Future Changes Research Articles

Convection-permitting climate model representation of severe convective wind gusts and future changes in southeastern Australia

Abstract. Previous research has suggested that the frequency and intensity of surface hazards associated with thunderstorms and convection, such as severe convective winds (SCWs), could potentially change in a future climate due to global warming. However, because of the small spatial scales associated with SCWs, they are unresolved in global climate models, and future climate projections are uncertain. Here, we evaluate the representation of SCW events in a convection-permitting climate model (Bureau of Meteorology Atmospheric Regional Projections for Australia, BARPAC-M) run over southeastern Australia for the months of December–February. We also assess changes in SCW event frequency in a projected future climate for the year 2050 and compare this with an approach based on identifying large-scale environments favourable for SCWs from a regional parent model (BARPA-R). This is done for three different types of SCW events that have been identified in this region, based on clustering of the large-scale environment. Results show that BARPAC-M representation of the extreme daily maximum wind gust distribution is improved relative to the gust distribution simulated by the regional parent model. This is due to the high spatial resolution of BARPAC-M output, as well as partly resolving strong and short-lived gusts associated with convection. However, BARPAC-M significantly overestimates the frequency of simulated SCW events, particularly in environments having steep low-level temperature lapse rates. A future decrease in SCW frequency under conditions with steep lapse rates is projected by BARPAC-M, along with less frequent favourable large-scale environments. In contrast, an increase in SCW frequency is projected under conditions of high surface moisture, with more frequent favourable large-scale environments. Therefore, overall changes in SCWs for this region remain uncertain, due to different responses between event types, combined with historical model biases.

The impacts of climate change, early agriculture and internal fluvial dynamics on paleo-flooding episodes in Central China

Floods clustered in episodes are the most prevalent natural disaster worldwide, causing substantial economic and human losses. Although these events are often linked to time-periods of extreme rainstorms and unique atmospheric circulation patterns, the river basin characteristics affected by anthropogenic land use changes could exert a strong influence. However, the way and extent of how land use changes across different time scales affect flooding periods are still unclear, especially considering the historical land use changes. This study uses the Landlab landscape evolution model, coupled with an evapotranspiration model, to investigate the forcing factors for the paleo-flooding trends in the Wei River catchment over the last 5000 years. The results indicate that the flooding period from 4400 to 4000 BP was caused by an increase of 28 % in antecedent moisture content as well as a decrease of 28 % in its spatial variability, which are primarily due to climate change, and that the contribution of land-use change is less than 5 %. The increases of about 14 % and 8 % in main channel sedimentation rate play a leading role in flood generation during the time periods from 3400 to 2800 BP and 2000–1400 BP, respectively. These two periods of increased flooding are primarily caused by the erosional effects of increasing anthropogenic land use, whose contributions range from 33 % to 64 %. Furthermore, based on our modelling results, we suggest that the downstream propagation of the main flooding locations, from the Wei River to the lower reaches of the Yellow River, can be explained by the downstream migrating sediment wave. In conclusion, our simulation results give new insights into the causes of Holocene flooding periods in the middle Yellow River from the perspective of dynamic changes in catchment characteristics, which is helpful to improve regional flood risk management under future climate change and anthropogenic activities.

Global invasion risk assessment of Lantana camara, a highly invasive weed, under future environmental change

Invasion risk assessments are essential for making informed decisions, allocating resources, and implementing targeted strategies to prevent or minimize the harmful effects of invasive species on native biodiversity, agricultural productivity, and natural ecosystems. In this study, the random forest algorithm was used to assess the spatial invasion risk of Lantana camara, one of the world’s top 100 worst invasive weeds, across all continents under current and future environmental conditions. The current invasion risk was relatively high on four continents (i.e., Africa, Australia, Oceania, and South America) within approximately 35°N and 35°S latitude, estimated to cover at least 68.98 % of the total land surface. Furthermore, projections for future environmental changes suggested a substantial increase in invasion risk across all continents, with the most significant changes (251.52 %) observed in Europe compared with current invasion levels. Additionally, invasion risk was predicted to extend beyond 35°N latitude. Categorizing 200 countries and territories into distinct risk levels, 27 countries had current invasion potential, and introduction and establishment was predicted in 114 countries. Moreover, at least 45 countries, including Canada, India, Italy, and United States, were projected to transition from no or low invasion risk to high invasion risk and 28 countries had a risk increase of over 50 %. Current study provides valuable insights into the global invasion risk posed by L. camara. These results are expected to be of great utility for invasive weed management, facilitating the development of control and sustainable management strategies for this notorious weed at both global and local scales.

