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As population-related climate change research increases, so does the need to nuance approaches to this complex phenomenon, including issues related to cultural and linguistic translations. To explore how climate change is understood in understudied societies, a case-study approach is taken to address social representations of climate change by inhabitants of a Maore village in the French island of Mayotte. The study explores how local fishers understand the issue when considering observed environmental changes. Based on analyses of 30 interviews, the study found that social representations and related climate change discourses are not well established, except for individuals in close contact with French institutions. Issues regarding local culture and language reveal the importance of understanding the different components of climate change. Climate change communication and awareness-raising on the island are explored, as well as considerations of culturally and linguistically complex settings with a Global North/Global South interface. [ABSTRACT FROM AUTHOR]

In the context of global climate warming, climate change is subtly reshaping the patterns of wildfires. Therefore, it is particularly urgent to conduct in-depth research on climate change, wildfires, and their management strategies. This study relies on detailed fire point data from 2001 to 2020, skillfully incorporating a spatial autocorrelation analysis to uncover the mysteries of spatial heterogeneity, while comprehensively considering the influences of multiple factors such as climate, terrain, vegetation, and socioeconomic conditions. To simulate fire conditions under future climates, we adopted the BCC-CSM2-MR climate model, presetting temperature and precipitation data for two scenarios: a sustainable low-development path and a high-conventional-development path. The core findings of the study include the following: (i) In terms of spatial heterogeneity exploration, global autocorrelation analysis reveals a striking pattern: within the southern forest region, 63 cities exhibiting a low–low correlation are tightly clustered in provinces such as Hubei, Anhui, and Zhejiang, while 48 cities with a high–high correlation are primarily distributed in Guangxi and Guangdong. Local autocorrelation analysis further refines this observation, indicating that 24 high–high correlated cities are highly concentrated in specific areas, 14 low–low correlated cities are located in Hainan, and there are only 3 sparsely distributed cities with a low–high correlation. (ii) During the model construction and validation process, this study innovatively adopted the LR-RF-SVM ensemble model, which demonstrated exceptional performance indicators: an accuracy of 91.97%, an AUC value of 97.09%, an F1 score of 92.13%, a precision of 90.75%, and a recall rate of 93.55%. These figures, significantly outperforming those of the single models SVM and RF, strongly validate the superiority of the ensemble learning approach. (iii) Regarding predictions under future climate scenarios, the research findings indicate that, compared to the current fire situation in southern forest areas, the spatial distribution of wildfires will exhibit a noticeable expansion trend. High-risk regions will not only encompass multiple cities in Hunan, Hubei, southern Anhui, all of Jiangxi, and Zhejiang but will also extend northward into southern forest areas that were previously considered low-risk, suggesting a gradual northward spread of fire risk. Notably, despite the relatively lower fire risk in some areas of Fujian Province under the SS585 scenario, overall, the probability of wildfires occurring in 2090 is slightly higher than that in 2030, further highlighting the persistent intensification of forest fire risk due to climate change. [ABSTRACT FROM AUTHOR]

The accurate prediction of global forest soil respiration (Rs) is critical for climate change research. Rs consists of autotrophic (Ra) and heterotrophic (Rh) respiration, which respond differently to environmental factors. Predicting Rs as a single flux can be biased; therefore, Ra and Rh should be predicted separately to improve prediction accuracy. In this study, we used the SRDB_V5 database and the random forest model to analyze the uncertainty in predicting Rs using a single global model (SGM) and Ra/Rh using a specific categorical model (SCM) and predicted the spatial dynamics of the distribution pattern of forest Ra, Rh, and Rs in the future under the two different climate patterns. The results show that Rs is higher under tropical and inland climatic conditions, while Rh fluctuates less than Ra and Rs. In addition, the SCM predictions better capture key environmental factors and are more consistent with actual data. In the SSP585 (high emissions) scenario, Rs is projected to increase by 19.59 percent, while in the SSP126 (low emissions) scenario, Rs increases by only 3.76 percent over 80 years, which underlines the need for SCM in future projections. [ABSTRACT FROM AUTHOR]

In climate studies, it is crucial to distinguish between changes caused by natural variability and those resulting from external forcing. Here we use a suite of numerical experiments based on the ECCO‐Darwin ocean biogeochemistry model to separate the impact of the atmospheric carbon dioxide (CO2) growth rate and climate on the ocean carbon sink — with a goal of disentangling the space‐time variability of the dominant drivers. When globally integrated, the variable atmospheric growth rate and climate exhibit similar magnitude impacts on ocean carbon uptake. At local scales, interannual variability in air‐sea CO2 flux is dominated by climate. The implications of our study for real‐world ocean observing systems are clear: in order to detect future changes in the ocean sink due to slowing atmospheric CO2 growth rates, better observing systems and constraints on climate‐driven ocean variability are required. Plain Language Summary: In climate research, it's important to determine whether changes in the ocean's ability to absorb carbon dioxide (CO2) are due to natural climate variations or human activities. This study uses a computer model to analyze how different factors affect the ocean's absorption of CO2. Our results show that changes in the rate of atmospheric CO2 growth from year to year and natural climate fluctuations have a similar amount of impact on ocean carbon sequestration when considering the entire globe. However, when zooming into smaller, more‐localized regions, natural climate variability plays a bigger role in driving short‐term changes. This means that to track changes in how much carbon the ocean is absorbing in the future — especially if we start to see slower increases in atmospheric CO2 due to efforts to reduce emissions — improved monitoring systems and a better understanding of climate‐related changes in the ocean will be needed. Key Points: Variable atmospheric growth rate drives variability in air‐sea CO2 fluxes at all ocean locations, integrating to globally significant impactClimate variability, both internally and externally forced, is the dominant driver of variability as spatiotemporal scales become smallerGlobal‐mean variability of air‐sea CO2 flux is equally forced by climate and atmospheric growth rate [ABSTRACT FROM AUTHOR]

Political partisanship is one of the strongest correlates of climate change attitudes in the United States, with Democrats expressing greater concern than Republicans. As the climate crisis progresses, surveys increasingly examine preparedness for environmental emergencies, instead of climate attitudes alone. Does partisan identification associate with environmental preparedness actions as it does other climate attitude measures? We find a paradox: Republicans are more likely to say they have taken preparedness actions than Democrats. We replicate this finding in three survey samples (n = 1,053; n = 1,201; n = 2,318) and provide two sociological explanations. Theoretically, a culture of emergency preparedness integrated with conservative belief systems could strengthen the partisanship association, especially if preparedness is not closely tied to other climate attitudes. Empirically, household income is associated with specific types of preparedness actions, possibly introducing bias based on social class. We discuss the importance of measuring climate preparedness for the future of climate opinion research and climate policy and call for preparedness measures that are applicable to the general population. [ABSTRACT FROM AUTHOR]

Accurate climate projections are critical for various applications and impact assessments in environmental science and management. This study presents HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China), a novel dataset tailored to China's specific needs. HiCPC leverages outputs from 22 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). To address inherent biases in daily GCM simulations, an advanced Bias Correction and Spatial Disaggregation (BCSD) method is employed, using the China Meteorological Forcing Dataset (CMFD) as a reference. HiCPC offers detailed daily precipitation and temperature data across China at an enhanced spatial resolution of 0.1° × 0.1°. It covers both the historical period (1979–2014) and future projections (2015–2100) based on four CMIP6 Shared Socioeconomic Pathways (SSPs) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Upon validation, HiCPC demonstrates good performance, surpassing CMIP6 GCMs across the historical period. This reinforces its significance for essential research in climate change evaluation and its associated implications within China. [ABSTRACT FROM AUTHOR]

