搜索结果：Climatological

This study investigates bioclimatic comfort conditions in Iran by integrating perspectives from traditional Persian Medicine (PM) with modern climatological methodologies. The primary objective is to develop the Persian Medicine (PMI) as a criterion for evaluating comfort zones across Iran and to compare its results with modern climatological indices, specifically the Terjung Index (TI) and the Standard Effective Temperature Index (SETI). Daily meteorological data from 2003 to 2018 were utilized. To construct the PMI, qualitative variables describing temperate regions were extracted from classical PM texts and implemented Python programming, Excel and ArcGIS 10.8 for spatial analysis. The analysis focuses on May, September, and December, months that exhibited the highest degree of similarity among the comfort zones identified by PMI, TI, and SETI. Results indicate that the spatiotemporal distribution of comfort zones derived from SETI corresponds most closely with those identified by PMI, representing the highest levels of climatic and physiological comfort throughout the year. Overall, the findings demonstrate a strong correspondence between modern climatological comfort indices and PMI-based assessments in temperate regions of Iran. This research highlights the potential of integrating traditional medical- environmental knowledge with modern climate science, providing valuable insights for medical geography, environmental health management, and climate adaptation planning under global warming conditions.

Heatwaves are becoming more frequent and intense in Bangladesh, particularly in urban areas such as Dhaka, where the combined effects of extreme heat and humidity pose serious public health risks. Temperature-based warning systems frequently underestimate physiological heat stress. This study proposes a machine-learning framework to estimate and project the Heat Index (HI) using 3-hourly meteorological records from 2014 to 2023. Because HI is a deterministic function of air temperature and relative humidity, the framework performs conditional estimation of HI based on meteorological predictors rather than independently forecasting HI itself. Five models: ARIMAX, SARIMAX, Random Forest Regressor (RFR), XGBoost, and Long Short-Term Memory (LSTM), were trained using air temperature, relative humidity, and seasonal indicators as predictors. For scenario-based projections beyond 2023, future temperature and humidity were approximated using historical monthly averages, generating scenario-based HI projections that preserve seasonal and diurnal patterns. These projections represent climatological scenarios rather than true meteorological forecasts. The Random Forest Regressor (RFR) achieved the highest conditional estimation accuracy, with the lowest RMSE (0.85°C) and highest R² (0.987) on the test set. Empirical 95% prediction intervals achieved 98.85% coverage, indicating slightly conservative uncertainty bounds. Scenario-based projections yielded mean HI values of 29.02°C (optimistic), 29.90°C (moderate), and 31.33°C (pessimistic). A substantial proportion of projected 3-hourly periods fall within the "Extreme Caution" category (32-41°C), indicating persistently elevated heat-stress exposure under climatological assumptions. The proposed framework demonstrates strong potential for generating high-resolution scenario-based HI projections by capturing nonlinear temporal dynamics and sub-daily variability. These findings can support scenario-based early warning systems and inform adaptive urban heat-management strategies in climate-vulnerable cities such as Dhaka, although results should be interpreted as conditional projections rather than deterministic forecasts. Unlike conventional HI studies, this framework translates meteorological inputs into high-resolution, operational heat-risk insights by modeling temporal persistence at sub-daily scales.

Heat stress limits for human survivability have been previously defined by a 6-hour exposure to a wet-bulb temperature of 35oC. However, the recently developed physiology-based HEAT-Lim model demonstrates that environmental heat stress thresholds may be cooler and drier than previously thought. We employ HEAT-Lim to determine whether non-survivable thresholds were surpassed during six historical events where conditions were climatologically extreme and/or high heat-related mortality was reported. Our results show that non-survivable conditions are occurring during present-day heat events, all of which are below 35oC wet-bulb temperature. Of concern is regular exceedances of deadly thresholds for older people directly exposed across all events. Moreover, extremely hot yet dry conditions are found to be just as deadly as hot and humid conditions. For future climatological assessments, we emphasise the importance of employing increasingly accurate physiology-derived methods to assess the risk of potentially deadly heat stress.

The timing of spring onset is a widely used indicator of climate change impacts, yet estimates of phenological trends depend critically on the choice of reference climate period. Using long-term daily air temperature records from 21 meteorological stations across Estonia, this study examines how different climatological normals influence estimates of spring onset timing and its change. Spring onset was defined as the sustained transition of mean daily air temperature above 5 °C and analysed across four overlapping reference periods (1965-1990, 1971-2000, 1981-2010, and 1991-2020). Mean spring onset dates vary substantially among reference periods, demonstrating that the concept of an "average" spring is sensitive to the selected normal. Cumulative changes derived from linear trends reveal a widespread advancement of spring onset over 1965-2020, with particularly strong shifts at several inland and coastal stations. When contrasting equal-length 30-year normals (1971-2000 vs. 1991-2020), trend magnitudes are generally weaker in the most recent period and, at some stations, near zero or positive. Comparison across reference periods indicates pronounced temporal heterogeneity in phenological responses. While some stations exhibit strong advances during the late twentieth century followed by weakening in the most recent climate normal, others show sustained strong change or weak and inconsistent responses. These patterns cannot be adequately described by a single linear trend and do not follow a simple geographic gradient. The results demonstrate that reference period selection can substantially affect both the magnitude and interpretation of phenological trends. It is therefore recommended that phenological studies explicitly report reference periods and, where possible, compare trend estimates across multiple climatological normals to improve the robustness and transparency of climate impact assessments.

