Large uncertainty in carbon uptake potential of land-based climate-change mitigation efforts.

Most climate mitigation scenarios involve negative emissions, especially those that aim to limit global temperature increase to 2°C or less. However, the carbon uptake potential in land-based climate change mitigation efforts is highly uncertain. Here, we address this uncertainty by using two land-based mitigation scenarios from two land-use models (IMAGE and MAgPIE) as input to four dynamic global vegetation models (DGVMs; LPJ-GUESS, ORCHIDEE, JULES, LPJmL). Each of the four combinations of land-use models and mitigation scenarios aimed for a cumulative carbon uptake of ~130 GtC by the end of the century, achieved either via the cultivation of bioenergy crops combined with carbon capture and storage (BECCS) or avoided deforestation and afforestation (ADAFF). Results suggest large uncertainty in simulated future land demand and carbon uptake rates, depending on the assumptions related to land use and land management in the models. Total cumulative carbon uptake in the DGVMs is highly variable across mitigation scenarios, ranging between 19 and 130 GtC by year 2099. Only one out of the 16 combinations of mitigation scenarios and DGVMs achieves an equivalent or higher carbon uptake than achieved in the land-use models. The large differences in carbon uptake between the DGVMs and their discrepancy against the carbon uptake in IMAGE and MAgPIE are mainly due to different model assumptions regarding bioenergy crop yields and due to the simulation of soil carbon response to land-use change. Differences between land-use models and DGVMs regarding forest biomass and the rate of forest regrowth also have an impact, albeit smaller, on the results. Given the low confidence in simulated carbon uptake for a given land-based mitigation scenario, and that negative emissions simulated by the DGVMs are typically lower than assumed in scenarios consistent with the 2°C target, relying on negative emissions to mitigate climate change is a highly uncertain strategy.

Precipitation extremes in recent decades impact cattle populations at the global and national scales.

Cattle populations are one of the most important global ecological drivers. The global cattle population tripled during the past century, leading to large impacts on nutrient cycling, greenhouse gas emissions and biodiversity loss. Nonetheless, their populations have not increased uniformly through the last seven decades (1961-2018), with large unexplained variation between years. We hypothesized a main driver for such fluctuation was climate variability and thus examined global and national level relationships between cattle population growth and precipitation anomalies for the period 1961-2017. We showed that the variation in the global cattle population growth rate was related to precipitation anomalies following a distinctive parabolic relationship, where extreme wetness or dryness decreased population growth. When the analysis was downscaled to the national level, we found the strength of such relationship to be determined by the background climate and management intensity. Countries in drier climates and with less intensive cattle management showed the largest susceptibility to extreme annual precipitation. We propose a general model to explain the relationship between precipitation extremes and cattle populations at multiple scales, based on ecological processes applicable to grazing systems.

The decreasing range between dry- and wet- season precipitation over land and its effect on vegetation primary productivity.

One consequence of climate change is the alteration of global water fluxes, both in amount and seasonality. As a result, the seasonal difference between dry- (p < 100 mm/month) and wet-season (p > 100 mm/month) precipitation (p) has increased over land during recent decades (1980-2005). However, our analysis expanding to a 60-year period (1950-2009) showed the opposite trend. This is, dry-season precipitation increased steadily, while wet-season precipitation remained constant, leading to reduced seasonality at a global scale. The decrease in seasonality was not due to a change in dry-season length, but in precipitation rate; thus, the dry season is on average becoming wetter without changes in length. Regionally, wet- and dry-season precipitations are of opposite sign, causing a decrease in the seasonal variation of the precipitation over 62% of the terrestrial ecosystems. Furthermore, we found a high correlation (r = 0.62) between the change in dry-season precipitation and the trend in modelled net primary productivity (NPP), which is explained based on different ecological mechanisms. This trend is not found with wet-season precipitation (r = 0.04), These results build on the argument that seasonal water availability has changed over the course of the last six decades and that the dry-season precipitation is a key driver of vegetation productivity at the global scale.