Currently, ground-based global navigation satellite system (GNSS) techniques have become widely recognized as a reliable and effective tool for atmospheric monitoring, enabling the retrieval of zenith total delay (ZTD) and precipitable water vapor (PWV) for meteorological and climate research. The International GNSS Service analysis centers (ACs) have initiated their third reprocessing campaign, known as IGS Repro3. In this campaign, six ACs conducted a homogeneous reprocessing of the ZTD time series spanning the period from 1994 to 2022. This paper primarily focuses on ZTD products. First, the data processing strategies and station conditions of six ACs were compared and analyzed. Then, formal errors within the data were examined, followed by the implementation of quality control processes. Second, a combination method is proposed and applied to generate the final ZTD products. The resulting combined series was compared with the time series submitted by the six ACs, revealing a mean bias of 0.03 mm and a mean root mean square value of 3.02 mm. Finally, the time series submitted by the six ACs and the combined series were compared with VLBI data, radiosonde data, and ERA5 data. In comparison, the combined solution performs better than most individual analysis centers, demonstrating higher quality. Therefore, the advanced method proposed in this study and the generated high-quality dataset have considerable implications for further advancing GNSS atmospheric sensing and offer valuable insights for climate modeling and prediction. [ABSTRACT FROM AUTHOR]

Species Distribution Modelling (SDM) is used to identify a species' potential current and future distribution. While numerous global studies have reported species distribution changes in various future climate change scenarios, regional relevance has often been overlooked. In this study, we conducted a systematic literature review to assess the future climate change impacts on India's biodiversity across all biogeographic zones. Our findings revealed a significant increase in research on climate change related SDM after 2015. These studies were published mainly in ecological, and biodiversity conservation journals. We found that (a) The Himalayan region is the most studied biogeographic zone (n = 49), followed by the Western Ghats (n = 15). (b) Plants are the most studied taxa (n = 77), followed by invertebrates (n = 11). (c) Gaps in the literature regarding the climate change impacts on the distribution of amphibians (n = 2) and reptiles (n = 1), and studies specific to Islands (n = 0) and Coastal (n = 2) biogeographic zones. (d) 60% of studies did not mention or calculate uncertainties arising from data gaps and model parameters. We highlight the need for more studies dedicated to lesser-studied taxa. We recommend that in the future the quality of SDM-related studies be critically reviewed to ensure that they are reproducible. [ABSTRACT FROM AUTHOR]

AbstractSmart housing and its consequences for tenants is still a largely absent field in smart city and housing research. Departing from the EU-funded project GrowSmarter, 2015–2019 and a renovation project of a 1960s housing complex in Stockholm, the article investigates tensions around retrofitting older apartment blocks to make them climate smart. The article presents the argument that top-down approaches of EU-funded climate-smart city interventions leave minimal space for different stakeholders to steer the process and limits the tenants’ role and influence. Researching the implementation of smart technology in housing renovations and how it ultimately effects tenants’ everyday lives, the article adds to previous knowledge of uneven development of climate smart solutions through bringing in the outcomes of housing renovation projects, and how ‘actual smart cities’ are played out within the housing sector. The article brings together research on climate urbanism, housing studies and smart cities with the purpose to understand the scalar politics of smart implementations and the effects on tenants. [ABSTRACT FROM AUTHOR]

The mesophilic methanogenic archaeal model organism Methanosarcina mazei strain Gö1 is crucial for climate and environmental research due to its ability to produce methane. Here, we establish a Ribo-seq protocol for M. mazei strain Gö1 under two growth conditions (nitrogen sufficiency and limitation). The translation of 93 previously annotated and 314 unannotated small ORFs, coding for proteins ≤ 70 amino acids, is predicted with high confidence based on Ribo-seq data. LC-MS analysis validates the translation for 62 annotated small ORFs and 26 unannotated small ORFs. Epitope tagging followed by immunoblotting analysis confirms the translation of 13 out of 16 selected unannotated small ORFs. A comprehensive differential transcription and translation analysis reveals that 29 of 314 unannotated small ORFs are differentially regulated in response to nitrogen availability at the transcriptional and 49 at the translational level. A high number of reported small RNAs are emerging as dual-function RNAs, including sRNA154, the central regulatory small RNA of nitrogen metabolism. Several unannotated small ORFs are conserved in Methanosarcina species and overproducing several (small ORF encoded) small proteins suggests key physiological functions. Overall, the comprehensive analysis opens an avenue to elucidate the function(s) of multitudinous small proteins and dual-function RNAs in M. mazei. Ribo-seq and peptidomics have the potential to advance archaeal genomic annotations. Here, the authors exploit these techniques to explore the small proteome of Methanosarcina mazei. [ABSTRACT FROM AUTHOR]

This study assesses the impact of climate change on flood frequency across seven sites in the Western Cape province of South Africa. The calibrated Water Resources Simulation Model (WRSM)/Pitman hydrological model was run using precipitation inputs from two representative concentration pathways (RCP) scenarios (RCP 4.5 and 8.5) using a combination of eight global circulatory models (GCM) for the two periods (2030–2060 and 2070–2100). GCMs were statistically downscaled using the delta change (DC), linear scaling (LS) and quantile delta mapping (QDM) approaches. Average daily discharge was estimated from each downscaled daily precipitation dataset using the Pitman/WRSM model with the Fuller and Sangal estimation methods used to calculate daily instantaneous peak flows. Flood frequency curves (FFC) were generated using the annual maximum series (AMS) for the GCM ensemble mean and individual GCMs for the return periods between 2 and 100 years. FFCs generated based on LS and QDM downscaling methods were aligned for the GCM ensemble mean in terms of the direction of FFCs. Further analysis was conducted using outputs based on the QDM approach, given its suitability in projecting peak flows. Under this method, both Fuller and Sangal FFCs exhibited a decreasing trend across the Jonkershoek and Little Berg River sites; however, estimated quantiles for low-probability events were higher under the Fuller method. This study noted the variation in FFCs from individual GCMs compared to the FFC representing the GCM ensemble mean. Further research on climate change flood frequency analysis (FFA) in South Africa should incorporate other advanced downscaling and instantaneous peak flow estimation (IPF) methods. [ABSTRACT FROM AUTHOR]

Selecting climate model projections is a common practice for regional and local studies. This process often relies on local rather than synoptic variables. Even when synoptic weather types are considered, these are not related to the variable or climate impact driver of interest. Therefore, most selection procedures may not sufficiently account for atmospheric dynamics and climate change impact uncertainties. This study outlines a selection methodology that addresses both these shortcomings. Our methodology first optimizes the Lamb Weather Type classification for the variable and region of interest. In the next step, the representation of the historical synoptic dynamics in Global Climate Models (GCMs) is evaluated and accordingly, low-performing models are excluded. In the last step, indices are introduced that quantify the climate change signals related to the impact of interest. Using these indices, a scoring method results in assessing the suitability of GCMs. To illustrate the applicability of the methodology, a case study of extreme heat in Belgium was carried out. This framework offers a comprehensive method for selecting relevant climate projections, applicable in model ensemble-based research for various climate variables and impact drivers. [ABSTRACT FROM AUTHOR]

