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Source camera identification can verify whether two videos were shot by the same device, which is of great significance in multimedia forensics. Most existing identification methods use convolutional neural networks to learn sensor noise patterns to identify the source camera in closed forensic scenarios. While these methodologies have achieved remarkable results, they are nonetheless constrained by two primary challenges: (1) the interference of semantic information and (2) the incongruity in feature distributions across different datasets. The former will interfere with the extraction of effective features of the model. The latter will cause the model to fit the characteristic distribution of the training data and be sensitive to unseen data features. To address these challenges, we propose a novel source camera identification framework that determines whether a video was shot by the same device by obtaining similarities between source camera features. Firstly, we extract video key frames and use the integral image to optimize the smoothing blocks selection algorithm of inter-pixel variance to remove the interference of video semantic information. Secondly, we design a residual neural network fused with a constraint layer to adaptively learn video source features. Thirdly, we introduce a triplet loss metric learning strategy to optimize the network model to improve the discriminability of the model. Finally, we design a multi-dimensional feature vector similarity fusion strategy to achieve highly generalized source camera recognition. Extensive experiments show that our method achieved an AUC value of up to 0.9714 in closed-set forensic scenarios and an AUC value of 0.882 in open-set scenarios, representing an improvement of 5% compared to the best baseline method. Furthermore, our method demonstrates effectiveness in the task of deepfake detection. [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]

Tree-ring blue intensity (BI) has the potential to provide information on past summer temperatures of a similar quality to that of tree-ring maximum latewood density and at a substantially reduced cost. To explore the applicability of BI in subtropical regions, the inverted BI for the earlywood, latewood, and the delta BI (DBI) parameters, together with tree-ring width of subalpine fir (Abies fargesii Franch.) in the Shennongjia area of China, were measured, and the corresponding chronologies were developed. The relationships between these chronologies and monthly precipitation, monthly mean minimum temperature, monthly mean temperature, and monthly mean maximum temperature were explored through correlation analysis. Results show that the DBI chronology is closely related to the temperature in August of the current year, indicating that BI, specifically delta BI, data are suitable for use in dendroclimatology studies in subtropical areas. The resultant mean temperature reconstruction for August, based on DBI, explains 40.8% of the temperature variance and is robustly validated using independent periods from the calibration. This pilot study not only highlights the potential of DBI for temperature reconstruction in China but also offers valuable insights into historical climate variations in the Shennongjia region. Moreover, it shows the potential for utilizing such tree-ring data from low-latitude regions to derive past climate data in subtropical warm-humid zones. [ABSTRACT FROM AUTHOR]

Operational forecasters desire information about how their reservoir and riverine systems will evolve over monthly to seasonal timescales. Seasonal traces of hydrometeorological variables at a daily or sub-daily resolution are needed to drive hydrological models at this timescale. Operationally available models such as the Climate Forecast System (CFS) provide seasonal precipitation forecasts, but their coarse spatial scale requires further processing for use in local or regional hydrologic models. We focus on three methods to generate such forecasts: (1) a bias-adjustment method, in which the CFS forecasts are bias-corrected by ground-based observations; (2) a weather generator (WG) method, in which historical precipitation data, conditioned on an index of the El Niño–Southern Oscillation, are used to generate synthetic daily precipitation time series; and (3) a historical analog method, in which the CFS forecasts are used to condition the selection of historical satellite-based mean areal precipitation (MAP) traces. The Yaque del Norte River basin in the Dominican Republic is presented herein as a case study, using an independent dataset of rainfall and reservoir inflows to assess the relative performance of the methods. The methods showed seasonal variations in skill, with the MAP historical analog method having the strongest overall performance, but the CFS and WG methods also exhibited strong performance during certain seasons. These results indicate that the strengths of each method may be combined to produce an ensemble forecast product. [ABSTRACT FROM AUTHOR]

Warming in southern California during the 21st century is unprecedented in the instrumental record. To place this warming in a multi-century historical context, we analyzed tree ring data sampled from Jeffrey pine (Pinus jeffreyi) and sugar pine (Pinus lambertiana) collected from minimally disturbed, old-growth high-elevation forests within Mt. San Jacinto State Park California, USA. Based on a calibration/verification period of 1960–2020 between earlywood radial growth and California Climate Division 6 climate data, we reconstructed annual (November–October) minimum temperature (Tmin) from 1658 to 2020. During the 61-year calibration/verification period, instrumental Tmin increased (r = 0.69, p < 0.01) and was positively associated with annual radial growth (r = 0.71, p < 0.01). Using regime shift analysis, we found that the 363-year reconstruction revealed Tmin stability until 1958 and then decreased until 1980, followed by the two warmest regimes (1981–2007, 2008–2020) on record. The last 13-year period was 0.77 °C warmer than the multi-century average with nine of the ten warmest years in the reconstruction recorded. These results suggest that 21st century warming in southern California is unique in the context of the past four centuries, indicating the rarity of exceptional warmth captured in the tree ring record. [ABSTRACT FROM AUTHOR]

This paper presents a quantitative assessment of the value of hydroelectric power plants (HPPs) in power systems with a significant penetration of variable renewable energy sources (VRESs). Through a capacity expansion planning (CEP) model that incorporates a detailed representation of HPP operating principles, the study investigates the construction and application of HPP rule curves essential for seasonal operation. A comparative analysis is also conducted between the proposed rule curve formulation and alternative modeling techniques from the literature. The CEP model optimizes installed capacities per technology to achieve predefined VRES penetration targets, considering hourly granularity and separate rule curves for each HPP. A case study involving twelve reservoir hydropower stations and two open-loop pumped hydro stations is examined, accounting for standalone plants and cascaded hydro systems across six river basins. The study evaluates the additional generation and storage required to replace the hydropower fleet under high VRES penetration levels, assessing the resulting increases in total system cost emanating from introducing such new investments. Furthermore, the study approximates the storage capabilities of HPPs and investigates the impact of simplified HPP modeling on system operation and investment decisions. Overall, the findings underscore the importance of reevaluating hydro rule curves for future high VRES penetration conditions and highlight the significance of HPPs in the energy transition towards carbon neutrality. [ABSTRACT FROM AUTHOR]

Infragravity (IG) waves are low-frequency water waves, which can propagate into harbors and estuaries, affecting currents and sediment transport processes. Understanding and predicting IG oscillations inside harbors and estuaries is critical to coastal management and estimating future resilience to climate change impacts. High-resolution water level and flow velocity observations collected within Seal Beach Wildlife Refuge in Southern California are analyzed for IG energy related to atmospheric parameters, water levels, and offshore wave conditions. A proof of concept approach for predicting infragravity oscillations within an estuary using machine learning (ML) is presented. [ABSTRACT FROM AUTHOR]

The presented paper is further focused on the presentation and subsequent assessment of utilising a proposed Neural Network (NN) with simple architecture in the role of a signal preprocessing algorithm for the Constant False Alarm Rate detector and the fixed threshold detector applied on a Range‐Doppler (RD) map with the aim of radar clutter impact reduction and minimisation of processing time. Based on a comparison of all tested algorithm results, it is possible to state that utilising the proposed NN with simple architecture led to reducing the impact of radar clutter when detecting radar targets on RD maps created from provided datasets. Comparing the mean processing time tmean values of all tested algorithms, the authors can state that employing the proposed NN in combination with the fixed threshold detector led to a significant improvement in the computation time needed for processing one RD map while preserving the suppression of radar clutter and detection of the radar targets. [ABSTRACT FROM AUTHOR]

