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This paper explores the relations between maritime labour, the circulation of struggles between different sites, and constructions of transnational subaltern agency. It does this through engaging with the interconnected strikes of Indian merchant seafarers in ports and ships across the British Empire in the Autumn of 1939. These strikes broke out after the outbreak of the Second World War and were in part mobilised against the racialised inequalities which structured maritime labour. The paper foregrounds the relations between practices of blockading ships in ports through refusing to crew them and the circulation of the strikes between ships. I argue that this combination of spatial tactics shaped transnational forms of networked subaltern agency (Balachandran 2012, Globalizing Labour? Indian Seafarers and World Shipping, c. 1870−1945). To engage with the dynamics of subaltern agency through the strikes the paper explores the relations between trajectories of organising, the circulation of demands, and the formation of solidarities. [ABSTRACT FROM AUTHOR]

Plant secondary metabolites (PSMs) are produced by plants to overcome environmental challenges, both biotic and abiotic. We were interested in characterizing how autumn seasonality in temperate and subtropical climates affects overall PSM production in comparison to herbivory. Herbivory is commonly measured between spring to summer when plants have high resource availability and prioritize growth and reproduction. However, autumn seasonality also challenges plants as they cope with limited resources and prepare survival for winter. This suggests a potential gap in our understanding of how herbivory affects PSM production in autumn compared to spring/summer. Using meta‐analysis, we recorded overall production of 22 different PSM subgroups from 58 published papers to calculate effect sizes from herbivory studies (absence to presence) and temperate to subtropical seasonal studies (summer to autumn), while considering other variables (e.g., plant type, increase in time since herbivory, temperature, and precipitation). We also compared production of five phenolic PSM subgroups – hydroxybenzoic acids, flavan‐3‐ols, flavonols, hydrolysable tannins, and condensed tannins. We wanted to detect a shared response across all PSMs and found that herbivory increased overall PSM production in herbaceous plants. Herbivory was also found to have a positive effect on individual PSM subgroups, such as flavonol production, while autumn seasonality was found to have a positive effect on flavan‐3‐ol and condensed tannin production. We discuss how these responses might stem from plants producing some PSMs constitutively, whereas others are induced only after herbivory, and how plants produce metabolites with higher costs only during seasons when other resources for growth and reproduction are less available, while other phenolic PSM subgroups serve more than one function for plants and such functions can be season dependent. The outcome of our meta‐analysis is that autumn seasonality changes some PSM production differently from herbivory, and we see value in further investigating seasonality–herbivory interactions with plant chemical defense. [ABSTRACT FROM AUTHOR]

Summary: Eupatorium japonicum Thunb. (Compositae: Eupatorieae: Eupatoriinae) is described and illustrated. The current synonymy is provided, together with type citations for each of the names and statements of the located type material, together with appropriate notes and comments on this material, including lectotypifications if present in the literature. A selection of verified illustrations of the species in the literature is provided. Statements of the species' distribution, habitat and ecological preferences, phenology, conservation status, and the etymology of both generic and specific epithets are given, along with an impressive array of vernacular names, reflecting its relatively wide distribution and long cultivation. Cultivation, propagation, and availability notes are provided. The probability of this plant having been 'exported' to China many centuries ago both for its scent and festival significance almost mirrors that of the Chrysanthemum, that came in the opposite direction a few centuries earlier. The author citation for this well‐known Japanese endemic is discussed, as is the long‐standing confusion between E. chinense L., E. japonicum and the separation of E. fortunei Turcz. in many references. Comments on Robert Fortune's Chinese collections relevant to the type material of Eupatorium fortunei are also provided. Included within the synonymy of E. fortunei is E. stoechadosmum Hance, published in a short paper, Manipulus ..., in the Annales des Sciences Naturelles, the Monitum, is dated '7 kal. Februar. 1863', suggesting publication of Cahier 4 post that date when it was published. Hance's novelties (some collected as late as November 1862), and those of Triana & Planchon in their Prodromus florae Novo‐Granatensis ... (from Cahier 5) are listed in major databases as having been published in 1862; there is no other internal evidence in the relevant papers to suggest, or confirm, this is true. Any suggestion that Hance's Manipulis was published in early 1862 is similarly incorrect. Hance's plant supports other records, including the publication of E. fortunei Turcz., that E. japonicum had been long in cultivation in China, but never known to grow in the wild. Eupatorium japonicum, now with E. fortunei (fujibakama) in synonymy, is one of the seven plants, from the Festival of Seven Flowers or Herbs (nanakusa no sekku) that takes place in the autumn – the Autumn's Seven Flowers or Herbs (aki no nanakusa). The autumn tradition, dating back over a millennium, mirrors the spring festival (haru no nanakusa) – when a seven‐herb rice porridge is eaten – but in autumn the simplicity of the flowers is just for visual enjoyment, not eating. The herbal and medicinal uses of this species also have a long history, are wide‐ranging, and clearly have great potential in a number of fields, not least in preventing insect predation, as an insecticide, or as an ovicide, together with antifungal and antimicrobial activity, as well as showing good anti‐inflammatory and cytotoxic activity. The plant's Lavender‐like smell is akin to that produced in sakuramochi (Prunus speciosa (Koidz.) Ingram, of cherry blossom fame), but is based on coumarin and its derivatives, not linalool and related volatiles in Lavandula. [ABSTRACT FROM AUTHOR]

The annular structure of Antarctic oscillation (AAO) is a research hotpot, but its asymmetry receives less attention. In this paper, the self‐organizing map method is employed to cluster the AAO patterns into symmetric and asymmetric modes in austral autumn. The asymmetry is mainly reflected in the Pacific‐Atlantic sector, and the AAO evolves toward asymmetric positive polarity, with the most pronounced asymmetry in May. Originating from near Australia, the asymmetry indicates a zonal wave train in the Pacific‐Atlantic sector. Both modeled and observed results demonstrate that the sea surface temperature anomaly in the equatorial western Pacific stimulates a local meridional circulation anomaly and induces anomalous Rossby wave sources near Australia subsequently. An anomalous wave train propagating toward the Antarctic Peninsula is formed, and the associated geopotential anomaly enhances the asymmetry of AAO. Asymmetric AAO is conducive to the Antarctic dipole, which modulates the air temperature and sea ice anomalies around Antarctica. Plain Language Summary: The zonal symmetry of the Antarctic oscillation (AAO; also named southern annular mode) is well known to researchers, while the zonal asymmetry has received less attention. We use a clustering method to cluster the AAO modes in austral autumn into symmetric modes and asymmetric modes. The asymmetry of AAO is mainly reflected in the Pacific‐Atlantic sector. The results show that the AAO mode evolves to a positive asymmetric mode, and the asymmetry is most obvious in May. Both simulations and observations suggest that this asymmetry stems from sea surface temperature anomaly in the equatorial western Pacific that stimulates an anomalous local meridional circulation near Australia, and in turn triggers an anomalous wave train propagating toward the Antarctic Peninsula. The associated geopotential anomalies enhance the asymmetry of AAO. The asymmetric AAO favors the Antarctic dipole, regulating the air temperature and sea ice anomalies around Antarctica. Key Points: Via a cluster method, we obtained the zonal asymmetric Antarctic oscillation (AAO) mode, and analyzed its trend and preferred monthThe source of its asymmetry is from the sea surface temperature anomaly in the tropical western PacificThe asymmetric AAO mode has a vital influence on the Antarctic dipole [ABSTRACT FROM AUTHOR]

This paper revealed a physical connection between the antiphase variation in the preceding autumn Tibetan Plateau (TP) and Lake Baikal snow cover anomalies (TBSA) and the following winter North Atlantic Oscillation (NAO) on interannual time scales during 1979–2021. The antiphase variation in TBSA, accounting for 44% of the total years, has a dipole structure in autumn, which prolonged into the following winter. The persistent antiphase TBSA associated diabatic forcing, disturbances and transient eddies favor a double wave train structure spanning the TP (east of Baikal) and North Atlantic from autumn to winter. Amid the wave train, the circulation anomalies over the North Atlantic extract more energy from the basic flow due to the seasonal increase in the westerly jet, which further evolves into the winter NAO pattern. Our results provide new insights into the formation and projection of winter NAO from the perspective of subtropical and extratropical Eurasia snow. Plain Language Summary: A robust link exists in the preceding autumn antiphase Tibetan Plateau (TP) and Lake Baikal snow cover anomalies (TBSA) and the winter North Atlantic Oscillation (NAO) during 1979–2021. There are 44% years of antiphase variation in TBSA in autumn, which shows a dipole structure with one positive center over the TP and another negative center over the Baikal. Larger (smaller) snow cover over the TP (Baikal) stimulates a local low (high) pressure system via diabatic cooling (heating). Due to the jet waveguide effect, the antiphase TBSA associated diabatic forcing and perturbation along the subtropical westerly jet favor the atmospheric wave train spanning the TP and North Atlantic. The antiphase TBSA associated transient eddies along the extratropical belt contribute to the atmospheric wave train lying between the eastern Baikal and the North Atlantic owing to the eddy‐flow interaction. Along with the seasonal increase in the subtropical westerly jet from autumn to winter, the geopotential height anomalies in the double wave train associated with the antiphase TBSA gradually develop into the winter large‐scale NAO circulation through more energy extraction from the stronger basic flow. Key Points: A robust link of winter North Atlantic Oscillation (NAO) to the preceding fall antiphase Tibetan Plateau‐Baikal snow cover anomalies (TBSA)The antiphase TBSA associated diabatic forcing, disturbances and transient eddies favor a double wave train structure around 30°N and 60°NMore energy extracts over the North Atlantic due to the seasonal increase in the westerly jet, favoring the development of the winter NAO [ABSTRACT FROM AUTHOR]