Cirque landforms, which are distinctive features of cold regions, play a pivotal role in understanding environmental changes and climate research. Currently, machine learning-based automatic glacier recognition models have significantly improved recognition speed. However, these models rely solely on elevation data from Digital Elevation Model (DEM), neglecting other topographic factors that influence cirque identification, such as slope and aspect. This study integrates DEM, slope, and aspect data through pseudo-color fusion technology and utilizes the RetinaNet network to achieve automatic cirque recognition. The pseudo-color fusion image-based recognition model outperforms methods solely relying on DEM, achieving mAP (Intersection over Union: IoU @0.5) of 82.3 %. In practical applications, the pseudo-color fusion image-based recognition model demonstrates balanced performance in identifying different categories of cirques, particularly excelling in recognizing intricate terrains and irregularly shaped cirques. [ABSTRACT FROM AUTHOR]

The majority of available climate data in global digital archives consist of data only from the 1940s or 1950s onwards, and many of these series have gaps and/or are available for only a subset of the variables which were actually observed. However, there exist billions of historical weather observations from the 1700s, 1800s, and early 1900s that are still in hard‐copy form and are at risk of being lost forever due to deterioration. An assessment of changes in climate extremes in several IPCC regions was not possible in IPCC AR6 WGI owing, in many cases, to the lack of available data. One such region is Africa, where the climate impact research and the ability to predict climate change impacts are hindered by the paucity of access to consistent good‐quality historical observational data. The aim of this innovative project was to use classroom‐based participatory learning to help transcribe some of the many meteorological observations from Africa that are thus far unavailable to researchers. This project transcribed quickly and effectively station series by enrolling the help of second‐year undergraduate students at Maynooth University in Ireland. The newly digitized African data will increase the temporal and spatial coverage of data in this important data‐sparse region. Students gained new skills while helping the global scientific community unearth new insight into past African climate. The project managed to transcribe 79 months of data at Andapa in Madagascar and 56 months of data for Macenta in Guinea. The digitized data will be openly and freely shared with the scientific and wider community via the Pangaea data repository, the C3S Climate Data Store, and the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Information (NCEI) data centre in the US. The project model has the potential for a broader roll‐out to other educational contexts and there is no shortage of data to be rescued. This paper provides details of the project, and all supporting information such as project guidelines and templates to enable other organizations to instigate similar programs. [ABSTRACT FROM AUTHOR]

All clouds influence the Earth's radiative budget, with their net radiative forcing being negative. However, high-level clouds warrant special attention due to their atmospheric warming effects. A comprehensive characterization of cirrus requires information on cloud coverage, obtainable from various data types. Active satellite sensors are presently the most accurate source for cirrus data, but their usefulness in climatological studies is limited. On the contrary, passive data, available for the past 40 years with sufficient temporal resolution for climatological research, were not specifically designed for cirrus detection. In this study, we assessed the utility of MODIS standard products for creating a cirrus mask by validating them against CALIOP data. Our objective was to determine if a MODIS product exists that detects cirrus with the same accuracy as CALIOP. Using CALIOP data as the reference, we evaluated six tests for cirrus detection considered in MODIS cloud masking algorithm and their combination (ALL TESTS CONSOLIDATION, ATC). Additionally we applied two ISCCP-originating tests: ISCCP3.6 and ISCCP23 tests. All tests have been applied to MODIS radiances. Study revealed that ATC test was the most effective resulting with the overall accuracy of 72.98 % during daytime and 59.50 % at night (probability of detection: 80.87 % and 25.46 %, false alarm rate of 34.86 % and 6.90 %, and Cohen's kapppa coefficient of 0.46 and 0.19 respectively). However, its effectiveness was notably reduced during nighttime compared to daytime. We conclude that the test is suitable for creating a mask of high-level clouds. [ABSTRACT FROM AUTHOR]

This study explores the performance of a dense optical flow method in comparison to pattern-matching techniques for retrieving atmospheric motion vectors (AMVs) from water vapor images. Using high-resolution simulated datasets that represent various weather phenomena, we assess the performance of these methods across different weather regimes, time intervals, and pressure levels and quantify the uncertainties associated with retrieved winds. The optical flow algorithm consistently outperforms the feature matching approach. Notably, it produces wind speeds and AMVs that closely resemble the wind fields from the simulations, and unlike the feature matching algorithm, the optical flow algorithm exhibits consistent performance across different time intervals. In contrast, the feature matching approach yields vector fields that exhibit oversmoothing in certain areas and erratic behavior in others, while also producing less detailed, regionally constant speed maps. Furthermore, unlike feature matching, the optical flow method effectively calculates AMV near regions with missing data, generating a dense AMV field for every pixel in a pair of images. This superior performance and flexibility significantly influence the planning for future satellite missions aimed at retrieving atmospheric winds. As such, our work plays a critical role in determining the mission architecture and projected instrument performance for future atmospheric wind retrieval satellite missions. The study underscores the potential of the optical flow algorithm as a robust and efficient approach for atmospheric motion retrieval, thus contributing to advances in climate research and weather prediction. Significance Statement: This research investigates the efficacy of two methods, optical flow and feature matching, for detecting atmospheric winds, referred to as atmospheric motion vectors, from satellite images of water vapor. Employing detailed simulated datasets that replicate real-world weather patterns, we found that optical flow consistently outperforms feature matching in various aspects. Notably, the optical flow method is not only more precise but also maintains its accuracy across different scenarios. These insights are critical for the design of future satellite missions focused on advancing our understanding of the atmosphere and enhancing weather predictions. This study contributes to advancements in climate research and supports improved weather forecasting, benefiting both scientific and societal needs. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) is a crucial parameter of Earth's atmosphere, with its spatial and temporal variations significantly impacting Earth's energy balance and weather patterns. Particularly during meteorological disasters such as typhoons, PWV and other meteorological parameters exhibit dramatic changes. Studying the response relationship between PWV and typhoon events, alongside other meteorological parameters, is essential for meteorological and climate analysis and research. To this end, this paper proposes a method for analyzing the response relationship between PWV and meteorological parameters based on Wavelet Coherence (WTC). Specifically, PWV and relevant meteorological parameters were obtained using GNSS and ERA5 data, and the response relationships between PWV and different meteorological parameters before and after typhoon events were studied in time–frequency domain. Considering that many GNSS stations are not equipped with meteorological monitoring equipment, this study interpolated meteorological parameters based on ERA5 data for PWV retrieval. In the experimental section, the accuracy of ERA5 meteorological parameters and the accuracy of PWV retrieval based on ERA5 were first analyzed, verifying the feasibility and effectiveness of this approach. Subsequently, using typhoon Lekima as a case study, data from six GNSS stations affected by the typhoon were selected, and the corresponding PWV was retrieved using ERA5. The WTC method was then employed to analyze the response relationship between PWV and meteorological parameters before and after the typhoon's arrival. The results show that the correlation characteristics between PWV and pressure can reveal different stages before and after the typhoon passes, while the local characteristics between PWV and temperature better reflect regional precipitation trends. [ABSTRACT FROM AUTHOR]