The probabilistic forecast skill level of statistically downscaled European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal‐to‐seasonal (S2S) forecasts is determined in predicting maximum and minimum temperatures for weeks 1–4 lead times during 20‐year December–January–February (DJF) seasons from 2001 to 2020 over South Africa. Skilful S2S forecasts are vital in assisting decision‐makers in the development of contingency planning for any eventualities that may arise because of weather and climate phenomena. Extreme high‐ and low‐temperature events over a prolonged period can lead to hyperthermia and hypothermia, respectively, and can lead to loss of life. The results from the relative operating characteristic (ROC) and reliability diagrams indicate that the ECMWF S2S model has skill in predicting maximum temperature up to week 3 ahead, particularly over the central and eastern parts of South Africa. The ROC scores indicate that the model has skill in predicting minimum temperature up to week 4 ahead for the above‐normal category, particularly over the central and eastern parts of South Africa. Reliability diagrams indicate that the model has a tendency of overestimating the below‐normal category when predicting both maximum and minimum temperatures for weeks 1–4 lead times over South Africa. Furthermore, canonical correlation analysis (CCA) pattern analysis suggests that when there are anomalously positive and negative predicted 850‐hPa geopotential heights located over South Africa, there are anomalously hot and cold conditions during the DJF seasons over most parts of South Africa, respectively. These results suggests that statistical downscaling of model forecasts can improve forecast skill. Moreover, the results suggest that there is potential for S2S predictions in South Africa, and as such, S2S prediction system for maximum and minimum temperatures can be developed. [ABSTRACT FROM AUTHOR]

Global climatic circulation indices play a major role in determining regional and global climate conditions. These atmospheric circulation patterns exhibit substantial variability, covering a wide geographical area and affecting weather-related events. The primary goal of this study was to examine and characterize various global climatic variability indices during the 1950 to 2020 period (El Niño Southern Oscillation, ENSO; Southern Oscillation Index, SOI; North Atlantic Oscillation, NAO; Atlantic Meridional Mode, AMM; and Indian Ocean Dipole, IOD). Also, this article try to investigating the link between these global climatic indices. Trend analysis showed that the ENSO index exhibits the highest recurrence frequency of correlation relationships with the other yearly global indices with significance at the 95% and 99% levels, while the NAO index exhibits the lowest recurrence frequency. On a seasonal basis, most indices demonstrate more abrupt changes during the winter season than during the summer. An increase occurred in events of abrupt changes in these indices over the last two decades (2000 to 2020), especially annually and in summer. The SOI exhibits the largest number of abrupt changes throughout the entire study period, spanning from positive to negative significant trends, whereas the IOD did not exhibit abrupt changes annually. Increasing and decreasing trends in the global climatic circulation indices may be related to natural and anthropogenic causes of climate change. Regarding both the correlation coefficient (CC) and partial correlation results, there existed a highly negative association between the ENSO and SOI in the annual, winter and summer time series. On the other hand, there is no relationship between ENSO and NAO. Furthermore, on an annual basis, there existed a highly negative association between the NAO and AMM and a less negative but still statistically significant association between these indices during the winter and summer seasons, respectively. Therefore, through the Azore high, the NAO could promote AMM. Moreover, when the NAO, AMM, and SOI are held constant, a positive and robust correlation is reached between the ENSO and IOD in winter season. Therefore, a developing IOD is intensified and sustained during the onset of an El Niño event in winter season. [ABSTRACT FROM AUTHOR]

Earth system predictions, from sub-seasonal to seasonal timescales, remain a challenging task, and the representation of predictability sources on seasonal timescales is a complex work. Nonetheless, advances in technology and science have been making continuous progress in seasonal forecasting. In a previous paper, a performance for temperature prediction by a modelling system named e-kmf® was carried out in comparison with observations and climatology for a year of data; a low level of predictability in the sub-seasonal range, particularly in the second month, was observed over the Italian peninsula. Therefore, in this study, we focus our investigations specifically on the performance between the fifth and the eighth week of temperature forecasts over six years of simulations (2012–2018) to investigate the capability of the weather model to better reproduce the behavior of temperatures in the second month of the forecast. Although some differences in seasons are present, results have globally shown how temperature predictions have the potential to be quite skillful, with an average skill score of about 68%, with climatology used as reference; additionally, an overall anomaly correlation coefficient equal to 0.51 was shown, providing useful information for applications in planning, sales, and supply of natural energy resources. [ABSTRACT FROM AUTHOR]

Climate change has far-reaching impacts that have created new challenges for water resource management. As an important measure to coordinate the relationship between society, economy, and environment, reservoir scheduling can reduce the future impact of climate change. It is, therefore, important to investigate the impacts of scheduling on reservoir operation. In this study, a reservoir system in the Pearl River Basin was selected to explore these impacts. Results show that the basin temperature significantly and abruptly increased in 2000, and precipitation and streamflow changed abruptly in 1983 and 1992. Historically, climate change has increased power generation, increased the risks to water supply security and ecological protection, and altered the relationship between power generation and ecological protection objectives. Based on 28 global climate models, the rank scoring method, Delta statistical downscaling, and the SWAT model, three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) in CMCC-CM were evaluated to assess the climate change impact. In the future, the temperature will continue to exhibit an increasing trend, and the amount and distribution of streamflow will be altered. Although climate change will increase power generation in the dry season, it will also bring about new challenges for ecological protection and water supply security. Accordingly, the Datengxia Reservoir operating rules may require significant amendments. [ABSTRACT FROM AUTHOR]

This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information. Methods: This retrospective study included 86 adult Hispanic men (60 ± 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL2) with PCa who underwent prebiopsy 3T MRI followed by targeted MRI–ultrasound fusion and systematic biopsy. Two observers performed 2D segmentation of lesions in T2WI/ADC images. We classified csPCa (GS ≥ 7) vs. non-csPCa (GS = 6). Univariate statistical tests were performed for different parameters, including prostate volume (PV), PSA-D, PI-RADS, and radiomic features. Multivariate models were built using the automatic feature selection algorithm Recursive Feature Elimination (RFE) and different classifiers. A stratified split separated the train/test (80%) and validation (20%) sets. Results: Radiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78). Conclusion: Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics' (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients. [ABSTRACT FROM AUTHOR]

Many global climate models, including the Climate Forecast System version 2 (CFSv2), have a biased representation of subseasonal modes of variability of the Indian summer monsoon. For instance, they simulate a weaker summer mean monsoon low‐pressure systems (LPS) climatology, faster than observed northward propagation of monsoon intraseasonal oscillations (MISOs), and a systematic dry bias over Indian landmass. The Bay of Bengal (BoB), with its shallow mixed layers and unique thermal stratification, significantly modulates the convective activity in this region at subseasonal‐to‐seasonal timescales through modulation of sea surface temperature. The highly stratified upper ocean in the BoB is due to the enormous freshwater it receives from rains and rivers. A river routing model is coupled to the CFSv2 to account for the riverine freshwater and the improvements in modelling the upper‐ocean structure are analysed. Model simulations indicate that inclusion of temporally varying riverine freshwater improves the upper‐ocean state in the BoB and the observed mixed‐layer temperature gradients in the Bay are simulated reasonably after incorporating the time varying river runoff. This resulted in increased LPS lifetime and track density, and enhanced rainfall over central India. Better representation of the upper‐ocean stratification in the model leads to larger post‐convection shoaling of mixed layers at intraseasonal timescales, thereby forming thick barrier layers. Enhanced air–sea interactions restricted to the shallow mixed layer are associated with stronger vorticity, specific humidity and low‐level convergence to the north of the intraseasonal convection band. This enhanced low‐level moisture convergence north of the convection centre results in realistic northward propagation of MISO and aids LPS activity. It is demonstrated that better simulation of the upper‐ocean structure in coupled climate models can improve the representation of subseasonal modes of monsoon variability. These results bear important implications for operational forecasting. [ABSTRACT FROM AUTHOR]

