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.

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity on the global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. Transpiration and gross primary productivity (GPP) that traditional LSMs simulate are not directly measurable from space and they are inferred from spaceborne observations using assumptions that are inconsistent with those of the LSMs, whereas canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we present the land model developed within the Climate Modeling Alliance (CliMA), which simulates global-scale GPP, transpiration, and hyperspectral canopy radiative transfer (RT). Thus, CliMA Land can predict any vegetation index or outgoing radiance, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given measurement geometry. Even without parameter optimization, the modeled spatial patterns of CliMA Land GPP, SIF, NDVI, EVI, and NIRv correlate significantly with existing observational products. CliMA Land is also very useful in its high temporal resolution, e.g., providing insights into when GPP, SIF, and NIRv diverge. Based on comparisons between models and observations, we propose ways to improve future land modeling regarding data processing and model development.

The livestock sector is the largest source of anthropogenic methane emissions, and is projected to increase in the future with increased demand for livestock products. Here, we compare livestock methane emissions and emission intensities, defined by the amount of methane emitted per unit of animal proteins, estimated by different methodologies, and identify mitigation potentials in different regions of the world based on possible future projections. We show that emission intensity decreased for most livestock categories globally during 2000-2018, due to an increasing protein-production efficiency, and the IPCC Tier 2 method should be used for capturing the temporal changes in the emission intensities. We further show that efforts on the demand-side to promote balanced, healthy and environmentally-sustainable diets in most countries will not be sufficient to mitigate livestock methane emissions without parallel efforts to improve production efficiency. The latter efforts have much greater mitigating effects than demand-side efforts, and hence should be prioritized in a few developing countries that contribute most of the mitigation potential.

Since the 1960s, carbon cycling in the high-latitude northern forest (HLNF) has experienced dramatic changes: most of the forest is greening and net carbon uptake from the atmosphere has increased. During the same time period, the COseasonal cycle amplitude (SCA) has almost doubled. Disentangling complex processes that drive these changes has been challenging. In this study, we substitute spatial sensitivity to temperature for time to quantify the impact of temperature increase on Gross Primary Production (GPP), total ecosystem respiration (TER), the fraction of Photosynthetic Active Radiation (fPAR), and the resulted contribution of these changes in amplifying the COSCA over the HLNF since 1960s. We use the spatial heterogeneity of GPP inferred from solar-induced chlorophyll Fluorescence in combination with net ecosystem exchange (NEE) inferred from column COobservations made between 2015 and 2017 from NASA’s Orbiting Carbon Observatory -2. We find that three quarters of the spatial variations in GPP and in the fPAR absorbed by the HLNF can be explained by the spatial variation in the growing season mean temperature (GSMT). The long term hindcast captures both the magnitude and spatial variability of the trends in observed fPAR. We estimate that between 1960 and 2010, the increase in GSMT enhanced both GPP and the SCA of NEE by ~20%. The calculated enhancement of NEE due to increase in GSMT contributes 56–72% of the trend in the CO SCA at high latitudes, much larger than simulations by most biogeochemical models.

2019 was both the hottest and driest year on record for Australia, leading to large forest fires in the southeast from November 2019 to January 2020. However, in early 2020, the fires and hot-dry conditions dissipated with above average rainfall and below average temperatures along Australia’s southeast coast. In this study, we utilize space-based measurements of trace gases (TROPOMI XCO, OCO-2 XCO2) and vegetation function (OCO-2 SIF, MODIS NDVI) to quantify the carbon cycle anomalies resulting from drought and fire in southeast Australia during the 2019/2020 growing season. During the austral spring, we find anomalous reductions in primary productivity and large biomass burning emissions in excess of bottom-up estimates from GFAS. This is then followed by a remarkable recovery and greening during early 2020, coincident with cooler and wetter conditions. We will further discuss different behaviors of recovery over fire-devasted and non-fire regions. This study showcases the capability of combining observations from multiple satellites to monitor the carbon and ecosystem anomalies resulting from extreme events. Finally, we will discuss the remaining challenges in monitoring the carbon cycle from space.