Observational Datasets Research Articles

Combined assimilation of NOAA surface and MIPAS satellite observations to constrain the global budget of carbonyl sulfide

Abstract. Carbonyl sulfide (COS), a trace gas in our atmosphere that leads to the formation of aerosols in the stratosphere, is largely taken up by terrestrial ecosystems. Quantifying the biosphere uptake of COS could provide a useful quantity to estimate gross primary productivity (GPP). Some COS sources and sinks still contain large uncertainties, and several top-down estimates of the COS budget point to an underestimation of sources, especially in the tropics. We extended the inverse model TM5-4DVAR to assimilate Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) satellite data, in addition to National Oceanic and Atmospheric Administration (NOAA) surface data as used in a previous study. To resolve possible discrepancies among the two observational data sets, a bias correction scheme is necessary and implemented. A set of inversions is presented that explores the influence of the different measurement streams and the settings of the prior fluxes. To evaluate the performance of the inverse system, the HIAPER Pole-to-Pole Observations (HIPPO) aircraft observations and NOAA airborne profiles are used. All inversions reduce the COS biosphere uptake from a prior value of 1053 GgS a−1 to much smaller values, depending on the inversion settings. These large adjustments of the biosphere uptake often turn parts of Amazonia into a COS source. Only inversions that exclusively use MIPAS observations, or strongly reduce the prior errors on the biosphere flux, maintain the Amazon as a COS sink. Inclusion of MIPAS data in the inversion leads to a better separation of land and ocean fluxes. Over the Amazon, these inversions reduce the biosphere uptake from roughly 300 to 100 GgS a−1, indicating a strongly overestimated prior uptake in this region. Although a recent study also reported reduced COS uptake over the Amazon, we emphasise that a careful construction of prior fluxes and their associated errors remains important. For instance, an inversion that gives large freedom to adjust the anthropogenic and ocean fluxes of CS2, an important COS precursor, also closes the budget satisfactorily with much smaller adjustments to the biosphere. We achieved better characterisation of biosphere prior and uncertainty, better characterisation of combined ocean and land fluxes, and better constraint of both by combining surface and satellite observations. We recommend more COS observations to characterise biosphere and ocean fluxes, especially over the data-poor tropics.

Spatial and temporal variability of martian water-ice cloud effective radius in EMIRS thermal infrared observations

New analyses of thermal infrared spectra obtained from the Emirates Mars Infrared Spectrometer (EMIRS) allow for retrieval of water-ice cloud effective radius, reff,ice, in the lower atmosphere of Mars across spatial, seasonal, and daytime diurnal scales. Using a one Martian-year observational dataset the mean retrieved reff,ice is 3.1 μm (standard deviation, σ = 1.5 μm) for an assumed effective variance, νeff,ice = 0.1, and with most individual values between 1 and 10 μm. Sensitivity analyses show the uncertainty associated with reff,ice is largely inversely proportional to water-ice cloud optical depth, τice. As a result, our retrieval is generally applicable to water-ice clouds with τice of at least ∼0.02 to 0.08 at 825 cm−1. Seasonal results show mean reff,ice values of 4.3 μm (σ = 1.9 μm) during aphelion season (Ls = 40°–140°), associated with larger particle sizes in the aphelion cloud belt (ACB), and 2.5 μm (σ = 0.8 μm) during perihelion season (Ls = 225°–360°). Diurnal analyses between roughly 06:00 and 20:00 local true solar time indicate the largest variability occurs in ACB clouds, with effective radii tending to be smaller during midday (zonal-mean reff,ice of 4–5 μm) as compared to the mornings and afternoons (zonal-mean reff,ice of 6–7 μm). Relatively large spatial gradients in reff,ice within the ACB are observed, including a seasonal mean reff,ice of ∼8 μm in the Tharsis region and regions in the southern mid-latitudes with seasonal mean reff,ice of ∼2 μm. Spatial variability in reff,ice tends to be smaller throughout the rest of the year, though with some similar patterns. This includes several time periods between regional dust storms when a low-latitude band of water-ice clouds formed in the afternoons near Tharsis, which grew in both optical depth and effective radius to maxima in the early evenings. Lastly, changes in reff,ice are found to be generally positively correlated with changes in τice and negatively correlated with estimated cloud height, though reff,ice distributions also show this to be a more complex relationship than simple averages might suggest.