We analyze the calibration stability of the 17-yr precipitation radar (PR) data on board the Tropical Rainfall Measuring Mission (TRMM) satellite to develop a precipitation climate record from the spaceborne precipitation radar data of the TRMM and following satellite missions. Since the PR measures the normalized radar cross section (NRCS) over the ocean surface, the temporal change in the NRCS whose variability is insensitive to the sea surface wind is regarded as the temporal change of the PR calibration. The temporal change of the PR calibration in TRMM, version 7, is found to be 0.19 dB decade−1 from 1998 to 2013. The calibration change is simply adjusted to evaluate the NRCS time series and the near-surface precipitation trend analysis within the latitudinal band between 35°S and 35°N. The NRCS time series at nadir and off-nadir are uncorrelated before the calibration adjustment, but they are correlated after the adjustment. The 0.19 dB decade−1 change of the PR calibration causes an overestimation of 0.08 mm day−1 decade−1 or 2.9% decade−1 for the linear trend of the near-surface precipitation. Even after the adjustment, agreement of the results among the satellite products depends on the analysis period. The temporal stability of the data quality is also important to evaluate the plausible trend analysis. The reprocessing of the PR data in TRMM, version 8 (or later), takes into account the temporal adjustment of the calibration change based upon the results of this study, which can provide more credible data for a long-term precipitation analysis. Significance Statement: The stability of long-term data is very important for climate research so that an account of temporal calibration changes in the sensor must be made. In this study, we investigate the calibration stability of the TRMM PR data and evaluate its impact on the precipitation trend analysis. The temporal change of the PR calibration is estimated to be 0.19 dB decade−1. Compensating for this change improves the consistency of precipitation trend analysis between the PR and other precipitation datasets. The reprocessed PR data provide more probable data for long-term precipitation analysis. [ABSTRACT FROM AUTHOR]

Problem: Investigations regarding perceptions of the institutional research integrity climate in the Arab Middle East remain underexplored. Subjects: We surveyed faculty from three Egyptian universities. Method: We utilized the Survey of Organizational Research Climate (SOuRCe) tool, which incorporates seven subscales that measure different aspects of the research integrity climate. Responses were obtained from a 5-point Likert scale. Findings: Of the 228 participants, the subscales 'Regulatory Quality' and '[Lack of] Integrity Inhibitors' received the highest mean scores, whereas the lowest scores pertained to 'Departmental Expectations,' 'Integrity Socialization,' and 'Responsible Conduct of Research´ indicating areas in need of improvement. Conclusions: Academic leaders should set fairer expectations for research and funding for their researchers, ensure junior researchers are socialized into research integrity practices, and promote effective RCR training and availability of RCR policies. We identify specific targeted interventions to enhance the research integrity climate within these institutions. [ABSTRACT FROM AUTHOR]

The reduction of in situ observations over the last few decades poses a potential risk of losing important information in regions where local effects dominate the climatology. Reanalyses face challenges in representing climatologies with highly localized effects, especially in regions with complex orography. Empirical downscaling methods offer a cost-effective and easier-to-implement alternative to dynamic downscaling methods and can partially overcome the aforementioned limitations of reanalyses by taking into account the local effects through statistical relationships. This article introduces RASCAL (Reconstruction by AnalogS of ClimatologicAL time series), an open-source Python tool designed to extend time series and fill gaps in observational climate data, especially in regions with limited long-term data and significant local effects, such as mountainous areas. Employing an object-oriented programming style, RASCAL's methodology effectively links large-scale circulation patterns with local atmospheric features using the analog method in combination with principal component analysis (PCA). The package contains routines for preprocessing observations and reanalysis data, generating reconstructions using various methods, and evaluating the reconstruction's performance in reproducing the time series of observations, statistical properties, and relevant climatic indices. Its high modularity and flexibility allow fast and reproducible downscaling. The evaluations carried out in central Spain, in mountainous and urbanized areas, demonstrate that RASCAL performs better than the ERA20C and ERA20CM reanalysis, as expected, in terms of R2 , standard deviation, and bias. When analyzing reconstructions against observations, RASCAL generates series with statistical properties, such as seasonality and daily distributions, that closely resemble observations. This confirms the potential of this method for conducting robust climate research. The adaptability of RASCAL to diverse scientific objectives is also highlighted. However, as with any other method based on empirical training, this method requires the availability of sufficiently long-term data series. Furthermore, it is susceptible to disruption caused by changes in land use or urbanization processes that might compromise the homogeneity of the training data. Despite these limitations, RASCAL's positive outcomes offer opportunities for comprehensive climate variability analyses and potential applications in downscaling short-term forecasts, seasonal predictions, and climate change scenarios. The Python code and the Jupyter Notebook for the reconstruction validation are publicly available as an open project. [ABSTRACT FROM AUTHOR]

This viewpoint analyses the relationship between the climate crisis and democratic quality, considering the various reports and studies that have been published on the role of disinformation in identifying the main global risks. To define the lines of action, a cross-cutting perspective is required to distinguish between obstructionist, denialist and sceptical narratives, but at the same time, another perspective, from the consequences for citizens in terms of apathy, disconnection or information anxiety. To this end, we define the main challenges in research on climate disinformation in the coming years and the types of social and academic response that have the greatest potential, including media literacy, specialized training for journalism and communication professionals, as well as the creation of institutional collaboration networks. [ABSTRACT FROM AUTHOR]

This paper explores how design can be used as a process of enquiry in climate research in collaboration with climate scientists. The research discusses the value of design in navigating the complexities of transdisciplinary climate research by engaging with the mess and researching everyday climate actions. The ordinary can be described as the realm of social life where the repetition of daily cycles that we learn is eventually taken for granted. Climate actions that are situated in this ordinary are vital to delivering larger‐scale transformations and achieving carbon reductions. However, climate scientists across disciplines often overlook the importance of engaging with the messiness of such climate initiatives – such as understanding how ordinary climate initiatives emerge, impact a place, and move across contexts. As a result, academics and policymakers tend to focus on global and high‐tech responses to climate issues. However, by shifting our focus on the mundane, we can research and present situated perspectives of climate actions that are crucial to achieving carbon emission reductions and improving resilience to the impact of climate change at different scales. This paper presents theoretical grounding for design to be used as an integral part of 'ordinary' climate research. [ABSTRACT FROM AUTHOR]

Many fundamental concepts of synoptic-scale extratropical dynamics are based on the quasi-geostrophic equations of a dry atmosphere. This "dry dynamics" provides the essential understanding of, for example, the formation of extratropical cyclones and the propagation of Rossby waves and makes potential vorticity (PV) a materially conserved quantity. Classically, for extratropical weather systems, the importance of so-called "diabatic effects", e.g. surface fluxes, phase changes of water in clouds, and radiation, has been regarded as secondary compared to the dry dynamical processes. As outlined in this review article, research during recent decades has modified this view of the role of diabatic processes. A combination of complementary research approaches revealed that the nonlinear dynamics of extratropical cyclones and upper-tropospheric Rossby waves is affected – in some cases strongly – by diabatic processes. Despite the violation of material PV conservation in the presence of diabatic processes, the concept of PV has been of utmost importance to identify and quantify the role of diabatic processes and to integrate their effects into the classical understanding based on dry dynamics. This review first summarises the theoretical concepts of diabatic PV modification, moist PV, and slantwise moist convection and provides a concise overview of early research on diabatic effects until the late 1970s. Two poorly predicted high-impact cyclones affecting eastern North America then triggered an impressive diversity of efforts to investigate the role of diabatic processes in rapid cyclone intensification in the last 2 decades of the 20th century. These research activities, including the development of sophisticated diagnostics, growing applications of the Lagrangian perspective, real-case and idealised numerical experiments, and dedicated field experiments, are reviewed in detail. This historical perspective provides insight about how societal relevance, international collaboration, technical development, and creative science contributed to establishing this important theme of dynamical meteorology. The second part of the review then more selectively outlines important achievements in the last 2 decades in our understanding of how diabatic effects, in particular those related to cloud microphysics, affect the structure, dynamics, and predictability of different types of extratropical cyclones and their mesoscale substructures, upper-tropospheric blocks, Rossby waves, and interactions. A novel aspect is the relevance of research on diabatic processes for climate change research. The review closes by highlighting important implications of investigating diabatic processes in extratropical weather systems for the broader field of weather and climate dynamics and its fundamentals and representation in numerical models. [ABSTRACT FROM AUTHOR]