The probabilistic prediction skill of the weekly forecasts of extreme cold events (ECE) is illustrated and measured in the form of the Brier Skill Score (BSS) and the area under Relative Operating Characteristics (ROC) curves based on the subseasonal-to-seasonal (S2S) prediction project database. The ROC scores show that six S2S models have the good potential predictability skill required for use in ECE probabilistic forecasts, and they were more useful than climatologic probabilistic models in creating forecasts of about 3–4 weeks in length. However, the BSS results show that the actual prediction skill of six models used in ECE probabilistic forecasts are different. The ECMWF model has a good performance, and its actual probabilistic prediction skill of ECE for forecasts of about 3–4 weeks in length was higher than those of climatology, which operates close to its potential predictability. The actual probabilistic prediction skill of the NCEP model for ECE was only about 2 weeks over the extra-tropics, and no skill was recorded over the tropics given its bad reliability, especially over the tropics. BoM, JMA, and CNRM models only have a 1-week actual prediction skill over the Northern Hemisphere extra-tropics, and they have no skill over the rest of the world's land area. The CNR-ISAC model has a 1-week actual prediction skill over the extra-tropics and about 4 weeks over the tropics. There is still much room for improvement in the prediction ability of models used for ECE. MJO in tropical regions has an important influence on the probabilistic prediction skill of ECE required at middle and high latitudes. When there is an MJO in the initial conditions, the potential predictability and actual prediction skill of ECE probabilistic forecasts over North America in the 3rd week and over Europe in the 3rd–4th weeks are higher than those without MJO. [ABSTRACT FROM AUTHOR]

Recent proliferation of GPS technology has transformed animal movement research. Yet, time-series data from this recent technology rarely span beyond a decade, constraining longitudinal research. Long-term field sites hold valuable historic animal location records, including hand-drawn maps and semantic descriptions. Here, we introduce a generalised workflow for converting such records into reliable location data to estimate home ranges, using 30 years of sleep-site data from 11 white-faced capuchin (Cebus imitator) groups in Costa Rica. Our findings illustrate that historic sleep locations can reliably recover home range size and geometry. We showcase the opportunity our approach presents to resolve open questions that can only be addressed with very long-term data, examining how home ranges are affected by climate cycles and demographic change. We urge researchers to translate historical records into usable movement data before this knowledge is lost; it is essential to understanding how animals are responding to our changing world., (© 2024 The Author(s). Ecology Letters published by John Wiley & Sons Ltd.)

The Greater Horn of Africa (GHA) is highly vulnerable to climate and weather hazards such as drought, heat waves, and floods. There is a need for accurate seasonal forecasts to prepare for risks (such as crop failure and reduced grazing opportunities) and take advantage of favorable conditions (rains arrive on time and where they are needed) when they arise. As such, information at finer spatial scales than current state‐of‐the‐art global prediction models can provide is needed. Dynamical downscaling is one method employed to obtain information at finer scales. However, providers of seasonal forecasts over the GHA are hampered by limited computational resources and time constraints that restrict the number of global model ensemble members that can be downscaled. Some ensemble subselection criteria must be employed. Currently, providers take an uninformed (or random) approach. Specifically, forecasters simply take the first ensemble member of the global model seasonal forecast ensemble. However, recent work, focused on decadal prediction, has shown that subselecting global model ensemble members in an informed way, that is, according to their ability to reproduce key features of the climate system, results in improved predictions. This emerges from the fact that the climate system is likely more predictable than our models would have us believe. Seeing an opportunity for improvement, we apply the same thinking to the seasonal context and assess several procedures for subselecting ensemble members from seasonal predictions with exchangeable members. Such informed subselections have the potential to take advantage of information in an ensemble of global simulations that might be missed by random selection. Three subselection methods are investigated, with a focus on seasonal predictions for rainfall over GHA. We demonstrate that informed subselection leads to systematically higher skill than random subselection. We find that (1) for small subsample sizes, such as would be chosen for dynamical downscaling and/or downstream impact modeling, informed subselection nearly always outperforms random subselection, (2) subselecting based on well‐known teleconnections benefits those seasons in which such pathways are active, such as OND and JJAS, and (3) k$$ k $$‐means subselection outperforms random selection for small ensemble sizes throughout all seasons, including the notoriously difficult to predict MAM season. These techniques require only input that is available at the time of the forecast release and are easy to apply operationally. [ABSTRACT FROM AUTHOR]

Understanding the influence of the El Niño–Southern Oscillation (ENSO) on the North Atlantic Oscillation (NAO) is of critical significance for seasonal prediction. The present study found that both Niño3.4 sea surface temperature anomaly (SSTA) intensity and east-west gradient in the mid-low latitude Pacific determine the linkage between ENSO and the NAO. Based on Niño3.4 SSTA intensity and the east-west gradient, ENSO events are classified into three types: strong intensity, weak intensity-strong gradient (WSG), and equatorial ENSOs. Note that the former two types are usually concurrent with a strong zonal SSTA gradient. In contrast, equatorial ENSO is often associated with weak intensity-weak gradient SSTAs confined in the equatorial Pacific. The anomalous circulation patterns in response to the three types of ENSO exhibit asymmetric features over the North Atlantic. The WSG-El Niño associated circulation anomaly resembles a negative NAO-like pattern, yet the strong and equatorial El Niño associated circulation anomalies show a neutral-NAO pattern. For La Niña events, their impact on the NAO mainly depends on the cold SSTA position rather than their intensity. The strong and WSG-La Niña associated negative SSTAs are centered in the equatorial-central Pacific and favor a steady positive NAO-like anomaly. The cold SSTA center of equatorial La Niña shifts to the equatorial-eastern Pacific and cannot profoundly influence the North Atlantic climate. The physical mechanisms are also investigated with a general circulation model. [ABSTRACT FROM AUTHOR]

Extreme events contribute significantly to rainfall variability in semi‐arid regions like South Africa. Here, following the definition of a novel typology of rainfall extremes, disentangling large‐ and small‐scale events in Part I, we use quality‐controlled observational databases in South Africa, the ERA5 reanalysis and satellite estimates TRMM‐3B42 to examine the relationship between these two types of rainfall extremes and different modes of climate variability at various timescales. At low frequencies, rainfall extremes are assessed at interannual (IV: 2–8 years) and quasi‐decadal (QDV: 8–13 years) timescales, which are primarily associated with the El Niño–Southern Oscillation (ENSO) and the Interdecadal Pacific Oscillation (IPO), respectively. At subseasonal timescales, the typology of rainfall extremes is analysed depending on the synoptic configurations, as inferred by seven convective regimes including tropical temperate troughs (TTTs: 3–7 days), and the intraseasonal variability associated with the Madden–Julien Oscillation (MJO: 30–60 days). At the IV timescale, the occurrence of large‐scale extremes is substantially higher during its wet phases thereby suggesting a 400% rise in the occurrence of large‐scale extremes as compared to its dry phases. At the QDV timescale, variability mostly relates to the modulation of small‐scale extremes during its wet phases. Teleconnections with global sea surface temperature (SST) confirm that La Niña conditions favour overall wet conditions and extremes in South Africa. The numbers of large‐scale extremes are consistently related to warmer SSTs in the North Atlantic, while their link with warmer Indian and tropical South Atlantic oceans is found to be statistically independent of the state of ENSO. At the subseasonal timescales, large‐scale extremes largely occur during three out of the seven convective regimes identified in the southern African region whereas small‐scale extremes are nearly equiprobable during all convective regimes. The occurrence of large‐scale extremes during continent‐rooted TTT is further enhanced during the locally wet phases of the MJO and is symmetrically weakened during its dry phases. [ABSTRACT FROM AUTHOR]