Identifying type II quasars at intermediate redshift with few-shot learning photometric classification

A sub-population of AGNs where the central engine is obscured are known as type II quasars (QSO2s). These luminous AGNs have a thick and dusty torus that obscures the accretion disc from our line of sight. Thus, their special orientation allows for detailed studies of the AGN-host co-evolution. Increasing the sample size of QSO2 sources in critical redshift ranges is crucial for understanding the interplay of AGN feedback, the AGN-host relationship, and the evolution of active galaxies. We aim to identify QSO2 candidates in the `redshift desert' using optical and infrared photometry. At this intermediate redshift range (i.e. $1 z 2$), most of the prominent optical emission lines in QSO2 sources (e.g. CIV$ OIII 4959, 5008$) fall either outside the wavelength range of the SDSS optical spectra or in particularly noisy wavelength ranges, making QSO2 identification challenging. Therefore, we adopted a semi-supervised machine learning approach to select candidates in the SDSS galaxy sample. Recent applications of machine learning in astronomy focus on problems involving large data sets, with small data sets often being overlooked. We developed a `few-shot' learning approach for the identification and classification of rare-object classes using limited training data (200 sources). The new AMELIA pipeline uses a transfer-learning based approach with decision trees, distance-based, and deep learning methods to build a classifier capable of identifying rare objects on the basis of an observational training data set. We validated the performance of AMELIA by addressing the problem of identifying QSO2s at $1 z 2$ using SDSS and WISE photometry, obtaining an F1-score above 0.8 in a supervised approach. We then used AMELIA to select new QSO2 candidates in the `redshift desert' and examined the nature of the candidates using SDSS spectra, when available. In particular, we identified a sub-population of NeV emitters at $z which are highly likely to contain obscured AGNs. We used X-ray and radio cross-matching to validate our classification and investigated the performance of photometric criteria from the literature showing that our candidates have an inherent dusty nature. Finally, we derived physical properties for our QSO2 sample using photoionisation models and verified the AGN classification using an SED fitting. Our results demonstrate the potential of few-shot learning applied to small data sets of rare objects, in particular QSO2s, and confirms that optical-IR information can be further explored to search for obscured AGNs. We present a new sample of candidates to be further studied and validated using multi-wavelength observations.

An insight from homogeneity testing of long-term rainfall datasets over East Java, Indonesia

Robust, reliable, and trustworthy ground observation datasets are the preliminary requirement for assessing the impact of climate change over regions. Principal testing to assess the quality of ground observation rely on the missing data and homogeneity result. The study used 40 years of monthly rainfall documented from different topographical features in the monsoonal region of East Java, Indonesia. The test included annual rainfall, early rainy season (October-November-December), and primary rain season (January-February-March). The homogeneity of rainfall determined by absolute technique: Pettitt’s test, the Standard Normal Homogeneity Test, the Buishand Rank Test, and the von Neumann Ratio. Among the time series, October-November-December observation results in better homogeneity. However, the rainfall datasets during primary rainy season showed the worst homogeneity. By performing annual and seasonal homogeneity test from 67 rainfall stations: 5 stations out of data length required, 5% stations ‘rejected’, 11% ‘suspect’, 11% ‘doubtful’, and 73% were ‘trusted’. Therefore, a total of 45 stations can be used as metadata for relative comparison and 7 stations can be considered to be useful for analysis despite ‘doubtful’. The remaining 10 stations need careful consideration to be used for future water management. Change point detected particularly between the year of 1997 through 2000. Pettitt’s test has outstanding results in the case of extreme climatic anomaly, but less sensitive of continuous abrupt change. The von Neumann test could detect abnormal data, but was not suitable for datasets containing few extreme values. The insights from homogeneity testing were: a) it is important to remove any outliers in the datasets before conducting homogeneity testing, b) both parametric and nonparametric homogeneity tests should be performed, and c) comparisons should be made with surrounding rainfall stations. Comparison with trusted long-term rainfall data is valuable for stations labeled as ‘doubtful’ or ‘suspect’ to mitigate false detections in individual homogeneity tests. The identified ‘useful’ rainfall data can then serve as reference stations for relative homogeneity tests. These findings suggest that reference stations should be assessed within similar rainfall zones.

Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes

Abstract. The current representation of key processes in land surface models (LSMs) for estimating water and energy balances still relies heavily on empirical equations that require calibration oriented to site-specific characteristics. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results, leading to a risk of obtaining “the right answers for the wrong reasons”, compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To address this problem and reduce the need for free parameters, here we present novel formulations based on first principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH model v.1.0 (Davis et al., 2017, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent. These quantities are derived from readily available meteorological data such as near-surface air temperature, precipitation, and solar radiation, as well as soil physical properties. Whenever empirical formulations were required, e.g., pedotransfer functions and albedo–snow cover relationships, we selected and optimized the best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs.

Long-term data reveal that grazer density mediates climatic stress in salt marshes.

Understanding how climate and local stressors interact is paramount for predicting future ecosystem structure. The effects of multiple stressors are often examined in small-scale and short-term field experiments, limiting understanding of the spatial and temporal generality of the findings. Using a 22-year observational dataset of plant and grazer abundance in a southeastern US salt marsh, we analyzed how changes in drought and grazer density combined to affect plant biomass. We found: (1) increased drought severity and higher snail density both correlated with lower plant biomass; (2) drought and snail effects interacted additively; and, (3) snail effects had a threshold, with additive top-down effects only occurring when snails were present at high densities. These results suggest that the emergence of multiple stressor effects can be density dependent, and they validate short-term experimental evidence that consumers can augment environmental stress. These findings have important implications for predicting future ecosystem structure and managing natural ecosystems.

Evaluation of the characteristics of Indian summer monsoon simulated by CMIP6 models

AbstractThe present research is aimed at evaluating the climate coupled models with respect to the observational datasets for the investigation of the characteristics of the Indian summer monsoon (ISM). The monthly averaged historical simulations from 12 coupled climate models that participated in Coupled Model Intercomparison Project Phase 6 (CMIP6) are compared with the ground based observational data, satellite and reanalysis datasets for the study period of 1980–2014. This study uses these high‐resolution models to investigate monsoon features, onset and withdrawal dates, primarily focusing on individual model performances rather than highly documented multi‐model mean performance. Performance evaluation of the models suggests that sea surface temperature (SST) simulations by the models are in better agreement for the Arabian Sea than the Bay of Bengal with respect to the ERA5 reanalysis data sets. Further, inter‐model differences amongst the CMIP6 models in estimating the spatial distribution of various monsoon system variables during pre‐monsoon and monsoon seasons are noted which may be attributed to the different model components and varying physics configuration, nonetheless, models like NESM3 and INM‐CN5 are able to reproduce ISM pattern reasonably well. The annual precipitation cycle demonstrates a good agreement between most climate models and IMD data. Evolution of tropospheric temperature gradient (ΔTT) estimated from the CMIP6 models mimics the temporal pattern of the annual rainfall and therefore, is used to estimate the onset and withdrawal dates from CMIP6 models; however, high variability is noted amongst the CMIP6 models in retrieving the onset and withdrawal dates when compared with the IMD observations. Most of the models show a shorter rainy season except NorESM2‐MM. Overall our results suggest that the CMIP6 models can be used for the seasonal mean evaluation of monsoon system parameters and process‐based studies to improve our present understanding of the ISM system.

Dark energy nature of logarithmic f(R,Lm)-gravity models with observational constraints

This work explores the dark energy nature of logarithmic [Formula: see text]-gravity models with observational constraints, where [Formula: see text] represents the matter Lagrangian for perfect fluid and R is the Ricci-scalar curvature. With a matter Lagrangian [Formula: see text], a flat FLRW space–time metric and an arbitrary function [Formula: see text], where [Formula: see text] is a positive model parameter, we have derived the field equations of this model. By using the Hubble function, we have been able to solve the energy conservation equation and create a relationship between [Formula: see text], [Formula: see text] and [Formula: see text]. Using the most recent two observational datasets as 32 [Formula: see text] and 1048 Pantheon SNe Ia datasets, we conducted MCMC analysis. We have obtained best fit values of model parameters with [Formula: see text] errors and using these values, we have explored the cosmological properties of the model. We have performed om diagnostic analysis for the model and estimated the present age of the universe. Thus our derived model presents a transit phase decelerating to accelerating model without dark energy term [Formula: see text]. We found that the effective equation of state parameter is in the range [Formula: see text] over [Formula: see text] with present value [Formula: see text].

