ALBERT

Description: Biogenic non-methane volatile organic compounds (NMVOCs) emitted from vegetation are a primary source for the chemical production of carbon monoxide (CO) in the atmosphere and these biogenic emissions account for about 18 % of the global CO burden. Partitioning CO fluxes to different source types in top-down inversion methods is challenging and typically a simple scaling of the posterior flux to prior flux values for fossil fuel, biogenic and biomass burning sources is used. Here we show top-down estimates of biogenic CO fluxes using a Bayesian inference approach, which explicitly accounts for both posterior and a priori CO flux uncertainties. This approach re-partitions CO fluxes following inversion of Measurements Of Pollution In The Troposphere (MOPITT) CO observations with the GEOS-Chem model, a global chemical transport model driven by assimilated meteorology from the NASA Goddard Earth Observing System (GEOS). We compare these results to the prior information for CO used to represent biogenic NMVOCs from GEOS-Chem, which uses the Model of Emissions of Gases and Aerosols from Nature (MEGAN) for biogenic emissions. We evaluate the a posteriori biogenic CO fluxes against top-down estimates of isoprene fluxes using Ozone Monitoring Instrument (OMI) formaldehyde observations. We find similar seasonality and spatial consistency in the posterior CO and top-down isoprene estimates globally. For the African savanna region, both top-down CO and isoprene seasonality vary significantly from the MEGAN apriori inventory. This method for estimating biogenic sources of CO will provide an independent constraint on modelled biogenic emissions and has the potential for diagnosing decadal-scale changes in emissions due to land-use change and climate variability.

Description: Understanding the processes controlling terrestrial carbon fluxes is one of the grand challenges of climate science. Carbon cycle process controls are readily studied at local scales, but integrating local knowledge across extremely heterogeneous biota, landforms and climate space has proven to be extraordinarily challenging. Consequently, top-down or integral flux constraints at process-relevant scales are essential to reducing process uncertainty. Future satellite-based estimates of greenhouse gas fluxes – such as CO2 and CH4 – could potentially provide the constraints needed to resolve biogeochemical process controls at the required scales. Our analysis is focused on Amazon wetland CH4 emissions, which amount to a scientifically crucial and methodologically challenging case study. We quantitatively derive the observing system requirements for testing wetland CH4 emission hypotheses at a process-relevant scale. To capture the spatial and temporal patterns of the major hydrological and carbon controls over wetland CH4 production, a satellite mission will need to resolve monthly CH4 fluxes at a 300 km resolution and with a 25 % flux precision. We simulate a range of low-earth orbit (LEO) and geostationary orbit (GEO) CH4 observing system configurations to evaluate the ability of these approaches to meet the CH4 flux requirements. Conventional LEO and GEO missions resolve monthly 300 km × 300 km Amazon wetland fluxes at a 186 % and 33 % median uncertainty level. Improving LEO CH4 measurement precision by √2 would only reduce the median CH4 flux uncertainty to 132 %. A GEO mission with targeted observing capability could resolve fluxes at a 21–27 % median precision by increasing the observation density in high cloud-cover regions at the expense of other parts of the domain. Process-driven greenhouse gas observing system simulations can enhance conventional uncertainty reduction assessments by providing the measurement needs for testing biogeochemical process hypotheses.