This paper analyzes the effect of the Australian High (AH) on the seasonal phase locking of Indian Ocean Dipole (IOD) events. The anomalous strong AH associated with the positive phase of the Antarctic Oscillation can cause significant easterly wind anomalies and northward cross‐equatorial flow over the western Maritime Continent (MC) by strengthening the Australian winter monsoon during May–August. The AH‐associated easterly anomalies and northward cross‐equatorial flow can create thermodynamic air‐sea feedback and contribute to a significant cooling anomaly in the western MC and the tropical eastern Indian Ocean. Without considering the effect of ENSO, these processes contribute to the occurrence of positive IOD events, which begin in early summer, peak in late summer, and decay rapidly thereafter. The effect of ENSO can extend the peak period of IOD into the boreal autumn of that year. An anomalous weak AH corresponds to the occurrence and seasonal phase locking of negative IOD events. Through combined empirical orthogonal function analysis, we find that the effect of the AH can well explain the seasonal phase locking of 34 IOD events (40 in total), which provides an important theoretical basis for the prediction of IOD events. Plain Language Summary: Seasonal phase locking is a critical characteristic of typical Indian Ocean Dipole (IOD) events. The IOD usually develops in boreal summer, peaks in autumn, and decays rapidly in winter. Note that the Australian High (AH) plays a key role on the seasonal phase locking of IOD events. It is found that The anomalous strong AH associated with the positive phase of the Antarctic Oscillation (AAO) can cause significant easterly wind anomalies and northward cross‐equatorial flow over the western Maritime Continent by strengthening the Australian winter monsoon during May–August. Key Points: Investigate the effect of Australian High (AH) the on the seasonal phase locking of Indian Ocean Dipole eventsThe Antarctic Oscillation can strengthen AH and Australian winter monsoonThe AH induced easterlies and cross‐equatorial flow contribute to a significant cooling anomaly in the tropical eastern Indian Ocean [ABSTRACT FROM AUTHOR]

Intense short‐term wind events can flush multiple‐inlet systems and even renew the water entirely. Nonetheless, little is known about the effect of wind variations at seasonal and interannual scales on the flushing of such systems. Here, we computed two Lagrangian transport time scales (LTTS), the residence and exposure times, for a multiple‐inlet system (the Dutch Wadden Sea) over 36 years using a realistic numerical model simulation. Our results reveal pronounced seasonal and interannual variability in both system‐wide LTTS. The seasonality of the LTTS is strongly anti‐correlated to the wind energy from the prevailing directions, which are from the southwesterly quadrant and coincidentally aligned with the geographical orientation of the system. This wind energy, which is stronger in autumn‐winter than in spring‐summer, triggers strong flushing (and hence low values of the LTTS) during autumn‐winter. The North Atlantic Oscillation (NAO) and the Scandinavia Pattern (SCAN) are shown to be the main drivers of interannual variability in the local wind and, ultimately, in both LTTS. However, this coupling is much more efficient during autumn‐winter when these patterns show larger values and variations. During these seasons, a positive NAO and a negative SCAN induce stronger winds in the prevailing directions, enhancing the flushing efficiency of the system. The opposite happens during positive SCAN and negative NAO, when weaker flushing during autumn‐winter is observed. Thus, large‐scale atmospheric patterns strongly affect the interannual variability in flushing and are potential drivers of the long‐term ecology and functioning of multiple‐inlet systems. Plain Language Summary: In multiple‐inlet coastal systems, strong wind events efficiently renew the water in these systems. In this paper, we investigate if the flushing of such systems has also a marked response to wind variability at longer time scales. To quantify the flushing, we compute the time that particles spend in the system before leaving it (known as the residence time), and the total time they spend within it considering future returns (known as the exposure time). Our 36‐year simulation of the hydrodynamics of the Dutch Wadden Sea (DWS) shows that the wind induces seasonal and interannual variations in both spatially‐averaged quantities. The seasonality is related to the wind energy from the dominant directions, which is much larger during autumn‐winter than during spring‐summer. This variation leads to a reduction of both time scales by, on average, a factor of 1.7 from spring‐summer to autumn‐winter. Two well‐known North Atlantic large‐scale atmospheric patterns, primarily active during autumn‐winter, induce interannual variations in the wind and consequently in both time scales. Thus, future changes in these patterns could strongly affect water transport and the ecology of the DWS. Similar situations are likely to occur in other multiple‐inlet systems. Key Points: The Lagrangian transport time scales in the Dutch Wadden Sea are typically 1.7 times smaller in autumn‐winter than in to spring‐summerThe seasonal and interannual variability of the Lagrangian transport time scales is attributed to the local windThe winter interannual variations are well explained by North Atlantic large‐scale atmospheric patterns [ABSTRACT FROM AUTHOR]

Monitoring and quantifying high‐latitude atmospheric circulation variability and trends are important towards understanding the anatomy of extreme events and constraining their probabilities under continued Arctic change. The greater Alaska region stands out as one region of enhanced warming and environmental changes over the Arctic amplification era, which has shown proclivity to extreme events and speaks to the need to consistently monitor overlying atmospheric variability. In this paper, we describe the creation and analysis of an updated, daily mid‐tropospheric Alaska Blocking Index (ABI) time series from 1948 to 2020. Sensitivity testing and index modifications from the initial dataset are discussed, and the new ABI is evaluated over the full period and subperiods. Additionally, we assess the role of the ABI as a regional climate indicator by analysing its variability against surface air temperature and precipitation from 13 climate divisions across Alaska as well as broader‐scale hemispheric temperatures. Months of the highest ABI means mainly transpired in the most recent climatological period, 1991–2020, and most notably during the spring and autumn transition seasons, reflecting the nonstationary nature of the jet stream magnitude through time atop Alaska. ABI trends are positive across all months, seasons, and annually only in the 1991–2020 period. Contemporaneous ABI values are strongly and positively correlated with air temperature across all Alaskan climate divisions during the warm season, but negatively correlated with winter temperatures over southeastern Alaska. Meanwhile, the ABI is significantly anticorrelated with summer precipitation over Interior and southern Alaska, but only over southeastern Alaska in other seasons. The ABI is statistically differentiated from the primary modes of atmospheric variability as shown by its generally weak correlations with the Arctic Dipole and the Arctic Oscillation (r ≤ 0.40). [ABSTRACT FROM AUTHOR]

Water availability to meet consumptive use demands over the next century in the Lower Basin of the Colorado River in the southwestern United States is considered under alternative climate and policy scenarios. Modeling reservoir storage and annual releases under scenario alternatives demonstrates that policy can only modestly mitigate losses from potential long run streamflow reductions. The major reservoirs Lake Powell and Lake Mead are treated as composite storage, and Upper Basin depletions are held constant over 97 annual periods beginning with calendar year 2020. Averaged over six climate change scenarios with mean inflow reductions of 22% (reaching 45% by end of century) estimated mean releases fall by 16% to 23% from year 2020 levels depending on decision scenario. Mean releases under the existing Law of the River are 6.1% below the benchmark decision scenario which uses a perfect forecast of future flows. This is largely the result of evaporation losses because reservoir storage across climate scenarios is about double that of the benchmark. In contrast, a decision scenario based on next year and future mean flow expectations shows a release reduction of only 2.6% below the benchmark, and a 2.8% decrease in standard deviation. Across the three decision scenarios, future reservoir storage is well below capacity in almost all years, and under the three higher impact climate scenarios mean storage across the century is only 35% to 40% of capacity. [ABSTRACT FROM AUTHOR]

The emerging malting industry in California needs adapted cultivars for a sustainable local barley production. 'UC‐Alameda' (Reg. no. CV‐381, PVP 202300263, PI 701932) is a two‐row, spring malting barley (Hordeum vulgare L.) released by the University of California, Davis, Agricultural Experiment Station, in 2022. UC‐Alameda is a highly productive cultivar with satisfactory malting quality for local craft maltsters. UC‐Alameda is adapted to the California Central Valley (Sacramento and San Joaquin Valleys) and is resistant to the most common diseases present in this region (e.g., Barley yellow dwarf virus, Cereal yellow dwarf virus, stripe rust, and powdery mildew) and moderately resistant to scald and net blotch. It was evaluated in preliminary trials, as B9K62, from 2017 to 2023 at Davis, CA, and by the University of California Regional Small Grains Testing program, as UC1911, from 2018 to 2023 for late fall planting in the Central Valley. UC‐Alameda is a non‐glycosidic nitrile producer, which is an important trait for craft‐maltsters and distillers. UC‐Alameda satisfies the quality criteria of the local craft malting and brewing industry interested in sourcing locally grown malting barley. Core Ideas: UC‐Alameda is a new two‐row spring malting barley for California.UC‐Alameda is a non‐glycoside nitrile producer.UC‐Alameda meets the requirements of California local maltsters and brewers.UC‐Alameda is resistant to the common barley pathogens present in the Central Valley of California.UC‐Alameda is resistant to stripe and stem rust. [ABSTRACT FROM AUTHOR]

A machine learning method is used to identify sources of long‐term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near‐surface zonal wind (U10)). Tropical SST represents the primary source of predictability skill. While U10 does not increase the skill when associated with SST, our analysis suggests U10 alone has apredictive skill comparable to that of SST between 11 and 21 months in advance, from late fall up to late spring. The long‐lead signal originates from coupled wind‐SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond 1 year ahead and identify the key role of U10 over the IO. Plain Language Summary: Many extreme events, such as floods or droughts, can be attributed to the El Niño Southern Oscillation, a mode of large‐scale ocean‐atmosphere coupled variability in the tropical Pacific Ocean occurring with a period of approximately 4 years. In this analysis, we use a machine learning methodology to disentangle the key atmospheric and oceanic ENSO components' relative contribution to its predictability, particularly the role of near‐surface 10‐m zonal wind. We quantify the potential for improved ENSO predictions for up to 2 years in advance and present a mechanistic understanding of the location of the sources of predictability. While equatorial sea surface temperature represents the primary source of ENSO predictability, the equatorial U10 plays a vital role from late spring to fall, from 1 to 2 years in advance. The enhanced predictability skill is shown to be linked to an SST anomaly originating in the Indian Ocean. The ML model used provides a new way to get new insights into the sources of predictability for ENSO and can be used as a simple but powerful tool to improve the underlying mechanistic understanding. Key Points: A deep learning‐based approach suggests near‐surface 10 m wind to play a significant role in providing skills for long‐term ENSO predictabilityThe skill of the 10‐m zonal wind is large from 12 months up to 23 months in advance (depending on the season)The signal is generated by coupled wind‐SST interactions in the Indian Ocean and later propagates across the Pacific [ABSTRACT FROM AUTHOR]

