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Bhikhari Tharu was not included as an author in the original publication [...]

Coral skeleton δ13C is a routinely measured indicator in modern insolation change research, but the knowledge of environmental and climatic signals recorded in its seasonality during geological time is sparse. In this study, we present eight Porites coral δ13C records from the mid‐late Holocene to the present from the northern South China Sea (NSCS). Compared with the main control factors for modern δ13C changes, coral δ13C seasonality in the NSCS since the mid‐Holocene shows a long‐term decreasing trend, which is consistent with the change trend as orbital‐induced (precession) insolation seasonal amplitude. By excluding other influencing factors, we speculate that the stronger coral δ13C seasonality (18.8%) over the mid‐Holocene than modern period is attributable to the metabolic effect, which predicts the stronger coral δ13C seasonality under stronger insolation seasonality. Our study has implications for coral δ13C seasonality as a potential indicator to record past insolation information under different climatic backgrounds. Plain Language Summary Numerous studies have documented the seasonal features of fast‐growing modern coral skeleton δ13C in response to insolation variation. The environmental and climatic signals recorded by coral δ13C seasonality in the geological period remain unclear owing to the relatively short length of observation records. Here we present monthly resolved δ13C records in seven fossil Porites corals (5.6–3.6 ka BP, before 1950 CE) and one modern Porites coral (1987–2001 CE) from the northern South China Sea (NSCS). Compared with the widely accepted prevailing control factors for modern coral δ13C, our results indicated that the stronger coral δ13C seasonality (18.8%) over the mid‐Holocene compared to the modern period is consistent with the orbital‐induced (precession) insolation changes, which indicate a declining seasonality from the mid‐Holocene to present. By excluding other influencing factors, we infer that a tendency toward higher δ13C seasonality during the mid‐Holocene was primarily driven by the metabolic fractionation caused by the enhanced insolation seasonality. This study highlighted coral δ13C seasonality as a potential indicator for recording past insolation information. Key Points The seasonal variation of modern coral skeleton δ13C in the northern South China Sea (NSCS) is primarily controlled by solar insolation Coral δ13C seasonality in the NSCS since the mid‐Holocene shows a long‐term decreasing trend The decrease of orbital‐induced (precession) insolation seasonal amplitude led to the declining trend of δ13C seasonality

The 4 common types of human coronaviruses (HCoVs)—2 alpha (HCoV-NL63 and HCoV-229E) and 2 beta (HCoV-HKU1 and HCoV-OC43)—generally cause mild upper respiratory illness. Seasonal patterns and annual variation in predominant types of HCoVs are known, but parameters of expected seasonality have not been defined. We defined seasonality of HCoVs during July 2014–November 2021 in the United States by using a retrospective method applied to National Respiratory and Enteric Virus Surveillance System data. In the 6 HCoV seasons before 2020–21, season onsets occurred October 21–November 12, peaks January 6–February 13, and offsets April 18–June 27; most (>93%) HCoV detection was within the defined seasonal onsets and offsets. The 2020–21 HCoV season onset was 11 weeks later than in prior seasons, probably associated with COVID-19 mitigation efforts. Better definitions of HCoV seasonality can be used for clinical preparedness and for determining expected patterns of emerging coronaviruses.

• A classic theory proposes that plant xylem cannot be both highly efficient in water transport and resistant to embolism, and therefore a hydraulic efficiency–safety trade-off should exist. However, the trade-off is weak, and many species exhibit both low efficiency and low safety, falling outside of the expected trade-off space. It remains unclear under what climatic conditions these species could maintain competitive fitness. • We compiled hydraulic efficiency and safety traits for 682 observations of 499 woody species from 178 sites world-wide and measured the position of each observation within the proposed trade-off space. • For both angiosperms and gymnosperms, observations from sites with high climatic seasonality, especially precipitation seasonality, tended to have higher hydraulic safety and efficiency than observations from sites with low seasonality. Specifically, high vapour pressure deficit, high solar radiation, and low precipitation during the wet season were driving factors. • Strong climatic seasonality and drought in both dry and wet seasons appear to be ecological filters that select for species with co-optimized safety and efficiency, whereas the opposite environmental conditions may allow the existence of plants with low efficiency and safety.

The aim of this study is to analyse and compare the seasonal pattern of activity and prices in the cruise industry, both globally and regionally (in the Mediterranean), as well as the impact of this seasonality on the cruise industry and cruise destinations. Methodologically, seasonality indices for activity and prices are calculated based on available statistics and a database of 51,129 observations from 2019. The results show that there is activity and price seasonality worldwide, although it is higher in the Mediterranean region. This supports the idea that cruise lines face less variation in activity due to seasonality, as they can reposition their ships in different regions throughout the year. The seasonality of cruise destinations is more pronounced, due to natural and institutional factors, but also to these repositioning decisions by cruise lines. A comparison of activity and price seasonality shows that prices fluctuate more globally, while activity fluctuates more regionally. The cruise lines' strategies are reviewed in order to draw some theoretical and practical implications for understanding future trends in the industry. Suggestions are also made as to how destinations can deal with the inconveniences of seasonality in cruise destinations, such as overtourism.

