We provide here a model-based estimate of the transit time of carbon through the terrestrial biosphere, since the time of carbon uptake through photosynthesis until its release through respiration. We explored the consequences of increasing productivity versus increasing respiration rates on the transit time distribution and found that while higher respiration rates induced by higher temperature increase the transit time because older carbon is respired, increases in productivity cause a decline in transit times because more young carbon is available to supply increased metabolism. The combined effect of increases in temperature and productivity results in a decrease in transit times, with the productivity effect dominating over the respiration effect. Using an ensemble of simulation trajectories from the Carbon Data Model Framework (CARDAMOM), we obtained time-dependent transit time distributions incorporating 20th century global change. In these simulations, transit time declined over the 20th century, suggesting an increased productivity effect that augmented the amount of respired young carbon, but also increasing the release of old carbon from high latitudes. The transit time distribution of carbon becomes more asymmetric over time, with more carbon transiting faster through tropical and temperate regions, and older carbon being respired from high latitude regions.

Radiocarbon (14C) is commonly used as a tracer of the carbon cycle to determine how fast carbon moves between different reservoirs such as plants, soils, rivers or oceans. However such studies mostly emphasize the mean value (as Δ14C) of an unknown probability distribution. We introduce a novel algorithm to compute Δ14C distributions from knowledge of the age distribution of carbon in compartmental systems at equilibrium. Our results demonstrate that the shape of the distributions might differ according to the speed of cycling of ecosystem compartments and their connectivity within the system, and are mostly non-normal. The distributions are also sensitive to the variations of Δ14C in the atmosphere over time, as influenced by the counteracting anthropogenic effects of fossil-fuel emissions (14C-free) and nuclear weapons testing (bomb 14C). Lastly, we discuss insights that such distributions can offer for sampling and design of experiments aiming to capture the precise variability of Δ14C values in ecosystems.

Tropical ecosystems strongly influence Earth’s climate and weather patterns. Most tropical ecosystems remain warm year-round; nonetheless, their plants undergo seasonal cycles of carbon and water exchange. Previous research has shown the importance of water and light as drivers of the seasonality of photosynthetic activity in the tropics. Although data are scarce, field-based studies have found that seasonal cycles at a handful of tropical forest sites do not match those in land surface model simulations. A comprehensive understanding and model comparison of how seasonal variations in tropical photosynthetic activity relate to climate is lacking. In this study, we identify the seasonal relationships of precipitation and light availability with satellite-based photosynthetic activity. Three dominant and spatially distinct seasonal relationships emerge between photosynthetic activity and these two environmental drivers: photosynthetic activity that is positively correlated with both drivers (36% of tropical pixels), activity that increases following rain but decreases with light (28%), and activity that increases following bright seasons but decreases with rain (14%). We compare distributions of these observed relationships with those simulated by land surface models. In general, model simulations of gross primary productivity (GPP) overestimate the extent of positive correlations of photosynthetic activity with water and underestimate positive correlations with light. The largest discrepancies between simulations and observations are in the representation of the regions where photosynthetic activity increases with light and decreases with rain. Our clear scheme for representing the relationship between climate and photosynthetic activity can be used to benchmark tropical seasonality of GPP in land models.

Mathematical models are essential for integrating different processes that control rates of soil C dynamics and for assessing C sequestration and related climate benefits. Many models have been proposed in the literature to predict C stocks and fluxes, with no overall consensus on the best model that can provide relevant insights at a large range of scales and for multiple questions. We reviewed general groups of models with their expected ranges of application. We also reviewed recent advances in using models of any level of detail to compute C sequestration, and the climate benefit of C sequestration. Using agricultural soils from Sweden and Hawai‘i as examples, we show that new C inputs to the soil do not remain for long timescales, and only small proportions are stabilized. Although soils are a promising reservoir to store C and mediate emissions, long timescales are required to store amounts of C of relevance to mitigate climate change. The magnitude of climate benefit to mitigate warming through soil C sequestration is less than that of avoiding direct emissions, however, remains an important component of climate change mitigation and adaptation portfolios. Beyond the direct warming mitigation benefits, improved soil health through soil organic matter aggradation brings many co-benefits to the environment and local comunities. Improved production practices and locally sourced food and energy feedstocks are associated directly with avoided emissions elsewhere in the food and energy system.

The drivers of tree growth are one critical question in forest ecology and conservation. However, the measurement of tree growth is a difficult task that requires novel methods to improve accuracy and broaden the understanding of the effect of climate on tree metabolism and carbon accumulation. In this context, isotopes variation along woody tissues is a strong tool that provides new information about tree metabolism, growth rate, and the effect of climatic variation on these processes at high temporal resolution. Here, we obtained woody samples of two tree species two individuals per species (n = 4) from the Biogeographic Choco; Region in Colombia, one of the most humid regions of the planet without dry periods (mean annual temperature 25.9C and rainfall over 7200 mm). We measured 18O and 13C on these samples across some rings in each one to obtain intra-annual variation. Using these data, we assessed if isotopes variation in wood is correlated with climatic variation, explicitly precipitation regimen indicators employing Pearson correlation and linear mixed effect models. We found that both isotopes are correlated negatively with ring width. We also found that 18O is high negative correlated with precipitation indicators, rather than 13C. Our results suggest that isotopes variation are surrogates of tree growth in humid and non-seasonal forests. Besides, the 18O accumulation, which is strongly related to rainfall during the less rainy month (February: 370 mm on average), could be a better indicator of the effect of precipitation on the woody tissue rate change. However, 13C is more related to tissue formation processes. In conclusion, we found evidence of intra-annual variation in isotopes and tree growth in one hyper-humid forest challenging the effect of the dry season of tree growth and potentially suggesting the water excess as an additional limiting factor controlling growth rhythms in tropical trees.