The South American monsoon approaches a critical transition in response to deforestation

Call for caution regarding the efficacy of large-scale afforestation and its hydrological effects

Large-scale afforestation programmes are generally presented as effective ways of increasing the terrestrial carbon sink while preserving water availability and biodiversity. Yet, a meta-analysis of both numerical and observational studies suggests that further research is needed to support this view. The use of inappropriate concepts (e.g., the biotic pump theory), the poor simulation of key processes (e.g., tree mortality, water use efficiency), and the limited model ability to capture recent observed trends (e.g., increasing water vapour deficit, terrestrial carbon uptake) should all draw our attention to the limitations of available theories and Earth System Models. Observations, either based on remote sensing or on early afforestation initiatives, also suggest potential trade-offs between terrestrial carbon uptake and water availability. There is thus a need to better monitor and physically understand the observed fluctuations of the terrestrial water and carbon cycles to promote suitable nature-based mitigation pathways depending on pre-existing vegetation, scale, as well as baseline and future climates.

Prospects for Neotropical Forest Birds and Their Habitats Under Contrasting Emissions Scenarios

Current and near future climate policy will fundamentally influence the integrity of ecological systems. The Neotropics is a region where biodiversity is notably high and precipitation regimes largely determine the ecology of most organisms. We modeled possible changes in the severity of seasonal aridity by 2100 throughout the Neotropics and used birds to illustrate the implications of contrasting climate scenarios for the region's biodiversity. Under SSP-8.5, a pessimistic and hopefully unlikely scenario, longer dry seasons (> 5%), and increased moisture stress are projected for about 75% of extant lowland forests throughout the entire region with impacts on 66% of the region's lowland forest avifauna, which comprises over 3000 species and about 30% of all bird species globally. Longer dry seasons are predicted to be especially significant in the Caribbean, Upper South America, and Amazonia. In contrast, under SSP-2.6-a scenario with significant climate mitigation-only about 10% of the entire region's forest area and 3% of its avifauna will be exposed to longer dry seasons. The extent of current forest cover that may plausibly function as precipitation-based climate refugia (i.e., < 5% change in length of dry periods) for constituent biodiversity is over 4 times greater under SSP-2.6 than with SSP-8.5. Moreover, the proportion of currently protected areas that overlap putative refugia areas is nearly 4 times greater under SSP-2.6. Taken together, our results illustrate that climate policy will have profound outcomes for biodiversity throughout the Neotropics-even in areas where deforestation and other immediate threats are not currently in play.

Charting the future of high forest low deforestation jurisdictions

High forest low deforestation jurisdictions (HFLDs) contain many of the world's last intact forests with historically low deforestation. Since carbon financing typically uses historical deforestation rates as baselines, HFLDs facing the prospect of future threats may receive insufficient incentives to be protected. We found that from 2002 to 2020, HFLDs (n = 310) experienced 44% higher deforestation rates than their historical baselines, and 60 HFLDs underwent periods of high deforestation (deforestation rate > 0.501%) at 0.983 ± 0.649% (mean ± SD)-a rate 7.5 times higher than the 10-y historical baseline of all HFLDs. For HFLDs to receive sufficient carbon finance requires baselines that can better reflect future deforestation trajectories of HFLDs. Using an empirical multifactorial model, we show that most contemporary HFLDs are expected to undergo higher deforestation from 2020 to 2038 than their historical baselines, with 72 HFLDs likely (>66% probability) to undergo high deforestation. Over the next 18 y, HFLDs are expected to lose 2.16 Mha y-1 of forests corresponding to 585 ± 74 MtCO2e y-1 (mean ± SE) of emissions. Efforts to protect HFLD forests from future threats will be crucial. In particular, improving baselining methods is key to ensuring that sufficient financing can flow to HFLDs to prevent deforestation.

Uncertainties too large to predict tipping times of major Earth system components from historical data

One way to warn of forthcoming critical transitions in Earth system components is using observations to detect declining system stability. It has also been suggested to extrapolate such stability changes into the future and predict tipping times. Here, we argue that the involved uncertainties are too high to robustly predict tipping times. We raise concerns regarding (i) the modeling assumptions underlying any extrapolation of historical results into the future, (ii) the representativeness of individual Earth system component time series, and (iii) the impact of uncertainties and preprocessing of used observational datasets, with focus on nonstationary observational coverage and gap filling. We explore these uncertainties in general and specifically for the example of the Atlantic Meridional Overturning Circulation. We argue that even under the assumption that a given Earth system component has an approaching tipping point, the uncertainties are too large to reliably estimate tipping times by extrapolating historical information.