The oceanographic conditions of the Southern California Bight (SCB) dictate the distribution and abundance of prey resources and therefore the presence of mobile predators, such as goose‐beaked whales (Ziphius cavirostris). Goose‐beaked whales are deep‐diving odontocetes that spend a majority of their time foraging at depth. Due to their cryptic behavior, little is known about how they respond to seasonal and interannual changes in their environment. This study utilizes passive acoustic data recorded from two sites within the SCB to explore the oceanographic conditions that goose‐beaked whales appear to favor. Utilizing optimum multiparameter analysis, modeled temperature and salinity data are used to identify and quantify these source waters: Pacific Subarctic Upper Water (PSUW), Pacific Equatorial Water (PEW), and Eastern North Pacific Central Water (ENPCW). The interannual and seasonal variability in goose‐beaked whale presence was related to the variability in El Niño Southern Oscillation events and the fraction and vertical distribution of the three source waters. Goose‐beaked whale acoustic presence was highest during the winter and spring and decreased during the late summer and early fall. These seasonal increases occurred at times of increased fractions of PEW in the California Undercurrent and decreased fractions of ENPCW in surface waters. Interannual increases in goose‐beaked whale presence occurred during El Niño events. These results establish a baseline understanding of the oceanographic characteristics that correlate with goose‐beaked whale presence in the SCB. Furthering our knowledge of this elusive species is key to understanding how anthropogenic activities impact goose‐beaked whales. [ABSTRACT FROM AUTHOR]

Obvious biases in simulating tropical cyclone (TC) genesis of the current climate models hamper our understanding of TC changes. In this study, we found a delay of the seasonal cycle of TC genesis frequency over the western North Pacific (WNP) in most Coupled Model Intercomparison Project Phase 6 models. During the active TC season, the simulated south‐warming and north‐cooling surface temperature bias amplifies the meridional gradient and excites thermal winds. This weakens the western North Pacific Subtropical High and easterly monsoon trough, which further reduces TC genesis frequency over the western WNP in summer. But in autumn, positive TC genesis biases were only observed in coupled models over the eastern WNP. Both seasons contribute to the delayed seasonal cycle of TC frequency in models. Our findings highlight the importance of accurate simulation of surface temperature by climate models to TC simulations and aid in future model improvements. Plain Language Summary: Tropical cyclone (TC) is a devastating weather system generated over the tropical ocean, and the climate model serves as an important tool to study the long‐term variability of TC activity. Hence, it is imperative to explore the biases of climate models in simulating TC genesis. In this investigation, we find a substantial influence of surface temperature bias in Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models on the simulation of the seasonal cycle of TC genesis frequency over the western North Pacific (WNP). The delay in the seasonal cycle of TC genesis frequency is widespread among most CMIP6 models. During boreal summer and autumn, the simulated bias of south‐warming and north‐cooling surface temperature increases its meridional gradient and triggers thermal winds. In summer, the weak western North Pacific Subtropical High and easterly monsoon trough, two crucial large‐scale circulation systems, cause a large negative TC genesis bias in models over the western WNP, substantially contributing to the delay of the seasonal cycle. Meanwhile, the relatively small positive bias of coupled models over the eastern WNP in autumn makes a secondary contribution. The combined contributions of these biases cause delays in seasonal simulation of TC genesis frequency. Key Points: Coupled Model Intercomparison Project Phase 6 models produce delayed seasonal cycles of tropical cyclone genesis frequency over the western North PacificBiases in East Asia summer monsoon and western North Pacific subtropical high lead to underestimation of tropical cyclone genesis during summerThe biased large‐scale circulation systems are further related to a warm southern and cold northern surface temperature bias [ABSTRACT FROM AUTHOR]

The role of internal variability in generating an apparent link between autumn Barents‐Kara sea (BKS) ice and the winter North Atlantic Oscillation (NAO) has been intensely debated. In particular, the robustness and causality of the link has been questioned by showing that BKS‐NAO correlations exhibit nonstationarity in both reanalysis and climate model simulations. We show that the lack of ice observations means nonstationarity cannot be confidently assessed using reanalysis prior to 1961. Model simulations are used to corroborate an argument that forced nonstationarity could result from ice edge changes due to global warming. Consequently, the observed change in BKS‐NAO correlations since 1960 might not be purely a result of internal variability and may also reflect that the ice edge has moved. The change could also reflect the availability of more accurate ice observations. We discuss potential implications for analysis based on coupled climate models, which exhibit large ice edge biases. Plain Language Summary: Does the amount of ice in the Barents‐Kara Sea influence European air pressure or are the patterns we see caused by random changes in the weather? In climate models and in estimates of the atmosphere's history these patterns change depending on which years we look at. This has been interpreted as evidence that the patterns are random. However, there are very few measurements of ice in this region before 1961, so we argue that looking at these years is not helpful. Since 1961, where we have more measurements, the winter sea ice edge has been moving Northwards because of global warming. When the ice in a particular region disappears, it changes the expected relationship with the atmosphere because heat can now quickly leave the ocean. We therefore hypothesize that some of the changes seen in the patterns may therefore not be random, but a result of ice edge changes. We also observe that different climate models put the ice edge in different places, and the same hypothesis therefore implies that models might not get potential ice‐air pressure relationships in specific regions correct. Key Points: A lack of observations means that ice‐NAO links cannot be confidently assessed with reanalysis prior to 1961Nonstationarity since 1961 may reflect forced changes to the ice edge, due to the dependence of heatflux anomalies on ice edge variabilityThe magnitude of this potential forced nonstationarity in the real world is currently unclear [ABSTRACT FROM AUTHOR]

Despite its importance for the global oxidative capacity, spatially resolved trends and variability of the hydroxyl radical (OH) are poorly constrained. We demonstrate the utility of a tropospheric column OH (TCOH) product, created from machine learning and satellite proxy data, in determining the spatial variability in trends of tropical OH over the oceans during September through November. While OH increases domain‐wide by 2.1%/decade from 2005–2019, we find significant spatial heterogeneity in regional trends, with decreases in some areas of 2.5%/decade. Our analysis of the trends in the proxy data indicate anthropogenic‐driven changes in emissions of OH drivers as well as increasing temperatures cause these trends. This OH product is potentially a significant advance in constraining OH spatial variability and serves as a useful complement to existing tools in understanding the atmospheric oxidative capacity. Comprehensive observations of TCOH are required to assess the fidelity of this method. Plain Language Summary: Hydroxyl is a chemical that removes many gases from the atmosphere, including methane, an important greenhouse gas. To understand recent trends in methane, we must also understand recent trends in hydroxyl. Because of various limitations, we unfortunately do not have long‐term, direct observations of hydroxyl. To address this problem, we have developed a machine learning model that uses satellite observations of variables relevant to hydroxyl chemistry and variability to calculate hydroxyl. We demonstrate that this product can be used to understand trends and variability of hydroxyl over the tropical oceans. While, on average, hydroxyl increases from 2005–2019, we show that this is not a universal trend and that hydroxyl actually decreases in multiple regions over the same time period. Using satellite observations of various chemicals, we demonstrate that changes in emissions due to human activity and increases in temperature cause many of these trends. This product is potentially a significant advance in understanding changes in hydroxyl and could be a useful complement to more traditional methods in understanding atmospheric methane. Key Points: Satellite proxies can constrain the spatial distribution of trends and variability of the hydroxyl radical in the tropicsChanges in biomass burning, temperature, and other emissions lead to large spatial heterogeneity in hydroxyl radical trendsMore direct observations of the hydroxyl radical and its drivers are needed to further validate the fidelity of our methodology [ABSTRACT FROM AUTHOR]

The October effect is known as a rapid and strong decrease in the signal amplitude of radio waves with very low frequency (VLF), reflected at the lowest edge of the ionosphere. This strong decrease can be observed only during the daytime. Although the October effect is long known, it is hardly investigated and its mechanism is still unknown. To get closer to a mechanism, we answer why the October effect does not occur during nighttime. Therefore, average characteristics of the October effect are obtained from different VLF transmitter‐receiver combinations. The occurrence of the October effect is then compared with characteristics of the neutral atmosphere temperature at VLF reflection heights as it seems to act as a proxy for the unknown mechanism. The temperature shows an asymmetric seasonal behavior at daytime VLF reflection heights poleward of 50°N but not during the nighttime, resulting in the October effect. Plain Language Summary: The October effect is known as a rapid and strong decrease in the signal amplitude of radio waves with very low frequency, reflected at the lowest edge of the ionosphere (60–90 km). This strong decrease can be observed only during the daytime. Although the October effect has been long known, it is hardly investigated and its mechanism is still unknown. To get one step closer to a mechanism, we want to answer the question of why the October effect does not occur during nighttime. There are two main reasons why the October effect does not occur during nighttime. First, the radio wave reflection height is at around 70 km during daytime and at 85 km during nighttime. The second is the different behavior of the temperature at these two altitudes. While the temperature follows the seasonal cycle of the sun at 85 km, it shows an asymmetric behavior between spring and autumn at 70 km. This unexpected behavior of the temperature at 70 km leads to the October effect during the daytime only. Key Points: Strong and rapid decrease in VLF amplitude in OctoberSpring‐fall asymmetry in VLF amplitude and lower mesospheric temperatureAsymmetry only at daytime VLF reflection height [ABSTRACT FROM AUTHOR]

During the Arctic night, clouds regulate surface energy budgets through longwave warming alone. During fall, any increase in low‐level clouds will increase surface cloud warming and could potentially delay sea ice formation. While an increase in clouds due to fall sea ice loss has been observed, quantifying the surface warming is observationally challenging. Here, we use a new observational data set of surface cloud warming at instantaneous 330 m × 90 m spatial resolution. By instantaneously co‐locating surface cloud warming and sea ice observations in regions where sea ice varies, we find October large surface cloud warming values (>80 W m−2) are much more frequent (∼+50%) over open water than over sea ice. Notably, in November large surface cloud warming values (>80 W m−2) occur more frequently (∼+200%) over open water than over sea ice. These results suggest more surface warming caused by low‐level opaque clouds in the future as open water persists later into the fall. Plain Language Summary: Over the past 40 years, Arctic sea ice cover has decreased in all months of the year, but especially in late summer and early fall. Through their impact on energy budgets, clouds have the potential to increase or decrease sea ice decline. More low‐level clouds over open water than over sea ice during non‐summer seasons have already been observed. But quantifying their radiative effect remains challenging. Therefore, this study seeks to answer the following question: By how much late fall Arctic clouds can change surface warming in response to sea ice loss? Using cloud surface warming data at high spatio‐temporal resolution, we found that large surface cloud warming values, higher than 80 W m−2, occurs much more frequently over open water than over sea ice during October and November months. This suggests that Arctic clouds favor sea ice loss by delaying sea ice recovery. As the Arctic continues to warm up due to human activities, cloud surface warming will delay sea ice freeze‐up later into the fall and may amplify Arctic sea ice loss. Key Points: During October, large surface cloud warming with values higher than 80 W m−2 occurs ∼+50% more often over open water than over sea iceCompared to October, November large surface cloud warming (>80 W m−2) occurs even more frequently (∼+200%) over open water than over sea iceMore frequent large surface warming caused by low‐level opaque clouds occurs as open water persists later into the fall [ABSTRACT FROM AUTHOR]