Changes in precipitation seasonality or redistribution of precipitation could exert significant influences on regional water resources availability and the well-being of the ecosystem. However, due to the nonuniform distribution of precipitation stations and intermittency of precipitation, precise detection of changes in precipitation seasonality on the global scale is absent. This study identifies and inter-compares trends in precipitation seasonality within seven precipitation datasets during the past three decades, including two gauge-based datasets derived from the Climatic Research Unit (CRU) and the Global Precipitation Climatology Centre (GPCC), one remote sensing-retrieval obtained from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), three reanalysis datasets obtained from National Centers for Environmental Prediction reanalysis II (NCEP2), European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), and Modern Era Reanalysis for Research and Applications Version 2 (MERRA2), and one precipitation dataset merged from above three types, Multi-Source Weighted Ensemble Precipitation Version 1.2 (MSWEP_V1.2). Values of two indices representing the precipitation seasonality, the normal seasonality index (SI) and the dimensionless seasonality index (DSI), are estimated for each land grid in each precipitation dataset. The results show that DSI is more sensitive to changes in the temporal distribution of precipitation as it considers both annual amount and monthly fluctuations of precipitation, compared to SI that only considers monthly fluctuations of precipitation. There are large differences in precipitation seasonality at annual and climatologic scales between precipitation datasets for both SI and DSI. Within the seven precipitation datasets, PERSIANN-CDR SI and DSI show high precipitation seasonality while CRU SI, and ERA-Interim and MERRA2 DSI show the low precipitation seasonality in all continental regions. During 1988–2013, PERSIANN-CDR, NCEP2 and ERA-Interim show more widespread, statistically significant trends in precipitation seasonality than other four precipitation datasets. PERSIANN-CDR and NCEP2 show statistically significant decreases in SI over Middle East and Central Asia, while ERA-Interim, MERRA2 and MSWEP_V1.2 SI increase over Central and South Africa. Increases in SI over the most of South America are significant. Regions of Canada/Greenland/Iceland, East and South Africa show significant increases in precipitation seasonality, while South Europe/Mediterranean and Central Africa show significant decreases in precipitation seasonality in most datasets. Although time series of seasonality indices values fluctuate correlatively in recent three decades, there are no regions on which all precipitation datasets show a consistent, statistically significant, positive or negative trend in indices of precipitation seasonality. These inconsistent changes in precipitation seasonality within various precipitation datasets imply the importance of choosing dataset when studying changes in regional precipitation seasonality.

Trog -Ferreira, A; Cun h a, C.L.N.; Sant'Ana, D.O.; Gon çalves, J.E., 2024. BOD-DO modelling and water quality analysis to sewage effluent in Guaratuba Bay, southern Brazil. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 1104-1108. Charlotte (North Carolina), ISSN 0749-0208. This study aims to evaluate the effects of sewage discharge into the Guaratuba Bay, located in the southern region of Brazil, using an environmental modeling tool. The cities of Matinhos and Guaratuba, municipalities located in the Guaratuba Bay region, attract thousands of tourists during the summer, causing an increase in the local population, reaching more than one million people throughout the region, causing several environmental problems. The main issue is the increase in sanitary sewage load reaching the bay, with high concentrations of organic matter; the accumulation and persistence of effluents constitute a threat to life, and severely degrades the environment. Correct characterization of hydrodynamic circulation is the first step in studying pollutant dispersion in bays. In this regard, hydrodynamic and water quality models developed to simulate long-term transport and evaluate sewage effluent pollution in the Guaratuba Bay are presented in this study. The hydrodynamic a nd water qu ality mod els used in this study are part of the Hydrodynamic Environmental System known as SisBAHIA® (Base System of Environmental Hydrodynamics). One of the wastewater discharge consequences in the environment is the oxygen deficit, which is caused by the consumption of oxygen by bacteria to oxidize the organic matter, indicated by biochemical oxygen demand (BOD), present in sewage. For the analysis, simulations consider variations in sewage load (low and high) and seasonality (summer and winter), taking into account the implications for pollutant loads on the resident population and a projected population with tourists during the summer; for both scenarios, a 60% removal of BOD load was assumed. The results showed that the sewage discharged in the channels reaches the bay, accumulates along the regions near the estuaries and compromises water quali ty. The outer Bay is less compromised by the discharges.