Characteristics and physical features related to low precipitation across many years in Southern Africa that lead to societal disruptions are diagnosed using observed analyses and an ensemble of historical coupled climate model simulations during 1921 to 2014. Four regions are evaluated, as identified through a hierarchical clustering algorithm applied to the Standardized Precipitation Index (SPI) during the October–April precipitation season. Although dryness spanning many October–April occurs periodically in each region, they seldom occur simultaneously, consistent with largely insignificant SPI cross‐correlations between them. However, characteristics relevant to low precipitation across many years are generalizable between the four regions, including the serial persistence of October–April precipitation, the likelihood of consecutive dry October–April, and the likelihood of dry October–April in temporal extents of up to 10 consecutive such 7‐month seasons. Systematic precipitation persistence is not a feature in any of the four Southern Africa regions, as serial correlations of October–April SPI are not statistically significant at any time lags. It follows that there is an exponential‐folding decay in the likelihood of consecutive October–April for various SPI thresholds and that there is a large spread in the likelihood of low October–April SPI across many years. In terms of physical features, low October–April SPI in each Southern Africa region is closely related to local atmospheric circulations; however, they are not as closely related to sea surface temperatures (SSTs). These results suggest that dryness spanning many years is determined primarily by persistent local circulations related to atmospheric variability and to a lesser extent variability related to SST anomalies, including the El Niño–Southern Oscillation. [ABSTRACT FROM AUTHOR]

Sea surface temperatures and vertical wind shear are essential to tropical cyclone formation. TCs need warm SSTs and low shear for genesis. Increasing SSTs and decreasing VWS influences storm development. This work analyzes SST and VWS trends for the Caribbean, surrounding region, and the Atlantic hurricane main developing region from 1982–2020. Storm intensity increases significantly during this period. Annual and seasonal trends show that regional SSTs in the MDR are warming annually at 0.0219 °C yr−1 and, per season, 0.0280 °C yr−1. Simultaneously, VWS decreases during the late rainfall season, at 0.056 m/s yr−1 in the MDR and 0.0167 m/s yr−1 in the Caribbean and surrounding area. The Atlantic Warm Pool is expanding at 0.51 km2 per decade, increasing upper atmospheric winds and driving VWS changes. Correlations of large-area averages do not show significant relationships between TC intensity, frequency, and SSTs/VWS during the LRS. The observed changes appear to be associated with regional warming SSTs impacting TC changes. Plain Language Abstract: Tropical cyclone (TC) formation requires warm ocean waters and low wind shear. Changes to sea surface anomalies and wind shear influences are essential to understanding storm development and intensification. The ability to forecast storm changes is vital to human lives and livelihoods. This work analyzes sea surface temperatures (SSTs) and vertical wind shear (VWS) trends in the Caribbean, surrounding areas, and the Atlantic main developing region (MDR). We found increasing SSTs, decreasing wind shears, an expanding Atlantic Warm Pool (AWP), and increased storm intensity during the Atlantic hurricane season. [ABSTRACT FROM AUTHOR]

In order to figure out the associated underlying dynamical processes of the 2014–2015 warming event, we used the ECCO (Estimating the Circulation and Climate of the Ocean) reanalysis from 1993 to 2016 and two combined scatterometers, QuikSCAT and ASCAT, to analysis hydrodynamic condition and ocean heat budget balance process in the equatorial tropical pacific. The spatiotemporal characteristics of that warming event were revealed by comparing the results with a composite El Niño. The results showed that the significant differences between the 2014 and 2015 warming periods were the magnitudes and positions of the equatorial easterly wind anomalies during the summer months. The abruptly easterly wind anomalies of 2014 that spread across the entire equatorial Pacific triggered the upwelling of the equatorial Kelvin waves and pushed the eastern edge of the warm pool back westward. These combined effects caused abrupt decreases in the sea surface temperatures (SST) and upper ocean heat content (OHC) and damped the 2014 warming process into an El Niño. In addition, the ocean budget of the upper 300 m of the El Niño 3.4 region showed that different dynamical processes were responsible for different warming phases. For example, at the beginning of 2014 and 2015, the U advection and subsurface processes played dominant roles in the positive ocean heat content tendency. During the easterly wind anomalies period of 2014, the U advection process mainly caused a negative tendency and halted the development of the warming phase. In regard to the easterly wind anomalies of 2015, the U advection and subsurface processes were weaker negatively when compared with that in 2014. However, the V advection processes were consistently positive, taking a leading role in the positive trends observed in the middle of 2015. [ABSTRACT FROM AUTHOR]

The Late Holocene fossil pollen records from the Zemu glacier, located in Yabuk, North Sikkim, in the eastern Himalayas, effectively generated quantitative climate reconstructions based on the transfer function model. The transfer function model was developed by establishing a modern pollen–climate calibration set from the temperate alpine belt of North Sikkim. A redundancy analysis was carried out to detect the pattern of variation of climatic variables in the modern pollen datasets. The mean annual precipitation (MAP) and mean temperature of the warming month (MTWA) had the strongest influence on the composition of the modern pollen samples among the climatic variables considered in the analysis. Proxy data in the form of fossil pollen records were analyzed for reconstructing past climate based upon the relationships between modern pollen vegetation assemblages and climatic patterns. Transfer functions for MAP and MTWA were developed with the partial least squares (PLS) approach, and model performance was assessed using leave-one-out cross-validation. The validated model was used to reconstruct MAP and MTWA for the last 2992 cal years BP (1042 BC) in North Sikkim. The variability observed in the reconstructions was analyzed for past global climatic events. It was further compared with the available regional and hemispheric proxy-based climate reconstructions. The reconstructions captured comparable Medieval Warm Period (MWP) and Little Ice Age (LIA)-like events from the Zemu glacier region. The fossil pollen data and climate reconstructions were further compared with the mineral magnetism data of the subsurface sediment profile. [ABSTRACT FROM AUTHOR]

Belligerent sides of current military conflicts are first and foremost counting on intelligence, air supremacy, and electromagnetic spectrum dominance. Actual concepts of conducting military operations can be characterised by the massive employment of air defence systems, radars, and electronic warfare systems. An approach is aimed to strengthen air‐defence potential by improving the resilience of pulse radar against electronic warfare threats, such as an electronic attack. To cope with the above‐mentioned situation, a radar intrapulse modulation was designed, where a combination of Costas codes and Walsh‐Hadamard codes is applied. Using a particular waveform together with the proposed model for radar signal processing results in increased resilience against an electronic attack, especially against repeater jamming methods. Furthermore, it is also possible to process radar echoes at low levels of Signal‐to‐Noise Ratio (SNR), which enhances radar electronic counter–countermeasure capability. The main scientific contribution of the paper is in the unique design of the Pulse Compression Filter for Combined Intrapulse Modulation (PCF‐CIM) and in its modelling. Based on the statistical evaluation of results, using the proposed radar waveform together with PCF‐CIM, it is possible to detect radar signals with combined intrapulse modulation in the case of SNR ≥ −14 dB with TPR ≥0.9999 and FPR ≤10−6, where SNR stands for Signal‐to‐Noise Ratio, TPR stands for True Positive Rate, and FPR stands for False Positive Rate. [ABSTRACT FROM AUTHOR]