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The escalating impact of climate change on global terrestrial ecosystems demands a robust prediction of the trees' growth patterns and physiological adaptation for sustainable forestry and successful conservation efforts. Understanding these dynamics at an intra‐annual resolution can offer deeper insights into tree responses under various future climate scenarios. However, the existing approaches to infer cambial or leaf phenological change are mainly focused on certain climatic zones (such as higher latitudes) or species with foliage discolouration during the fall season. In this study, we demonstrated a novel approach (INTRAGRO) to combine intra‐annual circumference records generated by dendrometers coupled to the output of climate models to predict future tree growth at intra‐annual resolution using a series of supervised and unsupervised machine learning algorithms. INTRAGRO performed well using our dataset, that is dendrometer data of P. roxburghii Sarg. from the subtropical mid‐elevation belt of Nepal, with robust test statistics. Our growth prediction shows enhanced tree growth at our study site for the middle and end of the 21st century. This result is remarkable since the predicted growing season by INTRAGRO is expected to shorten due to changes in seasonal precipitation. INTRAGRO's key advantage is the opportunity to analyse changes in trees' intra‐annual growth dynamics on a global scale, regardless of the investigated tree species, regional climate and geographical conditions. Such information is important to assess tree species' growth performance and physiological adaptation to growing season change under different climate scenarios. [ABSTRACT FROM AUTHOR]

Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice. Plain Language Summary: Antarctic sea ice has changed significantly since 2016, leading to a higher demand for sea ice forecasts. However, forecasting Antarctic sea ice has not received enough attention. Limited observations and lack of understanding of ice‐related physical mechanisms result in significant errors in sea ice predictions in dynamical models, particularly at the subseasonal timescale. We utilized a deep‐learning model to predict Antarctic sea ice at this timescale to fill this gap. Results showed that the deep‐learning model performed skillfully 1–8 weeks in advance, with the Weddell Sea being the best‐predicted region and the West Pacific being the worst. Furthermore, our study found that the model significantly outperformed mainstream dynamic models and a conventional statistical model. These findings build a foundation for developing more advanced prediction models at high resolutions for operational applications. Key Points: A deep‐learning model outperforms dynamic models, filling a subseasonal Antarctic sea ice prediction gap by bypassing physical mechanismsUnlike a Markov model that predicts time series of fixed spatial patterns, our model can extract ice synchronized spatiotemporal evolutionsThe deep‐learning model captures climate signals in sea ice, delivering the best prediction in fall season and the Weddell Sea region [ABSTRACT FROM AUTHOR]

During a disease survey in greenhouses of blueberry seedlings in Mohammad Shahr City, Alborz Province, Iran, in the autumn 2021, severe stem canker symptoms were observed in the cv. Emerald of highbush blueberry (Vaccinium corymbosum). The disease incidence in the affected greenhouses was more than 30%–40%. Diseased branches and stems were sampled and the associated fungus was isolated on potato dextrose agar. Based on the fungal morphological features of the colony, three isolates among which the representative isolate of UT2020 were identified as Ectophoma multirostrata. Phylogenetic analysis of the concatenated ITS‐rDNA and beta‐tubulin (tub2) gene sequence data confirmed the morphological identification of the fungal isolates. E. multirostrata differs from closely related species by having several ostiolar necks, textura angularis wall in pycnidia and oblong to ellipsoidal conidia. The pathogenicity of the isolate UT2020 in the stems of highbush blueberry cv. Emerald was confirmed by our artificial wound inoculation method. After 14 days, the same stem canker symptoms were developed on the inoculated stems, and the same fungal isolate was re‐isolated from the newly developed symptoms, fulfilling the Koch's postulates. This is the first report of E. multirostrata causing a stem canker disease in highbush blueberries. Also, Vaccinium corymbosum is reported as a new host (matrix nova) for E. multirostrata worldwide. [ABSTRACT FROM AUTHOR]

The need to evaluate global climate data has increased in recent times. In this study, we evaluate the ability of global precipitation products to monitor drought during three wet seasons (Belg/March–May, Kiremt/June–September and Autumn/September–November) and associated rainfall regions in Ethiopia. We employed statistical methods to quantify and evaluate precipitation products based on probability of drought detection (POD), the extent of false alarms (FAR) and the critical success index (CSI) to see the overall performance of the studied precipitation products. The majority of the studied precipitation datasets were relatively better in capturing the Autumn drought in southern Ethiopia, and 18 out of 21 precipitation products captured accurately more than 50% of observed droughts. The CSI scores for this season are also above 0.5 for 14 precipitation products. On the other hand, 15 and 14 precipitation products accurately captured more than 50% of the seasonal drought in Kiremt and Belg rainfall seasons in north‐eastern Ethiopia. In contrast, most precipitation products do not clearly represent the drought conditions of the Kiremt season in north‐western Ethiopia. Only 8 of the 21 precipitation products accurately captured more than 50% of the observed drought in this region, and only 6 precipitation products had a CSI score greater than 0.5. The results can facilitate the selection of precipitation products for drought monitoring purposes, for use in specific wet seasons and regions of Ethiopia. [ABSTRACT FROM AUTHOR]

Aim: How plants can disperse in response to global change is a critical question, yet major knowledge gaps persist about long‐distance dispersal (LDD) mechanisms. We studied the potential a migratory waterfowl has for LDD of flowering plants via gut passage of seeds (endozoochory), comparing spring and autumn migration. Location: United Kingdom and Iceland. Taxon: Pink‐footed Goose (Anser brachyrhynchus, Baillon) and Angiosperma. Methods: We studied endozoochory by Pink‐footed geese migrating within and between the UK and Iceland by faecal sampling and GPS tracking. We collected 614 faecal samples from 14 areas in the UK and one in Iceland. Using GPS tracks to and from these areas, we estimated how far seeds can be dispersed by Pink‐footed geese, and where to or from. Results: We recorded 5507 intact seeds of 35 species (27 terrestrial) from 15 plant families, with lower seed abundance per dropping when birds were migrating northwards in the UK during spring than upon their arrival in autumn. Species richness of plant seeds was highest in Iceland and in autumn. Only four plant species dispersed had an "endozoochory syndrome". GPS movements showed that seeds retained in guts for up to 24 h can be readily dispersed in both directions between the UK and Iceland, with maximum distances exceeding 2000 km, as well as between UK localities separated by 100 s of km. Movements northwards of ≤400 km were even recorded in autumn. While at stopover sites, daily movements between roost and feeding sites often exceed 20 km. Main Conclusions: Pink‐footed geese are LDD vectors for plants previously assumed to lack an LDD mechanism. Spring migration is not the only period when geese move plants to cooler latitudes. The pink‐footed goose can allow terrestrial and aquatic plants to cross the ocean and to keep pace with climate change. [ABSTRACT FROM AUTHOR]

The Southwestern Atlantic Ocean (SWA), is considered one of the most productive areas of the world, with a high abundance of ecologically and economically important fish species. Yet, the biological responses of this complex region to climate variability are still uncertain. Here, using 24 years of satellite‐derived Chl‐a data, we classified the SWA into 9 spatially coherent regions based on the temporal variability of Chl‐a concentration, as revealed by SOM (Self‐Organizing Maps) analysis. These biogeographical regions were the basis of a regional trend analysis in phytoplankton biomass, phenological indices, and environmental forcing variations. A general positive trend in phytoplankton concentration was observed, especially in the highly productive areas of the northern shelf‐break, where phytoplankton biomass has increased at a rate of up to 0.42 ± 0.04 mg m−3 per decade. Significant positive trends in sea surface temperature were observed in 4 of the 9 regions (0.08–0.26 °C decade−1) and shoaling of the mixing layer depth in 5 of the 9 regions (−1.50 to −3.36 m decade−1). In addition to the generally positive trend in Chl‐a, the most conspicuous change in the phytoplankton temporal patterns in the SWA is a delay in the autumn bloom (between 15 ± 3 and 24 ± 6 days decade−1, depending on the region). The observed variations in phytoplankton phenology could be attributed to climate‐induced ocean warming and extended stratification period. Our results provided further evidence of the impact of climate change on these highly productive waters. Plain Language Summary: The Southwestern Atlantic Ocean (SWA), is one of the most biologically productive areas of the world, with a high abundance of fish species. This important area might be affected by the environmental consequences of climate change. In this study, we addressed the influence of the already observed changes in environmental conditions on phytoplankton, which are the base of the marine food web, over the last 24 years. There has been an increase in phytoplankton biomass as well as the timing and intensity of the autumn bloom in some specific areas of the SWA. We have found that these changes in phenology might be related to the increase in sea surface temperature and the shoaling of the mixed layer depth. Consistent with previous studies, our results provided further evidence of the impact of climate change on these highly productive waters. Key Points: Chlorophyll‐a concentration has increased over the last 24 years in most of the Southwestern AtlanticPhytoplankton phenological changes have been observed, mainly during the austral autumn bloomPhytoplankton biomass and phenology might be affected by the increasing sea surface temperature and mixing layer depth shoaling [ABSTRACT FROM AUTHOR]

The production by microorganisms of nitrous oxide (N2O), a trace gas contributing to global warming and stratospheric ozone depletion, is enhanced around the oceanic oxygen minimum zones (OMZs). The production constitutes an important source of atmospheric N2O. Although an OMZ is found in the northern part of the eastern Indian Ocean, the Bay of Bengal (BoB), two earlier studies conducted during the later phase of winter monsoon (February) and spring intermonsoon (March–April) found quite different magnitudes of N2O accumulation. This study found two‐ to ten‐fold greater accumulation of N2O during the autumn intermonsoon (November) than for other seasons described in earlier reports. The maximum N2O concentration (136 nmol kg−1 at 16°N, 88°E) is comparable to those observed around the OMZ in the Arabian Sea or eastern tropical Pacific. Isotopic signatures suggest that bacterial denitrification and archeal nitrification play important roles in N2O production, but earlier studies using nitrate or nitrite analysis did not confirm denitrification in the BoB. Large seasonal variation of N2O implicates the BoB as an important N2O source, similar to the Arabian Sea and eastern tropical Pacific, if the accumulated N2O is emitted to the atmosphere during the subsequent monsoon season. Key Points: During the autumn intermonsoon, greenhouse gas/ozone depleting gas N2O accumulates around the oxygen minimum zone in the Bay of Bengal (BoB)Bacterial denitrification and archeal nitrification play important roles in the N2O productionLarge seasonal variation of N2O implicates the BoB as an important N2O source to the atmosphere [ABSTRACT FROM AUTHOR]