Ocean Heat Convergence and North Atlantic multidecadal heat content variability

Abstract We construct an upper ocean (0-1000m) North Atlantic heat budget (26°-67°N) for the period 1950-2020 using multiple observational datasets and an eddy-permitting global ocean model. On multidecadal timescales ocean heat transport convergence controls ocean heat content (OHC) tendency in most regions of the North Atlantic with little role for diffusive processes. In the subpolar North Atlantic (45°N-67°N) heat transport convergence is explained by geostrophic currents whereas ageostrophic currents make a significant contribution in the subtropics (26°N-45°N). The geostrophic contribution in all regions is dominated by anomalous advection across the time-mean temperature gradient although other processes make a significant contribution particularly in the subtropics. The timescale and spatial distribution of the anomalous geostrophic currents are consistent with a simple model of basin scale thermal Rossby waves propagating westwards/northwestwards in the subpolar gyre and multidecadal variations in regional OHC are explained by geostrophic currents periodically coming into alignment with the mean temperature gradient as the Rossby wave passes through. The global ocean model simulation shows that multidecadal variations in the Atlantic Meridional Overturning Circulation are synchronized with the ocean heat transport convergence consistent with modulation of the west-east pressure gradient by the propagating Rossby wave.

The impact of viscosity and anisotropy on cosmic expansion in [formula omitted] gravity

In modified gravity theories, such as f(Q,T) gravity, the concept of dark energy arises from alterations to the gravitational field equations, often manifesting as purely geometrical effects. This perspective opens up intriguing possibilities, suggesting that a universe undergoing cosmic acceleration could be explained by a bulk viscous fluid coupled with a modified gravity theory. In this paper, we introduce a model describing the LRS Bianchi type-I spacetime filled with a viscous fluid within the framework of f(Q,T) gravity. By assuming that the universe is dominated by non-relativistic viscous matter, we derive an exact solution to the field equations for the Hubble parameter H(z) as a function of redshift z. We assess the viability of the proposed f(Q,T) model by fitting it to observational datasets and explore several key physical properties of our cosmological model, including the deceleration parameter, jerk parameter, anisotropy parameter, and Om(z) diagnostics. For the present-day values of the parameters, we obtain: zt=0.80−0.003+0.004, q0=−0.45±0.01, j0=0.63±0.12, and Δ=0.38±0.09 at the 1−σ confidence level. In addition, our findings indicate that our f(Q,T) model, dominated by bulk viscous dark energy, displays quintessence-like behavior according to the Om(z) diagnostic, with a negative slope across all redshift values. We conclude that our study contributes to a deeper understanding of cosmic dynamics and the role of viscosity in cosmological models.

Developing novel ensemble models for predicting soil hydraulic properties in China’s arid region

