Search Results (148)

The Brazilian savanna, South America’s second-largest biome, is vital to Brazil’s economy but has suffered from environmental degradation due to unregulated agricultural and urban expansion. This study assesses climate change in the biome from 1961 to 2021 using the Köppen climate classification, drought indices, historical trend analyses, and the climatological water balance. Fourteen municipalities across the biome were analyzed. According to the Köppen classification, most municipalities were identified as Aw (tropical with dry winters) and Am (tropical monsoon), with Dourados, MS, and Sapezal, MT, alternating between Am and Aw. The standardized precipitation index (SPI) revealed changes in rainfall distribution. The Mann–Kendall test detected rising air temperatures in 13 of the 14 municipalities, with Sen’s slope ranging from 0.0156 to 0.0605 °C per year. Rainfall decreased in seven municipalities, with decreases from −4.54 to −12.77 mm per year. The climatological water balance supported the observed decrease in precipitation. The results indicated a clear warming trend and declining rainfall in most of the Brazilian savanna, highlighting potential challenges for water availability in the face of ongoing climate change. Full article

Complex networks of stochastic processes are crucial for modeling the dynamics of interacting systems, particularly those involving high-order interactions (HOIs) among three or more components. Traditional measures—such as mutual information (MI), interaction information (II), the redundancy-synergy index (RSI), and O-information (OI)—are typically limited to static analyses not accounting for temporal correlations and become computationally unfeasible in large networks due to the exponential growth of the number of interactions to be analyzed. To address these challenges, first a framework is introduced to extend these information-theoretic measures to dynamic processes. This includes the II rate (IIR), RSI rate (RSIR), and the OI rate gradient ($\Delta \mathrm{OIR}$), enabling the dynamic analysis of HOIs. Moreover, a stepwise strategy identifying groups of nodes (multiplets) that maximize either redundant or synergistic HOIs is devised, offering deeper insights into complex interdependencies. The framework is validated through simulations of networks composed of cascade, common drive, and common target mechanisms, modelled using vector autoregressive (VAR) processes. The feasibility of the proposed approach is demonstrated through its application in climatology, specifically by analyzing the relationships between climate variables that govern El Niño and the Southern Oscillation (ENSO) using historical climate data. Full article

Semi-annual climate oscillations in the Western Hemisphere (20 S–35 N, 150 W–20 E) were studied via empirical orthogonal function (EOF) eigenvector loading patterns and principal component time scores from 1980 to 2023. The spatial loading maximum for 850 hPa zonal wind extended from the north Atlantic to the east Pacific; channeling was evident over the southwestern Caribbean. The eigenvector loading maximum for precipitation reflected an equatorial trough, while the semi-annual SST formed a dipole with loading maxima in upwelling zones off Angola (10 E) and Peru (80 W). Weakened Caribbean trade winds and strengthened tropical convection correlated with a warm Atlantic/cool Pacific pattern (R = 0.46). Wavelet spectral analysis of principal component time scores found a persistent 6-month rhythm disrupted only by major El Nino Southern Oscillation events and anomalous mid-latitude conditions associated with negative-phase Arctic Oscillation. Historical climatologies revealed that 6-month cycles of wind, precipitation, and sea temperature were tightly coupled in the Western Hemisphere by heat surplus in the equatorial ocean diffused by meridional overturning Hadley cells. External forcing emerged in early 2010 when warm anomalies over Canada diverted the subtropical jet, suppressing subtropical trade winds and evaporative cooling and intensifying the equatorial trough across the Western Hemisphere. Climatic trends of increased jet-stream instability suggest that the semi-annual amplitude may grow over time. Full article

When planning a solar energy conversion system, having sufficiently reliable values of the monthly average daily solar radiation (MADSR) on a horizontal surface is essential. Traditionally, estimates based on other climatological variables for which more information is available have been relied upon to compensate for the lack of direct solar radiation measurements. Solar radiation varies widely, which requires the creation of site-specific forecast models. By using artificial neural network (ANN) models or similar methods using historical datasets, the monthly average daily solar radiation can be easily assessed. To verify the validity of the established ANN model, a series of analyses was performed using the mean squared error, the coefficient of determination (R²), and the mean absolute error. The study used a dataset collected from nine weather stations in Saudi Arabia from 1985 to 2000. The input parameters for the ANN model were the maximum air relative humidity, latitude, the maximum ambient air temperature, longitude, the minimum ambient air temperature, the minimum air relative humidity, sunshine duration, location altitude, and the corresponding month. The R² for the whole test dataset was 0.8449. Furthermore, a sensitivity analysis using the established ANN model showed that site elevation (location altitude) had the most significant effect on MADSR on a horizontal surface, with a contribution value of 14.66%. The analysis results show that the ANN model accurately estimates MADSR on horizontal surfaces regardless of seasonal variations in weather conditions. Furthermore, this work is important not only for its contribution to the shape of information in solar radiation forecasting but also for establishing the practical application of ANNs in renewable energy management. The results of this work will help improve the utilization of solar energy and support sustainable energy efforts. Furthermore, the proposed ANN model is believed to be useful for predicting MADSR on horizontal surfaces in other locations in Saudi Arabia with similar climatic conditions to the study sites. Furthermore, the ANN approach may be functional to the basic strategy of a solar arrangement and is suitable for forecasting other meteorological data. Full article