Climate change (Mal)adaptation in the Bangladesh’ Sundarbans – a Systematic Review

Bangladesh is among the countries most vulnerable to climate change. This vulnerability stems not only from its exposure to climate change and intensified extreme weather events but also from the way adaptation policies and projects have been designed using a technocratic, top-down approach. As a result, much of the adaptation has led to negative consequences for certain groups—often the most vulnerable—who depend directly on nature for their livelihoods. This outcome, known as maladaptation, has been particularly pronounced. This paper conducts a systematic review of peer-reviewed literature since 2014, the year when maladaptation first gained significant attention in the IPCC’s fifth report. The goal is to assess the current state of research on climate change adaptation in Southwestern coastal Bangladesh, particularly around the Sundarbans, the largest contiguous mangrove forest in the world. The review aims to identify research gaps and discuss key lessons learned. Keywords used in the review include 'maladaptation', 'barriers to adaptation', 'ecosystem-based adaptation', and 'gender', as these concepts are all interrelated to some degree. The review highlights significant research gaps across all topics, as well as a geographical imbalance in the distribution of studies within the region. Given the prevalence of maladaptation in Bangladesh, it is crucial that more research focuses on this issue to gather insights and reduce the risk of maladaptation in future climate change adaptation projects in the country.

China's provincial optimum population sizes under SSP‐RCP scenarios

AbstractClimate change poses new challenges to achieving a balanced population distribution. Predicting the impact of development patterns using climate change scenarios can offer more precise insights into environmental and social risks. By adopting the possibility–satisfiability (P–S) model, this study investigates the optimum population sizes for the 31 provincial districts of China by 2035 and 2050 under seven shared socioeconomic pathway‐representative concentration pathway (SSP‐RCP) scenarios. Results indicate that under SSP2‐4.5 scenario, which maintains current development patterns, the optimum human population (OHP) ranges from 985 million to 717 million in 2035 and from 902 million to 623 million by 2050 at different P–S values, respectively. The SSP1‐1.9 and SSP1‐2.6 scenarios offer promising prospects by carrying the largest optimum population size. Therefore, China's future development depends on choosing a sustainable path that prioritizes the growth of the OHP. The optimum population sizes of provinces under unbalanced development routes SSP4‐3.4 and SSP4‐6.0 scenarios are mainly limited by economic development, while those of the regional competition route SSP3‐7.0 scenario are limited by both economic and climatic pressures. Under the fossil fuel route SSP5‐8.5 scenario, climate deterioration imposes severe constraints on optimum population size. The optimum population scales of the eastern coastal area with high urbanization levels and the northwest area with harsh environments are subject to natural conditions. In addition, the central and western regions encounter population size limitations due to insufficient economic development. Notably, China's population distribution remains concentrated in the east and sparse in the west, flanking the Hu Huanyong Line (Hu Line), with minimal expected changes in the future. By studying the spatial distribution of the optimum population under different SSP‐RCP scenarios, this study provides theoretical support for government decision‐making in the context of future climate change.

Biochar and Straw Amendments over a Decade Divergently Alter Soil Organic Carbon Accumulation Pathways