Copyright of Functional Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The change in heat storage (Gc) is an essential component of a lake's energy balance, and its importance for lake evaporation (Ew) has been widely recognized. However, the effect of Gc on Ew exhibits diversity across time dimensions. The controls on Gc and the effects of Gc on Ew estimates at different time scales remain largely unexplored. To address these gaps, we identified the primary controls on Gc at an eddy covariance site in a large shallow lake (Lake Taihu) and quantified the role of Gc in estimating Ew on three (hourly, daily, and monthly) time scales based on two energy balance‐based Ew models. Our results indicate that the diurnal variation of Gc is dominated by net radiation and peaks around noon, while the seasonal variation of Gc is mainly controlled by air temperature and peaks in spring. In contrast, the daily variation of Gc is subjective to a confluence of factors—net radiation, wind speed, and relative humidity—displaying more stochasticity than that on the other two time scales. We also found that the importance of Gc for Ew estimates decreases as the time scale extends. Compared to the two models disregarding Gc, considering the effect of Gc enhances the average Kling‐Gupta efficiency (KGE) values of the two models by 1.33, 0.42, and 0.08 on the three time scales, respectively. Overall, this study highlights the importance of time scales in evaluating the effect of Gc on Ew estimates. Plain Language Summary: Although the importance of Gc for Ew has been widely recognized, little is known about the controls on Gc and the differences in the effect of Gc on Ew estimates at different time scales. Using two commonly used Ew models (i.e., the Priestley‐Taylor and De‐Bruin‐Keijman models), this study identified the primary controls on Gc at a lake flux site and quantified the effect of Gc on Ew estimation on hourly, daily, and monthly time scales. Our findings reveal that net radiation and air temperature are the most important factors controlling Gc on diurnal and seasonal scales, respectively. Both models perform poorly in estimating hourly and daily Ew when the effect of Gc is disregarded. In contrast, a significant model improvement is observed when the effect of Gc is considered. This enhancement remains applicable at the monthly scale, although its effect is not as significant as at the hourly and daily scales. Neglecting the effect of Gc on Ew estimates leads to an overestimation of Ew in spring and summer and an underestimation of Ew in autumn and winter. Key Points: We quantified the effect of heat storage change (Gc) on evaporation (Ew) estimates for a large shallow subtropical lakeThe importance of Gc on Ew estimation decreases as the time scale expandsNet radiation governs the diurnal variation of Gc, whereas air temperature dominates the seasonal variability of Gc [ABSTRACT FROM AUTHOR]

Autumn precipitation in the central region of China (APCC) can considerably influence on agricultural production and human livelihood. With reanalysis data from 1979 to 2020, it revealed that APCC is significantly affected by the second mode of the meridional wind field at 200 hPa over the Eurasian continent on the interannual scale, which featured a quasi‐barotropic "+‐+" wave‐like pattern from Europe to China (EC pattern) in extratropical zone. When the EC pattern is in a positive phase, central China lags behind the downstream positive geopotential height (anticyclone) anomaly. In this context, the transport of moisture from the south and east is enhanced and the advancement of the southerly monsoon is blocked, providing sufficient water vapor to support the occurrence of APCC. Further diagnosis revealed that local ascending motion is strengthened owing to the warm advection induced by anomalous southerly and positive vorticity advection associated with the anomalous anticyclone over East Asia. Additionally, wave flux analysis and model simulation suggest that the EC pattern results from the propagation of stationary Rossby wave, which could be excited by dipole sea surface temperature anomalies in the North Atlantic. These discoveries will contribute to improving the predictability of APCC. Plain Language Summary: The interannual variability of autumn precipitation in central China (APCC) is largely affected by a wave train over the Eurasian continent (EC pattern), which can influence the local moisture and dynamic uplift conditions by an anomalous anticyclone over East Asia. The North Atlantic dipole sea surface temperatur anomalies play an important role in the prediction of APCC, and could stimulate an extratropical teleconnection to shape the EC pattern over the Eurasian continent, as confirmed in the linear baroclinic model. Key Points: The autumn rainfall in central China is related to the second mode of V200 (EOF2) over the Eurasian continent on interannual timescaleEOF2 features in a "+‐+" wave‐like pattern, resulting in an anticyclone anomaly over East Asia in its positive phaseExtratropical EOF2 pattern wave train could be excited by the North Atlantic dipole sea surface temperature anomaly [ABSTRACT FROM AUTHOR]

Although a large part of Italy is characterized by a Mediterranean climate intrinsically highly suitable for olive cultivation, farmers may experience variable interannual yields with associated agronomic and management costs. A detailed overview of major climate stressors and their ongoing impacts on olive yield variability at a broad spatial–temporal scale across Italy could improve the understanding of how interannual olive yields are modulated by seasonal local climate and would enhance the development of actionable services to alert stakeholders of potential climate risks. We analysed aggregated olive yield data from the Italian National Statistics Institute (ISTAT) at the provincial level during 2006–2020, and several climatic variables from the Reanalysis v5 (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) as a basis to (i) explore olive yield trends and inter‐annual variations over the whole country; (ii) identify major seasonal climate stressors likely responsible for the largest variations in yield and (iii) develop a composite index that summarizes the risk of having exceptionally low yields due to the co‐occurrence of multiple stressors. To this end, we defined two extreme classes of yield, namely, exceptionally low and high yields (LY and HY, respectively) and explored the climatic variables aggregated on a bimonthly time scale that influenced yield outcomes. Our analysis showed that exceptional low yields have been erratically increasing since 2014 with temperature‐related variables having the highest impact, especially a relatively warm winter and cool early autumn. These period‐specific variables were major components of the resulting composite index predicting the likelihood of LY ranging from 28% to 49% due to increasing stress effects. Possible explanations of our findings are discussed, including the proliferation of the olive fly. We suggest the composite risk approach could lay the groundwork for an integrated meteorological seasonal forecasting system that provides timely insights on factors affecting within‐season olive yield development. [ABSTRACT FROM AUTHOR]

Release of dissolved organic carbon (DOC) by seaweed underpins the microbial food web and is crucial for the coastal ocean carbon cycle. However, we know relatively little of seasonal DOC release patterns in temperate regions of the southern hemisphere. Strong seasonal changes in inorganic nitrogen availability, irradiance, and temperature regulate the growth of seaweeds on temperate reefs and influence DOC release. We seasonally surveyed and sampled seaweed at Coal Point, Tasmania, over 1 year. Dominant species with or without carbon dioxide (CO2) concentrating mechanisms (CCMs) were collected for laboratory experiments to determine seasonal rates of DOC release. During spring and summer, substantial DOC release (10.06–33.54 μmol C · g DW−1 · h−1) was observed for all species, between 3 and 27 times greater than during autumn and winter. Our results suggest that inorganic carbon (Ci) uptake strategy does not regulate DOC release. Seasonal patterns of DOC release were likely a result of photosynthetic overflow during periods of high gross photosynthesis indicated by variations in tissue C:N ratios. For each season, we calculated a reef‐scale net DOC release for seaweed at Coal Point of 7.84–12.9 g C · m−2 · d−1 in spring and summer, which was ~16 times greater than in autumn and winter (0.2–1.0 g C · m−2 · d−1). Phyllospora comosa, which dominated the biomass, contributed the most DOC to the coastal ocean, up to ~14 times more than Ecklonia radiata and the understory assemblage combined. Reef‐scale DOC release was driven by seasonal changes in seaweed physiology rather than seaweed biomass. [ABSTRACT FROM AUTHOR]

Seasonal formation of Dense Shelf Water (DSW) in the Ross Sea is a direct precursor to Antarctic Bottom Water, which fills the deep ocean with atmospheric gases in what composes the southern limb of the solubility pump. Measurements of seawater noble gas concentrations during katabatic wind events in two Ross Sea polynyas reveal the physical processes that determine the boundary value properties for DSW. This decomposition reveals 5–6 g kg−1 of glacial meltwater in DSW and sea‐ice production rates of up to 14 m yr−1 within the Terra Nova Bay polynya. Despite winds upwards of 35 m s−1 during the observations, air bubble injection had a minimal contribution to gas exchange, accounting for less than 0.01 μmols kg−1 of argon in seawater. This suggests the slurry of frazil ice and seawater at the polynya surface inhibits air‐sea exchange. Most noteworthy is the revelation that sea‐ice formation and glacial melt contribute significantly to the ventilation of DSW, restoring 10% of the gas deficit for krypton, 24% for argon, and 131% for neon, while diffusive gas exchange contributes the remainder. These measurements reveal a cryogenic component to the solubility pump and demonstrate that while sea ice blocks air‐sea exchange, sea ice formation and glacial melt partially offset this effect via addition of gases. While polynyas are a small surface area, they represent an important ventilation site within the southern‐overturning cell, suggesting that ice processes both enhance and hinder the solubility pump. Plain Language Summary: Previous scientific studies have demonstrated that the water which fills the deep sea is created in isolated regions of the surface ocean where wind, evaporation, heat loss, and sea ice formation can work in concert to make very cold salty seawater at the ocean surface. As this water leaves the surface it can carry oxygen and carbon dioxide, as well as heat away from the atmosphere for nearly a millennium, suggesting the sequestration mechanism may impact earth's climate and human climate change. This study sought to reveal how different types of sea ice and glacier ice might influence the gases that are dissolved in seawater and sequestered in the ocean. We made measurements of the noble gases (helium, neon, argon, krypton, and xenon) in the Ross Sea in late fall of 2017, when the conditions are cold and windy, leading to lots of dense water production. The results reveal that sea ice interrupts the process of air‐sea exchange of gases, which can slow down the uptake of human‐generated carbon dioxide by dense water. But our results also revealed that sea ice formation and glacial ice melt can both add gas to dense water during its creation. Key Points: Noble gas tracers can infer the rate of sea ice production in polynyasFrazil ice in polynyas appears to block air‐sea gas exchange mechanismsThe solubility pump is influenced by glacial ice melt and sea ice formation [ABSTRACT FROM AUTHOR]