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Various databases have been developed to track national climate policy efforts. These datasets facilitate comparisons across countries regarding policy activity, instrument choice, and policy effectiveness. This article evaluates these datasets to see whether they converge in their observations about climate policy development. Our findings reveal that all datasets agree at the aggregate level in that they show that ever-more climate policies are being adopted. However, they diverge significantly when scrutinizing more nuanced elements like policy instrument types and their stringency. The main contributions of our review are to highlight what research endeavors are already possible with existing datasets and to identify the gaps that still remain. We also provide concrete suggestions on how to enhance the existing datasets, making them more useful for social science research on climate policy. The article provides the most comprehensive and up-to-date source for scholars and practitioners interested in the comparative analysis of governmental climate policy efforts. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) could revolutionize our ability to understand and address climate change. Studies to date have focused on specific AI applications to climate science, technologies, and policy. Yet despite the vast demonstrated potential for AI to change the way in which climate research is conducted, no study has presented a systematic and comprehensive understanding of the way in which AI is intersecting with climate research around the world. Using a novel merged corpus of scholarly literature which contains millions of unique scholarly documents in multiple languages, we review the community of knowledge at the intersection of climate change and AI to understand how AI methods are being applied to climate-related research and which countries are leading in this area. We find that Chinese research institutions lead the world in publishing and funding research at the intersection of climate and AI, followed by the United States. In mapping the specific AI tasks or methods being applied to specific climate research fields, we highlight gaps and identify opportunities to expand the use of AI in climate research. This paper can therefore greatly improve our understanding of both the current use and the potential use of AI for climate research. [ABSTRACT FROM AUTHOR]

Wet and cold biases on the Tibetan Plateau (TP) commonly exist in global and regional climate simulations. Previous studies have explored the possible causes of wet and cold biases and contributed to reducing these biases. However, the connection between wet and cold biases remains insufficiently addressed. Our research indicates that the TP wet bias converts into positive snow amount bias not continually but efficiently and concentratedly, under the control of snow phenology in different regions. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. Our research highlights the spatio‐temporally divergent transformation of wet to cold bias on the TP during snow season, providing a novel perspective to understand the intrinsic connection between wet and cold biases and improve climate simulations on the TP. Plain Language Summary: Due to the harsh environment, observational data are limited on the Tibetan Plateau (TP), so climate model is one of necessary tools for climate researches. However, both global and regional climate models generally overestimate precipitation (wet bias) and underestimate near surface air temperature (cold bias) on the TP. Previous studies have pointed out the possible causes of the wet and cold biases, and partly reduced these biases, leaving the connection between these biases unaddressed. Our study finds that the conversion of wet to cold bias during snow season is divergent in different areas of the TP. Under the control of snow duration in different areas, the wet bias efficiently and concentratedly transforms into larger snow amount. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. We reveal the spatio‐temporally divergent transformation of wet to cold bias on the TP during snow season, and these conclusions will contribute to a better improvement of snow and climate modeling on the TP. Key Points: WRF simulations significantly overestimate snow amount and delay snow phenology on the Tibetan Plateau (TP)Snowfall bias is not always transformable into snow amount bias, depending on snow phenologyComplex relationship between snow amount and surface albedo restricts the transformation of wet to cold bias on the TP [ABSTRACT FROM AUTHOR]

Climate change and Artificial Intelligence (AI) are both attracting great interest across society. There is also substantial interest in merging the two sciences, with evidence already that AI can identify earlier precursors to extreme weather events. There are a range of AI algorithms, and selection of the most appropriate one maximizes the amount of additional understanding extractable for any dataset. However, most AI algorithms are statistically based and even with careful splitting between data for training and testing, they arguably remain as emulators. Emulators may make unreliable predictions when driven by out-of-sample forcing and climate change is an example of this, requiring understanding responses to atmospheric Greenhouse Gas (GHG) concentrations that may be substantially higher than present or the recent past. Notable, though, is the emerging AI technique of "equation discovery". AI-derived equations from data also does not automatically guarantee good performance for new forcing regimes. However, access to equations rather than a statistical emulator guides system understanding, as their variables and parameters often have a better interpretation. Better process knowledge enables judgements as to whether equations are trusted under extrapolation. For many climate system attributes, descriptive equations are not yet fully available or may be unreliable. This uncertainty is hindering the development of Earth System Models (ESMs) which remain the main tool for projections of large-scale environmental change as GHGs rise. Here, we make the case for using AI-driven equation discovery in climate research, given that its outputs are more interpretable in terms of processes. As ESMs are based around the numerical discretisation of equations that describe climate components, equation discovery from new datasets provides a format amenable to direct inclusion into such models where representation of environmental systems is missing. We present three illustrative examples of how AI-led equation discovery may advance future climate science research. These are generating new equations related to atmospheric convection, parameter derivation for existing equations of the terrestrial carbon cycle, and (additional to ESM improvement) the creation of simplified models of large-scale oceanic features to assess Tipping Point (TP) risks. [ABSTRACT FROM AUTHOR]

This study investigated the relationship between publication pressure and questionable research practices (QRPs), focusing on moral disengagement (MD) and lab research ethics climate. A survey of 269 Chinese PhD students revealed that (1) publication pressure is significantly positively correlated with QRPs and MD; (2) MD mediates the relationship between publication pressure and QRPs; lab research ethics climate moderates both (3) the relationship between publication pressure and MD, and (4) the mediating role of MD between publication pressure and QRPs. Responsible conduct of research education should emphasize potential ethical conflicts in publication, the impact of cognitive patterns of MD, and lab-based interventions for QRPs. [ABSTRACT FROM AUTHOR]

This study examines the spatiotemporal variability of drought and associated physical processes over the Arabian Peninsula (AP). For this purpose, we computed the standardized precipitation evapotranspiration index (SPEI) for the period 1951–2020 using the Climate Research Unit and fifth generation ECMWF atmospheric reanalysis datasets. By applying rotated empirical orthogonal function analysis on the SPEI data, we identified four homogeneous and coherent drought regions. The droughts in the northern regions follow a relatively similar temporal evolution as compared to those in the southern region. All four sub-regions of the AP exhibit a significant drying trend (p < 0.01) with an abrupt acceleration in drought frequency and intensity over the last two decades. The increase in droughts is associated with the reduction of synoptic activity and an increase in the high pressure over the AP. Seasonally, potential evapotranspiration is the dominant driver of summer droughts in the AP, whereas both precipitation and temperature are important for driving winter droughts. The summer droughts, mainly over the northern AP, are due to the occurrence of an anomalous equivalent barotropic high associated with anomalous dry and hot conditions. However, anomalous dry conditions in winter are a result of an anomalous paucity of winter storms caused by the weakening of the sub-tropical jets. [ABSTRACT FROM AUTHOR]

This chapter provides an overview of the major themes, findings, and recommendations from NPCC4. It presents summary statements from each chapter of the assessment which identify salient and pressing issues raised and provides recommendations for future research and for enhancement of climate resiliency. The chapter also outlines a set of broader recommendations for future NPCC work and identifies some key topics for the next assessment. [ABSTRACT FROM AUTHOR]

