Large-scale Drivers Research Articles

Investigation of the Historical Trends and Variability of Rainfall Patterns during the March–May Season in Rwanda

This study explores the spatiotemporal variability and determinants of rainfall patterns during the March to May (MAM) season in Rwanda, incorporating an analysis of teleconnections with oceanic–atmospheric indices over the period 1983–2021. Utilizing the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset, the study employs a set of statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, the Mann–Kendall (MK) trend test, and Sen’s slope estimator to dissect the intricacies of rainfall variability, trends, and their association with large-scale climatic drivers. The findings reveal a distinct southwest to northwest rainfall gradient across Rwanda, with the MK test signaling a decline in annual precipitation, particularly in the southwest. The analysis for the MAM season reveals a general downtrend in rainfall, attributed in part to teleconnections with the Indian Ocean Sea surface temperatures (SSTs). Notably, the leading EOF mode for MAM rainfall demonstrates a unimodal pattern, explaining a significant 51.19% of total variance, and underscoring the pivotal role of atmospheric dynamics and moisture conveyance in shaping seasonal rainfall. The spatial correlation analysis suggests a modest linkage between MAM rainfall and the Indian Ocean Dipole, indicating that negative (positive) phases are likely to result in anomalously wet (dry) conditions in Rwanda. This comprehensive assessment highlights the intricate interplay between local rainfall patterns and global climatic phenomena, offering valuable insights into the meteorological underpinnings of rainfall variability during Rwanda’s critical MAM season.

Modelling non-stationarity in extreme rainfall using large-scale climate drivers

Flood estimates used in engineering design are commonly based on intensity–duration–frequency (IDF) curves derived from historical extreme rainfalls. Under global warming these extreme rainfalls are increasing, threatening the capacity of existing infrastructure to resist failure as IDF curves traditionally assume no change in rainfall magnitude. Hence, there is a need to investigate the implications of non-stationarity in extreme rainfalls used to derive IDFs across storm durations and annual exceedance probabilities (AEPs). One way of doing this is to incorporate covariates into the fitted probability distribution. However, there are few studies which examine non-stationarity in extreme rainfall using large-scale climate drivers as covariates, with little consensus on which covariate is the most appropriate. Here we evaluate non-stationarity in extreme rainfall across different durations, from the 1 in 5 AEP to the 1 in 100 AEP, using potential large-scale climate drivers including global and continental mean temperature, continental diurnal temperature range, continental dewpoint temperature, continental precipitable water, the Indian Ocean Dipole, the El Niño Southern Oscillation, and the Southern Annular Mode. These covariates are linked to the three parameters of the Generalized Extreme Value distribution to identify the most appropriate form of non-stationary probability model. We analyse 16 different storm durations from 6 min to 7 day annual maxima across 46 stations in Australia. Based on the Akaike Information Criteria, precipitable water is the superior covariate at a large proportion of stations irrespective of storm duration. However, when the modelled quantile changes are inspected, only global temperature is able to adequately capture the variability in changes across both storm duration and annual exceedance probability. Further, when regional average values of mean temperature, diurnal temperature range, dewpoint temperature and precipitable water were used as covariates, there was no improvement in the model performance compared to continental-wide covariates, particularly for short durations. The results using a mean global temperature covariate show that stationary historical rainfall quantile estimates are underestimated by 12 % – 9 % for frequent (1 in 5 AEP) and 23 % – 13 % for rare (1 in 100 AEP) short duration (6 min – 30 min) events. Moving forward our results suggests infrastructure design needs to incorporate non-stationarity in IDFs to ensure infrastructure and flood planning levels are not under-designed.

Examining the effects of climate change and human impacts on a high-resolution, late Holocene paleofire record from South Africa's winter rainfall zone

