Earths Future最新文献

Anthropogenic land use and land cover change (LULCC) is projected to continue in the future. However, the influence of forest management on forest productivity change and subsequent LULCC projections remains under-investigated. This study explored the impacts of forest management-induced change in forest productivity on LULCC throughout the 21st century. Specifically, we developed a framework to softly couple the Global Change Analysis Model and Global Timber Model to consider forest management-induced forest productivity change and projected future LULCC across the five Shared Socioeconomic Pathways (SSPs). We found future increases in forest management intensity overall drive the increase of forest productivity. The forest management-induced forest productivity change shows diverse responses across all SSPs, with a global increase from 2015 to 2100 ranging from 3.9% (SSP3) to 8.8% (SSP1). This further leads to an overall decrease in the total area with a change of land use types, with the largest decrease under SSP1 (−7.5%) and the smallest decrease under SSP3 (−0.7%) in 2100. Among land use types, considering forest management-induced change significantly reduces the expansion of managed forest and also reduces the loss of natural land in 2100 across SSPs. This suggests that ignoring forest management-induced forest productivity change underestimates the efficiency of wood production, overestimates the managed forest expansion required to meet the future demand, and consequently, potentially introduces uncertainties into relevant analyses, for example, carbon cycle and biodiversity. Thus, we advocate to better account for the impacts of forest management in future LULCC projections.

Climatic extreme events are important because they can strongly impact humans, infrastructure, and biodiversity and will be affected by a changing climate. Surface Solar Radiation (SSR) is the primary energy source for solar photovoltaics (PV), which will be indispensable in future zero-emissions energy systems. Despite their pivotal role, extreme events in SSR remain under-documented. We provide a starting point in extreme SSR analysis by focusing on events caused by internal variability alone and therefore building a baseline for future extreme SSR research. We analyze extreme SSR events using daily-mean data from the pre-industrial control simulations (piControl) of the Coupled Model Intercomparison Project—Phase 6. We investigate their role in PV energy generation using the Global Solar Energy Estimator with the intent of strengthening the energy system's resilience. Our results show a pronounced asymmetry between consecutive days with extremely high and extremely low solar radiation over land, the former occurring more frequently than the latter. Moreover, our results call for detailed PV generation modeling that includes panel geometry. Simple models based on linear SSR representations prove insufficient due to pronounced seasonal variations and strong non-linear SSR dependency of high extremes. Our results demonstrate how climate model results can be leveraged to understand persistent radiation extremes that are relevant for future energy systems.

The International Maritime Organization (IMO) introduced new regulations on the sulfur content of shipping emissions in 2020 (IMO2020). Estimates of the climatic impact of this global reduction in anthropogenic sulfate aerosols vary widely. Here, we contribute to narrowing this uncertainty with two sets of climate model simulations using UKESM1. Using fixed sea-surface temperature atmosphere-only simulations, we estimate an IMO2020 global effective radiative forcing of 0.139 ± 0.019 Wm⁻² and show that most of this forcing is due to aerosol-induced changes to cloud properties. Using coupled ocean-atmosphere simulations, we note significant changes in cloud top droplet number concentration and size across regions with high shipping traffic density, and—in the North Atlantic and North Pacific—these microphysical changes translate to a decrease in cloud albedo. We show that IMO2020 increases global annual surface temperature on average by 0.046 ± 0.010°C across 2020–2029; approximately 2–3 years of global warming. Furthermore, our model simulations show that IMO2020 helps to explain the exceptional warming in 2023, but other factors are needed to fully account for it. The year 2023 also had an exceptionally large decrease in reflected shortwave radiation at the top-of-atmosphere. Our results show that IMO2020 made that more likely, yet the observations are within the variability of simulations without the reduction in shipping emissions. To better understand the climatic impacts of IMO2020, a model intercomparison project would be valuable whilst the community waits for a more complete observational record.

