenic origins) is 1.6 Tg yr${}^{-\mathrm{1}}$ greater during 2017–2019 than during 2010–2012, with 70 % of this increase attributed to ${\mathrm{CH}}_{\mathrm{4}}$. By contrast, H${}_{\mathrm{2}}$ associated with anthropogenic activities decreases ($-$1.3 Tg yr${}^{-\mathrm{1}}$, Fig. S2a), mostly from transport ($-$1 Tg yr${}^{-\mathrm{1}}$) and industries ($-$0.4 Tg yr${}^{-\mathrm{1}}$). The decrease in ${\mathrm{H}}_{\mathrm{2}}$ emissions reflects the decline in CO emissions from these sectors. The interannual variability of the overall ${\mathrm{H}}_{\mathrm{2}}$ source during the 2010–2019 period is dominated by the variability of biomass burning emissions, which can result in interannual changes of $\simeq \mathrm{2}$ Tg yr${}^{-\mathrm{1}}$.

${\mathrm{H}}_{\mathrm{2}}$ sinks include chemical oxidation by OH and O(${}^{\mathrm{1}}$D) and soil uptake associated with microbial activity. The deposition velocity of H${}_{\mathrm{2}}$ ($v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$)) over land is calculated following the parameterization of and depends on temperature, soil moisture , and soil carbon . In the BASE configuration we use a monthly climatology of $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) calculated using monthly meteorological and soil outputs from the GFDL Earth System Model ESM4.1 over the 1989–2014 period . Soil uptake is estimated to account for 71 % of the overall ${\mathrm{H}}_{\mathrm{2}}$ sink. The overall lifetime of ${\mathrm{H}}_{\mathrm{2}}$ in the BASE configuration is 2.5 years. The lifetime of ${\mathrm{H}}_{\mathrm{2}}$ associated with anthropogenic emissions is 6 % shorter due to their geographical distribution.

Figure 2

Mean model bias at individual sites for the BASE model configuration (a) over the 2010–2019 period. Filled symbols denote sites where the correlation between observed and simulated H${}_{\mathrm{2}}$ concentrations exceeds 0.5. Squares and stars denote background sites and cruises, respectively. (b) Observed and simulated difference in ${\mathrm{H}}_{\mathrm{2}}$ at background sites relative to ${\mathrm{H}}_{\mathrm{2}}$ mole fraction measured at the South Pole Observatory. The average concentrations at background sites are indicated for each configuration in the legend.

[Figure omitted. See PDF]

2.2.2 Sensitivity simulations

In this section, we describe additional model simulations that are designed to explore the impact of uncertainties in the representation of ${\mathrm{H}}_{\mathrm{2}}$ emissions and deposition on the simulation of atmospheric ${\mathrm{H}}_{\mathrm{2}}$ (Table ). We focus on ${\mathrm{H}}_{\mathrm{2}}$ emissions and deposition as their representations in models are largely derived from limited observational constraints .

Table 1

Model configurations.

	${\mathrm{H}}_{\mathrm{2}}$ anthropogenic emission	${\mathrm{H}}_{\mathrm{2}}$ natural emission	${\mathrm{H}}_{\mathrm{2}}$ soil removal
BASE	Time-invariant H${}_{\mathrm{2}}$ $:$ CO	Ocean $+$ Soil: monthly climatology	Monthly climatology $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$)
	emission ratio (Table S1)	Biomass burning: GFED4s
REVISED	Revised H${}_{\mathrm{2}}$ $:$ CO emission ratio	Ocean: calculated from CO seawater concentration	Same as BASE
	Emission from industrial ${\mathrm{H}}_{\mathrm{2}}$ use	Soil: calculated from N fixation
	(Sect. S1.2 and Table S1)	(Sect. S1.3)
		Biomass burning: same as BASE
REVISED_GLDAS	Same as REVISED	Same as REVISED	Daily $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) calculated using
			land reanalysis with soil moisture
			sensitivity from
			(Sect. S1.4)
REVISED_GLDAS2	Same as REVISED	Same as REVISED	Same as REVISED_GLDAS with
			canopy $+$ litter resistance and
			a lower HA-HOB water-activation threshold (Sect. S1.4)

The REVISED configuration focuses on the representation of anthropogenic and natural ${\mathrm{H}}_{\mathrm{2}}$ emissions. The development of the REVISED emission inventory is guided by the biases of the BASE configuration against ${\mathrm{H}}_{\mathrm{2}}$ observations (Sect. S1.1 in the Supplement, ). In particular, we focus on the representation of transportation emissions (Table S1) and emissions associated with industrial ${\mathrm{H}}_{\mathrm{2}}$ use for refining and ammonia, methanol, and steel production. Further details regarding the treatment of anthropogenic and natural sources in the REVISED emission inventory can be found in the Supplement (Sects. S1.2 and S1.3).