Fire is central to the Cape Floristic Region's highly biodiverse and disturbance-adapted Fynbos Biome. However, prehistoric fire regimes, their ecological consequences, and their relationships with large-scale climate drivers and human activities remain poorly understood. Here, we use a high-resolution sedimentary charcoal record from Verlorenvlei, a coastal lake situated on the west coast, to interrogate links between fire, climate, and pastoralism in the Fynbos Biome. Our record has a robust chronology supported by 24 radiocarbon dates and provides a continuous sedimentary sequence spanning the last 4200 years, documenting fire activity before and after the local arrival of pastoralists in the Verlorenvlei area ∼1500 cal years BP. Fire at Verlorenvlei over the last 4200 years is variable, with relatively low activity until ∼2000 cal years BP, after which variable but generally higher fire activity occurs until the highest period of fire activity from ∼1450 to 1800 CE (∼500–150 cal years BP). The increase in fire activity ∼2000 years ago corresponds with a shift in the diatom assemblage at Verlorenvlei from marine towards brackish and freshwater species, reflecting increased precipitation derived from a strengthening of the southern westerly winds. The peak in fire activity beginning ∼1450 CE (∼500 cal years BP), near the onset of the Little Ice Age, tracks a second diatom-inferred strengthening of the westerly winds. Other southern hemisphere and Antarctic records further corroborate this increased westerly influence after ∼2000 years. Linear regression modeling on the fire record indicates that moisture availability is the primary driver of fire at Verlorenvlei, with little evidence that human populations influenced fire. Our reconstruction suggests that fire activity at Verlorenvlei is limited by moisture availability and that wetter conditions facilitate increased vegetation (i.e., fuel) and intensified fire at this otherwise fuel-limited site. This work has implications for management and conservation decisions in response to future predictions of a warmer and drier climate along South Africa's west coast.

Development of Indian summer monsoon precipitation biases in two seasonal forecasting systems and their response to large-scale drivers

Abstract. The Met Office Global Coupled Model (GC) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) are both widely used for predicting and simulating the Indian summer monsoon (ISM), and previous studies have demonstrated similarities in the biases in both systems at a range of timescales from weather forecasting to climate simulation. In this study, ISM biases are studied in seasonal forecasting setups of the two systems in order to provide insight into how they develop across timescales. Similarities are found in the development of the biases between the two systems, with an initial reduction in precipitation followed by a recovery associated with an increasingly cyclonic wind field to the north-east of India. However, this occurs on longer timescales in CFSv2, with a much stronger recovery followed by a second reduction associated with sea surface temperature (SST) biases so that the bias at longer lead times is of a similar magnitude to that in GC. In GC, the precipitation bias is almost fully developed within a lead time of just 8 d, suggesting that carrying out simulations with short time integrations may be sufficient for obtaining substantial insight into the biases in much longer simulations. The relationship between the precipitation and SST biases in GC seems to be more complex than in CFSv2 and differs between the early part of the monsoon season and the later part of the monsoon season. The relationship of the bias with large-scale drivers is also investigated, using the boreal summer intraseasonal oscillation (BSISO) index as a measure of whether the large-scale dynamics favour increasing, active, decreasing or break monsoon conditions. Both models simulate decreasing conditions the best and increasing conditions the worst, in agreement with previous studies and extending these previous results to include CFSv2 and multiple BSISO cycles.

Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán

There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine learning models can simulate high-impact weather events. We compare short to medium-range forecasts of Storm Ciarán, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numerical weather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of the warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciarán.

Synoptic and planetary-scale dynamics modulate antarctic atmospheric river precipitation intensity

Although rare, atmospheric rivers substantially influence the interannual variability of Antarctic surface mass balance. Here we use MERRA-2 reanalysis to identify characteristics unique to atmospheric river environments by comparing (1) Analog (environments that feature high-low pressure couplets, similar to Atmospheric River environments, but no Atmospheric River), (2) Atmospheric River, and (3) Top Atmospheric River (highest precipitation) timesteps during 1980–2019 around Antarctica. We find significant differences between Atmospheric River and Analog environments including more intense and poleward-shifted mid-tropospheric geopotential height couplets as well as larger atmospheric moisture anomalies. We find similar significant enhancement in synoptic-scale dynamic drivers of Top Atmospheric Rivers compared to all Atmospheric River environments, but no significant difference in local integrated water vapor anomalies. Instead, our results highlight the importance of large-scale dynamic drivers during Top Atmospheric River timesteps, including amplified Rossby waves excited by tropical convection.

Large-scale spatial drivers of avian schistosomes in Northern Michigan inland lakes.