Stratospheric ozone (O3) losses and consequent increases in surface ultraviolet (UV) radiation remain a health concern due to close association with skin cancers and cataracts. This study estimated how emissions of ozone-depleting substances (ODS) affect, via O3 reductions, surface UV radiation and associated health impacts in Australia compared to the United States. We used climatological atmospheric data and the U.S. Environmental Protection Agency's Atmospheric Health Effects Framework (AHEF) model to estimate these effects for historic and projected ODS emissions. Compared to the United States, the Australian population is potentially exposed to ca. 24% more biologically weighted UV radiation because of the closer proximity of its population to the equator, less stratospheric O3 at comparable latitudes, and seasonal variations in the Earth-Sun distance. Surface UV increments owing to ODS-caused stratospheric O3 depletion were largest in the 1990s, while the estimated health impacts peak several decades later at ca. 0.5% for cataracts, 1% for melanoma incidence, and 5% for keratinocyte cancer incidence (relative to 1980), and are similar in both countries. The study found that differences in population-weighted surface UV can only partly explain Australia's substantially higher incidence and mortality rates for UV-related maladies. Other factors like genetic susceptibility of skin types, exposure behaviors, and diagnosis practices likely play a role. Ozone in the upper atmosphere helps block harmful ultraviolet (UV) radiation from reaching Earth's surface. Chemicals known as ozone‐depleting substances (ODS) can reduce ozone levels, allowing more UV radiation through. Increased UV exposure increases the risk of health problems like skin cancer and cataracts. The amount of UV that reaches the surface varies by location. In this study, we used a model to compare UV exposure and resulting skin cancer and cataract rates between the United States and Australia. Australians receive about 24% more harmful UV radiation under clear skies. Our study explains that this is due mostly to Australia's closer proximity to the equator, and partly due to climatological differences in stratospheric ozone and seasonal variations in the Earth‐Sun distance. We further estimate that ozone depletion has increased incidence rates of UV‐induced illness similarly in both Australia and the USA, and by less than 5 percent. Overall, the study found that differences in UV radiation only partly explain the significantly higher rates of UV‐induced illness in Australia than in the USA, suggesting that other factors like genetic susceptibility of skin types, exposure behaviors, and diagnosis practices likely play a role.

Malaria remains a significant public health issue in sub&#x2011;Saharan Africa, influenced by climate variability, population movement and urbanisation. In South Africa, efforts to eliminate malaria operate within a dynamic environment where climatic fluctuations and population movements complicate risk patterns. However, existing approaches to analyzing climate-malaria relationships, often rely on seasonal averages, ignoring the potential influence of rare climatic extremes and migration flows. This study investigates how climatic extremes, climatic variability, and migration flows interact to shape malaria risk and inform adaptive surveillance and control strategies. Climate data (monthly minimum/maximum temperature, rainfall and relative humidity) for Limpopo, Mpumalanga and KwaZulu&#x2011;Natal (2000-2024) were obtained from the South African Weather Service. Laboratory&#x2011;confirmed malaria cases were sourced from provincial surveillance reports, and in&#x2011;migration data into Thembelihle and selected parts of Soweto (2017-2024) were obtained from the Soweto and Thembelihle Health and Demographic Surveillance System (SaT&#x2011;HDSS). Seasonal climate trajectories were evaluated using Ordinary Least Squares regressions. Climatic seasonality was assessed using diagnostic metrics, humidity plateaus, rainfall extremes and thermal season&#x2011;length indices. Shock&#x2011;year dynamics (KwaZulu&#x2011;Natal 2022 floods; Limpopo 2017 Cyclone Dineo) were analysed using monthly anomalies against 2000-2010 climatological envelopes. Pearson correlations examined same&#x2011;year and lag&#x2011;1 climate-malaria associations. Climate-malaria-migration co&#x2011;movement was assessed using min-max&#x2011;normalised rainfall, humidity, temperature, incidence and migration data (0-1). Malaria tail events were defined as years at or above the provincial 90th percentile. All statistical analyses were conducted using IBM Statistical Package for the Social Sciences (SPSS), Version 30; p&#x2009;<&#x2009;0.05 was considered significant. Climate trends across the three endemic provinces showed consistent minimum-temperature warming, declining warm-season humidity, and rainfall patterns increasingly dominated by short-duration extremes. Province-specific seasonality diagnostics revealed that KwaZulu-Natal experienced frequent multi-month humidity plateaus, Limpopo showed pronounced lagged hydrological effects following extreme rainfall events, and Mpumalanga displayed prolonged warm-season durations in recent years. Shock-year analyses (KwaZulu-Natal 2022 floods; Limpopo 2017 Cyclone Dineo) demonstrated compound climatic anomalies, combinations of intense rainfall, sustained humidity, and sub-seasonal cooling that extended environmental suitability beyond expected seasonal boundaries. Same-year humidity was positively associated with malaria incidence in KwaZulu-Natal and Mpumalanga, whereas lag-1 rainfall showed a significant relationship with malaria in Limpopo, reflecting hydrological carry-over effects. Following the April-May 2022 floods, migration into Thembelihle increased markedly, and a co-movement analysis indicated temporal alignment between malaria tail years, elevated rainfall, higher minimum temperatures, and increased migration during climatologically disrupted periods. Climatic extremes and post&#x2011;shock mobility reorganise malaria risk in South Africa. Tail&#x2011;aware, lag&#x2011;sensitive and mobility&#x2011;integrated surveillance is essential for adaptive elimination planning.