We further consider the impact of a different representation of ${\mathrm{H}}_{\mathrm{2}}$ soil uptake on the simulation of ${\mathrm{H}}_{\mathrm{2}}$. Here, we use the parameterization of the soil moisture response of HA-HOB activity recently developed by . This parameterization relates the minimum soil moisture required for ${\mathrm{H}}_{\mathrm{2}}$ uptake by HA-HOB to soil hydrological properties, which facilitates its incorporation into global models. This model also allows us to vary the strength of the diffusion barrier associated with soil litter, which can reduce ${\mathrm{H}}_{\mathrm{2}}$ transport to active sites . To quantify possible changes in $v_{\mathrm{d}}$(${\mathrm{H}}_{\mathrm{2}}$) over the 2010–2019 period, we calculate daily deposition velocity using 3-hourly soil moisture, soil temperature, and snow cover from the NASA Global Land Data Assimilation System . We focus on two different configurations. In REVISED_GLDAS, we neglect the litter resistance and assume that HA-HOB activity is inhibited when the soil matrix potential (${\mathrm{\Psi }}_{\mathrm{ws}}$) is less than the wilting point of plants in semiarid environments (${\mathrm{\Psi }}_{\mathrm{ws}}=$ $-$3000 kPa), as recommended by . The required soil moisture for the ${\mathrm{H}}_{\mathrm{2}}$ uptake is not well known and experimental studies have shown that HA-HOB are present in very arid environments . In REVISED_GLDAS2, we assume a much lower activation threshold for HA-HOB (${\mathrm{\Psi }}_{\mathrm{ws}}=$ $-$10 000 kPa) and account for the litter barrier. Note that both these configurations use the REVISED emission inventory. More details regarding the calculation of $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) can be found in the Supplement (Sect. S1.4).

Figure 3

Monthly standardized H${}_{\mathrm{2}}$ concentration for each cluster (a). The number of sites in each cluster is indicated by insets. The sites included in each cluster are shown in panel (b). The variation of source-tagged H${}_{\mathrm{2}}$ tracers in each cluster is shown in panel (c). Source-tagged H${}_{\mathrm{2}}$ tracers are normalized using the standard deviation of simulated ${\mathrm{H}}_{\mathrm{2}}$.

[Figure omitted. See PDF]

3 Results and discussion

3.1 BASE model evaluation

3.1.1 Climatology

Figure  shows the average model bias against surface observations from NOAA GML. In the BASE configuration, AM4.1 underestimates ${\mathrm{H}}_{\mathrm{2}}$ at all stations, with greater biases over continental regions (Fig. ). Correlations exceed 0.5 at more than 90 % of the background sites (squares) but only at 55 % of continental sites. Figure b further shows that the concentration at the South Pole is $\simeq$ 50 ppb greater than at the North Pole, which is well captured by the BASE configuration.

Figure 4

Trend in H${}_{\mathrm{2}}$ concentrations in observations (a) and in the BASE simulation (b) over the 2010–2019 period. (c) Observed (black) and simulated (red) trend in H${}_{\mathrm{2}}$ at background sites (squares) as well as the trend in tagged ${\mathrm{H}}_{\mathrm{2}}$ tracers associated with anthropogenic sources (green), biomass burning (purple), ocean and soil sources (black), and photochemical production (blue). Filled symbols denote trends that are significantly different from 0 ($p<\mathrm{0.01}$). The error bars show 1 SD for the estimated observed and simulated trends.