In July 2023, 19 continuous days of very hot days in Hong Kong brought inconvenience to citizens and disasters to society. This long-lasting heat wave event is closely linked to the atmospheric variability on the quasi-biweekly to intraseasonal timescales. While extreme weather has aroused the attention of scientists and society, limited studies focus on quasi-biweekly to intraseasonal extreme (QBIE) weather. Thus, to address this issue, this study aims at examining the climatology and long-term variability of these QBIE events in Hong Kong. This study serves as one of the very few fundamental works that construct a century-long record of QBIE temperature events, based on in situ observation in Hong Kong, and further examines the climatology, diversity, and variability of these QBIE temperature events. A total of 382 QBIE heat waves and 510 QBIE cold surges are identified from 1885 to 2022, exhibiting various characteristics in their occurring time and seasonality. Based on ARIMA model and time series analyses, we find that while apparent interannual variability exists in QBIE heat wave and cold surge activity, short-term climate prediction of QBIE temperature events based on past patterns or common climate indices is largely unfeasible. This research provides a valuable historical reference for understanding QBIE weather in the Guangdong–Hong Kong–Macau Greater Bay Area and highlights the need for further studies on the predictability of QBIE weather in the future. Full article

Due to their immense economic and recreational value, the monitoring of Great Lakes water quality is of utmost importance to the region. Historically, this has taken place through a combination of ship-based sampling, buoy measurements, and physical models. However, these approaches have spatial and temporal deficiencies which can be improved upon through satellite remote sensing. This study details a new approach for using long time series of satellite remote sensing data to identify historical and near real-time anomalies across a range of data products. Anomalies are traditionally detected as deviations from historical climatologies, typically assuming that there are no long-term trends in the historical data. However, if present, such trends could result in misclassifying ordinary events as anomalous or missing actual anomalies. The new anomaly detection method explicitly accounts for long-term trends and seasonal variability by first decomposing a 10-plus year data record of satellite remote sensing-derived Great Lakes water quality parameters into seasonal, trend, and remainder components. Anomalies were identified as differences between the observed water quality parameter from the model-derived expected value. Normalizing the anomalies to the mean and standard deviation of the full model remainders, the relative anomaly product can be used to compare deviations across parameters and regions. This approach can also be used to forecast the model into the future, allowing for the identification of anomalies in near real time. Multiple case studies are detailed, including examples of a harmful algal bloom in Lake Erie, a sediment plume in Saginaw Bay (Lake Huron), and a phytoplankton bloom in Lake Superior. This new approach would be best suited for use in a water quality dashboard, allowing users (e.g., water quality managers, the research community, and the public) to observe historical and near real-time anomalies. Full article

Streamflow forecasting is of great importance in water resource management and flood warnings. Machine learning techniques can be utilized to assist with river flow forecasting. By analyzing historical time-series data on river flows, weather patterns, and other relevant factors, machine learning models can learn patterns and relationships to present predictions about future river flows. In this study, an autoregressive integrated moving average (ARIMA) model was constructed to predict the monthly flows of the Athabasca River at three monitoring stations: Hinton, Athabasca, and Fort MacMurray in Alberta, Canada. The three monitoring stations upstream, midstream, and downstream were selected to represent the different climatological regimes of the Athabasca River. Time-series data were used for model training to identify patterns and correlations using moving averages, exponential smoothing, and Holt–Winters’ method. The model’s forecasting was compared against the observed data. The results show that the determination coefficients were 0.99 at all three stations, indicating strong correlations. The root mean square errors (RMSEs) were 26.19 at Hinton, 61.1 at Athabasca, and 15.703 at Fort MacMurray, respectively, and the mean absolute percentage errors (MAPEs) were 0.34%, 0.44%, and 0.14%, respectively. Therefore, the ARIMA model captured the seasonality patterns and trends in the stream flows at all three stations and demonstrated a robust performance for hydrological forecasting. This provides insights and predictions for water resource management and flood warnings. Full article