Avian schistosomes are snail-borne trematode parasites (Trichobilharzia spp.) that can cause a nasty skin rash in humans when their cercariae mistake us for their normal bird hosts. We sought to investigate drivers of the spatial distribution of Trichobilharzia cercaria abundance throughout Northern Michigan lakes. For 38 sites on 16 lakes, we assessed several dozen potential environmental predictors that we hypothesized might have direct or indirect effects on overall cercaria abundance, based on known relationships between abiotic and biotic factors in wetland ecosystems. We included variables quantifying local densities of intermediate hosts, temperature, periphyton growth rates, human land use and hydrology. We also measured daily abundance of schistosome cercariae in the water over a 5-week period, supported by community scientists who collected and preserved filtered water samples for qPCR. The strongest predictor of cercaria abundance was Lymnaea host snail density. Lymnaea density was higher in deeper lakes and at sites with more deciduous tree cover, consistent with their association with cool temperature habitats. Contrary to past studies of human schistosomes, we also found a significant negative relationship between cercaria abundance and submerged aquatic vegetation, possibly due to vegetation blocking cercaria movement from offshore snail beds. If future work shows that these effects are indeed causal, then these results suggest possible new approaches to managing swimmer's itch risk in northern MI lakes, such as modifying tree cover and shallow-water vegetation at local sites.

Coherent long-term body-size responses across all Northwest Atlantic herring populations to warming and environmental change despite contrasting harvest and ecological factors.

Body size is a key component of individual fitness and an important factor in the structure and functioning of populations and ecosystems. Disentangling the effects of environmental change, harvest and intra- and inter-specific trophic effects on body size remains challenging for populations in the wild. Herring in the Northwest Atlantic provide a strong basis for evaluating hypotheses related to these drivers given that they have experienced significant warming and harvest over the past century, while also having been exposed to a wide range of other selective constraints across their range. Using data on mean length-at-age 4 for the sixteen principal populations over a period of 53 cohorts (1962-2014), we fitted a series of empirical models for temporal and between-population variation in the response to changes in sea surface temperature. We find evidence for a unified cross-population response in the form of a parabolic function according to which populations in naturally warmer environments have responded more negatively to increasing temperature compared with those in colder locations. Temporal variation in residuals from this function was highly coherent among populations, further suggesting a common response to a large-scale environmental driver. The synchrony observed in this study system, despite strong differences in harvest and ecological histories among populations and over time, clearly indicates a dominant role of environmental change on size-at-age in wild populations, in contrast to commonly reported effects of fishing. This finding has important implications for the management of fisheries as it indicates that a key trait associated with population productivity may be under considerably less short-term management control than currently assumed. Our study, overall, illustrates the need for a comparative approach within species for inferences concerning the many possible effects on body size of natural and anthropogenic drivers in the wild.

Large-scale drivers of the exceptionally low winter Antarctic sea ice extent in 2023

The year 2023 marked a turning point for the Antarctic region as the Southern Hemisphere experienced a significant reduction in its sea ice extent, with a record-breaking sea ice minimum in July 2023 of approximately 2.4 million square kilometers below the long-term average. This study highlights the drivers behind this exceptional event by combining observational, satellite, and reanalysis data, with a special focus on the large-scale atmospheric circulation. Throughout the year, the Antarctic Sea ice extent broke record after record, ranking as the lowest sea ice on record from January to October, except for March and April. The exceptionally low sea ice extent from May to August was mainly driven by the prevalence of a zonal wave number 3 pattern, characterized by alternating surface high- and low-pressure systems, which favored the advection of heat and moisture, especially over the Ross Sea (RS), Weddell Sea (WS), and Indian Ocean (IO). The anomalous large-scale circulation was accompanied by record-high sea surface and subsurface temperatures over the regions with reduced sea ice extent. In addition to the air and ocean temperature, record-breaking heat, moisture, and sensible heat fluxes have been observed, especially over the WS, RS, and IO, which further amplified the reduction in the sea ice extent over these areas. Notably, over the Weddell Sea, we observed air temperature anomalies reaching up to 8°C and sea surface temperature anomalies of up to 3°C from May to July. Similar temperature anomalies were recorded over the Ross Sea, particularly in July and August. A change point analysis indicates that a regime shift in the Antarctic Sea ice, as well as in the average mean air temperature and (sub)surface ocean temperature over the Weddell Sea, started around 2015. The low sea ice extent in Antarctica in 2023 was a stark reminder of the ongoing changes in the polar regions. Thus, understanding the underlying mechanisms of these extreme events provides crucial insights into the changing dynamics of Antarctic Sea ice and its broader climatic significance.