[Figure omitted. See PDF]

To examine differences between the model and the observed seasonality, we first apply the Kmean$++$ clustering algorithm to the observed ${\mathrm{H}}_{\mathrm{2}}$ monthly climatology. Since our focus is on the seasonality of ${\mathrm{H}}_{\mathrm{2}}$, we transform the monthly climatology of ${\mathrm{H}}_{\mathrm{2}}$ at each site such that it has a mean of 0 and a standard deviation of 1. Using the within-cluster sum of squares and the silhouette score, we find that the standardized ${\mathrm{H}}_{\mathrm{2}}$ observations can be well represented using four clusters. Figure  shows the seasonality of the standardized ${\mathrm{H}}_{\mathrm{2}}$ concentration for each cluster (Fig. a) and their spatial distribution (Fig. b). Sites are found to cluster broadly by latitude based on the seasonality of ${\mathrm{H}}_{\mathrm{2}}$ with clusters 1, 2, 3, and 4 comprising primarily sites located in the southern mid-to-high latitudes, southern tropics, northern subtropics, and northern mid-to-high latitudes, respectively. The model captures the seasonality of ${\mathrm{H}}_{\mathrm{2}}$ well in the Southern Hemisphere (cluster 1) but peaks 1–3 months earlier than observations for clusters 2, 3, and 4. Figure c shows the contribution of different sources of ${\mathrm{H}}_{\mathrm{2}}$ to the simulated seasonality of ${\mathrm{H}}_{\mathrm{2}}$ (inferred from the tagged ${\mathrm{H}}_{\mathrm{2}}$ tracers). The seasonal bias for cluster 2 is primarily driven by ${\mathrm{H}}_{\mathrm{2}}$ emitted from biomass burning, which peaks $\sim$ 2 months earlier than observations. This delay may be associated with greater burning of woody material toward the end of the dry season, emitting more incompletely oxidized products such as ${\mathrm{H}}_{\mathrm{2}}$ . Figure c also shows that the seasonal bias in clusters 3 and 4 may be associated with ${\mathrm{H}}_{\mathrm{2}}$ emitted by anthropogenic activities. As we show in Sect. , this seasonal bias may also reflect errors in the removal of ${\mathrm{H}}_{\mathrm{2}}$.

Figure 5

Mean observed and simulated ${\mathrm{H}}_{\mathrm{2}}$ at background sites (see Fig.  for locations).

[Figure omitted. See PDF]

3.1.2 Time series

Figure  shows that ${\mathrm{H}}_{\mathrm{2}}$ has increased at most sites with an average trend at the background sites of 1.4 $\pm$ 0.7 ppb yr${}^{-\mathrm{1}}$ over the 2010–2019 period with little variability with latitude. Trends are calculated using ordinary least-square regression applied to the de-seasonalized monthly ${\mathrm{H}}_{\mathrm{2}}$ concentrations. By contrast, the simulated ${\mathrm{H}}_{\mathrm{2}}$ concentration in the BASE configuration changes little over this time period.

In the Northern Hemisphere, the lack of a trend at background sites in the model (Fig. c) reflects the near-cancellation between the increase of photochemically produced ${\mathrm{H}}_{\mathrm{2}}$ and the decrease in ${\mathrm{H}}_{\mathrm{2}}$ emitted from anthropogenic sources, consistent with the changes in anthropogenic emissions and the photochemical source of ${\mathrm{H}}_{\mathrm{2}}$ from the oxidation of ${\mathrm{CH}}_{\mathrm{4}}$ and biogenic VOCs (Fig. ). The simulated absolute trend in anthropogenic ${\mathrm{H}}_{\mathrm{2}}$ is $\simeq$ 50 % lower in the Southern Hemisphere relative to the Northern Hemisphere due to the higher relative areal density of anthropogenic sources in the Northern Hemisphere. By contrast, the change in photochemically produced ${\mathrm{H}}_{\mathrm{2}}$ exhibits little variability with latitude and matches the observed trend well. The simulated trend also shows little latitudinal variation due to a decrease in ${\mathrm{H}}_{\mathrm{2}}$ from biomass burning in the Southern Hemisphere.

3.2 Sensitivity simulations

In this section, we explore how uncertainties in the representation of ${\mathrm{H}}_{\mathrm{2}}$ emissions and deposition contribute to the biases in the BASE model run.

