Earths Future最新文献

Climate change is increasingly affecting the world's ocean ecosystems, necessitating urgent guidance on adaptation strategies to limit or prevent catastrophic impacts. The Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) is a network and framework that provides standardised ensemble projections of the impacts of climate change and fisheries on ocean life and the benefits that it provides to people. Since its official launch in 2013 as a small, self-organized project within the larger Inter-Sectoral Impact Model Intercomparison Project, the FishMIP community has grown substantially and contributed to key international policy processes, such as the Intergovernmental Panel on Climate Change Assessment Report, and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services Global Biodiversity Assessment. While not without challenges, particularly around comparing heterogeneous ecosystem models, integrating fisheries scenarios, and standardising regional-scale ecosystem models, FishMIP outputs are now being used across a variety of applications (e.g., climate change targets, fisheries management, marine conservation, Sustainable Development Goals). Over the next decade, FishMIP will focus on improving ecosystem model ensembles to provide more robust and policy-relevant projections for different regions of the world under multiple climate and societal change scenarios, and continue to be open to a broad spectrum of marine ecosystem models and modelers. FishMIP also intends to enhance leadership diversity and capacity-building to improve representation of early- and mid-career researchers from under-represented countries and ocean regions. As we look ahead, FishMIP aims to continue enhancing our understanding of how marine life and its contributions to people may change over the coming century at both global and regional scales.

Nights with high temperatures and humidity often bring more fatal consequences during the human sleeping process. A significantly increasing trend in the intensity and frequency of humid-heat nights has been observed in eastern China from 1961 to 2014. Detection analyses show that the fingerprinting of anthropogenic forcing, in which the greenhouse gas forcing is critical, can be identified for changes in the humid-heat nights in eastern China. However, the roles of anthropogenic aerosols and external natural forcings cannot be detected, and their effects on the observed changes in humid-heat nights over eastern China are generally negligible. Under different warming scenarios, there is a projected continuation of increasing intensity and frequency of humid-heat nights, although the magnitudes are reduced after constraining. In eastern China, the areal mean intensity of humid-heat nights is projected to increase by approximately 0.9, 1.5, 2.5, and 3.5°C (relative to 1995–2014) for 1.5, 2.0, 3.0, and 4.0°C global warming, while the occurrence of extreme humid-heat night is 1.1, 1.2, 1.2, and 1.3 times more than the current period. Moreover, the population exposed to humid-heat nights in eastern China, particularly in the northern regions, is expected to increase in the future. Our results enhance the understanding of the potential risks of humid-heat nights, which is critical for climate-change policy in China.

Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. Efforts to quantify the occurrence, drivers, and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll-a concentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in-lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll-a across the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll-a concentrations across US EPA's National Lakes Assessment (n = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of variation in chlorophyll-a. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll-a for ∼112,000 lakes across the CONUS. Risk for high chlorophyll-a concentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in nutrient pollution hot spots across the country. These catchment and lake-specific results can help managers identify potential nutrient pollution and chlorophyll-a hot spots that may fuel blooms, prioritize at-risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals.

Emerging fishing activity due to melting ice and poleward species distribution shifts in the rapidly-warming Arctic Ocean challenges transboundary management and requires proactive governance. A 2021 moratorium on commercial fishing in the Arctic high seas provides a 16-year runway for improved scientific understanding. Given substantial knowledge gaps, characterizing areas of highest uncertainty is a key first step. Marine ecosystem model ensembles that project future fish distributions could inform management of future Arctic fisheries, but Arctic-specific variation has not yet been examined for global ensembles. We use the Fisheries and Marine Ecosystem Intercomparison Project ensemble driven by two Earth System Models (ESMs) under two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) to illustrate the current state of and uncertainty among biomass projections for the Arctic Ocean over the duration of the moratorium. The models generally project biomass increases in more northern Arctic ecosystems and decreases in southern ecosystems, but wide intra-model variation exceeds projection means in most cases. The two ESMs show opposite trends for the main environmental drivers. Therefore, these projections are currently insufficient to inform policy actions. Investment in sustained monitoring and improving modeling capacity, especially for sea ice dynamics, is urgently needed. Concurrently, it will be necessary to develop frameworks for making precautionary decisions under continued uncertainty. We conclude that researchers should be transparent about uncertainty, presenting these model projections not as a source of scientific “answers,” but as bounding for plausible, policy-relevant questions to assess trade-offs and mitigate risks.

