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  1. <bold>Abstract</bold>

    The photocatalytic decomposition of atmospheric methane (CH4) and nitrous oxide (N2O) could be valuable tools for mitigating climate change; however, to date, few photocatalyst deployment strategies have had their costs modeled. Here, we construct basic cost models of three photocatalytic CH4and N2O decomposition systems: (1) a ground-based solar system with natural airflow over photocatalyst-painted rooftops, (2) a ground-based LED-lit system with fan-driven airflow, and (3) an aerosol-based solar system on solid particles dispersed in the atmosphere. Each model takes as inputs the photocatalyst’s apparent quantum yield (AQY; a measure of how efficiently photons drive a desired chemical reaction) and the local CH4or N2O concentration. Each model calculates an overall rate of greenhouse gas (GHG) drawdown and returns a levelized cost of GHG removal per equivalent ton of carbon dioxide (tCO2e). Based on prior studies of atmospheric carbon dioxide removal, we adopt $100/tCO2e as a target cost. We estimate that painting rooftops with photocatalysts might meet the target cost for decomposition of >10 ppm CH4with catalyst AQYs >4%. If painting and cleaning costs were reduced by a factor of ∼3 from our scenario, removal of ambient CH4could meet the cost target with AQYs >1% and removal of ambient N2O could do so with AQYs >0.1%. Fan-driven systems with LED illumination appear to be very challenging, achieving removal costs <$100/tCO2e only for AQYs of >10% for CH4and >1% for N2O. Dispersing photocatalytic aerosols in the troposphere could be cost-effective with AQYs of >0.4% for ambient CH4or >0.04% for ambient N2O. However, the mass of aerosols required is large and their side effects and social acceptability are uncertain. We note that, for any system, AQYs on the order of 1% will likely be extremely challenging to achieve with such dilute reagents.

     
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  2. Abstract

    Soil organic matter decomposition and its interactions with climate depend on whether the organic matter is associated with soil minerals. However, data limitations have hindered global-scale analyses of mineral-associated and particulate soil organic carbon pools and their benchmarking in Earth system models used to estimate carbon cycle–climate feedbacks. Here we analyse observationally derived global estimates of soil carbon pools to quantify their relative proportions and compute their climatological temperature sensitivities as the decline in carbon with increasing temperature. We find that the climatological temperature sensitivity of particulate carbon is on average 28% higher than that of mineral-associated carbon, and up to 53% higher in cool climates. Moreover, the distribution of carbon between these underlying soil carbon pools drives the emergent climatological temperature sensitivity of bulk soil carbon stocks. However, global models vary widely in their predictions of soil carbon pool distributions. We show that the global proportion of model pools that are conceptually similar to mineral-protected carbon ranges from 16 to 85% across Earth system models from the Coupled Model Intercomparison Project Phase 6 and offline land models, with implications for bulk soil carbon ages and ecosystem responsiveness. To improve projections of carbon cycle–climate feedbacks, it is imperative to assess underlying soil carbon pools to accurately predict the distribution and vulnerability of soil carbon.

     
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  3. Abstract

    The determinants of fire-driven changes in soil organic carbon (SOC) across broad environmental gradients remains unclear, especially in global drylands. Here we combined datasets and field sampling of fire-manipulation experiments to evaluate where and why fire changes SOC and compared our statistical model to simulations from ecosystem models. Drier ecosystems experienced larger relative changes in SOC than humid ecosystems—in some cases exceeding losses from plant biomass pools—primarily explained by high fire-driven declines in tree biomass inputs in dry ecosystems. Many ecosystem models underestimated the SOC changes in drier ecosystems. Upscaling our statistical model predicted that soils in savannah–grassland regions may have gained 0.64 PgC due to net-declines in burned area over the past approximately two decades. Consequently, ongoing declines in fire frequencies have probably created an extensive carbon sink in the soils of global drylands that may have been underestimated by ecosystem models.

     
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    Free, publicly-accessible full text available October 1, 2024
  4. Abstract

    Process‐based land surface models are important tools for estimating global wetland methane (CH4) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site‐level patterns of freshwater wetland CH4fluxes (FCH4) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model‐observation disagreements are mainly at multi‐day time scales (<15 days); (b) most of the models can capture the CH4variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH4production). Our evaluation suggests the need to accurately replicate FCH4variability, especially at short time scales, in future wetland CH4model developments.

     
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    Free, publicly-accessible full text available November 1, 2024
  5. Abstract

    The rooting-zone water-storage capacity—the amount of water accessible to plants—controls the sensitivity of land–atmosphere exchange of water and carbon during dry periods. How the rooting-zone water-storage capacity varies spatially is largely unknown and not directly observable. Here we estimate rooting-zone water-storage capacity globally from the relationship between remotely sensed vegetation activity, measured by combining evapotranspiration, sun-induced fluorescence and radiation estimates, and the cumulative water deficit calculated from daily time series of precipitation and evapotranspiration. Our findings indicate plant-available water stores that exceed the storage capacity of 2-m-deep soils across 37% of Earth’s vegetated surface. We find that biome-level variations of rooting-zone water-storage capacities correlate with observed rooting-zone depth distributions and reflect the influence of hydroclimate, as measured by the magnitude of annual cumulative water-deficit extremes. Smaller-scale variations are linked to topography and land use. Our findings document large spatial variations in the effective root-zone water-storage capacity and illustrate a tight link among the climatology of water deficits, rooting depth of vegetation and its sensitivity to water stress.

     
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  6. Consumption in industrialized regions contributes to land-use change and greenhouse gas emissions in low-income regions. 
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  7. Abstract

    Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4budget. Data‐driven upscaling of CH4fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH4emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH4flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH4fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4emissions of 146 ± 43 TgCH4 y−1for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH4 y−1) and overlaps with top‐down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4fluxes has the potential to produce realistic extra‐tropical wetland CH4emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).

     
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    Free, publicly-accessible full text available October 1, 2024