3.2.1 Emissions

Figure  shows that the BASE run exhibits a 10–15 ppb negative bias and fails to capture the $\simeq$ 15 ppb increase over the 2010–2019 period (Fig. ). From this bias, we estimate a missing source of H${}_{\mathrm{2}}$ of $\simeq$ 2–2.5 Tg yr${}^{-\mathrm{1}}$ in circa 2010 and 3–4 Tg yr${}^{-\mathrm{1}}$ in circa 2019 (Sect. S1.1). Similarly, recently reported that a missing ${\mathrm{H}}_{\mathrm{2}}$ source (5 Tg yr${}^{-\mathrm{1}}$ in 2020) was required to explain the observed increase in ${\mathrm{H}}_{\mathrm{2}}$ concentration at Mace Head and Cape Grim since 2010.

Figures  and show that the observed increase in ${\mathrm{H}}_{\mathrm{2}}$ can be well captured with the REVISED emission inventory. In this inventory, the increase in the missing source of ${\mathrm{H}}_{\mathrm{2}}$ is explained by a lower decrease in anthropogenic ${\mathrm{H}}_{\mathrm{2}}$ emissions associated with fossil fuel combustion (0.9 Tg yr${}^{-\mathrm{1}}$ lower in 2019 relative to 2010 compared to 1.6 Tg yr${}^{-\mathrm{1}}$ in the BASE inventory) and an increase in ${\mathrm{H}}_{\mathrm{2}}$ emissions associated with ${\mathrm{H}}_{\mathrm{2}}$ industrial usage (0.3 Tg yr${}^{-\mathrm{1}}$). We also increase the ${\mathrm{H}}_{\mathrm{2}}$ soil source from 3 to 4.5 Tg yr${}^{-\mathrm{1}}$ to reduce the model negative bias. This change is well within the large uncertainties in the minor ${\mathrm{H}}_{\mathrm{2}}$ sources surveyed by . In particular, it is a small fraction of the estimated geological source of ${\mathrm{H}}_{\mathrm{2}}$ (23 $\pm \mathrm{7}$ Tg yr${}^{-\mathrm{1}}$, ), which we do not account for here.

Figure 6

Same as Fig.  but for the REVISED configuration.

[Figure omitted. See PDF]

The REVISED emission inventory provides a possible explanation for the observed increase in atmospheric ${\mathrm{H}}_{\mathrm{2}}$. It highlights the importance of constraining ${\mathrm{H}}_{\mathrm{2}}$ emissions associated with ${\mathrm{H}}_{\mathrm{2}}$ industrial use, a sector that is expected to grow rapidly in coming decades.

3.2.2 Deposition

The BASE and REVISED experiments assume no interannual variability in $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$). However, we have recently shown that climate change may cause an increase in $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) . A recent analysis of observations at Mace Head also suggests that $v_{\mathrm{d}}$(H${}_{\mathrm{2}}$) has increased in the past few decades .

Figure  shows that the REVISED_GLDAS and REVISED_GLDAS2 $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) exhibit different meridional distributions relative to the BASE configuration with faster removal in the subtropics and northern high latitudes but slower removal in the tropics. This reflects more efficient removal of ${\mathrm{H}}_{\mathrm{2}}$ in arid regions and slower removal in the tropics. These spatial differences are the largest for the REVISED_GLDAS2 configuration due to the activation of HA-HOB at a lower soil moisture. Figure b further shows that the values of $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) in the REVISED_GLDAS and in the REVISED_GLDAS2 configuration both increase over the 2010–2019 period in the northern midlatitudes. This increase reflects drier and warmer conditions in Europe and the Western US as well as parts of Siberia, which result in faster biological uptake rates and promote ${\mathrm{H}}_{\mathrm{2}}$ diffusivity (Fig. S5). This mechanism may contribute to the reported 1.2 % yr${}^{-\mathrm{1}}$ increase in ${\mathrm{H}}_{\mathrm{2}}$ deposition velocity at Mace Head from 1994 to 2020 . Drier conditions in Australia trigger biotic limitations, which results in a large decrease in ${\mathrm{H}}_{\mathrm{2}}$ deposition velocity in the southern midlatitudes in the REVISED_GLDAS configuration. By contrast, we find no significant suppression of ${\mathrm{H}}_{\mathrm{2}}$ uptake in Australia over this time period in the REVISED_GLDAS2 configuration due a lower threshold for biotic limitations.