Climate indicators are essential for monitoring ongoing climate change, supporting climate impact research, conducting spatial hot spot analyses and assessing attribution questions. These efforts rely on high-quality, reliable datasets that adhere to FAIR data principles. We present a curated dataset of 117 climate indicators for Austria, covering the period from 1961 onward at a 1-km spatial resolution. The dataset includes climate indicators related to temperature, precipitation, radiation, snow, runoff and humidity, with spatial (area means) and temporal (climatological reference period means) aggregations to enable rapid climate impact analysis. The workflow used to compute these indices is supported by a careful technical validation procedure and is designed to ingest diverse climate datasets, enabling the creation of climate indices beyond the scope presented here. Both the dataset and the workflow thus offer a robust, flexible and user-friendly resource for advancing climate research and supporting informed decision-making.

Among the myriad consequences of climate change, global warming and extreme weather events are particularly critical due to their well-documented impact on neurological and psychological well-being. However, the severity of these impacts varies significantly by geography. This article assesses the potential adverse effects of climate change on "brain health" through a risk management framework. The analysis begins by evaluating existing governance and risk-assessment procedures, followed by an examination of human adaptive capacities and natural risks. The latter draws upon climatological data-specifically regarding global warming and the "tropicalization" of the Euro-Mediterranean region-as well as anthropological insights. Building on this foundation, we propose strategies for effective risk control, including adaptation, mitigation, and preparedness. Success depends on the mobilization of public health researchers and professionals to drive organizational change and implement preventative measures to address extreme events. Consequently, the article advocates for specific decisions regarding communication, education, and early-warning systems to enhance rescue efficiency and prevent disasters. The discussion concludes with a focus on mitigation strategies specifically tailored to the Euro-Mediterranean region to address the challenges of climate tropicalization.

The Madden-Julian oscillation (MJO) is a planetary-scale tropical weather disturbance marked by eastward-propagating cumulus cloud clusters over the Indo-Pacific region, causing severe weather and climate events worldwide. The mechanism and predictability of MJO propagation remain elusive, partly because relevant multiscale processes are poorly understood. Here, we reveal chaotic MJO propagation arising from cross-scale nonlinear interactions, based on 4000-member ensemble simulations of two MJO events with a global cloud-system-resolving model. Against conventional linear thinking, multiple regimes with distinct timings of MJO propagation emerge under a single atmosphere-ocean background. The emergence of regime bifurcation depends critically on the equatorial asymmetry of climatological sea surface temperature. Selection of the bifurcated regimes is probabilistic, influenced by whether tropical-extratropical interplay promotes moistening associated with westward-propagating tropical waves over the western Pacific. These results contribute to a more complete MJO conceptual model and help foresee when coherent MJO propagation emerges.