This article explores how sociologists of religion can respond to 'the animal turn' in studies of lived religion and nonreligion. We begin by considering how sociology has neglected the place of non-human animals and the 'more than human' in social life. We then turn to the sociology of religion, where animals have often been devalued or ignored as irrelevant to understanding religion in society. We argue that it is necessary to consider the ways in which human activities are shaped by non-human animals. This does not mean that animals should be thought of as nonreligious or religious. We contend that the failure to incorporate non-human animals in sociological considerations of religion and nonreligion replicates a hierarchical model, which sees human life as above or higher than non-human life and calls our attention to the place of sociological research amid the climate crisis. [ABSTRACT FROM AUTHOR]

Climate change is associated with the modification of the polar jet stream, cyclone tracks and corresponding circulation patterns over midlatitudes. To apply these synoptic changes on regional climate, the changing sensitivity of a specific location to different pressure centre regions must be studied. An automated, objective circulation pattern detection method was developed to investigate the evolution of cyclonic and anticyclonic influence at any specific point within the European domain. The algorithm was used to assign each location to influencing low‐ or high‐pressure centres within the domain. Pressure centre displacements and the frequency redistribution among different centres were studied for each location. The 180‐year (January 1836–December 2015) mean sea level pressure dataset on the European domain with a 0.703° × 0.702° spatial and daily temporal resolution was obtained from NOAA 20th Century Reanalysis project. The presented method can apply the continental‐scale changes to specific locations. A significant increase of anticyclonic influence was found in southern and central Europe, in line with the northward displacement of Atlantic cyclones. Hundred and eighty year (180‐year) change of the number of days with anticyclonic versus cyclonic influence was found to be between +10% and 15% in the Mediterranean and +2% and 10% in most of Europe. The increasing anticyclonic influence in central Europe was strongest in the spring and the winter and was most attributable to the eastern European anticyclone. Results highlight the importance of research on the dynamical climate response of anticyclonic pressure systems. [ABSTRACT FROM AUTHOR]

Scenario–neutral methods are commonly used to rapidly compare system responses to changes in climate. Using glacier mass balance as a system response, we present a bottom-up, scenario–neutral method as an effective tool for preliminary and overview studies on glacier sensitivity and a complementary approach to traditional top–down methods. The method's main characteristic is its visual result: two–dimensional response surfaces depicting glacier mass balance. Their axes represent perturbations in temperature and precipitation relative to a baseline. The simplicity of our approach makes it applicable to all global glaciers. As a proof–of–concept, the Open Global Glacier Model (OGGM) is used to perform a scenario–neutral glacier sensitivity analysis for four glaciers. In addition, the integration with a top–down approach is demonstrated by overlaying temperature and precipitation from four Coupled Model Intercomparison Project Phase 6 (CMIP6) models, under four Shared Socioeconomic Pathways (SSP). Finally, the benefits of the method are discussed for decision–making and science communication. Assessing results shows that overall, this scenario–neutral method can provide useful information for the research of climate change impact on glacier mass, from aiding study design to science communication. [ABSTRACT FROM AUTHOR]

The research literature indicates that school climate is important for student outcomes; however, research assessing school climate and achievement-related outcomes across time is limited. In this study, the relationship between school climate, students' psychosocial perceptions, and student achievement was examined across an academic school year in a cohort of 531 9th-grade students. A series of hierarchical linear regressions indicated that school climate predicted 10.5% of the variance in both hope and academic motivation, 8% of the variance in self-efficacy, 5% of the variance in academic self-concept, 3% of the variance in goal valuation, and 2% of the variance in spring semester GPA. Further, despite school climate being operationalized as school culture, attitude toward teachers, and attitude toward school, only attitude toward teachers significantly predicted any of the outcome variables. These results suggest that some aspects of school climate contribute more than others in crafting students' psychosocial perceptions and achievement. Impact Statement Despite the widespread belief that school climate is important for student outcomes, the existing school climate research lacks investigations across time. This study assessed the relationship between school climate, academic-oriented psychosocial perceptions, and student achievement across an academic school year. This study's results indicate that attitude toward teachers was the only significant predictor of psychosocial perceptions and that hope and academic motivation were most influenced by school climate perceptions. [ABSTRACT FROM AUTHOR]

Precipitation records in North Patagonia (Argentina) are scarce, which hinders climate research. This research aims to assess the performance of four novel local monthly datasets together with three commonly used global datasets for North Patagonia to evaluate the advantages and disadvantages of each one. First, four different local observed-interpolated datasets were built, two using the Angular Distance Weighted (ADW) method and two using ordinary Kriging. In addition, the global datasets CRU, ERA5-Land, and GPCC were evaluated. To assess the performance of the precipitation datasets, four metrics were used to evaluate the systematic errors (bias), the mean errors, the representation of time variations, and the representation of the probability density function. The ERA5-Land with a correction factor stands out as the best global dataset and it also presents the overall best representation of the probability density function (PDF). The built dataset with ADW using a precipitation index presents the overall best performance, especially in representing the time variations. Even though ADW presents an overall better performance, ERA5-Land with a correction factor presents a better performance in terms of errors in the southern region (south of 40°S). The novel dataset is freely available through the link provided in the conclusions section. [ABSTRACT FROM AUTHOR]

In light of the rapid Arctic warming and continuous reduction in Arctic Sea ice, the complex two-way Arctic–midlatitudes connection has become a focal point in recent climate research. In this paper, we review the current understanding of the interactive influence between midlatitude atmospheric variability and Arctic Sea ice or thermal conditions on interannual timescales. As sea ice diminishes, in contrast to the Arctic warming (cooling) in boreal winter (summer), Eurasia and North America have experienced anomalously cold (warm) conditions and record snowfall (rainfall), forming an opposite oscillation between the Arctic and midlatitudes. Both statistical analyses and modeling studies have demonstrated the significant impacts of autumn–winter Arctic variations on winter midlatitude cooling, cold surges, and snowfall, as well as the potential contributions of spring–summer Arctic variations to midlatitude warming, heatwaves and rainfall, particularly focusing on the role of distinct regional sea ice. The possible physical processes can be categorized into tropospheric and stratospheric pathways, with the former encompassing the swirling jet stream, horizontally propagated Rossby waves, and transient eddy–mean flow interaction, and the latter manifested as anomalous vertical propagation of quasi-stationary planetary waves and associated downward control of stratospheric anomalies. In turn, atmospheric prevailing patterns in the midlatitudes also contribute to Arctic Sea ice or thermal condition anomalies by meridional energy transport. The Arctic–midlatitudes connection fluctuates over time and is influenced by multiple factors (e.g., continuous melting of climatological sea ice, different locations and magnitudes of sea ice anomalies, internal variability, and other external forcings), undoubtedly increasing the difficulty of mechanism studies and the uncertainty surrounding predictions of midlatitude weather and climate. In conclusion, we provide a succinct summary and offer suggestions for future research. [ABSTRACT FROM AUTHOR]