Age, growth, and maturity of the bluewing searobin Prionotus punctatus were studied to assess its stock status in southern Brazil. Total length compositions, weight, sex, and maturity stage were recorded from commercial landings, and ages were estimated from transverse sections of the sagittal otolith. The oldest observed specimen was a 12‐year‐old female. Females grew to a larger asymptotic length (L∞ = 384.4 mm) than males (L∞ = 311.7 mm) but at a slower instantaneous rate (kfemales = 0.41 year−1, kmales = 0.75 year−1). Reproduction occurred from spring to early fall. Females size‐ and age‐at‐maturity was estimated at 246.8 mm and 1.66 years, while males were at 237.2 mm and 1.38 years. Based on a statistical catch‐at‐age stock assessment, the spawning biomass declined 84% from 1976 to 2000 being classified as overfished and suffering from overfishing (B/Bmsy = 0.62; F/Fmsy >1). P. punctatus was regularly discarded by industrial fisheries before 2000 but suffered from high exploitation in the last decades. These results highlight the need for the species to be included in Brazil's fishery management plans. [ABSTRACT FROM AUTHOR]

Recruitment success of marine fishes is generally considered to be highly dependent on larval growth and survival. In temperate ecosystems, fish larvae are sensitive to food limitation during the low productivity seasons, particularly if water temperatures and concomitant larval metabolic costs increase due to climate change. We combined 7 years of in situ sampling of larval fish, novel observations on zooplankton via automated image analyses, and larval physiological modeling to explore feeding conditions of Atlantic herring larvae (Clupea harengus) in the North Sea. The observed plankton size‐structure was close to the theoretical optimum for larval foraging, but not the biomass. Our results for autumn larvae supported Hjort's critical period hypothesis: small first‐feeding larvae were predicted to have a high probability of starvation, whereas larvae > 13 mm were able to reach their maximal growth capacity. In winter, the majority of herring larvae of all tested sizes (5–27 cm) experienced food‐limitation with over 35% probability of starvation. Sensitivity analysis suggested that young herring larvae improve their growth performance and probability of survival if feed not only on copepods and their life‐stages but include other microplankters in their diet. Given projected warming of the North Sea, our model predicts that herring larvae would require 28% (35%) more prey biomass in autumn (winter) to sustain their growth and survival in the future. This finding together with the ongoing low recruitment of North Sea herring underscore the importance of future micro‐ and mesoplankton monitoring within a scope of wintertime larval fish surveys. [ABSTRACT FROM AUTHOR]

The Siberian storm track is one of the drivers of the East Asian extreme weather events. Using the daily JRA‐55 reanalysis data from 1980 to 2021, this study examines roles of eddy generation and jet characteristics in setting the annual cycle of Siberian storm track. It is found that there are two peaks of Siberian storm track intensity in boreal spring and autumn. The possible reason for such an annual cycle is explored by analyzing the maximum Eady growth rate over the Siberian region and jet characteristics. The stronger Siberian Eady growth rate in boreal spring and autumn, favoring a stronger baroclinic eddy generation, could contribute to the stronger intensity of Siberian storm tracks in these two seasons. Furthermore, the Siberian jet stream cores during boreal spring and autumn are located north of 50° N and resembles more an eddy‐driven jet. While in winter, the subtropical jet stream enhanced and the eddy‐driven jet becomes relatively weaker, which is less efficient to generate midlatitude baroclinic eddies. Besides, the eddy‐driven jet can modulate the horizontal wave propagations from upstream, which also plays a role in amplifying the spring and autumn Siberian storm tracks. [ABSTRACT FROM AUTHOR]

Soil respiration (Rs) during daytime (Rsday) and nighttime (Rsnight) remains poorly understood owing to the limited availability of high‐resolution temporal measurements of Rs.Using continuous automatic chamber measurements at a half‐hourly interval, we explored Rs and its temperature sensitivity (Q10) during the daytime and nighttime over 10 years and determined the best sampling timeslot in a temperate deciduous broadleaf forest.We demonstrate a higher Rsnight than Rsday in spring and summer, a lower Rsnight than Rsday in autumn, but an insignificant difference between them in winter, contrasting with the consensus of a higher Rsday than Rsnight. Such discrepancy may result from the effect of factors other than temperature (e.g. reallocation of photosynthesis carbon, precipitation and soil moisture). We also reveal significant differences in apparent Q10 between seasons, indicating the highest Q10 in spring and significantly higher Q10 at night than at day in autumn. The Rs measured around midday (9:00–11:00 for autumn and 12:00–15:00 for other seasons) and midnight (20:00–22:00 for autumn and 0:00–2:00 for other seasons) provides the lowest bias to the daily mean Rs, whereas the measurement timeslots around midday are preferred due to practical considerations.Our findings highlight the significant diel variation of Rs and its temperature sensitivity across seasons and the necessity to account for Rsnight for a more accurate estimate of soil carbon budget. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

For instance, most or all Dolly Varden ( I Salvelinus malma i ) and Arctic grayling ( I Thymallus arcticus i ) overwintering sites in Alaska's Canning River (Figure 1) are associated with perennial springs (Brown et al., [3]). We are not aware of any previously published description of a mass mortality event among overwintering fish at an Arctic perennial spring, although such occurrences are known among the Iñupiat of the region, including at this particular site (North Slope Subsistence Regional Advisory Council Transcript, [18]). We counted 56 overflow fish, of which we were able to obtain fork lengths for 27 (median fork length: 27 cm, range: 15-35 cm; Glass et al., [13]), and >20 pool fish, although pool fish were frozen deeper in the ice precluding an accurate count. Keywords: Gulo gulo; overwintering habitat; perennial spring; Salvelinus malma; scavenging; Thymallus arcticus EN Gulo gulo overwintering habitat perennial spring Salvelinus malma scavenging Thymallus arcticus 1 5 5 07/05/23 20230701 NES 230701 Riverine perennial springs in the Arctic are often considered oases for aquatic species, providing refugia when rivers run dry during the summer and freeze in winter (Huryn et al., [15]; Power et al., [20]). [Extracted from the article]

The need for representative and accurate climate data such as precipitation useful for drought monitoring has been rapidly increasing among policymakers and practitioners to tackle climate‐change‐induced drought events. Hence, the objective of this article is to evaluate the drought monitoring performance of global precipitation products for the three wet seasons and rainfall regions in Ethiopia. Drought indices were calculated using the Standardized Precipitation Index (SPI) at 3‐month timescale for Belg (March–May) and Autumn (September–November) seasons and at 4‐month timescale for the Kiremt (June–September) seasons. Data products were evaluated for their accuracy in representing drought magnitude, geographical coverage and frequency using quasi‐objective (visual inspection), and frequency and correlation analysis methods. The performance of gridded precipitation products was compared against the SPI value computed for 126 reference stations and the Ethiopian satellite‐gauge merged precipitation data. Precipitation products showed different levels of performance in representing the magnitude, frequency and geographical coverage of drought events for the three wet seasons and rainfall regions. None of the data products outperformed in representing the occurrence of drought for all three wet seasons and the corresponding three rainfall regions. However, the Ethiopian merged precipitation, Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) and Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) precipitation products are relatively better than others. The study results generally indicate that no single data outperform the other precipitation products in representing the complex spatiotemporal characteristics of drought events in a mountainous region like Ethiopia. [ABSTRACT FROM AUTHOR]

High‐wind events predominantly cause the rapid breakdown of seasonal stratification on the continental shelf by the end of October. In particular the timing of events leads to considerable interannual variability in the stratification breakdown with a standard deviation of 15 days. Although previous studies have shown how coastal stratification depends on local wind‐forcing characteristics, the locally observed ocean forcing has not yet been linked to regional atmospheric weather patterns that determine the local wind characteristics. Establishing such a connection is a necessary first step toward examining how an altered atmospheric forcing due to climate change affects coastal ocean conditions. Here, we propose a categorization scheme for high‐wind events that links atmospheric forcing patterns with changes in stratification. We apply the scheme to the Southern New England shelf utilizing observations from the Ocean Observatories Initiative Coastal Pioneer Array (2015–2022). Impactful wind forcing patterns occur predominantly during early fall, have strong downwelling‐favorable winds, and are primarily of two types: (a) Cyclonic storms that propagate south of the continental shelf causing anticyclonically rotating winds, and (b) persistent large‐scale high‐pressure systems over East Canada causing steady north‐easterly winds. These patterns are associated with opposite temperature and salinity contributions to destratification, implying differences in the dominant processes driving ocean mixing based on a high‐wind pattern's overall strength and wind direction steadiness. The high‐wind event categorization scheme allows a transition from solely focusing on local wind forcing to considering realistic atmospheric weather patterns when investigating their impact on stratification in the coastal ocean. Plain Language Summary: While coastal waters are strongly density‐layered during the summer (called "seasonal stratification"), high‐wind events during the fall mix the water column and homogenize it. While it is known which local wind conditions tend to mix coastal waters the most, these conditions have not yet been linked to regional atmospheric weather patterns. Drawing such a connection is a necessary step toward understanding how atmospheric climate change may affect the coastal ocean. Here, we propose a categorization scheme to identify which atmospheric patterns have the strongest impact on coastal ocean stratification in the fall. The scheme is applied to the coastal ocean south of New England using 7 years of mooring observations. Two weather categories are particularly impactful: Storms passing south of the coastal ocean and large‐scale high‐pressure systems over East Canada. Both categories occur mainly during early fall and bring northeasterly winds associated with the onshore movement of more dense open‐ocean water which results in enhanced mixing. Differences in their ocean impact are likely caused by the difference in wind direction steadiness of the two categories. The categorization scheme allows a transition from solely investigating the ocean impacts from local wind forcing to incorporating more realistic atmospheric weather patterns. Key Points: The summer stratification on the outer shelf breaks down by the end of October ±15 days, induced mainly by high‐wind events during early fallCyclones passing south of the shelf and large‐scale high‐pressure systems over East Canada are most impactful in removing stratificationDifferences in the dominant mixing processes likely lead to opposite T/S‐contributions to destratification for the impactful wind patterns [ABSTRACT FROM AUTHOR]

North American nitrous oxide (N2O) emissions over 2011–2018 are estimated using the CarbonTracker‐Lagrange regional inversion framework. Emissions are strongest in the Midwestern corn/soybean belt and display a distinct dual maxima seasonal pattern. The first maximum occurs in late winter/early spring, suggestive of freeze‐thaw (FT) effects on denitrification rates and associated N2O emissions. The second maximum occurs in late spring/early summer, consistent with a growing season nitrogen fertilizer‐driven source, although fertilizer applied in late fall may contribute to the FT pulse as well. Interannual variability in the first maximum correlates significantly to soil freeze thaw status derived from remote sensing data. A requisite frozen period in the preceding early winter appears necessary to create conditions for the N2O pulse after thawing. The FT pulse is a prominent feature of the annual cycle in Canadian cropland, where it may be of comparable magnitude to growing season emissions. In contrast, the growing season peak in N2O dominates the FT peak in the Midwestern Corn‐Soybean region of the United States. Key Points: Early spring peaks in N2O flux from North American agriculture correlate to satellite soil freeze/thaw status from the previous winterA requisite frozen period in early winter appears necessary to prepare the soil for the freeze‐thaw (FT) N2O pulseGrowing season N2O emissions dominate FT emissions in U.S. agriculture but the two have comparable magnitude in Canada [ABSTRACT FROM AUTHOR]