Exogenous organic carbon (C) inputs and their subsequent microbial and mineral transformation affect the accumulation process of soil organic C (SOC) pool. Nevertheless, knowledge gaps exist on how different long-term forms of crop straw incorporation (direct straw return or pyrolyzed to biochar) modifies SOC composition and stabilization. This study investigated, in a 13-year long-term field experiment, the functional fractions and composition of SOC and the protection of organic C by iron (Fe) oxide minerals in soils amended with straw or biochar. Under the equal C input, SOC accumulation was enhanced with both direct straw return (by 43%) and biochar incorporation (by 85%) compared to non-amended conventional fertilization, but by different pathways. Biochar had greater efficiency in increasing SOC through stable exogenous C inputs and inhibition of soil respiration. Moreover, biochar-amended soils contained 5.0-fold greater SOCs in particulate organic matter (POM) and 1.2-fold more in mineral-associated organic matter (MAOM) relative to conventionally fertilized soils. Comparatively, although the magnitude of the effect was smaller, straw-derived OC was preserved preferentially the most in the MAOM. Straw incorporation increased the soil nutrient content and stimulated the microbial activity, resulting in greater increases in microbial necromass C accumulation in POM and MAOM (by 117% and 43%, respectively) compared to biochar (by 72% and 18%). Moreover, straw incorporation promoted poorly crystalline (Feo) and organically complexed (Fep) Fe oxides accumulation, and both were significantly and positively correlated with MAOM and SOC. The results address the decadal-scale effects of biochar and straw application on the formation of the stable organic C pool in soil, and understanding the causal mechanisms can allow field practices to maximize SOC content. These results are of great implications for better predicting and accurately controlling the response of SOC pools in agroecosystems to future changes and disturbances and for maintaining regional C balance.

Pearl millet phenology assessment: An integration of field, a review, and in silico approach

AbstractPearl millet [Pennisetum glaucum (L.) R.Br.] is an essential subsistence cereal for food security in dryland farming systems of the semiarid tropics (e.g., in sub‐Saharan Africa) and has improved tolerance to drought, heat, and salinity stress compared to other domesticated cereals. Assessing the variation on phenology is critical toward devising effective adaptative management strategies for crop adaptation to current and future climate change. In this context, pearl millet presents a vast genetic diversity, exhibiting sensitivity to temperature and photoperiod. Hence, this study aims to describe the genotypic variability in the phenological responses of pearl millet to temperature and photoperiod, particularly affecting leaf number with implications on the overall total time to flowering. The dataset encompassed 21 publications from seven countries, with experiments conducted from 1965 to 2023, including three field studies from the United States. Broad variability has been reported for phyllochron values ranging from 45 to 111°Cd leaf−1, with a mean value of 67°Cd leaf−1. Thermal time to panicle initiation ranged from 340 to 594°C, but no response to photoperiod duration was found due to the nature of dataset. Maximum number of leaves per shoot ranged from 11 to 25, showing response (1.55–2.15 leaf h−1) to photoperiod due to variations in thermal time to flowering (from 875 to 1346°Cd). Thermal time to flowering increased ca. 323°Cd h−1 under day durations longer than 13.3 h, below which basic vegetative phase duration was close to 1033°Cd. Based on the Agricultural Production Systems sIMulator simulations, different combinations of the above responses (in silico cultivars) generated a great range of times to flowering (44–120 days) for locations in Senegal, Brazil, India, and United States. The findings of this study can help breeders to explore the phenological genetic variability of pearl millet and provide inputs for crop growth models to evaluate future in silico scenarios.

Nonanthropocentric climate ethics

AbstractAnthropogenic climate change poses increasingly severe long‐term threats to living things worldwide. It may even contribute to a mass extinction that would leave biodiversity depleted for millions of years—quite possibly longer than the duration of the human species. Such effects are obviously of ethical concern, but because traditional ethical theories have focused on the relatively short‐term interests of human beings, they offer little guidance. In the late 20th century, a growing number of ethical activists and theorists sought to expand moral consideration to nonhuman animals and to the diverse life forms and habitats of wild nature. Simultaneously and at first independently, others began to develop long‐term (sometimes called “intergenerational”) ethics, which extends moral consideration into the distant future. Concerns about future climate change quickly became a central focus of this work. But only in this century have there been concerted efforts to integrate these lines of thought into far‐sighted nonanthropocentric climate ethics. These efforts are the subject of this review.This article is categorized under: Climate, Nature, and Ethics > Ethics and Climate Change Integrated Assessment of Climate Change > Assessing Climate Change in the Context of Other Issues Climate, Nature, and Ethics > Comparative Environmental Values

Tree diversity across the Minneapolis‐St. Paul Metropolitan Area in relation to climate and social vulnerability

