We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.

Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations

Bioaerosols in the Earth system: Climate, health, and ecosystem interactions

Recent pandemic outbreak of the corona-virus disease 2019 (COVID-19) has raised widespread concerns about the importance of the bioaerosols. They are atmospheric aerosol particles of biological origins, mainly including bacteria, fungi, viruses, pollen, and cell debris. Bioaerosols can exert a substantial impact on ecosystems, climate change, air quality, and public health. Here, we review several relevant topics on bioaerosols, including sampling and detection techniques, characterization, effects on health and air quality, and control methods. However, very few studies have focused on the source apportionment and transport of bioaerosols. The knowledge of the sources and transport pathways of bioaerosols is essential for a comprehensive understanding of the role microorganisms play in the atmosphere and control the spread of epidemic diseases associated with them. Therefore, this review comprehensively summarizes the up to date progress on the source characteristics, source identification, and diffusion and transport process of bioaerosols. We intercompare three types of diffusion and transport models, with a special emphasis on a widely used mathematical model. This review also highlights the main factors affecting the source emission and transport process, such as biogeographic regions, land-use types, and environmental factors. Finally, this review outlines future perspectives on bioaerosols.

/pdf/the-source-and-transport-of-bioaerosols-in-the-air-a-review-4ct78tur1u.pdf

The source and transport of bioaerosols in the air: A review

Aeolian dispersal of bacteria in southwest Greenland: their sources, abundance, diversity and physiological states

The atmosphere connects habitats across multiple spatial scales via airborne dispersal of microbial cells, propagules and biomolecules. Atmospheric microorganisms have been implicated in a variety of biochemical and biophysical transformations. Here, we review ecological aspects of airborne microorganisms with respect to their dispersal, activity and contribution to climatic processes. Latest studies utilizing metagenomic approaches demonstrate that airborne microbial communities exhibit pronounced biogeography, driven by a combination of biotic and abiotic factors. We quantify distributions and fluxes of microbial cells between surface habitats and the atmosphere and place special emphasis on long-range pathogen dispersal. Recent advances have established that these processes may be relevant for macroecological outcomes in terrestrial and marine habitats. We evaluate the potential biological transformation of atmospheric volatile organic compounds and other substrates by airborne microorganisms and discuss clouds as hotspots of microbial metabolic activity in the atmosphere. Furthermore, we emphasize the role of microorganisms as ice nucleating particles and their relevance for the water cycle via formation of clouds and precipitation. Finally, potential impacts of anthropogenic forcing on the natural atmospheric microbiota via emission of particulate matter, greenhouse gases and microorganisms are discussed. 

OUP accepted manuscript

. Model-simulated transport of atmospheric trace components can be combined with observed concentrations to obtain estimates of ground-based sources using various inversion techniques. These approaches have been applied in the past primarily to obtain source estimates for long-lived trace gases such as CO2. We consider the application of similar techniques to source estimation for atmospheric aerosols, using as a case study the estimation of bacteria emissions from different ecosystem regions in the global atmospheric chemistry and climate model ECHAM5/MESSy-Atmospheric Chemistry (EMAC). Source estimation via Markov Chain Monte Carlo is applied to a suite of sensitivity simulations, and the global mean emissions are estimated for the example problem of bacteria-containing aerosol particles. We present an analysis of the uncertainties in the global mean emissions, and a partitioning of the uncertainties that are attributable to particle size, activity as cloud condensation nuclei (CCN), the ice nucleation scavenging ratios for mixed-phase and cold clouds, and measurement error. For this example, uncertainty due to CCN activity or to a 1 μm error in particle size is typically between 10% and 40% of the uncertainty due to observation uncertainty, as measured by the 5–95th percentile range of the Monte Carlo ensemble. Uncertainty attributable to the ice nucleation scavenging ratio in mixed-phase clouds is as high as 10–20% of that attributable to observation uncertainty. Taken together, the four model parameters examined contribute about half as much to the uncertainty in the estimated emissions as do the observations. This was a surprisingly large contribution from model uncertainty in light of the substantial observation uncertainty, which ranges from 81–870% of the mean for each of ten ecosystems for this case study. The effects of these and other model parameters in contributing to the uncertainties in the transport of atmospheric aerosol particles should be treated explicitly and systematically in both forward and inverse modelling studies.

/pdf/estimating-bacteria-emissions-from-inversion-of-atmospheric-51izyoyhan.pdf

Estimating bacteria emissions from inversion of atmospheric transport: sensitivity to modelled particle characteristics

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Estimating bacteria emissions from inversion of atmospheric transport: sensitivity to modelled particle characteristics