Burned areas in the western US have increased ten‐fold since 1980s, which are attributable to multiple factors, including increasing heat, changing precipitation patterns, and extended drought. To better understand how these factors contribute to large fire emissions (gridded monthly fire emissions >95th percentile of all the fire emissions in the western US; 0.009 Gg/month), we build a machine learning model to predict fire emissions (PM2.5) over the western US at 0.25° resolution, interpreted using explainable artificial intelligence (XAI). From the predictor contributions derived from XAI, we conduct k‐means clustering analysis to identify four clusters of predictor variables representing different drivers of large fire emissions. The four clusters feature the contributions of fuel load (Cluster 1) and different levels of dryness (Cluster 2–4), controlled by fuel moisture, drought condition, and fire‐favorable large‐scale meteorological patterns featuring high temperature, high pressure, and low relative humidity. In the past two decades, large fire emissions peak in summer. However, large fire emissions increased significantly in September and October in 2010–2020 relative to 2000–2009, extending the peak large fire emissions from summer to autumn. The larger enhancements of large fire emissions during autumn compared to summer are contributed by decreased fuel moisture, along with more frequent concurrent fire‐favorable large‐scale meteorological patterns and drought. These results highlight fuel drying as a common driver supported by multiple drivers, such as warmer temperature and more frequent synoptic patterns favorable for fires, in increasing the autumn risk of large fire emissions across the western US. Plain Language Summary: Global warming has been raising temperature and drying out the western US. The increasingly warmer climate influences the seasonal water cycle over the western US and changes wildfire activity and its seasonality. Explainable artificial intelligence (XAI) is a set of useful tools for interpreting the predictions made by the machine learning (ML) models. Leveraging the power of XAI and a statistical clustering method, we built a ML model to predict the fire emissions over the western US and grouped the grids with large fire emissions by which predictors have larger contributions to the large fire emissions. We identified four groups of large fire emissions controlled by abundant fuel and extreme, moderate, and weak drying conditions, respectively. The drying conditions are contributed by multiple factors, including drought, local dryness, and fire‐favorable large‐scale meteorological patterns (high temperature, high pressure, and low relative humidity). Additionally, the large fire emission peak of the first three groups extends from summer to autumn. The increased fire emissions in autumn are caused by warmer temperature, decreased fuel moisture, along with concurrent fire‐favorable large‐scale meteorology and drought. These findings underscore the importance of drying in increasing the autumn risk of large fire emissions across the western US. Key Points: Using explainable artificial intelligence and k‐means clustering, we identify four clusters of different drivers of large fire emissionsThe peak large fire emissions of the first three clusters extended from summer to autumn in 2010–2020 relative to 2000–2009The increased fire emissions are caused by warmer temperature and concurrent drought and large‐scale pattern of high pressure and low relative humidity [ABSTRACT FROM AUTHOR]

A novel automated scheme for identifying occluded mid‐latitude cyclones from gridded datasets is described and employed to construct a limited climatology of such storms as well as composites of their thermodynamic and kinematic structures. The climatology (2006–2017) is derived from the MERRA‐2 reanalysis and reveals differences in the distribution of occlusions between the hemispheres. Northern Hemisphere occlusions are most frequent in winter (DJF) and are found poleward of the mean tropopause‐level jets in both the Atlantic and Pacific basins. In the Southern Hemisphere, however, occlusions are most frequent during autumn (MAM) and are almost never found equatorward of 40∘S$$ {40}^{\circ}\mathrm{S} $$. Using the identification scheme, wintertime occlusions are stratified based upon the value of 700 hPa θe$$ {\theta}_{\mathrm{e}} $$ (equivalent potential temperature) in their characteristic thickness ridges. Composites of six groups of occlusions, based upon this distinction, are constructed for each hemisphere. The composites reveal notable differences in the thermodynamic structures among these six groups with more poleward (lower θe$$ {\theta}_{\mathrm{e}} $$ or "colder") storms exhibiting shallower, less developed thermal structures as compared to their lower‐latitude (higher θe$$ {\theta}_{\mathrm{e}} $$ or "warmer") counterparts. These differences are attended by contrasts in the intensity of upward vertical motions in the occluded sectors of the various composite storms implying that "warm" storms are associated with greater latent heat release than "colder" storms. It is suggested that these coincident differences between "cold" and "warm" storms provide further evidence of the fundamental importance of latent heat release to the development of occluded thermal structures. [ABSTRACT FROM AUTHOR]

A total of 477 bones of the great auk (Pinguinus impennis) from 53 localities and 55 periods in Norway are studied. All but two, are archaeological sites from the Holocene, mainly from 6000–2000 cal years bp. The two non‐anthropogenic sites date to the Ice Age: probably 36,000–34,500 and 14,690–12,890 years bp. The bones are mainly unburned and well preserved but fractured. Except for the open‐air sites in northern Norway, the bones are mainly from rock‐shelters and caves. In combining archive data, chronological information, and morphometrical studies, we suggest the great auk disappeared from the most southern part of Norway (and Sweden and Denmark) prior to 4000 years bp: a decline in distribution 2000 years ago: It became absent from the Norwegian coast 1000 years ago. Data suggest that it was distributed on the coast and in the fjord systems in winter and early spring. The presence of bones of juveniles/subadults indicates that it was also distributed in northern Norway in the autumn. To evaluate possible size differences, in time and space, nine bone elements have been measured according to standard recommendations. Multiple imputation was used to handle missing data before any statistical analysis. Analyses indicate that bones from Nordland are larger than from the rest of the country, while bones from the northernmost sites are smaller. At some localities, size differences, especially in total length of the bones, are found. It has not been verified if this is due to individual variation or sexual differences. The great auk became extinct in the 19th century. The study supports the theory that human predation at breeding sites was the main cause of its extinction. [ABSTRACT FROM AUTHOR]

Animal movement is the mechanism connecting landscapes to fitness, and understanding variation in seasonal animal movements has benefited from the analysis and categorization of animal displacement. However, seasonal movement patterns can defy classification when movements are highly variable. Hidden Markov movement models (HMMs) are a class of latent‐state models well‐suited to modeling movement data. Here, we used HMMs to assess seasonal patterns of variation in the movement of pronghorn (Antilocapra americana), a species known for variable seasonal movements that challenge analytical approaches, while using a population of mule deer (Odocoileus hemionus), for whom seasonal movements are well‐documented, as a comparison. We used population‐level HMMs in a Bayesian framework to estimate a seasonal trend in the daily probability of transitioning between a short‐distance local movement state and a long‐distance movement state. The estimated seasonal patterns of movements in mule deer closely aligned with prior work based on indices of animal displacement: a short period of long‐distance movements in the fall season and again in the spring, consistent with migrations to and from seasonal ranges. We found seasonal movement patterns for pronghorn were more variable, as a period of long‐distance movements in the fall was followed by a winter period in which pronghorn were much more likely to further initiate and remain in a long‐distance movement pattern compared with the movement patterns of mule deer. Overall, pronghorn were simply more likely to be in a long‐distance movement pattern throughout the year. Hidden Markov movement models provide inference on seasonal movements similar to other methods, while providing a robust framework to understand movement patterns on shorter timescales and for more challenging movement patterns. Hidden Markov movement models can allow a rigorous assessment of the drivers of changes in movement patterns such as extreme weather events and land development, important for management and conservation. [ABSTRACT FROM AUTHOR]

Scavenging is an important part of food acquisition for many carnivore species that switch between scavenging and predation. In landscapes with anthropogenic impact, humans provide food that scavenging species can utilize. We quantified the magnitude of killing versus scavenging by gray wolves (Canis lupus) in Scandinavia where humans impact the ecosystem through hunter harvest, land use practices, and infrastructure. We investigated the cause of death of different animals utilized by wolves, and examined how the proportion of their consumption time spent scavenging was influenced by season, wolf social affiliation, level of inbreeding, density of moose (Alces alces) as their main prey, density of brown bear (Ursus arctos) as an intraguild competitor, and human density. We used data from 39 GPS‐collared wolves covering 3198 study days (2001–2019), including 14,205 feeding locations within space–time clusters, and 1362 carcasses utilized by wolves. Most carcasses were wolf‐killed (80.5%) while a small part had died from other natural causes (1.9%). The remaining had either anthropogenic mortality causes (4.7%), or the cause of death was unknown (12.9%). Time spent scavenging was higher during winter than during summer and autumn. Solitary wolves spent more time scavenging than pack‐living individuals, likely because individual hunting success is lower than pack success. Scavenging time increased with the mean inbreeding coefficient of the adult wolves, possibly indicating that more inbred individuals resort to scavenging, which requires less body strength. There was weak evidence for competition between wolves and brown bears as well as a positive relationship between human density and time spent scavenging. This study shows how both intrinsic and extrinsic factors drive wolf scavenging behavior, and that despite a high level of inbreeding and access to carrion of anthropogenic origin, wolves mainly utilized their own kills. [ABSTRACT FROM AUTHOR]

Temporal dynamics of Syndiniales Group II were investigated combining 18S rDNA amplicon sequencing and direct microscopy counts (fluorescence in situ hybridization‐tyramide signal amplification [FISH‐TSA]) during 5 years. The study was undertaken in meso‐eutrophic coastal ecosystem, dominated by diatoms, the haptophyte Phaeocystis globosa and exhibiting relatively low dinoflagellate abundance (max. 18.6 × 103 cells L−1). Consistent temporal patterns of Syndiniales Group II were observed over consecutive years highlighting the existence of local populations. According to sequencing data, Syndiniales Group II showed increasing abundance and richness in summer and autumn. Dinospores counted by microscopy, were present at low abundances and were punctuated by transient peaks. In summer dinospore highest abundance (559 × 103 L−1) and prevalence (38.5%) coincided with the peak abundance of the dinoflagellate Prorocentrum minimum (13 × 103 L−1) while in autumn Syndiniales Group II likely had more diversified hosts. Although, several peaks of dinospore and read abundances coincided, there was no consistent relation between them. Ecological assembly processes at a seasonal scale revealed that stochastic processes were the main drivers (80%) of the Group II community assembly, though deterministic processes were noticeable (20%) in June and July. This latter observation may reflect the specific Syndiniales—dinoflagellate interactions in summer. [ABSTRACT FROM AUTHOR]

