Observational Datasets Research Articles

Contrasting Trends in Colorado Fire Weather Index from Reanalysis and Observations

Abstract Recent wildfires in Colorado raise the question of whether rising global temperatures have increased fire weather occurrences in Colorado. The U.S. National Weather Service defines fire weather as when “forecast weather conditions will result in a significant threat for the ignition and/or spread of wildfires”. We use two datasets to address the question: “How has the occurrence of fire weather changed in Colorado?” Using 22 years of observed weather conditions from a meteorological tower at the National Renewable Energy Laboratory and 67 years of ERA5 reanalysis data, we assess changing trends in Colorado fire weather as defined by hot, dry, and windy conditions. Additionally, we explore if the difference in recorded wind speeds between observational data and reanalysis data can be explained by differences in spatial and temporal resolution, and what are the implications in the context of quantifying fire weather occurrences. The observational data are limited in temporal extent and spatial representativeness, but they capture exact real-world conditions at a location in complex terrain. The reanalysis data are available for an extended period of time and for the entire state, but the data are of relatively coarse spatial and temporal resolution and may fail to capture extremes. To quantify fire risk, we calculate the Hot-Dry-Windy Index (HDWI), which relies on wind speed and vapor pressure deficit. No statistically significant trend in the HDWI appears in the observational dataset. However, according to the reanalysis data, strong increasing trends in HDWI values emerge across all of Colorado. This apparent conflict between observational and reanalysis data suggests that reanalysis data may not be representative. Further, more long-term observational datasets are required to assess fire risk.

Deciphering the Relationship Between Moisture Flux and Monsoon Extreme Rainfall Over the West Coast of India

ABSTRACTThe west coast of India has recently been experiencing torrential monsoon rains, a trend that studies indicate is likely to continue under future warming scenarios. This study investigates the link between moisture flux and extreme rainfall over the west coast, using observational and reanalysis datasets for the monsoon seasons (June to September) from 1990 to 2023. The analysis shows that, over the Indian subcontinent, rainfall along the west coast is primarily influenced by large‐scale moisture flux from the Arabian Sea. By decomposing the vertically integrated moisture flux into dynamic and thermodynamic components, this study observes that the thermodynamic component of moisture flux exhibits an increasing trend over the southwest coast, while this increasing trend is more prominent for the dynamic component over the northwest coast. Extreme rainfall over the southwest coast is increasing at a rate of 0.23 mm per season, attributed primarily to the increase in the thermodynamic component of moisture flux. It is observed that the rate of sea surface temperature (SST) increase over the Arabian Sea is faster than over the Bay of Bengal, with the average SST over the southeast Arabian Sea exceeding 28°C in recent years. Observations indicate that warming over the southeast Arabian Sea is strongly coupled with moisture accumulation observed over the southwest coast. This study provides strong evidence of a link between moisture transport, extreme rainfall and SST, identifying the southwest coast as a region vulnerable to climate change. Over the northwest coast, the incidence of extreme rainfall is associated with the strengthening of dynamic processes, and the mean monsoon rainfall in this region is increasing in alignment with the rising dynamic component of moisture flux.

Evaluating a Hearing Loop Implementation for Live Orchestral Music.

Live music creates a sense of connectedness in older adults, which can help alleviate the social isolation frequently associated with hearing loss and aging. However, most hearing-aid (HA) users are dissatisfied with the sound quality of live music and rate sound quality as important to them. Assistive listening systems are frequently independent of a user's HAs and fall short in tailoring to each individual's hearing loss. The present study thus tested whether the use of a hearing loop would improve sound quality during an orchestral concert. Participants with symmetrical moderate-to-severe hearing loss were assigned to use Sonova-provided HAs with a telecoil (n = 20) or their own HAs (n = 8) without a telecoil during a performance by the Hamilton Philharmonic Orchestra. We changed loop input to use one of three feeds every 5 minutes: a mix of microphones from the hall's standard assistive feed on the first balcony (house condition), a mix of microphones located on the stage (stage condition), or no input to the loop (no feed). After each 5-minute interval, we collected sound quality and naturalness ratings for the previous 5 minutes. Sound quality and naturalness ratings were highly related (rRM = 0.81), though each provided unique insight. Repeated measures analysis of variance found significant differences among the loop feed conditions for sound quality and naturalness, with the no feed condition significantly outperforming the house condition on sound quality [t(18) = -3.73, adj. p = 0.005] and naturalness [t(18) = -4.15, adj. p = 0.002]. Mixed effects models allowed us to retain the richness of a repeated observation dataset and provided point estimates of the overall quality and naturalness among conditions; however, assumption violations of normality and homoskedasticity prevented further interpretation. Though HA-integrated assistive listening systems are a promising option for improving live music for people with hearing loss, a hearing loop does not seem to be crucial for orchestral music. Future directions include improving lyric understanding for music with vocals and customizing user experience via Bluetooth Low Energy Audio systems.

DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts

With climate extremes’ rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models. Despite recent progress in deep learning to ecosystem monitoring, there is a need for datasets specifically designed to analyse compound heatwave and drought extreme impact. Here, we introduce the DeepExtremeCubes database, tailored to map around these extremes, focusing on persistent natural vegetation. It comprises over 40,000 globally sampled small data cubes (i.e. minicubes), with a spatial coverage of 2.5 by 2.5 km. Each minicube includes (i) Sentinel-2 L2A images, (ii) ERA5-Land variables and generated extreme event cube covering 2016 to 2022, and (iii) ancillary land cover and topography maps. The paper aims to (1) streamline data accessibility, structuring, pre-processing, and enhance scientific reproducibility, and (2) facilitate biosphere dynamics forecasting in response to compound extremes.

Advancing Data Quality Assurance with Machine Learning: A Case Study on Wind Vane Stalling Detection

High-quality observational datasets are essential for climate research and models, but validating and filtering decades of meteorological measurements is an enormous task. Advances in machine learning provide opportunities to expedite and improve quality control while offering insight into non-linear interactions between the meteorological variables. The Cabauw Experimental Site for Atmospheric Research in the Netherlands, known for its 213 m observation mast, has provided in situ observations for over 50 years. Despite high-quality instrumentation, measurement errors or non-representative data are inevitable. We explore machine-learning-assisted quality control, focusing on wind vane stalling at 10 m height. Wind vane stalling is treated as a binary classification problem as we evaluate five supervised methods (Logistic Regression, K-Nearest Neighbour, Random Forest, Gaussian Naive Bayes, Support Vector Machine) and one semi-supervised method (One-Class Support Vector Machine). Our analysis determines that wind vane stalling occurred 4.54% of the time annually over 20 years, often during stably stratified nocturnal conditions. The K-Nearest Neighbour and Random Forest methods performed the best, identifying stalling with approximately 75% accuracy, while others were more affected by data imbalance (more non-stalling than stalling data points). The semi-supervised method, avoiding the effects of the inherent data imbalance, also yielded promising results towards advancing data quality assurance.

Seasonal forecasting of East African short rains

The variability of East African short rains (October–December) has profound socioeconomic and environmental impacts on the region, making accurate seasonal rainfall predictions essential. We evaluated the predictability of East African short rains using model ensembles from the multi-system seasonal retrospective forecasts from the Copernicus Climate Change Service (C3S). We assess the prediction skill for 1- to 5-month lead times using forecasts initialized in September for each year from 1993 to 2016. Although most models exhibit significant mean rainfall biases, they generally show skill in predicting OND (October–December) precipitation anomalies across much of East Africa. However, skill is low or absent in some northern and western parts of the focus area. Along the East African coasts near Somalia and over parts of the western Indian Ocean, models demonstrate skill throughout the late winter (up to December–February), likely due to the persistence of sea surface temperature anomalies in the western Indian Ocean. Years when models consistently outperform persistence forecasts typically align with the mature phases of El Niño Southern Oscillation (ENSO) and/or Indian Ocean Dipole (IOD). This latter mode, when tracked using the Dipole Mode Index, is generally able to predict the sign of the rainfall anomaly in all models. Despite East Africa’s proximity to the west pole of the IOD, the correlation between short rains and IOD maximizes when both east and west are considered. This finding confirms previous studies based on observational datasets, which indicate that broader-scale IOD variability associated with changes in the Walker Circulation, rather than local SST fluctuations, is the primary driver behind East African rainfall.