AbstractUrban tree canopy cover is often unequally distributed across cities such that more socially vulnerable neighborhoods often have lower tree canopy cover than less socially vulnerable neighborhoods. However, how the diversity and composition of the urban canopy affect the nature of social‐ecological benefits (and burdens), including the urban forest's vulnerability to climate change, remains underexamined. Here, we synthesize tree inventories developed by multiple organizations and present a species‐specific, geolocated database of more than 600,000 urban trees across the 7‐county Minneapolis‐St. Paul (MSP) metropolitan area in the Upper Midwest of the United States. We find that tree diversity across the MSP is variable yet dominated by a few species (e.g., Fraxinus pennsylvanica, Acer platanoides, and Gleditsia triacanthos), contributing to the vulnerability of the MSP urban forest to future climate change and disturbances. In contrast to tree canopy cover, tree diversity was not well predicted by socioeconomic or demographic factors. However, our analysis identified areas where both climate and social vulnerability are high. Our results add to a growing body of literature emphasizing the importance of considering how complex and interacting social and ecological factors drive urban forest diversity and composition when pursuing management objectives.

The influence of the solar wind electric and magnetic fields on the latitude and temporal variations of the current density, JZ, of the global electric circuit, with relevance to weather and climate

Observations have shown small day-to-day stratiform cloud opacity and atmospheric dynamical responses to variations in the ionosphere-earth current density (JZ). We model the day-to-day and seasonal/bi-decadal changes in the area-integrals of ionospheric potential (Vi) near the magnetic poles due to solar wind electric field inputs. The overhead value of Vi, divided by the local column resistance (R) determines JZ, where the conductivity of the column is the result of ionization by galactic cosmic rays (GCRs) and solar and magnetospheric energetic particle precipitation. These vary with time, due to varying solar wind magnetic field inputs, not only on the day-to-day timescale (e.g., Forbush decreases) but also on the decadal and bi-decadal and century timescales. The GCR and energetic particle inputs vary with latitude, due to filtering of particle energies in the geomagnetic field. We compare area-integrals of the amplitude of the JZ variations due to Vi changes to those due to the R changes, for evaluating their global effectiveness in affecting cloud microphysics and weather and climate changes. The day-to-day and bi-decadal correlated weather and climate variations indicate JZ rather than other solar forcings as mainly responsible for the correlations. The decadal and longer climate responses to space weather are not large; however, understanding them could help improve predictions of future climate change due to greenhouse gases.

Predicting the potential distribution of Arisaema heterophyllum in China under current and future climate change based on ArcGIS and MaxEnt model

Climate change significantly affects the distribution of medicinal plants, necessitating accurate models to predict future shifts in their habitats. This study employed the MaxEnt model and ArcGIS to predict the current and future distribution of Arisaema heterophyllum, a valuable perennial herb in traditional Chinese medicine, under two future climate scenarios: SSP 245 and SSP 585. Using 142 valid species occurrence records and critical environmental variables, we identified October precipitation, March precipitation, elevation and September precipitation as the major determinants of A. heterophyllum habitat suitability. The model demonstrated high predictive accuracy, with an average Area Under the Curve (AUC) value of 0.951, indicating the reliability of our findings. These results suggest a potential decrease in the total suitable habitat area for A. heterophyllum, with a noticeable shift toward higher altitudes and latitudes in response to climate change. This study highlighted the urgency of incorporating climate change projections into A. heterophyllum conservation strategies to mitigate the risk of resource depletion. Furthermore, our approach offers a robust framework for assessing the impacts of climate change on other medicinal plants, contributing valuable insights into their future conservation and sustainable use.

