Tracking 21st century anthropogenic and natural carbon fluxes through model-data integration.

Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21st century observation-based time series of woody vegetation carbon densities into a bookkeeping model (BKM). This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions. Estimated emissions (from land-use and land cover changes) between 2000 and 2019 amount to 1.4 PgC yr-1, reducing the difference to other carbon cycle model estimates by up to 88% compared to previous estimates with the BKM (without the data assimilation). Our estimates suggest that the global woody vegetation carbon sink due to environmental processes (1.5 PgC yr-1) is weaker and more susceptible to interannual variations and extreme events than estimated by state-of-the-art process-based carbon cycle models. These findings highlight the need to advance model-data integration to improve estimates of the terrestrial carbon cycle under the Global Stocktake.

Land-use emissions embodied in international trade.

International trade separates consumption of goods from related environmental impacts, including greenhouse gas emissions from agriculture and land-use change (together referred to as "land-use emissions"). Through use of new emissions estimates and a multiregional input-output model, we evaluated land-use emissions embodied in global trade from 2004 to 2017. Annually, 27% of land-use emissions and 22% of agricultural land are related to agricultural products ultimately consumed in a different region from where they were produced. Roughly three-quarters of embodied emissions are from land-use change, with the largest transfers from lower-income countries such as Brazil, Indonesia, and Argentina to more industrialized regions such as Europe, the United States, and China. Mitigation of global land-use emissions and sustainable development may thus depend on improving the transparency of supply chains.

Select strengths and biases of models in representing the Arctic winter boundary layer over sea ice: the Larcform 1 single column model intercomparison.

Weather and climate models struggle to represent lower tropospheric temperature and moisture profiles and surface fluxes in Arctic winter, partly because they lack or misrepresent physical processes that are specific to high latitudes. Observations have revealed two preferred states of the Arctic winter boundary layer. In the cloudy state, cloud liquid water limits surface radiative cooling, and temperature inversions are weak and elevated. In the radiatively clear state, strong surface radiative cooling leads to the build-up of surface-based temperature inversions. Many large-scale models lack the cloudy state, and some substantially underestimate inversion strength in the clear state. Here, the transformation from a moist to a cold dry air mass is modelled using an idealized Lagrangian perspective. The trajectory includes both boundary layer states, and the single-column experiment is the first Lagrangian Arctic air formation experiment (Larcform 1) organized within GEWEX GASS (Global atmospheric system studies). The intercomparison reproduces the typical biases of large-scale models: Some models lack the cloudy state of the boundary layer due to the representation of mixed-phase micro-physics or to the interaction between micro-and macrophysics. In some models, high emissivities of ice clouds or the lack of an insulating snow layer prevent the build-up of surface-based inversions in the radiatively clear state. Models substantially disagree on the amount of cloud liquid water in the cloudy state and on turbulent heat fluxes under clear skies. Observations of air mass transformations including both boundary layer states would allow for a tighter constraint of model behaviour.