During the last decades, the Baltic Sea has been among the fastest warming seas in the world. The warming is mainly driven by increasing air temperatures but deeper water layers can also be warmed by lateral advection of heat. By analyzing a 159 years long (1850–2008) hindcast simulation of the Baltic Sea, we link the exceptionally strong bottom water warming in the western Baltic Sea to a shift in the seasonality of saltwater inflows from the North Sea to the Baltic Sea. Over the model period, warm summer and early autumn inflows have increased while cold winter inflows have decreased. Sensitivity experiments reveal that these changes were partly driven by a shift in river runoff seasonality. The strong warming could lead to faster oxygen depletion in the affected layers and thus have ecological consequences. Plain Language Summary: The Baltic Sea is home to various marine and freshwater species and an important economic factor for the surrounding countries. Like other seas, the Baltic Sea is getting warmer due to climate change. The water at the surface warms especially fast because it takes up heat from the warming atmosphere. After some time, temperatures also increase in deeper layers. However, some deep parts in the western Baltic Sea are warming even faster than the sea surface. In our study, we investigate if the exceptional warming can be explained by an increase in warm saltwater inflows from the North Sea. Hence, we use a model simulation of the Baltic Sea for over 150 years to compare long time series of warm inflows and the temperatures in the deep layers of the western Baltic Sea. We find a strong correlation. Thus, we can link the exceptional warming in the deep layers of the western Baltic Sea during the last decades to an increase in warm inflows. The warming has ecological consequences since in warmer water, the oxygen is consumed faster and the deep water layers of the Baltic Sea are suffering from low oxygen concentrations. Key Points: Summer and early autumn salt import into the Baltic Sea increased significantly since 1851 compared to the annual salt importSalt import between June and October is highly correlated with the annual sub‐thermocline temperature maximum in the western Baltic SeaThe shift in inflow seasonality was partly caused by seasonal changes in river runoff [ABSTRACT FROM AUTHOR]

Based on reanalysis datasets and sea‐ice sensitivity experiments, this study has pointed out that the autumn sea ice loss in East Siberian‐Chukchi‐Beaufort (EsCB) Seas significantly increases the frequency of winter extreme low temperature over western‐central China. Autumn sea ice loss warms the troposphere and generates anticyclonic anomaly over the Arctic region 1 month later. Under the effects of synoptic eddy‐mean flow interaction and anomalous upward propagated planetary wave 2, the Arctic anticyclonic anomaly strengthens and develops toward Greenland‐Northern Europe, accompanied by a weakened stratospheric polar vortex. In winter, following intra‐seasonal downward propagation of stratospheric anomalies, the Northern European positive geopotential height anomalies enhance and expand downstream within 7 days, favoring Arctic cold air east of Novaya Zemlya southward (the hyperpolar path) accumulating in Siberia around Lake Baikal. In the subsequent 2–3 days, these cold anomalies rapidly intrude western‐central China and induce abrupt sharp cooling, thus more frequent extreme low temperature there. In contrast, the role of autumn Barents‐Kara (BK) sea ice loss exhibits quite different features, mainly favoring northwestern, northeastern and eastern China to experience increased frequency of extreme low temperature in winter. Plain Language Summary: Arctic sea ice change not only regulates the local ecosystem but extends its influences into mid‐even low‐latitudes through several complicated physical processes. Sea ice variation in EsCB Seas exhibits an increased amplitude and more crucial role in climate change under global warming. The new findings hinted that autumn EsCB sea ice decrease would significantly promote western‐central China to experience more frequent winter extreme low temperature. In responses, an Arctic anticyclonic anomaly occurs 1 month later and develops toward Greenland‐Northern Europe due to synoptic eddy‐mean flow interaction. Enhanced upward propagated planetary wave 2 and associated wave‐mean flow interaction maintains the tropospheric Arctic anomalies and weakens the stratospheric polar vortex. When entering winter, following intra‐seasonal downward propagated stratospheric anomalies, the Northern European anticyclonic anomaly strengthens downstream within 7 days, favoring Arctic cold air east of Novaya Zemlya rapidly invading western‐central China (the hyperpolar path) and sudden sharp cooling. Our results have understood how autumn EsCB sea ice loss contributes to extreme low temperature in China, including possible physical mechanisms and cold air pathways, unlike previous work focusing on BK Seas and winter‐mean temperature change. It provides a new factor and theoretical foundations for predicting winter extreme low temperature. Key Points: Autumn East Siberian‐Chukchi‐Beaufort sea ice loss favors cold air east of Novaya Zemlya invading western‐central China, thus more frequent extreme low temperatureSynoptic eddy‐mean flow interaction and anomalous upward planetary wave 2 provide favorable anticyclonic anomaly for extreme events outburstIntra‐seasonal downward propagated stratospheric anomalies are vital for the Ural anticyclonic anomaly to develop downstream within 7 days [ABSTRACT FROM AUTHOR]

The interannual variability in fluorescent dissolved organic matter (FDOM) was assessed in the Pacific‐ and Atlantic‐derived halocline, and Atlantic waters (AWs) of the Canada Basin during late summer/early fall expeditions in 2007–2017. Fluorescence spectroscopy excitation‐emission matrices coupled with parallel factor analysis were used to validate a seven‐component model. During the 11 years, humic‐like intensities increased by 0.0002–0.0006 r.u. (Raman unit) (p < 0.05) with greater annual rates of change in shallower waters (i.e., Pacific summer water) than in deeper waters (i.e., AWs). No significant temporal trends were observed for protein‐like intensities in any water layer (p > 0.05) due to the labile nature of these components. The increases in humic‐like fluorescence intensities were likely the results of changes in Bering Strait inflow and its modification over the Chukchi Shelf and/or the accumulation of freshwater under anticyclonic wind forcing in the Beaufort gyre. This 11‐year late summer/early fall survey shows the first evidence of changes in FDOM composition in the halocline of the Arctic Ocean basin. Plain Language Summary: The highly stratified Arctic Ocean receives a disproportionate amount of freshwater and plays a large role in the regulation of the global climate. Over the last decade, the effects of sea ice decline, hydrological cycle intensification, and vertical stratification have strengthened. Dissolved organic matter (DOM) is a heterogeneous mixture of non‐living organic compounds and is central to the marine carbon cycle. This study, which is part of the Joint Ocean Ice Study/Arctic Observing Network—Beaufort Gyre Observing System program, aims to assess the level and composition of fluorescent dissolved organic matter (FDOM) in water samples collected in the dark Canada Basin waters of the Arctic Ocean in late summer/early autumn between 2007 and 2017. The results of the present study indicate that microbial activity constitutes a significant source of humic‐like FDOM in the Atlantic—derived halocline waters. During the 11 years, the humic‐like component levels increased in both the Pacific and Atlantic‐origin halocline waters, which were likely the results of changes in Bering Strait inflow, heat transfer over the Chukchi Sea shelves, and/or the accumulation of FDOM‐rich freshwater under clockwise wind forcing in the Beaufort gyre. Key Points: Statistically significant temporal increases were observed in humic‐like fluorescence intensities in the halocline watersIncreases in halocline humic‐like reflect changes in Pacific‐origin flow, organic input, and, Chukchi shelf and advection processesEvidence of in situ microbial production of humic‐like fluorescence was found in the Atlantic‐origin halocline [ABSTRACT FROM AUTHOR]

The mobility provider MOIA operates Europe's largest contiguous electric ride‐pooling service in Hamburg, representing a testbed of how shared and digitized transport can help foster the transformation of urban mobility. The on‐demand service has been in operation since 2019 and was thus affected by the COVID‐19 pandemic in 2020. This study shows real‐world insights into travel behavior before and during the pandemic, contributing to the empirical evidence on recent mobility behavior. After the application of descriptive statistical analyses, several (spatial) regression models are estimated to understand the relationship between spatial variables and demand. MOIA trip data from three different time periods are used: (a) before the COVID‐19 pandemic in summer and autumn 2019, (b) during the time of the first lockdown in Germany in spring 2020, and (c) after the first lockdown in summer and autumn 2020. A significant positive effect on ride‐pooling demand is observed for number of inhabitants, workplaces, gastronomic facilities, and at the airport in all time periods. In the course of the pandemic, the main travel patterns remained stable. However, the positive influences of gastronomy and the airport on ride‐pooling demand diminished in 2020. In contrast, the impact of hospitals on ride‐pooling demand increased in the course of the pandemic. In areas with high car ownership, ride‐pooling demand declined compared to pre‐pandemic times. [ABSTRACT FROM AUTHOR]

Dinoflagellates constitute an abundant and diversified component of marine plankton, mostly associated with stratified conditions typical of late spring through autumn in temperate regions. Yet, difficulties with the identification of many species limit the knowledge of their composition and seasonal succession. Here we use a 3‐year V4‐18S rDNA metabarcoding dataset (4,366,007 reads, 4650 Amplicon Sequence Variants, ASVs) collected on 48 dates at the Long‐Term Ecological Research site MareChiara (LTER–MC) to explore the diversity and temporal distribution of dinoflagellates in the Gulf of Naples (Mediterranean Sea). A large fraction of dinoflagellate ASVs (55%, 68% of the reads) were assigned to 187 species in 85 genera, while 33% (23% of the reads) were attributed to undetermined Syndiniales, and the remainder to unidentified groups. A total of 147 ASVs were assigned at 100% similarity to 135 reference sequences, corresponding to 116 species, 46 of which were putative new records for the Gulf of Naples. Cluster analysis conducted on a normalized dataset of 1199 ASVs identified four sample clusters that corresponded largely to the spring, summer, autumn and winter seasons, respectively, each including samples from all 3 years. Syndiniales were particularly abundant (43.1%) in the winter cluster, whereas Gyrodinium reached higher percentages in the summer and autumn ones (38.6% and 57.8%), Gymnodiniales sensu stricto were more abundant in spring (11.5%) and Gonyaulacales in summer (13.5%). Almost half of the ASVs analysed (561) were significantly associated with one of the four seasonal clusters (p <.05). The winter group of ASVs was particularly rich (348 ASVs) and mainly consisted of naked and parasitic taxa, which are impossible to identify in routine phytoplankton observations in light microscopy. Despite biases mainly ascribed to both the incompleteness of the reference dataset and the limited resolution of the marker gene, the metabarcoding approach has provided new insights into the ecology and distribution of this important component of the plankton, highlighting its marked seasonality along with the existence of a diversified and previously overlooked dinoflagellate community in winter. [ABSTRACT FROM AUTHOR]