Accurately quantifying the mass (water, nutrients, and carbon) and energy exchange processes between the Earth’s atmosphere, biosphere, and lithosphere requires the accurate parameterization of soil hydraulic properties (SHPs) and their spatial heterogeneity. Because direct measurements of SHPs are difficult, time-consuming, and impossible at larger spatial scales, various pedotransfer functions (PTFs) have been developed in the last few decades, providing divergent estimates of SHPs from readily measurable variables. However, existing PTFs are mostly developed for specific regions and may not be suitable for other pedoclimatic conditions. Here, PTFs were developed using multiple machine-learning algorithms to estimate SHPs and examined the weaknesses and strengths of each method in estimating the average and variability of SHPs across China’s arid region. The optimal PTFs were applied to a 1 × 1 km2 regional map of texture and bulk density, thus producing maps of the saturated hydraulic conductivity (Ks), the parameters of the van Genuchten (VG) formulation, field capacity (θfc), wilting point (θwp), plant available water (θpa), and soil macroporosity (ϕm) in the 0–2 m soil profile throughout the region. The results indicate that the ensemble model with the averaging method (EMA) is the most robust for estimating all SHPs. The EMA-PTFs for Ks yielded the best performance for sand textures, followed by sandy loam, loam, silty loam, silty clay loam, loamy sand, and clay loam textures. There were no significant differences in estimating soil water retention curve parameters among the different soil texture classes. Using the same observed data set, we demonstrated that the new EMA-PTFs outperformed those of existing PTFs, such as Rosetta and HYPRES, with RMSE values decreasing by 25–83 % depending on the SHPs. Furthermore, our SHP datasets exhibited significantly deeper soil profiles and higher accuracy than other available regional and global SHP products. The VG retention parameter α shows the greatest variation vertically throughout the 0–2 m soil profile, followed by ln(Ks), θr, θpa, θfc, θwp, ϕm, θs and n. All SHPs exhibit much lower variability in the desert than in other areas, likely due to the homogeneous particle size distribution of desert areas. This study provides more accurate SHP estimates in China’s arid region than the existing SHP products by developing advanced PTFs and highlights the inconsistency in SHPs among different global or regional products; the uncertainties induced by PTFs should thus be considered in future terrestrial biosphere modeling.

The Role of Ocean Mesoscale Variability in Air‐Sea CO2 Exchange: A Global Perspective

AbstractOcean mesoscale flows significantly influence nutrient distribution and biological productivity, yet the scarcity of eddy‐permitting observational data sets and climate modeling hinders understanding their role in carbon sequestration. Using an eddy‐resolving global simulation, this study investigates the significance of ocean mesoscales in air‐sea carbon dioxide (CO2) exchange. Results show over 30% of CO2 flux variance in energetic regions attributed to flows with horizontal scales smaller than 2°. Mesoscale flows can drive a cumulative CO2 flux that is either a net carbon sink or source depending on region, with magnitudes on the order of 105 tonnes of carbon per year. Variations in this mesoscale‐related CO2 flux are correlated with local relative vorticity and the background gradient of ocean partial pressure of CO2. This analysis underscores the importance of considering ocean mesoscales in monitoring carbon flux, highlighting the need to explore the influence of increasing eddy activity on carbon uptake in a changing climate.

The majority of fact-checking labels in the United States are intense and this decreases engagement intention

Abstract Fact-checking labels have been widely accepted as an effective misinformation correction method. However, there is limited theoretical understanding of fact-checking labels’ impact. This study theorizes that language intensity influences fact-checking label processing and tests this idea through a multi-method design. We first rely on a large-scale observational dataset of fact-checking labels from 7 U.S. fact-checking organizations (N = 33,755) to examine the labels’ language intensity and then use a controlled online experiment in the United States (N = 656) to systematically test the causal effects of fact-checking label intensity (low, moderate, or high) and fact-checking source (professional journalists or artificial intelligence) on perceived message credibility of and the intention to engage with fact-checking messages. We found that two-thirds of existing labels were intense. Such high-intensity labels had null effects on messages’ perceived credibility, yet decreased engagement intention, especially when labels were attributed to AI. Using more intense labels may not be an effective fact-checking approach.

Freddy: breaking record for tropical cyclone precipitation?

Abstract Depending on the location on the Earth, the amount of precipitation associated with tropical cyclones (TCs) can reach 20% of the total yearly precipitation over land and up to 40% over some ocean regions. TC induced freshwater flooding has been suggested to be the largest threat to human lives due to TCs. Therefore, a reliable quantification of the precipitation amount associated with each past TC is important for a better definition of the TC fingerprint on the climate. The temporal and horizontal resolution of state-of-the-art observational datasets and atmospheric reanalysis gives the possibility to quantify precipitation associated with TCs globally following the observed TC tracks. In this work we compare the TC-related precipitation in various observational and reanalysis datasets. A particular focus is given to the record-breaking TC Freddy (Southern Indian Ocean, 2023). Here we show that the time-varying bias in TC associated precipitation, due to the positive trend in assimilated observations, makes it difficult to assess long-term trend investigation based on reanalysis. To this aim we need to build on state-of-the-art general circulation models, free to evolve under historical radiative forcing.