Observational constraints on freezing quintessence in a nonlinear f(R,Lm) gravity

In this paper, we investigate the freezing quintessence scenario in late-time cosmic expansion using a nonlinear [Formula: see text] gravity model, [Formula: see text], where [Formula: see text] is a free parameter. We consider a solution for this model using an appropriate parametrization of the scale factor, and then the model is constrained by observational datasets, including CC, Pantheon[Formula: see text] (SN), and CC[Formula: see text][Formula: see text][Formula: see text]SN[Formula: see text][Formula: see text][Formula: see text]BAO. Our analysis yields results aligning closely with observational data. The Hubble parameter, deceleration parameter, matter-energy density, and EoS parameter of our model exhibit expected trends over cosmic time, supporting its physical validity. Furthermore, the model demonstrates consistency with the [Formula: see text]CDM model in late times, displaying freezing behavior in the [Formula: see text] plane and stability against density perturbations. Our findings suggest that the modified [Formula: see text] gravity model is a credible approach to describing the universe’s accelerating phase.

A satellite-based analysis of semi-direct effects of biomass burning aerosols on fog and low-cloud dissipation in the Namib Desert

Abstract. In the Namib Desert, fog is the only regular water input and, thus, is a crucial water source for its fauna and flora. Each year, between June and October, absorbing biomass burning aerosols (BBAs) overlie the stratocumulus clouds in the adjacent Southeast Atlantic. In some synoptic settings, this layer of BBAs reaches Namibia and its desert, where it interacts with coastal fog and low clouds (FLCs). In this study, a novel 15-year data set of geostationary satellite observations of FLC dissipation time in the Namib Desert is used, along with reanalysis data, to better understand the potential semi-direct effects of BBAs on FLC dissipation in the Namib Desert, i.e., through adjustments of atmospheric stability and thermodynamics via the interaction of aerosols with radiation. This is done by investigating both the time of day when FLCs dissolve and the synoptic-scale meteorology depending on BBA loading. It is found that FLC dissipation time is significantly later on high-BBA-loading days. BBAs are transported to the Namib along moist free-tropospheric air by a large-scale anticyclonic recirculation pattern. At the surface, the associated longwave heating strengthens a continental heat low, which modifies the circulation and boundary layer moisture along the coastline, complicating the attribution of BBA effects. During high-BBA days, the vertical profiles of the temporal development of air temperatures highlight contrasting daytime and nighttime processes modifying the local inversion. These processes are thought to be driven by greenhouse warming as a result of the moisture in the BBA plumes and BBA absorption (only during the daytime). A statistical learning framework is used to quantify meteorological and BBA influences on FLC dissipation time. The statistical model is able to reproduce the observed differences in FLC dissipation time between high- and low-BBA days and attributes these differences mainly to differences in circulation, boundary layer moisture and near-surface air temperature along the coastline. However, the model is prone to underfitting and is not able to reproduce the majority of the FLC dissipation variability. While the model does not suggest that BBA patterns are important for FLC dissipation, the findings show how the moist BBA plumes modify local thermodynamics, to which FLC dissipation is shown to be sensitive. The findings highlight the challenges of disentangling meteorological and aerosol effects on cloud development using observations and invite detailed modeling analyses of the underlying processes, for example, with large-eddy simulations.

Climate model downscaling in central Asia: a dynamical and a neural network approach

Abstract. High-resolution climate projections are essential for estimating future climate change impacts. Statistical and dynamical downscaling methods, or a hybrid of both, are commonly employed to generate input datasets for impact modelling. In this study, we employ COSMO-CLM (CCLM) version 6.0, a regional climate model, to explore the benefits of dynamically downscaling a general circulation model (GCM) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), focusing on climate change projections for central Asia (CA). The CCLM, at 0.22° horizontal resolution, is driven by the MPI-ESM1-2-HR GCM (at 1° spatial resolution) for the historical period of 1985–2014 and the projection period of 2019–2100 under three Shared Socioeconomic Pathways (SSPs), namely the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) gridded observation dataset as a reference, we evaluate the performance of CCLM driven by ERA-Interim reanalysis over the historical period. The added value of CCLM, compared to its driving GCM, is evident over mountainous areas in CA, which are at a higher risk of extreme precipitation events. The mean absolute error and bias of climatological precipitation (mm d−1) are reduced by 5 mm d−1 for summer and 3 mm d−1 for annual values. For winter, there was no error reduction achieved. However, the frequency of extreme precipitation values improved in the CCLM simulations. Additionally, we employ CCLM to refine future climate projections. We present high-resolution maps of heavy precipitation changes based on CCLM and compare them with the CMIP6 GCM ensemble. Our analysis indicates an increase in the intensity and frequency of heavy precipitation events over CA areas already at risk of extreme climatic events by the end of the century. The number of days with precipitation exceeding 20 mm increases by more than 90 by the end of the century, compared to the historical reference period, under the SSP3-7.0 and SSP5-8.5 scenarios. The annual 99th percentile of total precipitation increases by more than 9 mm d−1 over mountainous areas of central Asia by the end of the century, relative to the 1985–2014 reference period, under the SSP3-7.0 and SSP5-8.5 scenarios. Finally, we train a convolutional neural network (CNN) to map a GCM simulation to its dynamically downscaled CCLM counterpart. The CNN successfully emulates the GCM–CCLM model chain over large areas of CA but shows reduced skill when applied to a different GCM–CCLM model chain. The scientific community interested in downscaling CMIP6 models could use our downscaling data, and the CNN architecture offers an alternative to traditional dynamical and statistical methods.