Characteristics of air temperature on the southern and southeastern slopes of the Greater Caucasus in the modern period

In the paper, the characteristics of air temperature in the south part of the Greater Caucasus region and the effects of regional climate changes on this area in the modern period were studied. In the research, the air temperature observation data of 8 hydrometeorological stations operating in the region for the years 1961-2022 were used, and the modern classification of the average monthly, seasonal, annual and altitude time-space distributions of temperature was given.Based on the data of hydrometeorological stations in the south and southeast piedmonts of the Greater Caucasus Mountains, an electronic map was prepared for the distribution of average annual air temperature indicators on the territory, and for determining the indicators of areas without data. DEM (Digital Elevation Model) files obtained fromthe decoding of satellite images (satellite data) were used in the prepared map. The research shows that July and August are the hottest months when the temperature is 22.80C, while January is the coldest by temperature about 0.50C in this region. In this part of the Greater Caucasus province, in the cold months of the year, the average monthly temperature is indicating below 00C in the area located above the moderately mountainous part of the region. The dynamics of the average indicators of temperature of the multi-year period (1961-2022) are expressed by a positive trend. The average annual temperature distribution map of the territory shows that the air temperature is 15.00C in the low mountainous part of the region, while it decreases upwards and, according to the adiabatic law, equals 00C at altitudes above 3500 m. According to the climate studies conducted in the region, compared to the years 1981-2010, it was found that there was a temperature increase of 1.20C in the mountainous region in 2011-2022. Also, a comparative analysis of 10-year averages of air temperature trends, using the Holt- Winters model, shows that temperatures in 2100 are expected to increase by 3.00C compared to 2030. In the region, climate changes that worsen every year lead to water scarcity, desertification, drought, etc. and can expand the area of recurrence of environmental crises and destructive natural events. This research can be used in future climate change mitigation , climate atlases, infrastructure, agricultural and industrial projects in the region.

Abstract C041: A systematic review of cardiovascular health among cancer survivors

Abstract Purpose: The most common non-cancer cause of death among cancer survivors is cardiovascular disease. Health behaviors (e.g., diet, physical activity, sleep, smoking) and health factors (e.g., blood pressure, hemoglobin A1C, cholesterol) associated with cancer mortality are also associated with cardiovascular mortality. The American Heart Association has termed these behaviors and factors “cardiovascular health” (CVH) or “Life’s Essential 8,” providing a metric to determine poor, intermediate, and high (ideal) CVH. Less than ideal CVH is associated with higher cancer mortality; however, little is known about the CVH of cancer survivors. The purpose of this systematic review is to examine the extant literature on CVH among cancer survivors. MethodsIn accordance with PRISMA guidelines, we are systematically examining cardiovascular health among cancer survivors (including patients) within PubMed, Scopus, CINAHL, and Embase databases without date limitations. Two reviewers are accessing articles at each level of review to determine concordance with inclusion criteria, coming to consensus via a shared discussion. Data from studies that meet inclusion criteria will be abstracted, assessed for risk of bias. and synthesized to understand themes of CVH among cancer survivors. Results To date, we have retrieved 1,990 studies from the named databases. Preliminary review of the title and abstracts suggests that CVH as a metric may be seldom used within extant literature. This review is ongoing. Conclusion: Given the co-morbid and iatrogenic nature of cardiovascular disease among cancer survivors, it is important to understand metrics that can be applied to both identify cancer survivors with increased risk of poor outcomes and educate/empower cancer survivors to make modifiable changes to reduce their risk. This review will provide much needed evidence to support future research, practice changes, and policy implications to support the long-term health of cancer survivors. Citation Format: Miriam A. Miles, Le’Andrea Anderson-Tolbert, Abigail A. Ninson, Cha-Breia Means, Tuchondriana Smith, Reagan Coleman, Laura Rogers, Joshua Joseph, Timiya S. Nolan. A systematic review of cardiovascular health among cancer survivors [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C041.

Cardiovascular Disease, Brain Glucose Metabolism, and Neurocognitive Decline in People With Human Immunodeficiency Virus.