In the Northwest Pacific (NWP), where a unique monsoon climate exists and where both typhoons and extratropical storms occur frequently, hazardous waves pose a significant risk to maritime safety. To analyze the 20-year variability of hazardous waves in this region, this study utilized hourly reanalysis data from the ECMWF ERA5 dataset covering the period from 2001–2020, alongside the wave risk assessment method. The ERA5 data exhibits better consistency, in both the temporal and spatial dimensions, than satellite data. Although hazardous wind seas occur more frequently than hazardous swells, swells make hazardous waves travel further. Notably, the extreme wave height (EWH) shows an increasing trend in high- and low-latitude areas of the NWP. The change in meridional wind speeds is the primary reason for the change in the total wind speed in the NWP. Notably, the maximum annual increase rate of 0.013 m/year for EWH exists in the region of the Japanese Archipelago. This study elucidated the distributions of wave height intensity and wave risk levels, noting that the EWHs of the 50-year and 100-year return periods can reach 20.92 m and 23.07 m, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: Discordance and conversion of receptor expressions in metastatic lesions and primary tumors is often observed in patients with brain metastases from breast cancer. Personalized therapy requires continuous monitoring of receptor expressions and dynamic adaptation of applied targeted treatment options. This study sought to evaluate if quantitative MR image features can predict the receptor status of brain metastases from breast cancer using machine learning algorithms. Results indicate that receptor status can be differentiated non-invasively based on routine MR imaging data. The proposed approach could allow non-invasive expression tracking at high frequencies, and may support dynamic treatment optimization for personalized therapies. Discordance and conversion of receptor expressions in metastatic lesions and primary tumors is often observed in patients with brain metastases from breast cancer. Therefore, personalized therapy requires continuous monitoring of receptor expressions and dynamic adaptation of applied targeted treatment options. Radiological in vivo techniques may allow receptor status tracking at high frequencies at low risk and cost. The present study aims to investigate the potential of receptor status prediction through machine-learning-based analysis of radiomic MR image features. The analysis is based on 412 brain metastases samples from 106 patients acquired between 09/2007 and 09/2021. Inclusion criteria were as follows: diagnosed cerebral metastases from breast cancer; histopathology reports on progesterone (PR), estrogen (ER), and human epidermal growth factor 2 (HER2) receptor status; and availability of MR imaging data. In total, 3367 quantitative features of T1 contrast-enhanced, T1 non-enhanced, and FLAIR images and corresponding patient age were evaluated utilizing random forest algorithms. Feature importance was assessed using Gini impurity measures. Predictive performance was tested using 10 permuted 5-fold cross-validation sets employing the 30 most important features of each training set. Receiver operating characteristic areas under the curves of the validation sets were 0.82 (95% confidence interval [0.78; 0.85]) for ER+, 0.73 [0.69; 0.77] for PR+, and 0.74 [0.70; 0.78] for HER2+. Observations indicate that MR image features employed in a machine learning classifier could provide high discriminatory accuracy in predicting the receptor status of brain metastases from breast cancer. [ABSTRACT FROM AUTHOR]

Areas of high ice water content (HIWC) within cloud systems may cause power loss events and engine damage in jet aircraft due to ice crystal icing (ICI). The Algorithm for Prediction of HIWC Areas (ALPHA) was developed to identify these regions and enable provision of guidance to airlines. ALPHA combines numerical weather prediction model data, satellite data, and radar data (where available), and applies fuzzy logic to identify the likely presence of HIWC. In a collaboration between the U.S. National Center for Atmospheric Research, Australian Bureau of Meteorology, U.S. Federal Aviation Administration, and Australian airlines, a trial of ALPHA was conducted for an area across Indonesia, Papua New Guinea (PNG), and northern Australia, a region with frequent deep convection and a relatively high incidence of ICI events. ALPHA was adapted to ingest data from the Australian Community Climate and Earth System Simulator model and the Japanese Himawari-8 geostationary meteorological satellite. Radar data was not used. The HIWC product was made available to stakeholder groups for evaluation. Independent validation of the HIWC product was undertaken by comparing it with retrieved profiles of ice water content (IWC) from the cloud profiling radar on the NASA polar-orbiting CloudSat satellite. Conduct of the ALPHA trial and results from validation of the HIWC product provides confidence in the potential utility for flight planning, maintaining situational awareness, and flight monitoring. [ABSTRACT FROM AUTHOR]

Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering. In this paper, we compared the properties and performance of different dissimilarity measures D using three different representations of texts—vocabularies, word frequency distributions, and vector embeddings—and three simple tasks—clustering texts by author, subject, and time period. Using the Project Gutenberg database, we found that the generalised Jensen–Shannon divergence applied to word frequencies performed strongly across all tasks, that D's based on vector embedding representations led to stronger performance for smaller texts, and that the optimal choice of approach was ultimately task-dependent. We also investigated, both analytically and numerically, the behaviour of the different D's when the two texts varied in length by a factor h. We demonstrated that the (natural) estimator of the Jaccard distance between vocabularies was inconsistent and computed explicitly the h-dependency of the bias of the estimator of the generalised Jensen–Shannon divergence applied to word frequencies. We also found numerically that the Jensen–Shannon divergence and embedding-based approaches were robust to changes in h, while the Jaccard distance was not. [ABSTRACT FROM AUTHOR]

The ground deformation rate is an important index for evaluating the stability and degradation of permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost areas on the Tibetan Plateau is a challenge. Thus, the technique of time-series interferometric synthetic aperture radar (InSAR) is often adopted for measuring the ground deformation rate of the permafrost area, the effectiveness of which is, however, degraded in areas with geometric distortions in synthetic aperture radar (SAR) images. In this study, a method that integrates InSAR and the random forest method is proposed for an improved permafrost stability mapping on the Tibetan Plateau; to demonstrate the application of the proposed method, the permafrost stability mapping in a small area located in the central region of the Tibetan Plateau is studied. First, the ground deformation rate in the concerned area is studied with InSAR, in which 67 Sentinel-1 scenes taken in the period from 2014 to 2020 are collected and analyzed. Second, the relationship between the environmental factors (i.e., topography, land cover, land surface temperature, and distance to road) and the permafrost stability is mapped with the random forest method based on the high-quality data extracted from the initial InSAR analysis. Third, the permafrost stability in the whole study area is mapped with the trained random forest model, and the issue of data scarcity in areas where the terrain visibility of SAR images is poor or InSAR results are not available in permafrost stability mapping can be overcome. Comparative analyses demonstrate that the integration of the InSAR and the random forest method yields a more effective permafrost stability mapping compared with the sole application of InSAR analysis. [ABSTRACT FROM AUTHOR]

The assessment of seasonal streamflow forecasting is essential for appropriate water resource management. A suitable seasonal forecasting system requires the evaluation of both numerical weather prediction (NWP) and hydrological models to represent the atmospheric and hydrological processes and conditions in a specific region. In this paper, we evaluated the ECMWF-SEAS5 precipitation product with four hydrological models to represent seasonal streamflow forecasts performed at hydropower plants in the Legal Amazon region. The adopted models included GR4J, HYMOD, HBV, and SMAP, which were calibrated on a daily scale for the period from 2014 to 2019 and validated for the period from 2005 to 2013. The seasonal streamflow forecasts were obtained for the period from 2017 to 2019 by considering a daily scale streamflow simulation comprising an ensemble with 51 members of forecasts, starting on the first day of every month up to 7 months ahead. For each forecast, the corresponding monthly streamflow time series was estimated. A post-processing procedure based on the adjustment of an autoregressive model for the residuals was applied to correct the bias of seasonal streamflow forecasts. Hence, for the calibration and validation period, the results show that the HBV model provides better results to represent the hydrological conditions at each hydropower plant, presenting NSE and NSElog values greater than 0.8 and 0.9, respectively, during the calibration stage. However, the SMAP model achieves a better performance with NSE values of up to 0.5 for the raw forecasts. In addition, the bias correction displayed a significant improvement in the forecasts for all hydrological models, specifically for the representation of streamflow during dry periods, significantly reducing the variability of the residuals. [ABSTRACT FROM AUTHOR]