Hantavirus Expansion Trends in Natural Host Populations in Brazil

Hantaviruses are zoonotic agents responsible for causing Hantavirus Cardiopulmonary Syndrome (HCPS) in the Americas, with Brazil ranking first in number of confirmed HCPS cases in South America. In this study, we simulate the monthly spread of highly lethal hantavirus in natural hosts by conjugating a Kermack-McCormick SIR model with a cellular automata model (CA), therefore simultaneously evaluating both in-cell and between-cell infection dynamics in host populations, using recently compiled data on main host species abundances and confirmed deaths by hantavirus infection. For both host species, our models predict an increase in the area of infection, with 22 municipalities where no cases have been confirmed to date expected to have at least one case in the next decade, and a reduction in infection in 11 municipalities. Our findings support existing research and reveal new areas where hantavirus is likely to spread within recognized epicenters. Highlighting spatial-temporal trends and potential expansion, we emphasize the increased risk due to pervasive habitat fragmentation and agricultural expansion. Consistent prevention efforts and One Health actions are crucial, especially in newly identified high-risk municipalities.

Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems

Real-world non-autonomous systems are open, out-of-equilibrium systems that evolve in and are driven by temporally varying environments. Such systems can show multiple timescale and transient dynamics together with transitions to very different and, at times, even disastrous dynamical regimes. Since such critical transitions disrupt the systems' intended or desired functionality, it is crucial to understand the underlying mechanisms, to identify precursors of such transitions, and to reliably detect them in time series of suitable system observables to enable forecasts. This review critically assesses the various steps of investigation involved in time-series-analysis-based detection of critical transitions in real-world non-autonomous systems: from the data recording to evaluating the reliability of offline and online detections. It will highlight pros and cons to stimulate further developments, which would be necessary to advance understanding and forecasting nonlinear behavior such as critical transitions in complex systems.

Rainfall seasonality dominates critical precipitation threshold for the Amazon forest in the LPJmL vegetation model

Understanding the Amazon Rainforest's response to shifts in precipitation is paramount with regard to its sensitivity to climate change and deforestation. Studies using Dynamic Global Vegetation Models (DGVMs) typically only explore a range of socio-economically plausible pathways. In this study, we applied the state-of-the-art DGVM LPJmL to simulate the Amazon forest's response under idealized scenarios where precipitation is linearly decreased and subsequently increased between current levels and zero. Our results indicate a nonlinear but reversible relationship between vegetation Above Ground Biomass (AGB) and Mean Annual Precipitation (MAP), suggesting a threshold at a critical MAP value, below which vegetation biomass decline accelerates with decreasing MAP. We find that approaching this critical threshold is accompanied by critical slowing down, which can hence be expected to warn of accelerating biomass decline with decreasing rainfall. The critical precipitation threshold is lowest in the northwestern Amazon, whereas the eastern and southern regions may already be below their critical MAP thresholds. Overall, we identify the seasonality of precipitation and the potential evapotranspiration (PET) as the most important parameters determining the threshold value. While vegetation fires show little effect on the critical threshold and the biomass pattern in general, the ability of trees to adapt to water stress by investing in deep roots leads to increased biomass and a lower critical threshold in some areas in the eastern and southern Amazon where seasonality and PET are high. Our findings underscore the risk of Amazon forest degradation due to changes in the water cycle, and imply that regions that are currently characterized by higher water availability may exhibit heightened vulnerability to future drying.

The new record of drought and warmth in the Amazon in 2023 related to regional and global climatic features

In 2023 Amazonia experienced both historical drought and warm conditions. On October 26th 2023 the water levels at the port of Manaus reached its lowest record since 1902 (12.70 m). In this region, October monthly maximum and minimum temperature anomalies also surpassed previous record values registered in 2015 (+ 3 °C above the normal considering the 1981-2020 average). Here we show that this historical dry and warm situation in Amazonia is associated with two main atmospheric mechanisms: (i) the November 2022-February 2023 southern anomaly of vertical integrated moisture flux (VIMF), related to VIMF divergence and extreme rainfall deficit over southwestern Amazonia, and (ii) the June-August 2023 downward motion over northern Amazonia related to extreme rainfall deficit and warm conditions over this region. Anomalies of both atmospheric mechanisms reached record values during this event. The first mechanism is significantly correlated to negative sea surface temperature (SST) anomalies in the equatorial Pacific (November-February La Niña events). The second mechanism is significantly correlated to positive SST anomalies in the equatorial Pacific, related to the impacts of June-September El Niño on the Walker Circulation. While previous extreme droughts were linked to El Niño (warmer North Tropical Atlantic SST) during the austral summer (winter and spring), the transition from La Niña 2022-23 to El Niño 2023 appears to be a key climatic driver in this record-breaking dry and warm situation, combined to a widespread anomalous warming over the worldwide ocean.

Modeling early warning signs of possible Amazon Forest dieback

Deforestation of the Amazon may reach a critical point where abrupt declines in rainfall could cause widespread forest dieback.