With the advancement of urbanization and acceleration of global warming, extreme weather and climate events are becoming increasingly frequent and severe, and climate risk continues to rise. Each community is irreplaceable and important in coping with extreme climate risk and improving urban resilience. In this study, the Dongsi Community in the functional core area of Beijing was explored, and the risk assessment of high temperatures and rainstorm waterlogging was implemented at the community scale. Local navigation observations were integrated into a theoretical framework for traditional disaster risk assessment. The risk assessment indicator system for community-scale high-temperature and rainstorm waterlogging disasters was established and improved from a microscopic perspective (a total of 22 indicators were selected from the three dimensions of hazard, exposure, and vulnerability). Geographic Information Systems (GIS) technology was used to integrate geographic information, meteorological, planning, municipal, socioeconomic and other multisource information layers, thus enabling more detailed spatial distribution characteristics of the hazard, exposure, vulnerability, and risk levels of community-scale high temperatures and rainstorm waterlogging to be obtained. The results revealed that the high-risk area and slightly high-risk area of high-temperature disasters accounted for 13.5% and 15.1%, respectively. The high-risk area and slightly high-risk area of rainstorm waterlogging disasters accounted for 9.8% and 31.6%, respectively. The high-risk areas common to high temperatures and waterlogging accounted for 3.9%. In general, the risk of high-temperature and rainstorm waterlogging disasters at the community scale showed obvious spatial imbalances; that is, the risk in the area around the middle section of Dongsi Santiao was the lowest, while a degree of high temperatures or rainstorm waterlogging was found in other areas. In particular, the risk of high-temperature and rainstorm waterlogging disasters along Dongsi North Street, the surrounding areas of Dongsi Liutiao, and some areas along the Dongsi Jiutiao route was relatively high. These spatial differences were affected to a greater extent by land cover (buildings, vegetation, etc.) and population density within the community. This study is a useful exploration of climate risk research for resilient community construction, and provides scientific support for the planning of climate-adaptive communities, as well as the proposal of overall adaptation goals, action frameworks, and specific planning strategies at the community level. [ABSTRACT FROM AUTHOR]

The CICERO Simple Climate Model (CICERO-SCM) is a lightweight, semi-empirical model of global climate. Here we present a new open-source Python port of the model for use in climate assessment and research. The new version of CICERO-SCM has the same scientific logic and functionality as the original Fortran version, but it is considerably more flexible and also open-source via GitHub. We describe the basic structure and improvements compared to the previous Fortran version, together with technical descriptions of the global thermal dynamics and carbon cycle components and the emission module, before presenting a range of standard figures demonstrating its application. A new parameter calibration tool is demonstrated to make an example calibrated parameter set to span and fit a simple target specification. CICERO-SCM is fully open-source and available through GitHub (https://github.com/ciceroOslo/ciceroscm , last access: 23 August 2024). [ABSTRACT FROM AUTHOR]

Paleoclimate model simulations provide reference data to help interpret the geological record and offer a unique opportunity to evaluate the performance of current models under diverse boundary conditions. Here, we present a dataset of 35 climate model simulations of the warm early Eocene Climatic Optimum (EECO; ~ 50 million years ago) and corresponding preindustrial reference experiments. To streamline the use of the data, we apply standardised naming conventions and quality checks across eight modelling groups that have carried out coordinated simulations as part of the Deep-Time Model Intercomparison Project (DeepMIP). Gridded model fields can be downloaded from an online repository or accessed through a new web application that provides interactive data exploration. Local model data can be extracted in CSV format or visualised online for streamlined model-data comparisons. Additionally, processing and visualisation code templates may serve as a starting point for advanced analysis. The dataset and online platform aim to simplify accessing and handling complex data, prevent common processing issues, and facilitate the sharing of climate model data across disciplines. [ABSTRACT FROM AUTHOR]

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Through synthesizing contemporary campus climate and digital campus climate research, we conceptualize a Model of Digital Campus Climate for higher education leaders to assess their institutions' equity and inclusivity. Our digital campus climate model accounts for platforms institutions use in communicating information, dynamic levels existing in digital spaces, and five factors shaping digital spaces: accessibility, depth, engagement, relevance, and representation. Recommendations will guide practitioners to reenvision their digital spaces to promote equity. [ABSTRACT FROM AUTHOR]

Six ancestral Pueblo community water supply sources were investigated by a team of engineers, scientists, archeologists, and other specialists affiliated with Wright Water Engineers, Inc. (WWE), and the Wright Paleohydrological Institute (WPI) from 1996 to 2021. The team members applied their various technical backgrounds and research methods to gain more insight into the water available to the ancestral Pueblo people living in the Four Corners area of the United States between 750 and 1280 CE, and how these indigenous people managed the water. Using lab analyses, field research, surveys, and analyses of sediment layers, the WWE/WPI team determined that four mounded areas discovered at Mesa Verde National Park had been ancestral Pueblo reservoirs. Through climate research, lab analyses, and investigations at these and two other sites, the team learned that water in this region was limited, and the community had to work diligently to harvest this water and maintain access to it. In the case of the four reservoirs studied, for example, the runoff used as water supply carried a high volume of sediment that required the water storage basins to be frequently dredged to maintain adequate capacity. These and other examples indicate that the ancestral Pueblo people were resourceful, hardworking, and organized water harvesters. [ABSTRACT FROM AUTHOR]

Unpredicted observations in the climate system, such as recent excessive ocean warming, are often lacking immediate causal explanations and are challenging numerical models. As a highly advanced mathematical tool, the Thermodynamic Equation of Seawater – 2010 (TEOS-10) was established by international bodies as an interdisciplinary standard and is recommended for use in geophysics, such as, and in particular, in climate research. From its very beginning, the development of TEOS-10 was supported by Ocean Science through publishing successive stages and results. Here, the history and properties of TEOS-10 are briefly reviewed. With focus on the air–sea interface, selected current problems of climate research are discussed, and tutorial examples for the possible use of TEOS-10 in the associated context are presented, such as topics related to ocean heat content, latent heat, and the rate of marine evaporation; properties of sea spray aerosol; or climatic effects of low-level clouds. Appended to this article, a list of publications and their metrics is provided for illustrating the uptake of TEOS-10 by the scientific community, along with some continued activities, addressing still pending, connected issues such as uniform standard definitions of uncertainties of relative humidity, seawater salinity, or pH. This article is dedicated to the jubilee celebrating 20 years of Ocean Science. This article is also dedicated to the memory of Wolfgang Wagner, who sadly and unexpectedly passed away on 12 August 2024. His contributions to TEOS-10 are truly indispensable constituents; Wolfgang was an essential co-author of various related documents and articles. He will be deeply missed. All the rivers run into the sea; yet the sea is not full; unto the place from whence the rivers come, thither they return again. The King James Bible: Ecclesiastes, 450–150 BCE He wraps up the waters in his clouds, yet the clouds do not burst under their weight. Holy Bible: New International Version, Job 26:8 Of the air, the part receiving heat is rising higher. So, evaporated water is lifted above the lower air. Leonardo da Vinci: Primo libro delle acque, Codex Arundel, ca. 1508 Two-thirds of the Sun's energy falling on the Earth's surface is needed to vaporize ... water ... as a heat source for a gigantic steam engine. Heinrich Hertz: Energiehaushalt der Erde, 1885 The sea-surface interaction is obviously a highly significant quantity in simulating climate. Andrew Gilchrist and Klaus Hasselmann: Climate Modelling, 1986 The climate of the Earth is ultimately determined by the temperatures of the oceans. Donald Rapp: Assessing Climate Change, 2014 [ABSTRACT FROM AUTHOR]

This article provides, firstly, an overview of Arctic traditional horse herding as one of the Indigenous subsistence practices of the Republic of Sakha (Yakutia). It discusses the origins, characteristics, and spiritual and material importance of Sakha horses and horse herding practices to inform the overall understanding of this traditional subsistence activity, which remains largely unexplored. Secondly, by conducting in-depth semi-structured interviews with Indigenous Sakha horse herders, this study explores the ways in which Indigenous subsistence practices are evolving and reacting to the climate and environmental changes. Results show that climate change is altering the local ecosystem and introducing new challenges to communities in Central Yakutia. Local herders describe climate change as a complex interplay of diverse transformations rather than a singular phenomenon. While historical adaptation strategies relied on the flexibility of traditional practices, today, this flexibility is often hindered by non-climatic factors. This article further discusses adaptability of Indigenous practices to climate change and offers recommendations for their development, particularly traditional horse herding. Future research related to climate change and Arctic Indigenous communities should encompass deeper and broader aspects, covering historical, cultural, social, and economic contexts and the worldviews of Indigenous peoples, distinct from Western perspectives. [ABSTRACT FROM AUTHOR]