The continental United States is a global hotspot of severe thunderstorms and therefore is particularly vulnerable to social and economic damages from high-impact severe convective weather (SCW), such as tornadoes, thunderstorm winds, and large hail. However, our knowledge of the spatiotemporal climatology and variability of SCW occurrence is still lacking, and the potential change in SCW frequency and intensity in response to anthropogenic climate warming is highly uncertain due to deficient and sparse historical records and the global and regional climate model’s inability to resolve thunderstorms. This study investigates SCW in the Central and Eastern United States in spring and early summer for the current and future warmed climate using two multi-year continental-scale convection-permitting Weather Research and Forecasting (WRF) model simulations. The pair of simulations consist of a retrospective simulation, which downscales the ERA-Interim reanalysis during October 2000–September 2013, and a future climate sensitivity simulation based on the perturbed reanalysis-derived boundary conditions with the CMIP5 ensemble-mean high-end emission scenario climate change. A proxy based on composite reflectivity and updraft helicity threshold is applied to infer the simulated SCW occurrence. Results indicate that the retrospective simulation captures reasonably well the spatial distributions and seasonal variations of the observed SCW events, with an exception of an overestimate along the Atlantic and Gulf coast. In a warmer-moister future, most regions experience intensified SCW activity, most notably in the early-middle spring, with the largest percentage increase in the foothills and higher latitudes. In addition, a shift of simulated radar reflectivity toward higher values, in association with the significant thermodynamic environmental response to climatic warming, potentially increases the SCW severity and resultant damage. Full article

This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from remote automated weather stations (RAWS) allowed predictions of 10-h fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model, with a mean absolute error prediction of 0.05 g/g. For context, the values of DFMC in California are commonly between 0.05 g/g and 0.30 g/g. The presented product provides gridded hourly moisture estimates for 1-h, 10-h, 100-h, and 1000-h fuels, essential for analyzing historical fire activity and understanding climatological trends. The methodology presented here demonstrates significant advancements in the accuracy and robustness of fuel moisture estimates, which are critical for fire forecasting and management. Full article

Iran is a country characterized by high biodiversity and complex biogeographic patterns. Its diverse landscape and steep climatic gradients have resulted in significant faunal diversity and high level of endemism. To better understand these patterns, we investigated the historical environmental drivers that have shaped Iran’s current geological and climatological conditions, and, consequently, have shaped the current zoological distribution patterns. Furthermore, we provide an overview of the country’s zoological diversity and zoogeography by reviewing published studies on its fauna. We analyzed nearly all available catalogs, updated checklists, and relevant publications, and synthesized them to present a comprehensive overview of Iran’s biodiversity. Our review reports approximately 37,500 animal species for Iran. We also demonstrated that the country serves as a biogeographic transition zone among three zoogeographical realms: the Palearctic, Oriental, and Saharo-Arabian, where distinct faunal elements intersect. This biogeographic complexity has made it challenging to delineate clear zoogeographical zones, leading to varying classifications depending on the taxon. The uplift of mountain ranges, in particular, has played a crucial role in shaping faunal diversity by serving as barriers, corridors, and glacial refugia. These mountains are largely the result of orogeny and plate collisions during the Mesozoic and Cenozoic eras, coupled with the development of the Tethyan Sea and the uplift of several ranges during the Miocene. Despite these insights, our understanding of biodiversity distribution in Iran remains incomplete, even for some well-studied taxa, such as certain vertebrate families and arthropods. We highlight the existing gaps in knowledge regarding zoogeographical patterns and propose approaches to address these gaps, particularly concerning less-studied species and the highly diverse group of insects. Full article

This study aimed to determine the amount of As and Pb in the water in the Meoqui-Delicias’ aquifer and their spatiotemporal dynamics. Twenty-one water sampling points were selected. Seventeen samples were from wells and four were from surface water; two were used for human consumption and the rest for agricultural use. The samples were taken from May 2019 to January 2020 in four sampling events, one for each climatological season of the year. The studied geochemical anomalies seem to be linked to the nature and mechanism of volcanic emplacement. Several samples exhibited high concentrations of arsenic ranging from 1.20 to 156.54 ppb, unlike lead, with low values being the maximum value of 26.32 ppb. These elements (As and Pb) are in the water in Naica, part of the mining district where tons of Au, Ag, Pb, Cu, and Zn were obtained. From a geographical standpoint, it is impossible to establish that these elements are related, even though these elements (As and Pb) are present in the water in Naica, a mining zone where tons of Au and Ag were historically mined. Full article