This paper reviews applications of optimization techniques connected with reservoir simulation models to search for optimal rule curves. The literature reporting the search for suitable reservoir rule curves is discussed and examined. The development of optimization techniques for searching processes are investigated by focusing on fitness function and constraints. There are five groups of optimization algorithms that have been applied to find the optimal reservoir rule curves: the trial and error technique with the reservoir simulation model, dynamic programing, heuristic algorithm, swarm algorithm, and evolutionary algorithm. The application of an optimization algorithm with the considered reservoirs is presented by focusing on its efficiency to alleviate downstream flood reduction and drought mitigation, which can be explored by researchers in wider studies. Finally, the appropriate future rule curves that are useful for future conditions are presented by focusing on climate and land use changes as well as the participation of stakeholders. In conclusion, this paper presents the suitable conditions for applying optimization techniques to search for optimal reservoir rule curves to be effectively applied in future reservoir operations. [ABSTRACT FROM AUTHOR]

The current understanding of wind-generated wave climate from buoy-based measurements is mainly focused on a limited number of locations and has not been updated to include measurements in the past decade. This study quantifies wave climate variability and change during the historical period of 1980–2020 through a comprehensive analysis of wave height measurements at 43 buoys off the U.S. Pacific, Atlantic, and Gulf of Mexico Coasts. Variabilities and trends in the annual and monthly mean and 95th percentile significant wave heights (SWH) and the number of extreme wave events are quantified for the cold and warm seasons. We calculate the SWH long-term and decadal trends, and temporal variabilities using the ordinary least squares regression and coefficient of variation, respectively. Independent extreme wave events are identified using a method based on the peaks-over-threshold and the autocorrelation function, which accounts for the geographical variation in the timespan between independent extreme events. Results show that the warm season's interannual variabilities in monthly and annual SWH are smaller in the Pacific while larger in the Atlantic and Gulf, with the largest variabilities observed at buoys in the Gulf and lower latitudes of the Atlantic. Strong significant alternating decadal trends in SWH are found in the Pacific and Atlantic regions. Buoys in the Atlantic and Gulf regions have experienced higher numbers of extreme wave events (anomalies) compared to the Pacific region. In general, the long-term trend in the number of extreme events during the cold season is positive at buoys located at higher latitudes but negative at lower latitudes. [ABSTRACT FROM AUTHOR]

The wintertime multidecadal land precipitation (WMLP) variability is obtained using the ensemble empirical mode decomposition, and then the leading spatial pattern and temporal evolution of the WMLP in the northern hemisphere during the period of 1891–2014 are investigated. The time series shows a pronounced enhancement of the variability since the mid‐1970s, coincident with the 1976/1977 climate transition, with the variance increasing from 0.33 in the former period (P1) to 2.57 in the latter period (P2). The spatial structure in P2 resembles that in the entire period. The precipitation anomaly is positive in the North America between 30° and 50°N, South America and southern Europe; while it is negative in the southern North America, majority of Russia, Arabian Peninsula, Iran and South China. However, the spatial pattern in P1 differs from the entire period. An Interdecadal Pacific Oscillation (IPO)‐like sea surface temperature (SST) variability in cold phase and an Atlantic Multidecadal Oscillation (AMO)‐like SST variability in warm phase are associated with the leading mode of WMLP in P2 and the entire period. Yet, the relationship of IPO and AMO on the WMLP is barely found in P1. The in‐phase change of IPO and AMO in P1 may contribute to the smaller amplitude of WMLP variability in P1. In the end, the coupled models from CMIP6 historical runs are evaluated. The good‐models average is superior to any single good model in capturing the observed spatial features of WMLP in both P1 and P2. The poor‐models average performs better than any single poor model. Both good‐ and poor‐models averages are able to reproduce the IPO‐ and AMO‐like SST variabilities in P2. [ABSTRACT FROM AUTHOR]

The sea-gale process (SGP) is a significant and disastrous weather event for the marine industry. However, the sub-seasonal predictability of SGP remains unclear. In this study, we investigate the influence of low-frequency oscillation on SGP in the Yangtze River estuary from November to April, and its implications for sub-seasonal prediction. We noted that SGPs have a close relationship with the 10~30 day low-frequency component of the 10-m wind speed in the Yangtze River estuary, and typically occur during the peak phase of the low-frequency oscillation. The 10~30 day low-frequency oscillation of 10-m wind was found to be linked to the eastward propagation of extratropical Rossby waves from the North Atlantic across Europe to East Asia. This Rossby wave leads to the low-frequency oscillation of the Siberian high pressure and Japan Sea low pressure, which is indicative of the 10~30 day low-frequency oscillations of the 10-m wind speed in the Yangtze River Estuary. A sea-gale process index (SGPI) was constructed based on the low-frequency oscillation of the Siberian high and the Japan Sea low in order to predict SGPs at the sub-seasonal time scale. Hindcast and real-time forecasts showed that 2/3 of SGPs can be predicted with a leading time of 10~30 days, and that good sub-seasonal predictions of SGPs are connected with strong low-frequency oscillations at the initial forecast time. Therefore, SGPI can be adopted for the sub-seasonal prediction of SGPs in the Yangtze River Estuary. [ABSTRACT FROM AUTHOR]

Several aspects of global climate change, e.g., the rise of sea level and water temperature anomalies, suggest the advantages of studying wave distributions. In this study, WAVEWATCH-III (WW3) (version 6.07), which is a well-known numerical wave model, was employed for simulating waves over global seas from 1993–2020. The European Centre for Medium-Range Weather Forecasts (ECMWF), Copernicus Marine Environment Monitoring Service (CMEMS), current and sea level were used as the forcing fields in the WW3 model. The validation of modelling simulations against the measurements from the National Data Buoy Center (NDBC) buoys and Haiyang-2B (HY-2B) altimeter yielded a root mean square error (RMSE) of 0.49 m and 0.63 m, with a correlation (COR) of 0.89 and 0.90, respectively. The terms calculated by WW3-simulated waves, i.e., breaking waves, nonbreaking waves, radiation stress, and Stokes drift, were included in the water temperature simulation by a numerical circulation model named the Stony Brook Parallel Ocean Model (sbPOM). The water temperature was simulated in 2005–2015 using the high-quality Simple Ocean Data Assimilation (SODA) data. The validation of sbPOM-simulated results against the measurements obtained from the Array for Real-time Geostrophic Oceanography (Argo) buoys yielded a RMSE of 1.12 °C and a COR of 0.99. By the seasonal variation, the interrelation of the currents, sea level anomaly, and significant wave heights (SWHs) were strong in the Indian Ocean. In the strong current areas, the distribution of the sea level was consistent with the SWHs. The monthly variation of SWHs, currents, sea surface elevation, and sea level anomalies revealed that the upward trends of SWHs and sea level anomalies were consistent from 1993–2015 over the global ocean. In the Indian Ocean, the SWHs were obviously influenced by the SST and sea surface wind stress. The rise of wind stress intensity and sea level enlarges the growth of waves, and the wave-induced terms strengthen the heat exchange at the air–sea layer. It was assumed that the SST oscillation had a negative response to the SWHs in the global ocean from 2005–2015. This feedback indicates that the growth of waves could slow down the amplitude of water warming. [ABSTRACT FROM AUTHOR]