Simulating the Western North America heatwave of 2021 with analogue importance sampling

During the summer of 2021, the North American Pacific Northwest was affected by an extreme heatwave that broke previous temperature records by several degrees. The event caused severe impacts on human life and ecosystems, and was associated with the superposition of concurrent drivers, whose effects were amplified by climate change. We evaluate whether this record-breaking heatwave could have been foreseen prior to its observation, and how climate change affects North American Pacific Northwest worst-case heatwave scenarios. To this purpose, we use a stochastic weather generator with empirical importance sampling. The generator simulates extreme temperature sequences using circulation analogues, chosen with an importance sampling based on the daily maximum temperature over the region that recorded the most extreme impacts. We show how some of the large-scale drivers of the event can be obtained form the circulation analogues, even if such information is not directly given to the stochastic weather generator.

Climate Driver Influences on Prediction of the Australian Fire Behaviour Index

Fire danger poses a pressing threat to ecosystems and societies worldwide. Adequate preparation and forewarning can help reduce these threats, but these rely on accurate prediction of extreme fire danger. With the knowledge that climatic conditions contribute heavily to overall fire danger, this study evaluates the skill with which episodes of extreme fire danger in Australia can be predicted from the activity of large-scale climate driver patterns. An extremal dependence index for extreme events is used to depict the historical predictive skill of the Australian Bureau of Meteorology’s subseasonal climate prediction system in replicating known relationships between the probability of top-decile fire danger and climate driver states at a lead time of 2–3 weeks. Results demonstrate that the El Niño Southern Oscillation, Southern Annular Mode, persistent modes of atmospheric blocking, Indian Ocean Dipole and Madden-Julian Oscillation are all key for contributing to predictability of fire danger forecasts in different regions during critical fire danger periods. Northwest Australia is found to be particularly predictable, with the highest mean index differences (>0.50) when certain climate drivers are active, compared with the climatological index mean. This integrated approach offers a valuable resource for decision-making in fire-prone regions, providing greater confidence to users relying on fire danger outlooks for key management decisions, such as those involved in the sectors of national park and forest estate management, agriculture, emergency services, health and energy. Furthermore, the results highlight strengths and weaknesses in both the Australian Fire Danger Rating System and the operational climate model, contributing additional information for improving and refining future iterations of these systems.

A two-terminal fault location method for gas switch prefire in linear transformer driver.

Large-scale linear transformer drivers (LTDs) are composed of numerous high-power gas switches, and switch prefire is a frequent operational fault. To detect and locate the faulty switch accurately and efficiently, a two-terminal location method is proposed. A B-dot sensor is integrated on the gas switch's shell to collect the discharging signal. All the B-dot sensors are connected in parallel through cables of equal length. The fault position can be determined by the time delay of the signals at the two terminals. A diode is inserted between the B-dot sensor's coil and the cable core to ensure low-loss transmission of the signal. Two methods are applied in fault location, including time-of-arrival (TOA) and time reversal (TR). For the TOA method, an energy criterion and a phase criterion are applied and compared. The accuracy of the energy criterion is greatly influenced by the signal-to-noise ratio, while the phase criterion requires a reasonable estimate of the actual delay to account for the impact of phase periodicity. The TR method based on a precise simulation model is established, which demonstrates high precision in location. The TR method has been tested and validated on a single stage LTD module. Moreover, the location method for double switches prefire is discussed theoretically. The method proposed in this paper will be helpful to improve the efficiency of the commissioning, operation, and maintenance of the large-scale LTD devices.