This study examines the representation of blocking events in the Southeast Pacific and Southwest Atlantic regions using a set of 13 global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). Historical runs were employed to analyze blocking conditions in the recent past climate, spanning from 1985 to 2014, with ERA5 data utilized to represent observed blocking events. The majority of CMIP6 models underestimate the total number of blocking events in the Southeast Pacific. The MPI–ESM1–2–HR and MPI–ESM1–2–LR models come closest to replicating the number of blocking events observed in ERA5, with underestimations of approximately −10% and −9%, respectively. Nonetheless, these models successfully capture the seasonality and overall duration of blocking events, as well as accurately represent the position of blocking heights over the Southeast Pacific. Conversely, CMIP6 models perform poorly in representing blocking climatology in the Southwest Atlantic. These models both overestimate and underestimate the total number of blocking events by more than 25% compared to ERA5. Furthermore, they struggle to reproduce the seasonal distribution of blockings and face challenges in accurately representing the duration of blocking events observed in ERA5. Full article

Snow is an indicator of climate change. Its variation can affect surface energy, water balance, and atmospheric circulation, providing important feedback on climate change. There is a lack of assessment of the spatial characteristics of multi-source snow data in Eurasia, and these data exhibit high spatial variability and other differences. Therefore, using data obtained from the Global Historical Climatology Network Daily (GHCND) from 1980 to 2018, snow depth information from ERA5, MERRA2, and GlobSnow is assessed in this study. The spatiotemporal variation characteristics and the primary spatial modes of seasonal variations in snow depth are analyzed. The results show that the snow depth, according to GlobSnow data, is closer to that of the measured site data, while the ERA5_Land and MERRA2 data are overestimated. The annual variations in snow depth are consistent with seasonal variations in winter and spring, with an increasing trend in the mountains of Central Asia and Siberia and a decreasing trend in most of the rest of Eurasia. The dominant patterns of snow depth in late autumn, winter, and spring are all north–south dipole patterns, and there is overall consistency in summer. Full article

Statistical downscaling of climate data has been widely described in the literature, with the aim of improving the reliability of local climatic parameters from coarse-resolution (often >20 km) global datasets. In this article, we present ClimateDT, a dynamic downscaling web tool for monthly historical and future time series at a global scale. The core of ClimateDT is the 1 km 1981–2010 climatology from CHELSA Climate (version 2.1), where the CRU-TS layers for the period 1901-current are overlayed to generate a historic time series. ClimateDT also provides future scenarios from CMIP5 using UKCP18 projections (rcp2.6 and rcp8.5) and CMIP6 using 5 GCMs, also available on the CHELSA website. The system can downscale the grids using a dynamic approach (scale-free) by computing a local environmental lapse rate for each location as an adjustment for spatial interpolation. Local predictions of temperature and precipitation obtained by ClimateDT were compared with climate time series assembled from 12,000 meteorological stations, and the Mean Absolute Error (MAE) and the explained variance (R²) were used as indicators of performance. The average MAEs for monthly values on the whole temporal scale (1901–2022) were around 1.26 °C for the maximum monthly temperature, 0.80 °C for the average monthly temperature, and 1.32 °C for the minimum monthly temperature. Regarding monthly total precipitation, the average MAE was 19 mm. As for the proportion of variance explained, average R² values were always greater than 0.95 for temperatures and around 0.70 for precipitation due to the different degrees of temporal autocorrelation of precipitation data across time and space, which makes the estimation more complex. The elevation adjustment resulted in very accurate estimates in mountainous regions and areas with complex topography and substantially improved the local climatic parameter estimations in the downscaling process. Since its first release in November 2022, more than 1300 submissions have been processed. It takes less than 2 min to calculate 45 locations and around 8 min for the full dataset (512 records). Full article

Marine cold spells (MCSs) are extreme ocean temperature events impacting marine organisms, yet their characteristics and trends in the South China Sea (SCS) historical period remain unclear. This study systematically analyzes sea surface temperature (SST) and MCSs in the SCS using satellite observation data (OISSTv2.1) from 1982 to 2022. The climatological mean SST ranges from 22 °C near the Taiwan Strait to 29 °C near the Nansha Islands, showing notable variations. Annual SST anomalies demonstrate a heterogeneous spatial trend of approximately 0.21 ± 0.16 °C/decade (p < 0.01) across the SCS, indicating an increase in SST over time. MCS analysis uncovers spatial non-uniformity in frequency, with higher values near the Beibu Gulf and Hainan Island, and longer durations in the northeastern coastal areas. Statistical analysis indicates normal distributions for frequency and duration trends but skewness for intensity and cumulative intensity, reflecting extreme values. Winter months exhibit larger MCS occurrence areas and higher mean intensities, illustrating seasonal variability. Anticipated changes will significantly impact the ecological structure and functioning of the SCS. Full article