Description: While heterotrophic respiration (Rh) makes up about a quarter of gross global terrestrial carbon fluxes, it remains among the least-observed carbon fluxes, particularly outside the midlatitudes. In situ measurements collected in the Soil Respiration Database (SRDB) number only a few hundred worldwide. Similarly, only a single data-driven wall-to-wall estimate of annual average heterotrophic respiration exists, based on bottom-up upscaling of SRDB measurements using an assumed functional form to account for climate variability. In this study, we exploit recent advances in remote sensing of terrestrial carbon fluxes to estimate global variations in heterotrophic respiration in a top-down fashion at monthly temporal resolution and 4∘×5∘ spatial resolution. We combine net ecosystem productivity estimates from atmospheric inversions of the NASA Carbon Monitoring System-Flux (CMS-Flux) with an optimally scaled gross primary productivity dataset based on satellite-observed solar-induced fluorescence variations to estimate total ecosystem respiration as a residual of the terrestrial carbon balance. The ecosystem respiration is then separated into autotrophic and heterotrophic components based on a spatially varying carbon use efficiency retrieved in a model–data fusion framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting dataset is independent of any assumptions about how heterotrophic respiration responds to climate or substrate variations. It estimates an annual average global average heterotrophic respiration flux of 43.6±19.3 Pg C yr−1. Sensitivity and uncertainty analyses showed that the top-down Rh are more sensitive to the choice of input gross primary productivity (GPP) and net ecosystem productivity (NEP) datasets than to the assumption of a static carbon use efficiency (CUE) value, with the possible exception of the wet tropics. These top-down estimates are compared to bottom-up estimates of annual heterotrophic respiration, using new uncertainty estimates that partially account for sampling and model errors. Top-down heterotrophic respiration estimates are higher than those from bottom-up upscaling everywhere except at high latitudes and are 30 % greater overall (43.6 Pg C yr−1 vs. 33.4 Pg C yr−1). The uncertainty ranges of both methods are comparable, except poleward of 45∘ N, where bottom-up uncertainties are greater. The ratio of top-down heterotrophic to total ecosystem respiration varies seasonally by as much as 0.6 depending on season and climate, illustrating the importance of studying the drivers of autotrophic and heterotrophic respiration separately, and thus the importance of data-driven estimates of Rh such as those estimated here.

Description: Global terrestrial nitrogen (N) and phosphorus (P) cycles are coupled to the global carbon (C) cycle for net primary production (NPP), plant C allocation and decomposition of soil organic matter, but N and P have distinct pathways of inputs and losses. Current C-nutrient models exhibit large uncertainties in their estimates of pool sizes, fluxes and turnover rates of nutrients, due to a lack of consistent global data for evaluating the models. In this study, we present a new model-data fusion framework called Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines the CARbon DAta MOdel fraMework (CARDAMOM) data-constrained C-cycle analysis with spatially explicit data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. We calculated the steady-state N- and P-pool sizes and fluxes globally for large biomes. Our study showed that new N inputs from biological fixation and deposition supplied 〉20% of total plant uptake in most forest ecosystems but accounted for smaller fractions in boreal forests and grasslands. New P inputs from atmospheric deposition and rock weathering supplied a much smaller fraction of total plant uptake than new N inputs, indicating that the terrestrial C sink may ultimately be constrained by low P. Nutrient-use efficiency, defined as the ratio of gross primary production (GPP) to plant nutrient uptake, can be diagnosed from our model results and compared between biomes. Tropical forests had the lowest N-use efficiency and the highest P-use efficiency of the forest biomes. An analysis of sensitivity and uncertainty indicated that the NPP-allocation fractions to leaves, roots and wood contributed the most to the uncertainties in the estimates of nutrient-use efficiencies. Correcting for biases in NPP-allocation fractions produced more plausible gradients of N- and P-use efficiencies from tropical to boreal ecosystems and highlighted the critical role of accurate measurements of C allocation for understanding the N and P cycles.

Description: Multi-model averaging techniques provide opportunities to extract additional information from large ensembles of simulations. In particular, present-day model skill can be used to evaluate their potential performance in future climate simulations. Multi-model averaging methods have been used extensively in climate and hydrological sciences, but they have not been used to constrain projected plant productivity responses to climate change, which is a major uncertainty in Earth system modelling. Here, we use three global observationally orientated estimates of current net primary productivity (NPP) to perform a reliability ensemble averaging (REA) method using 30 global simulations of the 21st century change in NPP based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) ”business as usual” emissions scenario. We find that the three REA methods support an increase in global NPP by the end of the 21st century (2095–2099) compared to 2001–2005, which is 2–3 % stronger than the ensemble ISIMIP mean value of 24.2 Pg C y−1. Using REA also leads to a 45–68 % reduction in the global uncertainty of 21st century NPP projection, which strengthens confidence in the resilience of the CO2 fertilization effect to climate change. This reduction in uncertainty is especially clear for boreal ecosystems although it may be an artefact due to the lack of representation of nutrient limitations on NPP in most models. Conversely, the large uncertainty that remains on the sign of the response of NPP in semi-arid regions points to the need for better observations and model development in these regions.