Burn severity is fundamental to post-fire impact assessment and emergency response. Vegetation Burn Severity (VBS) can be derived from satellite observations. However, Soil Burn Severity (SBS) assessment—critical for mitigating hydrologic and geologic hazards—requires costly and laborious field recalibration of VBS maps. Here, we develop a physics-informed Machine Learning model capable of accurately estimating SBS while revealing the intricate relationships between soil and vegetation burn severities. Our SBS classification model uses VBS, as well as climatological, meteorological, ecological, geological, and topographical wildfire covariates. This model demonstrated an overall accuracy of 89% for out-of-sample test data. The model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. VBS had the largest control on SBS, followed by weather (e.g., wind, fire danger, temperature), climate (e.g., annual precipitation), topography (e.g., elevation), and soil characteristics (e.g., soil organic carbon content). The relative control of processes on SBS changes across regions. Our model revealed nuanced relationships between VBS and SBS; for example, a similar VBS with lower wind speeds—that is, higher fire residence time—translates to a higher SBS. This transferrable model develops reliable and timely SBS maps using satellite and publicly accessible data, providing science-based insights for managers and diverse stakeholders.

Tipping elements are features of the climate system that can display self-reinforcing and non-linear responses if pushed beyond a certain threshold (the “tipping point”). Models suggest that we may surpass several of these tipping points in the next few decades, irrespective of which emissions pathway humanity follows. Some tipping elements reside in the Arctic and Antarctic and could potentially be avoided or arrested via a stratospheric aerosol injection (SAI) program applied only at the poles. This paper considers the utility of proactively developing the capacity to respond to emergent tipping element threats at the poles as a matter of risk management. It then examines both the air and ground infrastructure that would be required to operationalize such capability by 2040 and finds that this would require a funded launch decision by a financially credible actor by roughly 2030.

Vegetation distribution and composition are expected to change in northern high latitudes under rapid warming, which regulates ecosystem functions but remains challenging to predict. Vegetation change arises from the interplay of chronic climate trends such as warming and transient demographic processes of recruitment, growth, competition, and mortality. Most predictive models overlooked the role of demographic dynamics controlled by plant traits. Here, we simulate vegetation dynamics at the Kougarok Hillslope site in Alaska under historical and future climates using the E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator (ELM-FATES). To evaluate the roles of plant traits, we parameterize the model with 5,265 trait configurations representing diverse physiological and demographic strategies. Results show current modeled biomass, composition, and productivity are most sensitive to traits controlling photosynthetic capacity, carbon allocation, allometry, and phenology. Among all trait configurations, ∼5% reproduce in situ biomass and plant functional type (PFT) composition measured in 2016, that are indistinguishable from these two observed ecosystem states. Notably, these same trait configurations produce diverging biomass, composition, and productivity under future climate, where the uncertainty attributable to traits is twice the change attributable to climate change. The variation of projected productivity arises from emerging PFT composition under novel climate regimes, primarily explained by traits controlling cold-induced mortality, recruitment, and allometry. Our findings highlight the importance and uncertainty of demographic dynamics and its interaction with climate change in shaping Arctic vegetation change. Improved model predictions will likely benefit from explicit consideration of vegetation demography and better constraints of critical traits.

The future evolution of the Amazon rainforest remains uncertain not only due to uncertain climate projections, but also owing to the intricate balance between tree growth and mortality. Many Earth System Models inadequately represent forest demography processes, especially drought-induced tree mortality. In this study, we used ORCHIDEE-CAN-NHA, a land surface model featuring a mechanistic hydraulic architecture, a tree mortality sub-model linked to a critical loss of stem conductance and a forest demography module for simulating regrowth. The model was forced by bias-corrected climate forcing data from the ISIMIP-2 program, considering two scenarios and four different climate models to project biomass changes in the Amazon rainforest until 2100. These climate models display diverse patterns of climate change across the Amazon region. The simulation conducted with the HadGEM climate model reveals the most significant drying trend, suggesting that the Guiana Shield and East-central Amazon are approaching a tipping point. These two regions are projected to transition from carbon sinks to carbon sources by the mid-21st century, with the Brazilian Shield following suit around 2060. This transition is attributed to heightened drought-induced carbon loss in the future. This study sheds light on uncertainties in the future carbon sink in the Amazon forests, through a well-calibrated model that incorporates tree mortality triggered by hydraulic damage and the subsequent recovery of drought-affected forests through demographic processes.