South Africa spans the subtropics at the interface between tropical, subtropical, and temperate weather systems, and consequently experiences distinct summer-, winter- and year-round rainfall zones (SRZ, WRZ and YRZ). Spatio-temporal characteristics of the various weather systems are broadly understood, however, the rainfall seasonality classification at the transition between these rainfall zones remains disputed. This surrounds the complexity of rainfall regimes, however, metrics with dissimilar rainfall seasonality definitions have been applied, hindering comparability. To address this dispute, meteorological data spanning 1987–2016 from 46 weather stations is used to assess the utility of a metric posited to quantify rainfall seasonality through a seasonality score derived from a ratio of monthly rainfall: temperature. This score statistically discriminates SRZ, WRZ and YRZ conditions, fulfilling an important requirement for a metric applied to South Africa. Nelspruit (NEL; score = 1.59) represents the strongest SRZ conditions across 30 eastern and central locations with scores > 0.30. Cape Town Wo (CTW; score = − 1.04) represents the strongest WRZ conditions across seven southwestern Cape and west coast locations with scores < − 0.30. Characterising the SRZ-to-WRZ transition region with scores from − 0.30 to 0.30, nine YRZ locations were classified. With the weakest score, Oudtshoorn (OUD; score = − 0.05), within the Cape Fold mountains, most represents YRZ conditions. Applicability across all weather stations, compatibility with known rainfall drivers, and agreement with known spatial rainfall seasonality characteristics demonstrates the ratio’s utility. Strong correspondence of scores between station and gridded data applications demonstrates additional confidence in the ratio, establishing its value for further application.

1. The environmental filtering hypothesis predicts that the abiotic environment selects species withsimilar trait values within communities. Testing this hypothesis along multiple – and interacting –gradients of climate and soil variables constitutes a great opportunity to better understand and predictthe responses of plant communities to ongoing environmental changes.2. Based on two key plant traits, maximum plant height and specific leaf area (SLA), we assessedthe filtering effects of climate (mean annual temperature and precipitation, precipitation seasonality),soil characteristics (soil pH, sand content and total phosphorus) and all potential interactions on thefunctional structure and diversity of 124 dryland communities spread over the globe. The functionalstructure and diversity of dryland communities were quantified using the mean, variance, skewnessand kurtosis of plant trait distributions.3. The models accurately explained the observed variations in functional trait diversity across the124 communities studied. All models included interactions among factors, i.e. climate–climate (9%of explanatory power), climate–soil (24% of explanatory power) and soil–soil interactions (5% ofexplanatory power). Precipitation seasonality was the main driver of maximum plant height, andinteracted with mean annual temperature and precipitation. Soil pH mediated the filtering effects ofclimate and sand content on SLA. Our results also revealed that communities characterized by a lowvariance can also exhibit low kurtosis values, indicating that functionally contrasting species canco-occur even in communities with narrow ranges of trait values.4. Synthesis. We identified the particular set of conditions under which the environmental filteringhypothesis operates in drylands world-wide. Our findings also indicate that species with functionallycontrasting strategies can still co-occur locally, even under prevailing environmental filtering. Interactionsbetween sources of environmental stress should be therefore included in global trait-basedstudies, as this will help to further anticipate where the effects of environmental filtering will impactplant trait diversity under climate change.

Precipitation exhibits a pronounced seasonal cycle, of which the phase and amplitude are closely associated with water resource management. While previous studies suggested an emerged delaying phase in the past decades, whether the amplified amplitude has emerged is controversial. Using multiple observational data sets and climate simulations, here we show that the amplification of precipitation annual cycle has emerged in most global land areas since the 1980s, especially in the tropics. These amplifications are mainly driven by anthropogenic emissions, and will be further intensified by 17.6% in the future (2081–2100) under high emission scenario (Shared Socioeconomic Pathways, SSP585), and limited to 7.2% under SSP126 scenario, relative to the historical period (1982–2014). Precipitation seasonality will be amplified by 4.2% for each 1°C of global warming, which is seen in all emission scenarios. The mitigation of lower emissions is helpful for alleviating the amplification of precipitation seasonality in a warming world. Plain Language Summary Precipitation displays pronounced seasonal cycle, and its phase and amplitude are closely associated with ecosystems and our society by redistributing water resources. The phase of precipitation cycle has been well understood in previous studies, but how its magnitude changes remain largely unknown. In this study, we use multiple observational data sets and climate simulations to show that precipitation annual cycle has been amplified in most parts of global land area since the 1980s. These amplifications are especially strong in the tropical regions, and are mainly driven by the increases in anthropogenic greenhouse gas and aerosol emissions. In the future (2081–2100) under high emission scenario (SSP585), they will be further intensified by 17.6% relative to the historical period (1982–2014), and will be limited to 7.2% under low emission scenario (SSP126). We also estimate that the amplitude of precipitation seasonality will be increased by around 4.2% for each 1°C of global warming, and suggest that keeping lower emissions is helpful for alleviating the amplification of precipitation seasonality. Key Points Precipitation annual cycle has been amplified in most global land areas since the 1980s, especially in the tropics Precipitation seasonality amplification will be intensified in the future, mainly driven by anthropogenic emissions The amplitude of precipitation seasonality will be amplified by ∼4.2% for each 1°C of global warming