The extraordinary atmospheric rivers analysis over the Middle East: Large-scale drivers, structure, effective sources, and precipitation characterization

The study investigates the characteristics of Extraordinary Atmospheric Rivers (EARs), including large-scale atmospheric patterns, structure, effective sources, and precipitation in the Middle East. For this purpose, ARs with maximum Vertically Integrated Water Vapor Transport (IVT) ≥ 1000 kg m−1 s−1 were extracted from 1981 to 2020. ERA5 and PERSIANN-CCS-CDR data were used to analyze the characteristics of the EARs. The latter was applied to show the precipitation risk level. Atmospheric patterns indicated the state of the merging cyclones. Sudan's low pressure was the more recurrent system in all the patterns and alternately integrated with one or two cyclonic tongues over the Mediterranean, the Black Sea, the Caspian Sea, and then the polar vortex. Atmospheric blocking was present in all events, affecting the lifetime of the EARs and the maximum IVT anomaly, which averaged 4.5 days and 622 kg m−1 s−1, respectively. The EARs are the result of the simultaneous feeding of several moisture pathways from different sources. Both regional (mainly below 850 hPa) and trans-regional (above 700 hPa, except for the western Mediterranean) water sources played a crucial role in their formation. Dynamically, most events were characterized by merging the subtropical and polar jets, with maximum central speeds between 70 and 85 m s−1. EARs were also accompanied by strong near surface wind gusts up to 28 m s−1. In the IVT core wind structure, the low-level jet with a speed of 30 m s−1 deepened to a maximum of 925 hPa. There were also intense upward velocities between − 3 and − 5 Pa s−1 near the precipitation maxima areas in the EARs. The spatial character of the precipitation was of a continuous or intermittent nature, and a considerable part of it fell in short periods (up to 286 mm in 3 h). The daily maximum was 390 mm. Accordingly, the importance of using high-resolution data was represented for such events with devastating hydrological effects.

Water isotopic characterisation of the cloud–circulation coupling in the North Atlantic trades – Part 1: A process-oriented evaluation of COSMOiso simulations with EUREC4A observations

Abstract. Naturally available, stable, and heavy water molecules such as HDO and H218O have a lower saturation vapour pressure than the most abundant light water molecule H216O; therefore, these heavy water molecules preferentially condense and rain out during cloud formation. Stable water isotope observations thus have the potential to provide information on cloud processes in the trade-wind region, in particular when combined with high-resolution model simulations. In order to evaluate this potential, nested COSMOiso (isotope-enabled Consortium for Small Scale Modelling; Steppeler et al., 2003; Pfahl et al., 2012) simulations with explicit convection and horizontal grid spacings of 10, 5, and 1 km were carried out in this study over the tropical Atlantic for the time period of the EUREC4A (Elucidating the role of clouds-circulation coupling in climate; Stevens et al., 2021) field experiment. The comparison to airborne in situ and remote sensing observations shows that the three simulations are able to distinguish between different mesoscale cloud organisation patterns as well as between periods with comparatively high and low rain rates. Cloud fraction and liquid water content show a better agreement with aircraft observations with higher spatial resolution, because they show strong spatial variations on the scale of a few kilometres. A low-level cold-dry bias, including too depleted vapour in the subcloud and cloud layer and too enriched vapour in the free troposphere, is found in all three simulations. Furthermore, the simulated secondary isotope variable d-excess in vapour is overestimated compared to observations. Special attention is given to the cloud base level, which is the formation altitude of shallow cumulus clouds. The temporal variability of the simulated isotope variables at cloud base agrees reasonably well with observations, with correlations of the flight-to-flight data as high as 0.7 for δ2H and d-excess. A close examination of isotopic characteristics under precipitating clouds, non-precipitating clouds, clear-sky and dry-warm patches at the altitude of cloud base shows that these different environments are represented faithfully in the model with similar frequencies of occurrence, isotope signals, and specific-humidity anomalies as found in the observations. Furthermore, it is shown that the δ2H of cloud base vapour at the hourly timescale is mainly controlled by mesoscale transport and not by local microphysical processes, while the d-excess is mainly controlled by large-scale drivers. Overall, this evaluation of COSMOiso, including the isotopic characterisation of different cloud base environments, suggests that the simulations can be used for investigating the role of atmospheric circulations on different scales for controlling the formation of shallow cumulus clouds in the trade-wind region, as will be done in part 2 of this study.

An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection.

To avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggregating the personal data of these drivers may lead to insufficient data usage and pose a risk of privacy breaches. To address these issues, a framework called Group Federated Learning (Group-FL) for large-scale driver drowsiness detection is proposed, which can efficiently utilize diverse client data while protecting privacy. First, by arranging the clients into different levels of groups and gradually aggregating their model parameters from low-level groups to high-level groups, communication and time costs are reduced. In addition, to solve the problem of notable variations in EEG signals among different clients, a global-personalized deep neural network is designed. The global model extracts shared features from various clients, while the personalized model extracts fine-grained features from each client and outputs classification results. Finally, to address special issues such as scale/category imbalance and data pollution, three checking modules are designed for adjusting grouping, evaluating client data, and effectively applying personalized models. Through extensive experimentation, the effectiveness of each component within the framework was validated, and a mean accuracy, F1-score, and Area Under Curve (AUC) of 81.0%, 82.0%, and 87.9% was achieved, respectively, on a publicly available dataset comprising 11 subjects.

Habitat-based biodiversity responses to macroclimate and edaphic factors in European fen ecosystems.

Understanding large-scale drivers of biodiversity in palustrine wetlands is challenging due to the combined effects of macroclimate and local edaphic conditions. In boreal and temperate fen ecosystems, the influence of macroclimate on biodiversity is modulated by hydrological settings across habitats, making it difficult to assess their vulnerability to climate change. Here, we investigate the influence of macroclimate and edaphic factors on three Essential Biodiversity Variables across eight ecologically defined habitats that align with ecosystem classifications and red lists. We used 27,555 vegetation plot samples from European fens to assess the influence of macroclimate and groundwater pH predictors on the geographic distribution of each habitat type. Additionally, we modeled the relative influence of macroclimate, water pH, and water table depth on community species richness and composition, focusing on 309 plant specialists. Our models reveal strong effects of mean annual temperature, diurnal thermal range, and summer temperature on biodiversity variables, with contrasting differences among habitats. While macroclimatic factors primarily shape geographic distributions and species richness, edaphic factors emerge as the primary drivers of composition for vascular plants and bryophytes. Annual precipitation exhibits non-linear effects on fen biodiversity, with varying impact across habitats with different hydrological characteristics, suggesting a minimum requirement of 600 mm of annual precipitation for the occurrence of fen ecosystems. Our results anticipate potential impacts of climate warming on European fens, with predictable changes among habitat types and geographic regions. Moreover, we provide evidence that the drivers of biodiversity in boreal and temperate fens are closely tied to the ecological characteristics of each habitat type and the dispersal abilities of bryophytes and vascular plants. Given that the influence of macroclimate and edaphic factors on fen ecosystems is habitat specific, climate change research and conservation actions should consider ecological differentiation within functional IUCN ecosystems at continental and regional scales.

Vulnerability assessment of the pink shrimp small-scale fishery to climate change in southwestern Atlantic brackish coastal lagoons (Uruguay)

Assessing vulnerability using semi-quantitative approaches provide an opportunity to analyze conditions of exposure, sensitivity and adaptive capacity of socio-ecological systems concerning climate change drivers and anthropogenic factors. We assessed the vulnerability of the small-scale pink shrimp fishery of Castillos and Rocha lagoons, two coastal Uruguayan ecosystems of the southwest Atlantic, considering social factors and climate change drivers which affect atmospheric-oceanographic conditions regionally and locally. Using a risk approach, we evaluated the ecological system's exposure to environmental drivers. Then we estimated the sensitivity and adaptive capacity of fishers and the local community (social system). High vulnerability found for this fishery results from a combination of large-scale environmental drivers impacting the ecological system, and social factors causing high sensitivity and low adaptive capacity of the social system. High risk level with adverse ecosystem effects triggered by climate change drivers acting on the distribution of this migratory crustacean in the southwest Atlantic region could promote adverse consequences on harvest duration and shrimp catch levels in the Uruguayan coastal lagoons. Fishers and their communities showed high sensitivity to a set of (demographic, economic, governance, cultural and technological) factors. The social system of both lagoons showed a low adaptive capacity to prevent social impacts or to mitigate climate change hazards. A broad set of social indicators was identified in Rocha lagoon to enhance fishers and community resilience, while the indicator set was narrower in Castillos lagoon. Lastly, weak governance, dormant fisheries laws, non-compliance of fisheries regulations and lack of climate early warning monitoring hamper the adaptability of these small-scale fisher communities under global change scenarios. Comparative evaluation of spatial, environmental and social settings between lagoons can offer lessons for upscaling the assessment and adaptation measures to other small-scale fisheries regionally and globally.