Integration of surface-based and space-based atmospheric CO2 measurements for improving carbon flux estimates using a new developed 3-GAS inversion model

The ongoing necessity and developmental trajectory in atmospheric CO2 assimilation system revolve around enhancing the optimization efficacy of assimilation inversions through the synergistic integration of distinct observational datasets. In this study, we developed an new inversion system-Global Greenhouse-Gases Assimilation System (3-GAS), by coupling the global atmospheric transport model GEOS-Chem with the four-dimensional local ensemble transform Kalman filter (4D-LETKF) algorithm. We compiled an integrated dataset utilizing the widely recognized carbon observation data from ObsPack (Observation Package), GOSAT (The Greenhouse Gases Observing Satellite), and OCO-2 (Orbiting Carbon Observatory-2) to estimate global surface fluxes. The integration of multiple observational data significantly amplifies spatial coverage, enhances the variability in flux inversion, and fosters improvements in localized assimilation efficacy. The spatial distribution of the carbon flux from the combined assimilation emerges as a comprehensive outcome, born from the assimilation with individual observational data. This distribution is primarily influenced by the coverage attributes of the original observations and the meticulous selection of observations during the assimilation process. The validation with surface and aircraft observations reveals that the results of combined assimilation possess composite advantages, which are manifested in alleviating certain intrinsic errors inherent to individual assimilation. The combined assimilation improves the accuracy based on root-mean-square error reduced by 9%, 22% and 33% in comparison to surface-only, GOSAT-only and OCO-2-only assimilation. Eventually, predicated upon preliminary inversion results and regional characteristics, we endeavor to propose a more refined filtration scheme and error determination method for the integrated dataset, as the initiative for the efficient assimilation of multi-source data.

Beyond order-based nursing workload: A retrospective cohort study in intensive care units.

In order to be positioned to address the increasing strain of burnout and worsening nurse shortage, a better understanding of factors that contribute to nursing workload is required. This study aims to examine the difference between order-based and clinically perceived nursing workloads and to quantify factors that contribute to a higher clinically perceived workload. A retrospective cohort study was used on an observational dataset. We combined patient flow, nurse staffing and assignment, and workload intensity data and used multivariate linear regression to analyze how various shift, patient, and nurse-level factors, beyond order-based workload, affect nurses' clinically perceived workload. Among 53% of our samples, the clinically perceived workload is higher than the order-based workload. Factors associated with a higher clinically perceived workload include weekend or night shifts, shifts with a higher census, patients within the first 24 h of admission, and male patients. The order-based workload measures tended to underestimate nurses' clinically perceived workload. We identified and quantified factors that contribute to a higher clinically perceived workload, discussed the potential mechanisms as to how these factors affect the clinically perceived workload, and proposed targeted interventions to better manage nursing workload. By identifying factors associated with a high clinically perceived workload, the nurse manager can provide appropriate interventions to lighten nursing workload, which may further reduce the risk of nurse burnout and shortage.

Capability of the building-resolving PALM model system to capture micrometeorological characteristics of an urban environment in Vienna, Austria

Understanding and evaluating possible changes in thermal conditions of urban settlements are crucial for risk assessment, adaptation to climate change and sustainable urban development. This study presents the results of a micro-scale measurement campaign conducted in Vienna, Austria, to investigate the effects of natural and artificial surfaces on temperature and humidity variability. The observational data set is used for the evaluation of the newly developed building-resolving urban climate model system PALM. Dragino LHT65 LoRaWAN air temperature and humidity sensors were installed to monitor local-scale variations. On selected summer days, drones equipped with thermal imaging cameras were used to provide hourly surface temperature. The monitoring data show higher temperatures near buildings and overall strong spatial and temporal variations. The model demonstrates the capability to simulate the main thermal characteristics of the study area, although it slightly overestimates temperatures at night. Compared to the measurements, it has a less pronounced spatial variability in air temperature and relative humidity, but a larger one in surface temperature. The analysis confirmed that the urban climate model has the potential to appropriately assess microclimate conditions and therefore, contribute to future-oriented urban planning.