Figure 7

Meridional distribution of $v_{\mathrm{d}}$(H${}_{\mathrm{2}}$) in the BASE, REVISED_GLDAS, and REVISED_GLDAS2 simulations (a) and (b) simulated change in $v_{\mathrm{d}}$(H${}_{\mathrm{2}}$) between the periods 2017–2019 and 2010–2012.

[Figure omitted. See PDF]

Changes to the spatial distribution of $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) and the increase in ${\mathrm{H}}_{\mathrm{2}}$ removal in the northern midlatitudes (Fig. b) in REVISED_GLDAS result in a larger pole-to-pole difference in surface ${\mathrm{H}}_{\mathrm{2}}$ (Fig. ) and a reduction in the simulated trend (Fig. ) in the northern mid-to-high latitudes. Both of these changes tend to degrade the model performance relative to the REVISED configuration. By contrast, the REVISED_GLDAS configuration better captures the timing of the ${\mathrm{H}}_{\mathrm{2}}$ maximum in the Northern Hemisphere (clusters 3 and 4, Fig. a).

Figure 8

Same as Fig.  but for the REVISED_GLDAS configuration.

[Figure omitted. See PDF]

Figure  shows the systematic assessment of the sensitivity of $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) to ${\mathrm{\Psi }}_{\mathrm{ws}}$ and the strength of the litter barrier. We find that a lower soil moisture threshold for HA-HOB activation (i.e., a lower ${\mathrm{\Psi }}_{\mathrm{ws}}$) favors ${\mathrm{H}}_{\mathrm{2}}$ removal in the Northern Hemisphere relative to the Southern Hemisphere (Fig. a) and results in a larger increase in $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) over the 2010–2019 period (Fig. b), especially in the Southern Hemisphere (Fig. c). This suggests that a lower ${\mathrm{\Psi }}_{\mathrm{ws}}$ would tend to worsen the model performance in the absence of a litter barrier (given the REVISED emissions). The litter barrier tends to increase the importance of arid regions for ${\mathrm{H}}_{\mathrm{2}}$ removal. This makes ${\mathrm{H}}_{\mathrm{2}}$ uptake more susceptible to moisture inhibition, such that a stronger litter barrier tends to result in a lower increase or even a decrease in $v_{\mathrm{d}}$(H${}_{\mathrm{2}}$) over the 2010–2019 period. Under all scenarios, the litter barrier tends to increase the gradient in $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) between the Northern Hemisphere and the Southern Hemisphere.

Figure 9

Simulated sensitivity of $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) to ${\mathrm{\Psi }}_{\mathrm{ws}}$ and the strength of the litter diffusive barrier. Panel (a) shows the response of the $\mathrm{north}/\mathrm{south}$ ratio of $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$). Panel (b) shows the change in $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) between 2010–2012 and 2017–2019. Panel (c) shows the change in the north/south ratio of $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) between 2010–2012 and 2017–2019. The REVISED_GLDAS configuration uses ${\mathrm{\Psi }}_{\mathrm{ws}}=$ $-$3000 kPa and no litter resistance (diamond). The REVISED_GLDAS2 uses ${\mathrm{\Psi }}_{\mathrm{ws}}=$ $-$10 000 kPa and the default (scale $=$ 1) litter resistance (star). The litter scale reflects the perturbation to the default litter resistance (see Sect. S1.4).

[Figure omitted. See PDF]

It is notable that no configuration results in a small change in $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) without producing large and increasing gradients between the Northern Hemisphere and Southern Hemisphere. As a result, our model cannot capture the observed trends, meridional gradient, and seasonality together given our REVISED estimate of ${\mathrm{H}}_{\mathrm{2}}$ emissions. This is illustrated by the REVISED_GLDAS2 configuration (${\mathrm{\Psi }}_{\mathrm{ws}}$ $=$ $-$10 000 kPa, litter_scale $=$ 1), which is found to improve the simulated trend relative to the REVISED_GLDAS (not shown) and the simulated seasonality relative to the REVISED configuration (Fig. ) but results in a large overestimate of the hemispheric gradient (Fig. ). This highlights the need for a more detailed representation of the factors that modulate $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) to help interpret changes in ${\mathrm{H}}_{\mathrm{2}}$ concentrations.