In the context of climate change, coastal flood risk is intensifying globally, particularly in China, where intricate coastlines and frequent tropical cyclones make storm surges a major concern. Despite local government's efforts to initiate coastal monitoring networks and qualitative risk guidelines, there remains a gap in detailed and efficient quantitative assessments for combinations of multiple sea-level components. To address this, we develop the Tropical Cyclone Storm Surge-based Flood Risk Assessment under Combined Scenarios (TCSoS-FRACS). This framework integrates impacts of storm surges, high tides, and sea-level rise using a hybrid of statistical and dynamic models to balance reliability and efficiency. By combining hazard, exposure, and vulnerability, it incorporates economic and demographic factors for a deeper understanding of risk composition. Applying TCSoS-FRACS to Hainan Island reveals that the combined effects of storm surges, high tides, and sea-level rise significantly amplify local coastal flood risk, increasing economic losses to 4.27–5.90 times and affected populations to 4.96–6.23 times. Additionally, transitioning from Fossil-fueled Development (SSP5-8.5) to Sustainability (SSP1-1.9) can reduce the risk increase by approximately half. The equivalence in flood hazard between current high tides and future sea level under a sustainable scenario boosts confidence in climate change adaptation efforts. However, coastal cities with low hazard but high exposure need heightened vigilance in flood defense, as future risk could escalate sharply. Our study provides new insights into coastal flood risk on Hainan Island and other regions with similar profiles, offering a transferable and efficient tool for disaster risk management and aiding in regional sustainable development.

Sea-level rise (SLR) poses a severe threat to the coastal environment through seawater intrusion into freshwater aquifers. The rising groundwater table also exacerbates the risk of pluvial, fluvial, and groundwater flooding in coastal regions. However, current Earth system models (ESMs) commonly ignore the exchanges of water at the land-ocean interface. To address this gap, we developed a novel land-ocean hydrologic coupling scheme in a state-of-the-science ESM, the Energy Exascale Earth System Model version 2 (E3SMv2). The new scheme includes the lateral exchange between seawater and groundwater and the vertical infiltration of seawater driven by the SLR-induced inundation. Simulations were performed with the updated E3SMv2 for the global land-ocean interface to assess the impacts of SLR on coastal groundwater under a high CO₂ emission scenario. By the middle of this century, seawater infiltration on the inundated areas will be the dominant component in the land-ocean coupling process, while the lateral subsurface flow exchange will be much smaller. The SLR-induced seawater infiltration will raise the groundwater levels, enhance evapotranspiration, and increase runoff with distinct spatial patterns globally in the future. Although the coupling process is induced by SLR, we found topography and warming temperature have more control on the coupling impacts, probably due to the relatively modest magnitude of SLR during the selected future period. Overall, our study suggests significant groundwater and seawater exchange at the land-ocean interface, which needs to be considered in ESMs.

Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM-LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM-LIN method improved R² of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM-LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias-corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub-periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade-offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques.

In low-lying coastal regions, the joint occurrence of high river flow and high water levels can cause coastal flooding with substantial economic and social implications. Recent studies over Canada's coasts have shown that neglecting the interdependency between flood drivers can underestimate the risk of flooding by up to 50%. However, to date, such interdependency has not been investigated for the coasts of the St. Lawrence River, Estuary and Gulf system (StL), where Sea Level Rise (SLR), along with intensified river peaks, are already threatening these communities. In this study, a copula-based bivariate frequency analysis was applied to quantify the likelihood of occurrence of flooding events under dependent and independent assumptions, for 26 sites along the StL. Furthermore, to quantify the impact of anthropogenic climate change, the joint return period in historical period was compared with that of projected SLR associated with RCP 8.5 for the year 2100. Results show that (a) the independence assumption can underestimate the likelihood of occurrence of flooding event in the Fluvial Section of the StL by up to 30 times and (b) the SLR can increase the likelihood of occurrence of flooding event by up to 50 times in the Estuary and the Gulf and by up to 5 times in the Fluvial Section of the StL. This study highlights the need for explicit consideration of the dependence between flood drivers and of SLR in the delineation of flood maps along the coast of the St. Lawrence.

Ocean warming and associated deoxygenation caused by anthropogenic global warming are impacting marine ecosystems. This article contextualizes and provides perspectives on key insights from a recently published study by Fröb et al. in Earth's Future (2024). The authors employ historical and high-emission scenario simulations through a state-of-the-art Earth system model to detect abrupt and persistent changes in the viability of marine habitats by leveraging an ecophysiological framework that quantifies how temperature and oxygen jointly limit the distribution of life in the ocean for a number of ecophysiotypes. A changepoint analysis is used to objectively detect shifts in decadal to multi-decadal mean states in potential marine habitats. They observe a decrease in the ocean volume capable of providing viable habitats for those ecophysiotypes with positive sensitivity to hypoxia. About half of these decreases occur abruptly, thus highlighting potential risks on the capacity of marine organisms to cope with a changing environment.