Latent class analysis of the capacity of countries to manage diabetes and its relationship with diabetes-related deaths and healthcare costs

BackgroundThe prevalence of diabetes is escalating globally, underscoring the need for comprehensive evidence to inform health systems in effectively addressing this epidemic. The purpose of this study was to examine the patterns of countries’ capacity to manage diabetes using latent class analysis (LCA) and to determine whether the patterns are associated with diabetes-related deaths and healthcare costs.MethodsEight indicators of country-level capacity were drawn from the World Health Organization Global Health Observatory dataset: the widespread availability of hemoglobin A1C (HbA1c) testing, existence of diabetes registry, national diabetes management guidelines, national strategy for diabetes care, blood glucose testing, diabetic retinopathy screening, sulfonylureas, and metformin in the public health sector. We performed LCA of these indicators, testing 1–5 class solutions, and selecting the best model based on Bayesian Information Criteria (BIC), entropy, corrected Akaike Information Criteria (cAIC), as well as theoretical interpretability. Multivariable linear regression was used to assess the association between capacity to manage diabetes (based on the latent class a country belongs) and diabetes-related deaths and healthcare costs.ResultsWe included 194 countries in this secondary analysis. Countries were classified into “high capacity” (88.7%) and “limited capacity” (11.3%) countries based on the two-class solution of the LCA (entropy = 0.91, cAIC = 1895.93, BIC = 1862.93). Limited capacity countries were mostly in Africa. Limited capacity countries had significantly higher percentage of their deaths attributable to diabetes (adjusted beta = 1.34; 95% CI: 0.15, 2.53; p = 0.027) compared to high capacity countries even after adjusting for income status and diabetes prevalence.ConclusionsOur findings support the report by the Lancet commission on diabetes, which suggests that differences in diabetes outcomes among countries may be explained by variations in the capacity of and investments made in their health systems. Future studies should evaluate initiatives such as the WHO Global Diabetes Compact that are currently underway to improve the capacity of resource-limited countries.

Emergence of the North Pacific heat storage pattern delayed by decadal wind-driven redistribution

Storage of anthropogenic heat in the oceans is spatially inhomogeneous, impacting regional climates and human societies. Climate models project enhanced heat storage in the mid-latitude North Pacific (MNP) and much weaker storage in the tropical Pacific. However, the observed heat storage during the past half-century shows a more complex pattern, with limited warming in the MNP and enhanced warming in the northwest tropical Pacific. Here, based on observational datasets, ocean model experiments, and climate models, we show that the emergence of human-induced heat storage is likely postponed in the North Pacific by natural variability until the late-21st century. Specifically, phase shifts of the Pacific Decadal Oscillation have vitally contributed to trends in the North Pacific winds during recent decades. Changes in surface winds drove meridional heat redistribution via Rossby wave dynamics, leading to regional warming and cooling structures and a more complex historical heat storage than models predict. Despite this, enhanced anthropogenic warming has already been emerging in marginal seas along the North Pacific basin rim, for which we shall prepare for the pressing consequences such as increasing marine heatwaves.