Unveiling an underground climate change in the Chicago Loop with a district-wide sensing network

Unveiling an underground climate change in the Chicago Loop with a district-wide sensing network

Large-Scale Controls on the Leaf Economic Spectrum of the Overstory Tree Species Metrosideros polymorpha

The role of intraspecific trait variation in functional ecology has gained traction in recent years as many papers have observed its importance in driving community diversity and ecology. Yet much of the work in this field relies on field-based trait surveys. Here, we used continuous canopy trait information derived from remote sensing data of a highly polymorphic tree species, Metrosideros polymorpha, to quantify environmental controls on intraspecific trait variation. M. polymorpha, an endemic, keystone tree species in Hawai’i, varies morphologically, chemically, and genetically across broad elevation and soil substrate age gradients, making it an ideal model organism to explore large-scale environmental drivers of intraspecific trait variation. M. polymorpha canopy reflectance (visible to shortwave infrared; 380–2510 nm) and light detection and ranging (LiDAR) data collected by the Global Airborne Observatory were modeled to canopy trait estimates of leaf mass per area, chlorophyll a and b, carotenoids, total carbon, nitrogen, phosphorus, phenols, cellulose, and top of canopy height using previously developed leaf chemometric equations. We explored how these derived traits varied across environmental gradients by extracting elevation, slope, aspect, precipitation, and soil substrate age data at canopy locations. We then obtained the feature importance values of the environmental factors in predicting each leaf trait by training random forest models to predict leaf traits individually. Of these environmental factors, elevation was the most important predictor for all canopy traits. Elevation not only affected canopy traits directly but also indirectly by influencing the relationships between soil substrate age and canopy traits as well as between nitrogen and other traits, as indicated by the change in slope between the variables at different elevation ranges. In conclusion, intraspecific variation in M. polymorpha traits derived from remote sensing adheres to known leaf economic spectrum (LES) patterns as well as interspecific LES traits previously mapped using imaging spectroscopy.

Predictability of Marine Heatwaves off Western Australia Using a Linear Inverse Model

Abstract Marine heatwaves (MHWs) off Western Australia (110°–116°E, 22°–32°S; herein, WA MHWs) can cause devastating ecological impacts, as was evidenced by the 2011 extreme event. Previous studies suggest that La Niña is the major large-scale driver of WA MHWs, while the Indian Ocean dipole (IOD) may also play a role. Here, we investigate historical WA MHWs and their connections to these large-scale climate modes in an ocean model (ACCESS-OM2) simulation driven by a prescribed atmosphere from JRA-55-do over 1959–2018. Rather than analyzing sea surface temperature, the WA MHWs and climate mode indices were characterized and investigated in vertically averaged temperature (VAT) to ∼300-m depth to afford the longer ocean dynamic time scales, including remote oceanic connections. We develop a cyclostationary linear inverse model (CS-LIM; from 35°S to 10°N, across the Indo-Pacific Ocean), to investigate the relative contributions of La Niña VAT and positive IOD VAT to the predictability of WA VAT MHWs. Using a large ensemble of CS-LIM simulations, we found that ∼50% of WA MHWs were preceded about 5 months by La Niña, and 30% of the MHWs by positive IOD about 20 months prior. While precursor La Niña or positive IOD, on their own, were found to correspond with increased WA MHW likelihood in the months following (∼2.7 times or ∼1.5 times more likely than by chance, respectively), in combination these climate mode phases were found to produce the largest enhancement in MHW likelihood (∼3.2 times more likely than by chance). Additionally, we found that stronger and longer La Niña and/or positive IOD tend to lead stronger and longer WA MHWs. Significance Statement This study examines seasonal-to-interannual time-scale predictability of marine heatwaves off Western Australia. We developed and applied a linear inverse model, informed by numerical model results, to generate a large number of 60-yr temperature simulations across the broader Indian–Pacific Ocean region to quantify this marine heatwave predictability. We found that La Niña typically increases the likelihood of marine heatwaves off Western Australia about 5 months (3–7 months) later, while positive Indian Ocean dipole events increase their likelihood about 20 months (18–22 months) later. Marine heatwaves can severely impact local marine ecosystems and the economy. Our findings are expected to be valuable for marine heatwave prediction system development on time scales that can be beneficial to marine ecosystem conservation and fishery management.