Description: Global terrestrial nitrogen (N) and phosphorus (P) cycles are coupled to the global carbon (C) cycle for net primary production (NPP), plant C allocation, and decomposition of soil organic matter, but N and P have distinct pathways of inputs and losses. Current C-nutrient models exhibit large uncertainties in their estimates of pool sizes, fluxes, and turnover rates of nutrients, due to a lack of consistent global data for evaluating the models. In this study, we present a new model–data fusion framework called the Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines the CARbon DAta MOdel fraMework (CARDAMOM) data-constrained C-cycle analysis with spatially explicit data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. We calculated the steady-state N- and P-pool sizes and fluxes globally for large biomes. Our study showed that new N inputs from biological fixation and deposition supplied 〉20 % of total plant uptake in most forest ecosystems but accounted for smaller fractions in boreal forests and grasslands. New P inputs from atmospheric deposition and rock weathering supplied a much smaller fraction of total plant uptake than new N inputs, indicating the importance of internal P recycling within ecosystems to support plant growth. Nutrient-use efficiency, defined as the ratio of gross primary production (GPP) to plant nutrient uptake, were diagnosed from our model results and compared between biomes. Tropical forests had the lowest N-use efficiency and the highest P-use efficiency of the forest biomes. An analysis of sensitivity and uncertainty indicated that the NPP-allocation fractions to leaves, roots, and wood contributed the most to the uncertainties in the estimates of nutrient-use efficiencies. Correcting for biases in NPP-allocation fractions produced more plausible gradients of N- and P-use efficiencies from tropical to boreal ecosystems and highlighted the critical role of accurate measurements of C allocation for understanding the N and P cycles.

Description: While heterotrophic respiration (Rh) makes up about a quarter of gross global terrestrial carbon fluxes, it remains among the least observed carbon fluxes, particularly outside the mid-latitudes. In situ measurements collected in the Soil Respiration Database (SRDB) number only a few hundred worldwide. Similarly, only a single data-driven wall-to-wall estimate of annual average heterotrophic respiration exists, based on bottom-up upscaling of SRDB measurements using an assumed functional form to account for climate variability. In this study, we exploit recent advances in remote sensing of terrestrial carbon fluxes to estimate global variations in heterotrophic respiration in a top-down fashion at monthly temporal resolution and 4 x 5° spatial resolution. We combine net ecosystem productivity estimates from atmospheric inversions of the NASA Carbon Monitoring System- Flux (CMS-Flux) with an optimally-scaled gross primary productivity dataset based on satellite-observed solar-induced fluorescence variations to estimate total ecosystem respiration as a residual of the terrestrial carbon balance. The ecosystem respiration is then separated into autotrophic and heterotrophic components based on a spatially-varying carbon use efficiency retrieved in a model-data fusion framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting dataset is independent of any assumptions about how heterotrophic respiration responds to climate or substrate variations. It estimates an annual average global average heterotrophic respiration flux of 43.6 ± 19.3 Pg C/yr. These top-down estimates are compared to bottom-up estimates of annual heterotrophic respiration, with new uncertainty estimates that partially account for sampling and model errors. Top-down heterotrophic respiration estimates are higher than those from bottom-up upscaling everywhere except at high latitudes, and are 30 % greater overall (43.6 Pg C/yr vs. 33.4 Pg C/yr). The uncertainty ranges of both methods are comparable, except poleward of 45 degrees North, where bottom-up uncertainties are greater. The ratio of top-down heterotrophic to total ecosystem respiration varies seasonally by as much as 0.6 depending on season and climate, illustrating the importance of studying the drivers of autotrophic and heterotrophic respiration separately, and thus the importance of data-driven estimates of Rh such as those estimated here.