Extended Reconstructed Sea Surface Temperature Version 6 (ERSSTv6): Part II. Upgrades on Quality Control and Large-scale Filter

Abstract NOAA’s Extended Reconstructed Sea Surface Temperature (SST; ERSST) is a monthly 2° SST product starting from 1850. Our Part I study indicated that the performance scores of spatial correlation coefficient (SCC) and root-mean-square-difference (RMSD) dropped clearly after the mid-1970s in the analysis of ERSST with an artificial neural network (ANN) method. In this Part II study, we demonstrate that ERSST with the ANN method can further be improved progressively in the final ERSSTv6 by the following steps: 1) applying a nearest neighbor check (NNC) quality control algorithm on ship observations, 2) applying a large-scale (>200km) filter (LS200) on SST super-observations, and 3) upgrading algorithms in proxy SST from ice concentration. These progressive improvements were assessed against validation and observation datasets. In comparison with ERSST with the ANN method alone, the quality of ERSSTv6 improves in the statistical metrics of SCC and RMSD by 2–11% and 0.01°–0.24°C, respectively, in the global oceans. In the ice-covered regions, SST bias and RMSD decrease by 0.67°C and 0.29°C, respectively.

The Performance of a High-Resolution WRF Modelling System in the Simulation of Severe Tropical Cyclones over the Bay of Bengal Using the IMDAA Regional Reanalysis Dataset

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the BoB, having their genesis in the southeast BoB, and the intensity and duration of these storms have increased in recent times. The Advanced Research version of the Weather Research and Forecasting (ARW) model is utilized to simulate the five extremely severe cyclonic storms (ESCSs) over the BoB during the past two decades using the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. The initial and lateral boundary conditions are derived from the IMDAA datasets with a horizontal resolution of 0.12° × 0.12°. Five ESCSs from the past two decades were considered: Sidr 2007, Phailin 2013, Hudhud 2014, Fani 2019, and Amphan 2020. The model was integrated up to 96 h using double-nested domains of 12 km and 4 km. Model performance was evaluated using the 4 km results, compared with the available observational datasets, including the best-fit data from the India Meteorological Department (IMD), the Tropical Rainfall Measuring Mission (TRMM) satellite, and the Doppler Weather Radar (DWR). The results indicated that IMDAA provided accurate forecasts for Fani, Hudhud, and Phailin regarding the track, intensity, and mean sea level pressure, aligning well with the IMD observational datasets. Statistical evaluation was performed to estimate the model skills using Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Probability of Detection (POD), the Brier Score, and the Critical Successive Index (CSI). The calculated mean absolute maximum sustained wind speed errors ranged from 8.4 m/s to 10.6 m/s from day 1 to day 4, while mean track errors ranged from 100 km to 496 km for a day. The results highlighted the prediction of rainfall, maximum reflectivity, and the associated structure of the storms. The predicted 24 h accumulated rainfall is well captured by the model with a high POD (96% for the range of 35.6–64.4 mm/day) and a good correlation (65–97%) for the majority of storms. Similarly, the Brier Score showed a value of 0.01, indicating the high performance of the model forecast for maximum surface winds. The Critical Successive Index was 0.6, indicating the moderate model performance in the prediction of tracks. It is evident from the statistical analysis that the performance of the model is good in forecasting storm structure, intensity and rainfall. However, the IMDAA data have certain limitations in predicting the tracks due to inadequate representation of the large-scale circulations, necessitating improvement.

The long lives of subducted spice and vorticity anomalies in the subtropical oceans

Abstract Subtropical cells, which exist in nearly all ocean basins, connect subducting subtropical waters to upwelling sites along the equator. This tight link between the subtropics and the tropics, on a scale of 5-15 years, is well-established in a time-averaged sense by modeling and observations. Recently, evidence has emerged of spice and potential vorticity anomaly persistence along mean flow pathways on isopycnals. We provide the first global view of subtropical water mass anomaly propagation, using both an observational dataset and the Estimating the Circulation and Climate of the Ocean (ECCO) state estimate Version 4 Release 4. In this global synthesis that complements the existing body of largely regional studies, we find long-lived interannual water mass anomalies that translate along mean advective pathways in all ventilated subtropical gyres. They are detectable over multiple years and several thousand kilometers. Some anomalies are persistent enough to reach both the western boundary and equatorial current systems before being entirely eroded, and thus could form ocean “tunnels” equivalent to the well-studied atmospheric bridge to impact remote climate variability. Analysis of ocean tunnel propagation of a passive tracer (spice) and an active tracer (potential vorticity) confirms earlier model results that the active tracer decays more quickly than the passive tracer. Similarities and differences between timing and frequency of the two tracers could provide clues to anomaly formation mechanisms in various subduction regions. The success of ECCO in capturing these phenomena is encouragement to further explore their upstream sources and downstream impacts within this framework.