Accurate prediction of the El Niño–Southern Oscillation (ENSO) is crucial for climate change research and disaster prevention and mitigation. In recent decades, the prediction skill for ENSO has improved significantly; however, accurate forecasting at a lead time of more than six months remains challenging. By using a machine learning method called eXtreme Gradient Boosting (XGBoost), we corrected the ENSO predicted results from the First Institute of Oceanography Climate Prediction System version 2.0 (FIO−CPS v2.0) based on the satellite remote sensing sea surface temperature data, and then developed a dynamic and statistical hybrid prediction model, named FIO−CPS−HY. The latest 15 years (2007–2021) of independent testing results showed that the average anomaly correlation coefficient (ACC) and root mean square error (RMSE) of the Niño3.4 index from FIO−CPS v2.0 to FIO−CPS−HY for 7− to 13−month lead times could be increased by 57.80% (from 0.40 to 0.63) and reduced by 24.79% (from 0.86 °C to 0.65 °C), respectively. The real−time predictions from FIO−CPS−HY indicated that the sea surface state of the Niño3.4 area would likely be in neutral conditions in 2023. Although FIO−CPS−HY still has some biases in real−time prediction, this study provides possible ideas and methods to enhance short−term climate prediction ability and shows the potential of integration between machine learning and numerical models in climate research and applications. [ABSTRACT FROM AUTHOR]

The spatial–temporal characteristics of changes in lengths and onsets of the four seasons in China during 1961–2020 are investigated using daily temperature observations from weather stations. The relationship between changes in season lengths and climate change is also explored based on annual temperature variation, the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO). The results show that lengths of spring and autumn are shorter in the plateau region but longer in the southwest compared with other regions. In the past 60 years, the onset was generally advanced for spring and summer, whereas it was delayed for autumn and winter. Lengths of summer (winter) showed an overall increase (decrease) as 10.78 days (11.74 days) from the 1960s to the 2010s, which is positively (negatively) correlated with the temperature variation. Comparatively, changes in lengths of spring and autumn are much smaller and weakly correlated with the temperature variation. However, lengths of spring and autumn significantly increased in the plateau region and parts of the southwest and decreased in the south, which is positively and negatively correlated with the temperature trend, respectively. Changes in season lengths in China are also related to the AMO and modulated by the PDO phases. At the ~55‐year scale, summer lengths and winter lengths are simultaneously affected by the AMO and the PDO. The impact of the AMO on changes in lengths of spring and autumn is mainly due to its effect on the nonlinear trend associated with long‐term climate change. [ABSTRACT FROM AUTHOR]

Accounting studies have inconsistent conclusions about whether including Key Audit Matters (KAM) improves the communicative value of audit reports. We experimentally investigate the joint effect of investors' trait scepticism and the familiarity and readability of KAM on the communicative value of audit reports. Our results indicate that KAM readability enhances the communicative value of audit reports for less (more) sceptical investors when they are less (more) familiar with the issues discussed in the KAM. Additionally, using an eye-tracking device, we find that the communicative value of audit reports for nonprofessional investors depends on their visual attention paid to the KAM. These findings show that the positive effect of including KAM in audit reports is contingent on the user's trait scepticism and the content and form of the KAM. Our conclusions should be of interest to regulators and auditors when considering what and how to communicate to different financial statement users. [ABSTRACT FROM AUTHOR]

A critical study of the herbarium material representing the orchid genus Dactylorhiza Necker ex Nevski in Kazakhstan was conducted in 2019–2020. The information on the species composition was clarified. Dactylorhiza fuchsii subsp. hebridensis (Wilmott) Soó and D. × kerneri (Soó) Soó were identified for the first time in the country. New taxa were noted for individual botanical and geographical areas. All taxa were presented in the list and annotated with studied herbarium materials from the Kazakhstan area. Based on the collected and available locations for the studied taxa, distribution modeling was carried out for the four taxa (D. incarnata, D. majalis subsp. baltica, D. salina, and D. umbrosa). Bioclimatic data for the present and future (2041–2060) based on four possible scenarios were used. The occurrence of Dactylorhiza representatives in Kazakhstan is threatened by global climate warming. It is likely that some of them may not occur in the country in the future (D. incarnata and D. majalis subsp. baltica), losing up to 99.87% of their modern range or their range may be significantly reduced (D. salina and D. umbrosa), losing up to 80.83% of their present distribution. It is worth considering global changes in planning conservation activities and identifying areas that may play a significant role in the functioning of the national flora in the future. [ABSTRACT FROM AUTHOR]

Sea surface temperatures (SSTs) in the western part of the South China Sea (SCS) are cooler than in the eastern part in boreal winter, owing to a winter climatological cold tongue (CT). In this study, using a regional atmospheric model configured for the Maritime Continent, we assess the atmospheric impacts of local (or SCS) SSTs versus those from remote drivers (e.g., western tropical Pacific SSTs) during strong CT events with anomalously cool SSTs. In the local run, more rainfall is observed over the eastern SCS, but no significant atmospheric impacts are found over the CT region when SSTs associated with strong CT events are imposed within the SCS while climatological conditions are imposed elsewhere. SCS SST anomalies during strong CT events do not significantly modify the regional wind circulation. The lack of atmospheric response to SSTs over the CT region may be explained by the wintertime mean SSTs (i.e., <27–28°C) over the CT region that are inadequate to trigger deep atmospheric convection, while eastern SCS SSTs are high enough. The increase of anomalous positive moist static energy (MSE) near the sea level over the eastern SCS indicates underlying warm eastern SCS SST anomalies could be influencing positive rainfall anomalies. In the remote run, imposing climatological SCS SSTs but remote SSTs and lateral boundary conditions linked to strong CT events results in cyclonic wind and positive rainfall anomalies over the eastern SCS and Philippines, which are a Matsuno–Gill response to the diabatic heating anomalies over the warm western tropical Pacific SST anomalies. Positive rainfall and cloud cover anomalies associated with the cyclonic wind anomalies are due to the anomalous positive MSE import into the eastern SCS by horizontal advection. [ABSTRACT FROM AUTHOR]

In this work, the role of initial conditions (ICs) and boundary conditions (BCs) in the predictability of natural flows of large river basins (>1000 km2) in South America using the South American Large Basins Model (MGB-SA) was investigated. We proposed an analysis based on a simple metric: predictability was estimated by the number of days in which the streamflow forecasts performed above a given threshold. The role of ICs was assessed through null precipitation experiments and the role of BCs through climatological rainfall. For experiments with a Kling-Gupta efficiency metric and threshold of 0.95, the study shows that streamflow predictability from the ICs is relatively high (up to 40 days) in the main river reaches of flat-relief basins. The increase in predictability due to climatology-based BCs mostly occurs in areas with high predictability based on ICs. Basins with fast response present low streamflow predictability (up to three days). [ABSTRACT FROM AUTHOR]

The effect of climate prevails on a diverse time scale from days to seasons and decades. Between 1993 and 2013, global warming appeared to have paused even though there was an increase in atmospheric greenhouse gases. The variations in oceanographic variables, like current speed and sea surface temperature (SST), under the influence of the global warming hiatus (1993–2013), have drawn the attention of the global research community. However, the magnitude of ocean current and SST characteristics oscillates and varies with their geographic locations. Consequently, investigating the spatio-temporal changing aspects of oceanographic parameters in the backdrop of climate change is essential, specifically in coastal regions along Kuroshio current (KC), where fisheries are predominant. This study analyzes the trend of ocean current and SST induced mainly during the global warming hiatus, before and till the recent time based on the daily ocean current data from 1993 to 2020 and SST between 1982 and 2020. The Kuroshio extent is delineated from its surrounding water masses using an aggregation of raster classification, stretching, equalization, and spatial filters such as edge detection, convolution, and Laplacian. Finally, on the extracted Kuroshio extent, analyses such as time series decomposition (additive) and statistical trend computation methods (Yue and Wang trend test and Theil–Sen's slope estimator) were applied to dissect and investigate the situations. An interesting downward trend is observed in the KC between the East coast of Taiwan and Tokara Strait (Tau = −0.05, S = −2430, Sen's slope = −5.19 × 10−5, and Z = −2.61), whereas an upward trend from Tokara Strait to Nagoya (Tau = 0.89, S = 4344, Sen's slope = 8.4 × 10−5, and Z = 2.56). In contrast, a consistent increasing SST in trend is visualized in the southern and mid-KC sections but with varying magnitude. [ABSTRACT FROM AUTHOR]