4 Conclusions

The recently released ${\mathrm{H}}_{\mathrm{2}}$ dry air mole fraction measurements from the NOAA Global Cooperative Air Sampling Network expand the spatial coverage of the WMO Global Atmospheric Watch observations. This offers the opportunity to assess the representation of the ${\mathrm{H}}_{\mathrm{2}}$ atmospheric budget in the state-of-the-art GFDL-AM4.1 global atmospheric chemistry-climate model. Observations show that ${\mathrm{H}}_{\mathrm{2}}$ has increased on average by 1–2 ppb yr${}^{-\mathrm{1}}$ over the 2010–2019 period. This change can be explained by the increase in photochemically produced ${\mathrm{H}}_{\mathrm{2}}$ (mostly from ${\mathrm{CH}}_{\mathrm{4}}$), provided direct anthropogenic ${\mathrm{H}}_{\mathrm{2}}$ emissions have remained stable during this time period. We hypothesize that this stability reflects the compensation between declining emissions associated with fossil fuel combustion (mostly from the transport sector) and increasing emissions associated with ${\mathrm{H}}_{\mathrm{2}}$-producing facilities (for refining and ammonia, methanol, and steel production). This is notable since ${\mathrm{H}}_{\mathrm{2}}$ release from ${\mathrm{H}}_{\mathrm{2}}$ production facilities is poorly understood yet important for assessing the climate benefits of ${\mathrm{H}}_{\mathrm{2}}$ .

We show that the observed trend, seasonality, and meridional gradient of H${}_{\mathrm{2}}$ provide complementary constraints on the global ${\mathrm{H}}_{\mathrm{2}}$ biogeochemical cycle. We find that our model fails to capture all three constraints together, which most likely reflects fundamental gaps in our representation of the soil removal of ${\mathrm{H}}_{\mathrm{2}}$ by microorganisms (HA-HOB). Such uncertainties are important, as an increase in $v_{\mathrm{d}}({\mathrm{H}}_{\mathrm{2}}$) would require a commensurate increase in ${\mathrm{H}}_{\mathrm{2}}$ sources to explain the observed change in ${\mathrm{H}}_{\mathrm{2}}$ concentration.

This study highlights the need for coordinated field and laboratory data collection efforts to help improve models of the distribution and activity of HA-HOB in global models . Such work is critical for quantifying the response of atmospheric ${\mathrm{H}}_{\mathrm{2}}$ to increasing anthropogenic ${\mathrm{H}}_{\mathrm{2}}$ usage as well as hydrological changes associated with climate change ; however, it is hindered by the lack of sensors that offer higher time resolution and maintain good sensitivity and stable response.

Code and data availability

The code for the GFDL ESM4.1 model is available at 10.5281/zenodo.5347705 (Robinson, 2021). NOAA Global Cooperative Network Flask Air ${\mathrm{H}}_{\mathrm{2}}$ can be downloaded at 10.15138/WP0W-EZ08.

The supplement related to this article is available online at: https://doi.org/10.5194/acp-24-4217-2024-supplement.

Author contributions

FP designed the research and developed and analyzed the model simulations. GP and AC collected and processed ${\mathrm{H}}_{\mathrm{2}}$ observations from the NOAA network and provided guidance regarding their interpretation. MB developed the soil moisture parameterization of HA-HOB used in the REVISED_GLDAS and REVISED_GLDAS2 configurations. All authors contributed to the drafting of the manuscript.

Competing interests

The contact author has declared that none of the authors has any competing interests.

Disclaimer

The views expressed in this paper do not necessarily represent the views of the US Department of Energy or the United States Government.Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

Acknowledgements

We thank Vaishali Naik for her help with generating model-ready ${\mathrm{H}}_{\mathrm{2}}$ emissions. We thank Larry Horowitz, Vaishali Naik, Amilcare Porporato, and Xinning Zhang for their helpful comments on the paper.

Financial support

This research has been supported in part by NOAA cooperative agreements NA17OAR4320101 and NA22OAR4320151 and by the US Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), specifically the Hydrogen and Fuel Cell Technologies Office.

Review statement

This paper was edited by Manvendra Krishna Dubey and reviewed by two anonymous referees.