Beside climate-change-induced sea-level rise (SLR), land subsidence can strongly amplify coastal risk in flood-prone areas. Mapping and quantifying contemporary vertical land motion (VLM) at continental scales has long been a challenge due to the absence of gridded observational products covering these large domains. Here, we fill this gap by using the new European Ground Motion Service (EGMS) to assess the current state of coastal VLM in Europe. First, we compare the InSAR-based EGMS Ortho (Level 3) with nearby global navigation satellite systems (GNSS) vertical velocity estimates and show that the geodetic reference frame used to calibrate EGMS strongly influences coastal vertical land velocity estimates at the millimeter per year level and this needs to be considered with caution. After adjusting the EGMS vertical velocity estimates to a more updated and accurate International Terrestrial Reference Frame (ITRF2014), we performed an assessment of VLM in European low elevation coastal flood plains (CFPs). We find that nearly half of the European CFP area is, on average, subsiding at a rate faster than 1 mm/yr. More importantly, we find that urban areas and populations located in the CFP experience a near −1 mm/yr VLM on average (excluding the uplifting Fennoscandia region). For harbors, the average VLM is even larger and increases to −1.5 mm/yr on average. This demonstrates the widespread importance of continental-scale assessments based on InSAR and GNSS to better identify areas at higher risk from relative SLR due to coastal subsidence.

In this study, we present a machine-learning model capable of predicting food insecurity in the Horn of Africa, which is one of the most vulnerable regions worldwide. The region has frequently been affected by severe droughts and food crises over the last several decades, which will likely increase in future. Therefore, exploring novel methods of increasing early warning capabilities is of vital importance to reducing food-insecurity risk. We present a XGBoost machine-learning model to predict food-security crises up to 12 months in advance. We used >20 data sets and the FEWS IPC current-situation estimates to train the machine-learning model. Food-security dynamics were captured effectively by the model up to 3 months in advance (R² > 0.6). Specifically, we predicted 20% of crisis onsets in pastoral regions (n = 96) and 20%–50% of crisis onsets in agro-pastoral regions (n = 22) with a 3-month lead time. We also compared our 8-month model predictions to the 8-month food-security outlooks produced by FEWS NET. Over a relatively short test period (2019–2022), results suggest the performance of our predictions is similar to FEWS NET for agro-pastoral and pastoral regions. However, our model is clearly less skilled in predicting food security for crop-farming regions than FEWS NET. With the well-established FEWS NET outlooks as a basis, this study highlights the potential for integrating machine-learning methods into operational systems like FEWS NET.

Groundwater in north India remains a vital food and water security resource for more than one billion people. Both summer monsoon drying, and winter warming pose considerable challenges for rapidly declining groundwater. However, their impacts on irrigation water demands and groundwater storage under the observed and projected future climate remain unexplored. Using in situ observations, satellite data, and a hydrological model that considers the role of irrigation and groundwater pumping, we show that summer monsoon drying and winter warming accelerate groundwater depletion in north India during the observed climate, which will continue in the projected future climate. Summer monsoon precipitation has significantly (P-value = 0.04) declined (∼8%) while winters have become warmer in north India during 1951–2021. Both satellite (GRACE/GRACE-FO) and hydrological model-based estimates show a rapid groundwater depletion (∼1.5 cm/year) in north India with a net loss of 450 km³ of groundwater during 2002–2021. The summer monsoon drying followed by winter warming cause a substantial reduction in groundwater storage due to reduced groundwater recharge and enhanced pumping to meet irrigation demands. Summer monsoon drying and winter warming will continue to affect groundwater storage in north India in the future. For instance, summer monsoon drying (10%–15% deficit for near-far periods) followed by substantial winter warming (1–4°C) in the future will further accelerate groundwater depletion by increasing (6%–20%) irrigation water demands and reducing groundwater recharge (6%–12%). Groundwater sustainability measures including reducing groundwater abstraction and enhancing the groundwater recharge during the summer monsoon seasons are needed to ensure future agricultural production.