Increasing temporal stability of global tropical cyclone precipitation

Tropical cyclone (TC) precipitation has led to escalating urban flooding and transportation disruptions in recent years. The volatility of the TC rain rate (RR) over short periods complicates accurate forecasting. Here, we use satellite-based observational rainfall datasets from 1998 to 2019 to calculate changes in TC 24-h RR and quantify the temporal stability of TC precipitation. We demonstrate a significant global increase in the annual temporal stability of TC RR across the total rainfall area, inner-core, and rainband areas. Specifically, the probabilities of rapid RR increase and decrease events in the TC total rainfall area decreased at rates of –1.74 ± 0.57% per decade and –2.23 ± 0.55% per decade, respectively. Based on the reanalysis dataset, we propose that the synergistic effects of increased atmospheric stability and total column water vapor—both resulting from anthropogenic warming at low latitudes—are potentially associated with this trend.

Fostering employment match? Graduates’ internships and early professional experiences between individuals’ choices and institutional constraints

PurposeIn Italy, internships were introduced in higher education to ease graduates’ entry into the labor market, addressing youth unemployment and overeducation attributed to low job-specific skills. We investigate whether internships during and after the degree program improve employment quality, specifically in terms of coherence between the job obtained and the educational-level achieved.Design/methodology/approachWe employ both a longitudinal approach and Propensity Score Matching (PSM) to examine the associations between curricular and extracurricular internships, and the number of job episodes after graduation, with the risk of overeducation among master’s graduates.FindingsThe findings suggest that curricular internships, if at all, do not affect the likelihood of obtaining a matching qualified job. Extracurricular internships are instead associated with a higher risk of overeducation. Additionally, the risk of overeducation does not decrease with more job episodes and even rises in the South, where opportunities are scarcer. We conclude that internships may help secure employment but at the cost of overeducation for graduates.Originality/valueThis study fills a gap by analyzing the impact of both curricular and extracurricular internships on employment quality in terms of occupational coherence. In doing so, it assesses policies on graduates' professional experiences over the last 20 years. By employing both longitudinal analysis and PSM quasi-experimental models, it aims to address selection bias related to unobserved characteristics in observational datasets and panel attrition. Theoretically, it advances by analyzing the determinants of overeducation while accounting for territorial differences in labor demand, and better assessing the contrasting predictions of human capital vs credentialist theories.

Using predictive models to identify kelp refuges in marine protected areas for management prioritization.

Kelp forests serve as the foundation for shallow marine ecosystems in many temperate areas of the world but are under threat from various stressors, including climate change. To better manage these ecosystems now and into the future, understanding the impacts of climate change and identifying potential refuges will help to prioritize management actions. In this study, we use a long-term dataset of observations of kelp percentage cover for two dominant canopy-forming species off the coast of Victoria, Australia: Ecklonia radiata and Phyllospora comosa. These observations were collected across three scuba sampling programs that extend from 1998 to 2019. We then associated those observations with habitat and environmental variables including depth, seafloor structure, wave climate, currents, temperature, and population connectivity in generalized additive mixed-effects models and used these models to develop predictive maps of kelp cover across the Victorian marine protected areas (MPAs). These models were also used to project kelp coverage into the future by replacing wave climate and temperature with future projections (2090, Representative Concentration Pathways [RCPs] 4.5 and 8.5). Once the spatial predictions were compiled, we calculated percent cover change from 1998 to 2019, stability over the same period, and future predicted change in percent cover (2019-2090) to understand the dynamics for each species across the MPAs. We also used the current percentage cover, stability, and future percentage cover to develop a ranking system for classifying the maps into very unlikely refugia, unlikely refugia, neutral, potential refugia, and likely refugia. A management framework was then developed to use those refugia ranking values to inform management actions, and we applied this framework across three case studies: one at the scale of the MPA network and two at the scale of individual MPAs, one where management decisions were the same for both species, and one where the actions were species-specific. This study shows how species distribution models, both contemporary and with future projections, can help to identify potential refugia areas that can be used to prioritize management decisions and future-proof restoration actions.