Currently, the "spring predictability barrier" (SPB) is still a controversial problem in many atmosphere–ocean coupled models and has significant impacts on degrading the El Niño–Southern Oscillation (ENSO) predictions across the boreal spring. In this study, unlike previous studies that viewed the SPB issue from the perspective of sea surface temperature (SST), based on the Bjerknes feedback theory and the decadal variations in Walker circulation over the tropical Pacific, a new perspective of the SPB is revealed by the seasonal variations in the observed zonal sea level pressure (SLP) gradient, which can reflect the stability and variability of the atmosphere–ocean interactions during the ENSO's evolution. More importantly, a significant decadal variation of SPB strength (SPBS) is exhibited in the last 3 decades, from 1991 to 2020, which is strongly controlled by the dominant patterns of sea surface temperature (SST) and Walker circulation, and associated with the background mean atmosphere–ocean states. That is to say, the atmosphere–ocean interaction pattern over the tropical Pacific has undergone decadal variations over the past 3 decades which determine the decadal variations in SPBS. International Research Institute for Climate and Society/Climate Prediction Center (IRI/CPC) multi-models show stronger SPBS during 2001–2010 than during 2011–2020, indicating that the decadal variations in SPBS from statistical analysis also exist in actual model predictions, which further confirms the rationality of this perspective of SPB based on the zonal SLP gradient. [ABSTRACT FROM AUTHOR]

Background: Patient safety climate constitutes an important element for quality improvement. Its current evidence base has been generated in hospital settings in developed countries. Studies in dentistry are limited. Objective: To systematically explore the evidence regarding assessing patient safety climate in dentistry. Methods: We developed a search strategy to explore MEDLINE, SCOPUS, and Web of Science databases from January 1st, 2002, to December 31st, 2022, to include observational studies on patient safety culture or patient safety climate assessment. Methodological features and item data concerning the dimensions employed for assessment were extracted and thematically analyzed. Reported scores were also collected. Results: Nine articles out of 5584 were included in this study. Most studies were generated from high‐income economies. Our analysis revealed methodological variations. Non‐randomized samples were employed (ranging from 139 to 656 participants), and response rates varied from 28% to 93.7%. Three types of measurement instruments have been adapted to assess patient safety climate. These mainly consisted of replacing words or rewording sentences. Only one study employed an instrument previously validated through psychometric methods. In general, patient safety climate levels were either low or neutral. Only one study reported scores equal to or greater than 75. Discussion: Despite diverse assessment tools, our two‐decade analysis reveals a lag compared with medicine, resulting in methodological variations for assessing patient safety climate. Collaboration is vital to elevate standards, prioritize patient safety across oral healthcare services, and advocate for integrating safety climate into local and national quality and patient safety strategies. [ABSTRACT FROM AUTHOR]

Significant advancements in satellite‐based precipitation retrieval algorithms have led to the development of continuous, quasi‐global precipitation products, offering unique opportunities for hydrometeorological and climate research. In this context, six satellite‐based precipitation products (SPPs), including CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were thoroughly investigated for their application in hydrological simulations over the Wardha River basin in India. The observed gridded precipitation product developed by India Meteorological Department (IMD) has been used as reference data to evaluate the performance of SPPs. Hydrological variables such as runoff, soil moisture (SM) and evapotranspiration (ET) were simulated using the Variable Infiltration Capacity model. The performances of SPPs are critically assessed using various statistical metrics, including Pearson's correlation coefficient (R), Klinga‐Gupta Efficiency (KGE), Percent Bias (PBIAS), root mean square error (RMSE), and the RMSE to standard deviation ratio (RSR). The model‐simulated discharge was compared with streamflow observations at a single gauging site, while a spatially distributed comparison was conducted between simulated SM and ET and satellite‐based SM and ET. The IMD dataset consistently shows superior performance for discharge simulation at both daily and monthly scales, with KGE values of 0.85 and 0.91, respectively. Among SPPs, MSWEP and CHIRPS excel in simulating daily and monthly discharge, respectively. For ET simulation, CHIRPS outperforms IMD and other SPPs, achieving an overall KGE value of 0.37. In contrast, PERSIANN is the most effective for simulating SM compared to other precipitation products. [ABSTRACT FROM AUTHOR]

Salinity is an essential variable for characterizing and understanding the state of the ocean and its role in the climate system. Gridded ocean salinity products, heavily reliant on Argo float measurements since the early 2000s, are widely used in oceanographic and climate research. However, a concerning issue of instrument drift leading to spurious salinity increases ("salty drift") has been identified in a significant number of Argo floats since 2015. This study investigates the potential consequences of this "salty drift" issue on various gridded salinity products. We compare a suite of these products and evaluate their consistency, particularly from 2015 to 2019. Our analysis reveals two major issues with the gridded salinity products after 2015: a sudden increase in global mean salinity and elevated inconsistencies between gridded salinity products. In 2015–2019, the North Indian and North Atlantic Oceans emerged as regions displaying particularly large disagreements between gridded products compared to the prior period, 2010–2014. These findings highlight the substantial impact of the "salty drift" on the reliability of the gridded salinity products. They also underline the critical need for the oceanography community to address these issues to ensure the validity of future ocean and climate studies that utilize gridded salinity products. Plain Language Summary: Salinity is crucial for understanding our planet's oceans and climate. Since 2015, some Argo floats have produced inaccurate salinity measurements that drift to higher values. This study examined several widely used ocean salinity products that were produced primarily based on Argo float measurements to assess how they are affected by the "salty drift" floats. We found that between 2015 and 2019, most of the examined products showed unrealistic increases in global mean salinity and became less consistent than before. Regions most likely affected include the North Indian and North Atlantic Oceans. This "salty drift" issue can impact previous and future global and regional studies using gridded salinity products after 2015 and needs further detailed study by the oceanography and climate communities. Key Points: Multiple gridded salinity products show an unrealistic rise in global mean salinity after 2015, with increases of up to 1.3 × 10−3 per yearThe spread of global mean salinity tripled between 2015 and 2019, translating to an uncertainty of about 1 cm in halosteric sea levelAfter 2015, over 60% of the global ocean, including the North Atlantic and North Indian Oceans, exhibited increased uncertainty in salinity change [ABSTRACT FROM AUTHOR]

Climate change and physical activity have emerged as focal points of contemporary research. This study adopts a scientometric approach to review 1,268 bibliographic records and 70,522 citation records from the WoS Core Collection database, aiming to outline the research domains of climate change and physical activity through co-authorship, co-citation, and keyword co-occurrence analyses. Our findings highlight that: firstly, the research landscape is predominantly shaped by developed nations in Europe and North America, with the USA, Canada, and England being key contributors; secondly, the top ten authors in this field are spotlighted based on co-citation frequency, burst intensity, and centrality. Additionally, we identified four primary research themes and further unveiled the evolving research hotspots, delineating two promising research trajectories. In conclusion, potential future directions for research in climate change and physical activity are proposed. Furthermore, the study's principal contributions and limitations are elaborated upon, acknowledging the constraints imposed by utilized tools and data sources, and the influence of the researchers' expertise on result interpretation. [ABSTRACT FROM AUTHOR]