Cardiovascular disease (CVD) and neuroinflammation are thought to exacerbate neurocognitive dysfunction in treated people with human immunodeficiency virus (PWH). Here, we longitudinally measured brain glucose metabolism as a measure of neuronal integrity in treated PWH using [18F]Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in correlation with atherosclerotic cardiovascular disease (ASCVD) scores, cerebrospinal fluid (CSF) neuroinflammatory markers, neurocognitive outcomes, and other clinical and laboratory variables (CLVs). Well-controlled PWH (n = 36) underwent baseline and follow-up FDG PET/CT obtained 3.5 years apart on average. Longitudinal changes in whole brain and regional relative FDG uptake, brain volumes, CLVs, CSF cytokines, and neuropsychological measures were measured. A variable selection model identified baseline variables related to future brain metabolic changes while multivariable models explored neuropsychological implications of brain metabolism and volumetrics. High ASCVD scores predicted future decreased thalamic uptake (slope = -0.0068, P = .027) and decreasing thalamic uptake correlated with worsening cognition (slope = 15.80, P = .020). Despite longitudinal greater than expected gray matter loss, whole brain FDG uptake did not change over the follow-up period. Most CSF cytokines decreased longitudinally but were not predictive of FDG changes. We found that high ASCVD scores in a group of treated PWH were related to thalamic hypometabolism, which in turn correlated with neurocognitive decline. Our findings support the contribution of CVD to neurocognitive dysfunction. More proactive CVD management may have a role in mitigating progression of cognitive impairment. Lack of change in global brain glucose metabolism despite documented accelerated gray matter volume loss over the same period suggests that FDG PET might underestimate neuronal injury in PWH compared to structural magnetic resonance imaging.

South American monsoon intensification during the last millennium driven by joint Pacific and Atlantic forcing

The South American summer monsoon (SASM) profoundly influences tropical South America’s climate, yet understanding its low-frequency variability has been challenging. Climate models and oxygen isotope data have been used to examine the SASM variability over the last millennium (LM) but have, at times, provided conflicting findings, especially regarding its mean-state change from the Medieval Climate Anomaly to the Little Ice Age. Here, we use a paleoclimate data assimilation (DA) method, combining model results and δ 18 O observations, to produce a δ 18 O-enabled, dynamically coherent, and spatiotemporally complete austral summer hydroclimate reconstruction over the LM for tropical South America at 5-year resolution. This reconstruction aligns with independent hydroclimate and δ 18 O records withheld from the DA, revealing a centennial-scale SASM intensification during the MCA-LIA transition period, associated with the southward shift of the Atlantic Intertropical Convergence Zone and the strengthening Pacific Walker circulation (PWC). This highlights the necessity of accurately representing the PWC in climate models to predict future SASM changes.

The effect of extreme temperatures on evictions

Using data on evictions in the United States, we estimate the relationship between temperature and eviction filings. We find that extreme heat days result in a statistically significant increase in filings, while extreme cold days do not have the same relationship. To explain these findings, we show that residential energy expenditures are more sensitive to extreme heat than extreme cold, and that energy assistance programs in the United States prioritize funding for heating rather than cooling. These findings suggest that relative to today, future climate change scenarios with more hot days and fewer cold days will increase eviction filings without other policy or private adaptation.

Breaking Rossby waves drive extreme precipitation in the world’s arid regions

More than a third of the world’s population lives in drylands and is disproportionately at risk from hydrometeorological hazards such as drought and flooding. While weather systems governing precipitation formation in humid regions have been widely explored, our understanding of the atmospheric processes generating precipitation in arid regions remains fragmented at best. Here we show, using a variety of precipitation datasets, that Rossby wave breaking is a key atmospheric driver of precipitation in arid regions worldwide. Rossby wave breaking contributes up to 90% of daily precipitation extremes and up to 80% of total precipitation amounts in arid regions equatorward and downstream of the midlatitude storm tracks. The relevance of Rossby wave breaking for precipitation increases with increasing land aridity. Contributions of wave breaking to precipitation dominate in the poleward and westward portions of arid subtropical regions during the cool season. Our findings imply that Rossby wave breaking plays a crucial role in projections and uncertainties of future precipitation changes in societally vulnerable regions that are exposed to both freshwater shortages and flood hazards.