Spatiotemporal patterns in precipitation whiplash over China during 1901–2100 based on CMIP6 simulations

Abstract The increasing risk of precipitation whiplash, characterized by rapid transitions between extreme drought and wet conditions, is largely related to atmospheric circulation changes due to anthropogenic greenhouse gas emissions. This study aims to examine the changing patterns of precipitation whiplash events for future projections (2021–2100) with regard to the current climate (1981–2020), and to unveil the relations between intensity, duration, and frequency of these events for various return periods across different regions of China. Multi-source observational datasets were also used to analyze the trend of precipitation whiplash indices for the current climate and to assess the ability of the CMIP6 ensemble model for reproducing the characteristics of precipitation whiplash events. The Intensity-Duration-Frequency (IDF) curves were estimated by using the nonstationary Generalized Extreme Value (GEV) model combined with Bayesian inference. The results show an increasing trend in frequency, intensity, and severity of precipitation whiplash events, particularly for the long-term future, while the duration of these transitions shows a decreasing trend. The peak occurrence months during the long-term future period exhibit notable changes in most parts of China. Specifically, the dry-to-wet whiplash events occur approximately two months earlier than in the current period, while the wet-to-dry whiplash occurs approximately two months later. The results also indicated that IDF curves shifted upward, particularly in the long-term future projections, suggesting an increased likelihood of more severe events. The findings of this study will serve as an essential reference for local authorities to develop more effective water resource management and disaster mitigation policies to tackle the impact of future climate change.

Utilising causal inference methods to estimate effects and strategise interventions in observational health data.

Randomised controlled trials (RCTs) are the gold standard for evaluating health interventions but often face ethical and practical challenges. When RCTs are not feasible, large observational data sets emerge as a pivotal resource, though these data sets may be subject to bias and unmeasured confounding. Traditional statistical (or non-causal) learning methods, while useful, face limitations in fully uncovering causal effects, i.e., determining if an intervention truly has a direct impact on the outcome. This gap is bridged by the latest advancements in causal inference methods, building upon machine learning-based approaches to investigate not only population-level effects but also the heterogeneous effects of interventions across population subgroups. We demonstrate a causality approach that utilises causal trees and forests, enhanced by weighting mechanisms to adjust for confounding covariates. This method does more than just predict the overall effect of an intervention on the whole population; it also gives a clear picture of how it works differently in various subgroups. Finally, this method excels in strategising and optimising interventions, by suggesting precise and explainable approaches to targeting the intervention, to maximise overall population health outcomes. These capabilities are crucial for health researchers, offering new insights into existing data and assisting in the decision-making process for future interventions. Using observational data from the 2017-18 Australian National Health Survey, our study demonstrates the power of causal trees in estimating the impact of exercise on BMI levels, understanding how this impact varies across subgroups, and assessing the effectiveness of various intervention targeting strategies for enhanced health benefits.

F(T, TG) gravity theory: observational constraints for Barrow holographic dark energy with Hubble and Granda-Oliveros cutoff

Abstract The reconstruction technique and stability of various well-known cosmological parameters of f(T, T G ) gravity are investigated in this paper, where T and T G denote the torsion scalar and Gauss-Bonnet invariant torsion component. In this model, we consider Barrow holographic dark energy (BHDE) as candidates of IR cut-offs and develop the corresponding field equations. In our analysis, we have examined two well-known IR cutoffs Hubble and GrandaOliveros (GO) cutoffs. Our findings indicate a transition from a deceleration phase to an accelerated phase in the Universe for both cutoff scenarios, aligning with recent observational data. A reconstruction method is introduced to analyze the parameters linked to BHDE within the framework of viscous cosmology. Our investigation revealed that the power-law cosmology effectively aligns with the observational datasets. By combining the Markov Chain Monte Carlo (MCMC) technique with Bayesian analysis and likelihood function, we were able to determine the model parameters H 0 = 69.6 ± 1.8 km/s/Mpc; q = − 0.281 ± 0.034 and M = 23.846 ± 0.010. We have solved the modified Einstein’s field equations by taking the bulk-viscosity component ξ = ξ 0 + ξ 1 H. Additionally, we have examined the energy conditions to assess the consistency of our model.