Precipitation Seasonality Amplifies as Earth Warms

AbstractPrecipitation exhibits a pronounced seasonal cycle, of which the phase and amplitude are closely associated with water resource management. While previous studies suggested an emerged delaying phase in the past decades, whether the amplified amplitude has emerged is controversial. Using multiple observational data sets and climate simulations, here we show that the amplification of precipitation annual cycle has emerged in most global land areas since the 1980s, especially in the tropics. These amplifications are mainly driven by anthropogenic emissions, and will be further intensified by 17.6% in the future (2081–2100) under high emission scenario (Shared Socioeconomic Pathways, SSP585), and limited to 7.2% under SSP126 scenario, relative to the historical period (1982–2014). Precipitation seasonality will be amplified by 4.2% for each 1°C of global warming, which is seen in all emission scenarios. The mitigation of lower emissions is helpful for alleviating the amplification of precipitation seasonality in a warming world.

Contemporary Predictors of Major Adverse Cardiovascular Events Following Percutaneous Coronary Intervention: A Nationally Representative US Sample.

Background: Patient outcomes after percutaneous coronary intervention (PCI) have improved over the last 30 years due to better techniques, therapies, and care processes. This study evaluated contemporary predictors of post-PCI major adverse cardiovascular events (MACE) and summarized risk in a parsimonious risk prediction model. Methods: The Cardiovascular Patient-Level Analytical Platform (CLiPPeR) is an observational dataset of baseline variables and longitudinal outcomes from the American College of Cardiology's CathPCI Registry® and national claims data. Cox regression was used to evaluate 2-6 years of patient follow-up (mean: 2.56 years), ending in December 2017, after index PCI between 2012 and 2015 (N = 1,450,787), to examine clinical and procedural predictors of MACE (first myocardial infarction, stroke, repeat PCI, coronary artery bypass grafting, and mortality). Cox analyses of post-PCI MACE were landmarked 28 days after index PCI. Results: Overall, 12.4% (n = 179,849) experienced MACE. All variables predicted MACE, with cardiogenic shock, cardiac arrest, four diseased coronary vessels, and chronic kidney disease having hazard ratios (HRs) ≥ 1.50. Other major predictors of MACE were in-hospital stroke, three-vessel disease, anemia, heart failure, and STEMI presentation. The index revascularization and discharge prescription of aspirin, P2Y12 inhibitor, and lipid-lowering medication had HR ≤ 0.67. The primary Cox model had c-statistic c = 0.761 for MACE versus c = 0.701 for the parsimonious model and c = 0.752 for the parsimonious model plus treatment variables. Conclusions: In a nationally representative US sample of post-PCI patients, predictors of longitudinal MACE risk were identified, and a parsimonious model efficiently encapsulated them. These findings may aid in assessing care processes to further improve care post-PCI outcomes.

Climate of the Pyrenees: Extremes indices and long-term trends

We utilized an extensive, multisource, cross-border dataset of daily meteorological observations from over 1500 stations in the Pyrenees, spanning from the mid-20th century to 2020, to examine the spatial and temporal climate patterns. Our focus was on 17 indices related to extreme precipitation and temperature events across the mountain range. The original data underwent rigorous quality control and homogenization processes, employing a comprehensive workflow that included spatial modeling based on environmental predictors. This process yielded two main outcomes: 1) a high-resolution gridded dataset (1 km2) of daily precipitation, maximum and minimum temperature from 1981 to 2020, allowing for a detailed analysis of spatial variations; and 2) an evaluation of long-term annual and seasonal trends from 1959 to 2020, using selection of high-quality data series that were homogenized to preserve their temporal structure and coherence. The findings revealed a clear elevation-related pattern in temperature indices (with the exception of tropical nights, which were predominantly observed on the Mediterranean side) and a distinct north-south latitudinal disparity in precipitation, turning longitudinal when focusing on extreme precipitation events. Overall, there was a notable and significant warming trend of 0.2 to 0.4 °C per decade, and a non-significant change of precipitation, with the exception of the southern and Mediterranean regions, where there was a notable decrease, approximately −3 % per decade, observed on an annual basis.