Epidemiology and SARIMA model of deaths in a tertiary comprehensive hospital in Hangzhou from 2015 to 2022

BackgroundBy analysing the deaths of inpatients in a tertiary hospital in Hangzhou, this study aimed to understand the epidemiological distribution characteristics and the composition of the causes of death. Additionally, this study aimed to predict the changing trend in the number of deaths, providing valuable insights for hospitals to formulate relevant strategies and measures aimed at reducing mortality rates.MethodsIn this study, data on inpatient mortality at a tertiary hospital in Hangzhou from 2015 to 2022 were obtained via the population information registration system of the Chinese Center for Disease Control and Prevention. The death data of inpatients were described and analysed through a retrospective study. Excel 2016 was utilized for data sorting, and SPSS 22.0 software was employed for data analysis. The statistical inference of single factor differences was conducted via χ2 tests. The SARIMA model was established via the forecast, aTSA, and tseries software packages (version 4.3.0) to forecast future changes in the number of deaths.ResultsA total of 1938 inpatients died at the tertiary hospital in Hangzhou, with the greatest number of deaths occurring in 2022 (262, 13.52%). The sex ratio was 2.22:1, and there were significant differences between sexes in terms of age, marital status, educational level, and place of residence (P < 0.05). The percentage of males in the groups aged of 20 to 29 and 30 to 39 years was significantly greater than that of females (χ2 = 46.905, P < 0.001). More females than males died in the widowed group, and divorced and married males experienced a greater number of deaths than divorced and married females did (χ2 = 61.130, P < 0.001). The proportions of male students with a junior college and senior high school education were significantly greater than that of female students (χ2 = 12.310, P < 0.05). The primary causes of mortality within the hospital setting included circulatory system diseases, injury, poisoning, tumours, and respiratory system diseases. These leading factors accounted for 86.12% of all recorded deaths. Finally, the SARIMA (2, 1, 1) (1, 1, 1)12 model was determined to be the optimal model, with an AIC of 380.23, a BIC of 392.79, and an AICc of 381.81. The MAPE was 14.99%, indicating a satisfactory overall fit of this model. The relative error between the predicted and actual number of deaths in 2022 was 8.02%. Therefore, the SARIMA (2, 1, 1) (1, 1, 1)12 model demonstrates good predictive performance.ConclusionsHospitals should enhance the management of sudden cardiac death, acute myocardial infarction, severe craniocerebral injury, lung cancer, and lung infection to reduce the mortality rate. The SARIMA model can be employed for predicting the number of deaths.

Negative self-referent cognition predicts future depression symptom change: an intensive sampling approach

ABSTRACT Cognitive theories of depression assert that negative self-referent cognition has a causal role in the development and maintenance of depression symptoms, but few studies have examined temporal associations between these constructs using intensive, longitudinal sampling strategies. In three samples of undergraduate students, we examined associations between change in self-referent processing and depression across 5 daily assessments (Sample 1, N = 303, 1,194 measurements, 79% adherence), 7 daily assessments (Sample 2, N = 313, 1,784 measurements, 81% adherence), and 7 weekly assessments (Sample 3; N = 155, 833 measurements, 81% adherence). Random intercept cross-lagged panel models indicated large cross-lagged effects in two of the three samples (Samples 1 and 3 but not Sample 2), such that more negative self-referent thinking than usual was significantly associated with a subsequent increase in depression symptoms at the next time lag. Notably, change in depression from usual was not associated with increases in negative self-referent processing at the next time point in any sample. These findings suggest that change in negative self-referent processing may be causally linked to future increases in depression on a day-to-day and week-to-week basis, although confidence in this conclusion is tempered somewhat by a lack of replication in Sample 2.

Emergent constraints on future Amazon climate change-induced carbon loss using past global warming trends

Reducing uncertainty in the response of the Amazon rainforest, a vital component of the Earth system, to future climate change is crucial for refining climate projections. Here we demonstrate an emergent constraint (EC) on the future response of the Amazon carbon cycle to climate change across CMIP6 Earth system models. Models that overestimate past global warming trends, tend to estimate hotter and drier future Amazon conditions, driven by northward shifts of the intertropical convergence zone over the Atlantic Ocean, causing greater Amazon carbon loss. The proposed EC changes the mean CMIP6 Amazon climate-induced carbon loss estimate (excluding CO2 fertilisation and land-use change impacts) from −0.27 (−0.59–0.05) to −0.16 (−0.42–0.10) GtC year−1 at 4.4 °C warming level, reducing the variance by 34%. This study implies that climate-induced carbon loss in the Amazon rainforest by 2100 is less than thought and that past global temperature trends can be used to refine regional carbon cycle projections.