Selective logging, fragmentation, and understory fires directly degrade forest structure and composition. However, studies addressing the effects of forest degradation on carbon, water, and energy cycles are scarce. Here, we integrate field observations and high-resolution remote sensing from airborne lidar to provide realistic initial conditions to the Ecosystem Demography Model (ED–2.2) and investigate how disturbances from forest degradation affect gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H). We used forest structural information retrieved from airborne lidar samples (13,500 ha) and calibrated with 817 inventory plots (0.25 ha) across precipitation and degradation gradients in the Eastern Amazon as initial conditions to ED-2.2 model. Our results show that the magnitude and seasonality of fluxes were modulated by changes in forest structure caused by degradation. During the dry season and under typical conditions, severely degraded forests (biomass loss ≥ 66%) experienced water-stress with declines in ET (up to 34%) and GPP (up to 35%), and increases of H (up to 43%) and daily mean ground temperatures (up to 6.5°C) relative to intact forests. In contrast, the relative impact of forest degradation on energy, water, and carbon cycles markedly diminishes under extreme, multi-year droughts, as a consequence of severe stress experienced by intact forests. Our results highlight that the water and energy cycles in the Amazon are not only driven by climate and deforestation, but also the past disturbance and changes of forest structure from degradation, suggesting a much broader influence of human land use activities on the tropical ecosystems.

Across temperate North America, interannual variability (IAV) in gross primary production (GPP) and net ecosystem exchange (NEE), and their relationship with environmental drivers, are poorly understood. Here, we examine IAV in GPP and NEE and their relationship to environmental drivers using two state-of-the-science flux products: NEE constrained by surface and space-based atmospheric CO2 measurements over 2010–2015 and satellite up-scaled GPP from FluxSat over 2001-2017. We show that the arid western half of temperate North America provides a larger contribution to IAV in GPP (104% of east) and NEE (127% of east) than the eastern half, in spite of smaller magnitude of annual mean GPP and NEE. This occurs because anomalies in western ecosystems are temporally coherent across the growing season leading to an amplification of GPP and NEE. In contrast, IAV in GPP and NEE in eastern ecosystems are dominated by seasonal compensation effects, associated with opposite responses to temperature anomalies in spring and summer. Terrestrial biosphere models in the MsTMIP ensemble generally capture these differences between eastern and western temperate North America, although there is considerable spread between models.

Top-down estimates of CO2 fluxes are typically constrained by either surface-based or space-based CO2 observations. Both of these measurement types have spatial and temporal gaps in observational coverage that can lead to biases in inferred fluxes. Assimilating both surface-based and space-based measurements concurrently in a flux inversion framework improves observational coverage and reduces sampling biases. This study examines the consistency of flux constraints provided by these different observations and the potential to combine them by performing a series of six-year (2010–2015) CO2 flux inversions. Flux inversions are performed assimilating surface-based measurements from the in situ and flask network, measurements from the Total Carbon Column Observing Network (TCCON), and space-based measurements from the Greenhouse Gases Observing Satellite (GOSAT), or all three datasets combined. Combining the datasets results in more precise flux estimates for sub-continental regions relative to any of the datasets alone. Combining the datasets also improves the accuracy of the posterior fluxes, based on reduced root-mean-square differences between posterior-flux-simulated CO2 and aircraft-based CO2 over midlatitude regions (0.35–0.50~ppm) in comparison to GOSAT (0.39–0.57~ppm), TCCON (0.52–0.63~ppm), or in situ and flask measurements (0.45–0.53~ppm) alone. These results suggest that surface-based and GOSAT measurements give complementary constraints on CO2 fluxes in the northern extratropics and can be combined in flux inversions to improve observational coverage. This stands in contrast with many earlier attempts to combine these datasets and suggests that improvements in the NASA Atmospheric CO2 Observations from Space (ACOS) retrieval algorithm have significantly improved the consistency of space-based and surface-based flux constraints.

The retrieval of atmospheric composition from remote sensing measurements is a complex process that requires the integration of cross cutting domain knowledge into a coherent software package. The complexity is increased many times over when the software has to handle multiple types of instruments, operating in different spectral regions, each with their own peculiarities. This is further compounded when trying to combine information from multiple instruments for joint retrievals. Yet, there is enough overlap between the radiative transfer and retrieval techniques used by various missions that it is wasteful to continually reinvent the wheel every time. The Reusable Framework for Atmospheric Composition (ReFRACtor) is an extensible multi-instrument atmospheric composition retrieval framework that supports and facilitates data fusion of radiance measurements from different instruments in the ultraviolet, visible, near- and thermal-infrared. This framework is being developed to provide a community available software package that uses robust software engineering practices with well tested, community accepted algorithms and techniques. ReFRACtor is geared not only for the creation of end to end production systems, but also towards independent investigative scientists who need a software package to help answer atmospheric composition questions. We will explain how the use of succint interfaces between components provides advantages for future proofing, flexibility and reusability. Examples will be given for translating the logical separation of mathematical and scientific concepts into software components. We will describe how having a Python interface to fast compiled algorithms is helpful for rapid prototyping of new systems. The experience of early adopter scientists will also be discussed to give a perspective from outside the software development team.

Efforts to stem the spread of COVID-19 in China hinged on severe restrictions to human movement starting January 23rd, 2020 in Wuhan and subsequently to other provinces. Here, we quantify the ancillary impacts on air pollution and human health using inverse emissions estimates based on multiple satellite observations. We find that Chinese NOx emissions were reduced by 36% from early January to mid-February, with more than 80% of reductions occurring after their respective lockdown in most provinces. These emissions declines increased surface ozone by up to 16 ppb over northern China but decreased PM2.5 by up to 23 µgm nationwide. Air pollution appears to have substantially offset hospital admissions related to COVID-19, augmenting mitigation efforts, such as in the Hubei province with ~400 reduced admissions. Changes in human exposure are associated with about 2,100 increased ozone-related morbidity incidences and avoidance of at least 60,000 PM2.5-related morbidity incidences.

The ACT-America Earth Venture mission conducted five airborne campaigns across four seasons from 2016-2019, to study the transport and fluxes of Greenhouse gases across the eastern United States (US). Unprecedented spatial sampling of atmospheric tracers (CO2, CO, and COS) related to biospheric processes offers opportunities to improve our qualitative and quantitative understanding of seasonal and spatial patterns of biospheric carbon uptake. Here, we examine co-variation of boundary layer enhancements of CO2, CO, and COS across three diverse regions: the crop-dominated Midwest, evergreen-dominated South, and deciduous broadleaf-dominated Northeast. To understand the biogeochemical processes controlling these tracers, we compare the observed co-variation to simulated co-variation resulting from model- and satellite- constrained surface carbon fluxes. We found indication of a common terrestrial biogenic sink of CO2 and COS and secondary production of CO from biogenic sources in summer throughout the eastern US. Stomatal conductance likely drives fluxes through diffusion of CO2 and COS into leaves and emission of biogenic volatile organic compounds into the atmosphere. ACT-America airborne campaigns filled a critical sampling gap in the southern US, providing information about seasonal carbon uptake in southern temperate forests, and demanding a deeper investigation of underlying biological processes and climate sensitivities. Satellite- constrained carbon fluxes capture much of the observed seasonal and spatial variability, but underestimate the magnitude of net CO2 and COS depletion in the Southeast, indicating a stronger than expected net sink in late summer.