Description: Understanding the processes controlling terrestrial carbon fluxes is one of the grand challenges of climate science. Carbon cycle process controls are readily studied at local scales, but integrating local knowledge across extremely heterogeneous biota, landforms and climate space has proven to be extraordinarily challenging. Consequently, top-down or integral flux constraints at process-relevant scales are essential to reducing process uncertainty. Future satellite-based estimates of greenhouse gas fluxes – such as CO2 and CH4 – could potentially provide the constraints needed to resolve biogeochemical process controls at the required scales. Our analysis is focused on Amazon wetland CH4 emissions, which amount to a scientifically crucial and methodologically challenging case study. We quantitatively derive the observing system (OS) requirements for testing wetland CH4 emission hypotheses at a process-relevant scale. To distinguish between hypothesized hydrological and carbon controls on Amazon wetland CH4 production, a satellite mission will need to resolve monthly CH4 fluxes at a ∼ 333 km resolution and with a ≤ 10 mg CH4 m−2 day−1 flux precision. We simulate a range of low-earth orbit (LEO) and geostationary orbit (GEO) CH4 OS configurations to evaluate the ability of these approaches to meet the CH4 flux requirements. Conventional LEO and GEO missions resolve monthly ∼ 333 km Amazon wetland fluxes at a 17.0 and 2.7 mg CH4 m−2 day−1 median uncertainty level. Improving LEO CH4 measurement precision by 2 would only reduce the median CH4 flux uncertainty to 11.9 mg CH4 m−2 day−1. A GEO mission with targeted observing capability could resolve fluxes at a 2.0–2.4 mg CH4 m−2 day−1 median precision by increasing the observation density in high cloud-cover regions at the expense of other parts of the domain. We find that residual CH4 concentration biases can potentially reduce the ∼ 5-fold flux CH4 precision advantage of a GEO mission to a ∼ 2-fold advantage (relative to a LEO mission). For residual CH4 bias correlation lengths of 100 km, the GEO can nonetheless meet the  ≤  10 mg CH4 m−2 day−1 requirements for systematic biases ≤ 10 ppb. Our study demonstrates that process-driven greenhouse gas OS simulations can enhance conventional uncertainty reduction assessments by quantifying the OS characteristics required for testing biogeochemical process hypotheses.

Description: Wetland emissions contribute the largest uncertainties to the current global atmospheric CH4 budget, and how these emissions will change under future climate scenarios is also still poorly understood. Bloom et al. (2017b) developed WetCHARTs, a simple, data-driven, ensemble-based model that produces estimates of CH4 wetland emissions constrained by observations of precipitation and temperature. This study performs the first detailed global and regional evaluation of the WetCHARTs CH4 emission model ensemble against 9 years of high-quality, validated atmospheric CH4 observations from GOSAT (the Greenhouse Gases Observing Satellite). A 3-D chemical transport model is used to estimate atmospheric CH4 mixing ratios based on the WetCHARTs emissions and other sources. Across all years and all ensemble members, the observed global seasonal-cycle amplitude is typically underestimated by WetCHARTs by −7.4 ppb, but the correlation coefficient of 0.83 shows that the seasonality is well-produced at a global scale. The Southern Hemisphere has less of a bias (−1.9 ppb) than the Northern Hemisphere (−9.3 ppb), and our findings show that it is typically the North Tropics where this bias is the worst (−11.9 ppb). We find that WetCHARTs generally performs well in reproducing the observed wetland CH4 seasonal cycle for the majority of wetland regions although, for some regions, regardless of the ensemble configuration, WetCHARTs does not reproduce the observed seasonal cycle well. In order to investigate this, we performed detailed analysis of some of the more challenging exemplar regions (Paraná River, Congo, Sudd and Yucatán). Our results show that certain ensemble members are more suited to specific regions, due to either deficiencies in the underlying data driving the model or complexities in representing the processes involved. In particular, incorrect definition of the wetland extent is found to be the most common reason for the discrepancy between the modelled and observed CH4 concentrations. The remaining driving data (i.e. heterotrophic respiration and temperature) are shown to also contribute to the mismatch with observations, with the details differing on a region-by-region basis but generally showing that some degree of temperature dependency is better than none. We conclude that the data-driven approach used by WetCHARTs is well-suited to producing a benchmark ensemble dataset against which to evaluate more complex process-based land surface models that explicitly model the hydrological behaviour of these complex wetland regions.

Description: We conduct a global inverse analysis of 2010–2018 GOSAT observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes anthropogenic methane emissions and their 2010–2018 trends on a 4∘×5∘ grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane). We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. We verify our inversion results with independent methane observations from the TCCON and NOAA networks. Our inversion successfully reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high for China (coal) and Russia (oil and gas) and too low for Venezuela (oil and gas) and the US (oil and gas). We show large 2010–2018 increases in anthropogenic methane emissions over South Asia, tropical Africa, and Brazil, coincident with rapidly growing livestock populations in these regions. We do not find a significant trend in anthropogenic emissions over regions with high rates of production or use of fossil methane, including the US, Russia, and Europe. Our results indicate that the peak methane growth rates in 2014–2015 are driven by low OH concentrations (2014) and high fire emissions (2015), while strong emissions from tropical (Amazon and tropical Africa) and boreal (Eurasia) wetlands combined with increasing anthropogenic emissions drive high growth rates in 2016–2018. Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.