Converting floodwater into power without increasing flood risk is critical for energy-stressed regions. Over the past decades, numerous methods have been proposed to solve this problem. However, few studies have investigated the theoretical explanation of the trade-offs between power generation and flood risk. This study establishes an analytical framework to derive optimal hedging rules (OHR) and explains the economic insights into flood risk reduction and power generation improvement. A two-stage model based on the concept of dynamic control of carryover storage (DCCS) was developed as part of the framework, considering forecast uncertainty and risk tolerance. The results illustrated that hedging and trade-offs between power generation and flood risk during DCCS only occurs when the forecasted inflow and forecast uncertainty are within certain ranges, beyond which there is no hedging and trade-offs analysis; either power generation or flood risk becomes the dominant objective. The OHR was divided into three cases under different levels of forecast uncertainty and risk tolerance. Compared to forecast uncertainty, downstream risk tolerance plays a more important role in determining which case of the OHR is adopted in real-world operations. The analysis revealed what and how intense trade-offs are between power generation and flood risk under different scenarios of forecasted inflow, forecast uncertainty, and risk tolerance. The framework serves as a guideline for less abundant water resources or energy-stressed areas of operational policy. Nierji Reservoir (located in northeast China) was taken as a case study to illustrate the analysis, and the application results showed that OHR increases the average annual power generation by 4.09% without extra flood risk compared to current operation rules. [ABSTRACT FROM AUTHOR]

The inter-annual stable carbon isotope ratio (δ13C) of three tree-ring cores of P. euphratica (Populus euphratica Oliv.) was determined from Ejina Oasis in Northwest China. A robust and representative δ13C chronology is generated from the three δ13C series using an arithmetic mean method. After eliminating the influence of the δ13C from elevated atmospheric carbon dioxide (CO2) concentration, we obtained a carbon isotopic discrimination (Δ13C) chronology. According to the significant correlation between the tree-ring Δ13C and instrumental data, we reconstructed the mean maximum temperature anomalies from previous December to current September (TDS) for the period 1901–2011. The reconstruction explained 43.6% of the variance over the calibration period. Three high-temperature periods (1929–1965, 1972–1974, and 1992–2006) and three low-temperature periods (1906–1926, 1966–1968, and 1975–1991) were found in the reconstructed series. Comparisons between the reconstructed TDS and the observed mean temperature from previous December to current September in Anxi meteorological station and the temperature index in north-central China demonstrated the reconstructed TDS has the advantage of reliability and stability. The significant spatial correlation declared that the reconstruction has a broad spatial representation and can represent the temperature variation characteristics in a wide geographical area. In addition, we found that the area of Ejina Oasis is smaller (larger) when the mean maximum temperature is higher (lower), which may be due to a conjunction effect of natural and anthropogenic activities. Significant periodicities and correlations suggested that the TDS variations in Ejina Oasis were regulated by solar radiation and atmospheric circulations at the interannual and interdecadal time scales. [ABSTRACT FROM AUTHOR]

A coupled tropical channel model is developed to forecast the Madden–Julian Oscillation (MJO) event during the Dynamics of the MJO (DYNAMO) campaign period. Eight sets of 30‐day hindcast experiments are performed using different combinations of cumulus parameterization and planetary boundary layer (PBL) schemes. The results indicate that a well‐simulated mean state is a prerequisite for MJO forecast, and the model's ability to reproduce the mean state is sensitive to the parameterization schemes. The experiment with Tiedtke (TDK) cumulus parameterization and University of Washington (UW) PBL schemes can successfully predict the MJO event. Over the equatorial Indian Ocean, this experiment has a good representation of zonal sea surface temperature (SST) gradient and low‐level westerly wind, favouring a reasonable atmospheric circulation. As a result of boundary‐layer convergence in the eastern Indian Ocean, more water vapour is gathered in the low‐middle troposphere by updrafts, contributing to the PBL moistening ahead of the MJO convection and the eastward‐propagation of MJO precipitation. Further analysis reveals that the high cloud is accompanied by strong convection. A better simulation in tropical high cloud fraction helps trap the cloud radiation heating to warm middle‐upper troposphere, which promotes the MJO propagation. Our results suggest that the selection of proper parameterization schemes with a better‐simulated mean state is crucial to predicting the MJO event in the tropical channel model. [ABSTRACT FROM AUTHOR]

In the Indo‐Pacific Ocean (IPO), extreme significant wave heights (SWHs) can substantially induce coastal erosion, flooding, and devastating impacts on coastal livelihoods. This study examines the seasonal influence of the El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Southern Annular Mode (SAM), and Pacific North American (PNA) pattern on extreme wind‐wave parameters. The climatic extremes are calculated utilizing ERA5 reanalysis datasets from 1979 to 2019 and a nonstationary generalized extreme value distribution. Significant increases in extreme wind‐sea (Hmax) and swell SWH (HmaxSw) response to ENSO occur over the northeast North Pacific (NP) during December–February (DJF) and western NP during June–August (JJA). The PNA influence exhibits a similar pattern to ENSO during DJF yet is inactive in JJA. Hmax and HmaxSw responses to the IOD during September–November (SON) include significant increases over the western Pacific, southern Indian Ocean (IO), and southwest tropical IO, yet decreases over the central tropical IO. The HmaxSw response to the SAM is larger than that for Hmax over the southern IPO during DJF, which extends toward the eastern Pacific in March–May (MAM). Overall, extreme wind‐sea and swell parameter responses are found to be associated with sea level pressure (SLP) and SLP gradients. [ABSTRACT FROM AUTHOR]

The greatest warm body in the world exists in the tropical oceans, which stimulates deep convection, resulting in abundant water vapor and precipitation in the tropical atmosphere. Using multiple SST datasets and related precipitation and atmospheric parameter data, this study examines multi-scale variabilities of the Indo-Pacific warm pool (IPWP) as well as the associated rain pool (IPRP). The results show that the IPWP and IPRP are spatially analogous and have significant increasing trends of intensity and coverage. Seasonal variations of the IPWP and IPRP are the strongest and almost coincident with each other. Our results also confirm previous findings that the most important interannual variations of the IPWP and IPRP are associated with various types of ENSO. The composite analysis reveals that the IPWP's SST structure is linked to the ENSO-induced trade wind anomaly and that SST structural changes cause changes in the position and intensity of the ascending branch of the Walker circulation, which in turn drives changes in the position and intensity of the IPRP. [ABSTRACT FROM AUTHOR]

Natural time analysis enables the introduction of an order parameter for seismicity, which is just the variance of natural time χ , κ 1 = 〈 χ 2 〉 − 〈 χ 〉 2 . During the last years, there has been significant progress in the natural time analysis of seismicity. Milestones in this progress are the identification of clearly distiguishable minima of the fluctuations of the order parameter κ 1 of seismicity both in the regional and global scale, the emergence of an interrelation between the time correlations of the earthquake (EQ) magnitude time series and these minima, and the introduction by Turcotte, Rundle and coworkers of EQ nowcasting. Here, we apply all these recent advances in the global seismicity by employing the Global Centroid Moment Tensor (GCMT) catalog. We show that the combination of the above three milestones may provide useful precursory information for the time of occurrence and epicenter location of strong EQs with M ≥ 8.5 in GCMT. This can be achieved with high statistical significance (p-values of the order of 10 − 5 ), while the epicentral areas lie within a region covering only 4% of that investigated. [ABSTRACT FROM AUTHOR]