This study presents a comprehensive spatiotemporal drought assessment for &#xc7;anakkale province, T&#xfc;rkiye, utilizing multi-index remote sensing approaches over a 20-year period (2005-2024) coupled with predictive risk modeling for 2025-2027. Four key environmental parameters were derived through the Google Earth Engine platform: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Standardized Precipitation Index (SPI), and Soil Moisture Condition Index (SMCI). Multiple satellite data sources were integrated, including Landsat 7 ETM&#x2009;+&#x2009;, MODIS/MOD11A1, CHIRPS precipitation dataset, and TerraClimate hydrological data. The retrospective analysis revealed significant climatic variability characterized by inter-annual LST fluctuations, progressive NDVI enhancement toward 2024, and pronounced negative trends in both SPI and SMCI indices during recent years. Particularly, SMCI reached&#x2009;-&#x2009;1.14 in 2023, indicating severe soil moisture deficit. Spatial heterogeneity was evident across the province, with differential vegetation dynamics and precipitation patterns between coastal and interior regions. A Principal Component Analysis-based integrated drought index was developed, explaining 68.7% of total variance, providing comprehensive drought characterization beyond univariate approaches. A hybrid trend-based forecasting framework incorporating seasonal decomposition, climatological constraints, and stochastic variability was implemented. Model validation demonstrated robust performance for LST (R2&#x2009;=&#x2009;0.85) and NDVI (R2&#x2009;=&#x2009;0.88), while SPI and SMCI exhibited challenges inherent to normalized indices with small-magnitude variations. Prospective projections indicate systematic elevation in composite drought risk from 2.58 (2025) to 2.67 (2026-2027), representing a 3.5% increase and persistent moderate-to-high drought vulnerability. These findings provide critical insights for regional water resource management, agricultural planning, and climate adaptation strategies in Mediterranean ecosystems facing intensifying drought pressures.

As projected by climate models in the high emission scenarios, the El Ni&#xf1;o-Southern Oscillation (ENSO) exhibits a non-monotonic amplitude shift. However, its key drivers remain poorly quantified. Here we introduce a framework using an intermediate coupled model (ICM) that coherently represents mean-state oceanic climatologies in the tropical Pacific, derived from eight selected climate models across three periods (1940-1990, 2040-2090 and 2240-2290). By applying vertical baroclinic mode decomposition to ocean density, we extract wind projection coefficients (pn; n is mode number) governing upper-ocean dynamical responses. The ICM with the explicitly prescribed climatological fields, including stratification and the thermocline structure, successfully reproduces the non-monotonic ENSO shifts, which is illustrated to be primarily driven by opposite changes in p1 and p2 post 2140. Sensitivity experiments further confirm stratification as the dominant modulator. This study establishes a coherent mechanistic framework for disentangling stratification impacts on ENSO in climate model projections under global warming.

Increases in area of extent, severity, and frequency of wildfires across the western United States are presenting challenges to socio-ecological systems, including shifts to alternative ecological states, loss of homes, and compromising human health. Wildfire suppression operations, such as constructing hand lines to limit the spread of fire, are an important part of wildland fire management, particularly in the wildland urban interface. Like other attributes of wildland fire activity and effects, suppression strategies and their effectiveness vary with ecological, topographic, climatological, and sociopolitical factors. However, there has been little research that examines the efficacy of suppression operations, specifically as they relate to forest composition. Here, we ask about the effectiveness of fire line construction based on adjacent stand composition. Specifically, we ask: (1) Are wildfire suppression lines preferentially constructed in stands with specific tree species? (2) How does species identity influence the probability that suppression lines hold when also considering differences in topography, climate, and extreme fire weather? We anticipated that suppression operations will be biased towards-and more effective in-stands with quaking aspen because they are often associated with less extreme fire behavior than many conifer species. We conducted our study in the southern Rocky Mountain ecoregion using fires (n&#x2009;=&#x2009;36) that burned during 2019-2023 and included records of fire suppression operations (n&#x2009;=&#x2009;4295). We used nonparametric statistical models to elucidate biases in the construction of fire lines and the effects of stand composition. We found quaking aspen was the least common tree species to be within fire footprints, yet fire lines were placed near quaking aspen 1.68-5.30 times more than commonly co-occurring tree species. Fire growth, independent of stand composition, was the most important predictor for whether fire suppression lines were likely to hold but the percentage of fire lines that held differed between fires >40,500 ha and smaller events (65% vs. 82%, respectively). This research suggests that wildland firefighters preferentially located fire lines near aspen stands, perhaps due to the long-held notion that aspen stands are less flammable. However, during extreme burning conditions fire lines are unlikely to hold regardless of stand composition.

Understanding the temporal dependence of precipitation is key to improving weather predictability and developing efficient stochastic rainfall models. We introduce an information-theoretic approach to quantify memory effects in discrete stochastic processes and apply it to daily precipitation records across the contiguous United States. The method is based on the predictability gain, a quantity derived from block entropy that measures the additional information provided by higher-order temporal dependencies. This statistic, combined with a bootstrap-based hypothesis testing and Fisher's method, enables a robust memory estimator from finite data. Tests with generated sequences show that this estimator outperforms other model-selection criteria such as Akaike information criterion and Bayesian information criterion. Applied to precipitation data, the analysis reveals that daily rainfall occurrence is well described by low-order Markov chains, exhibiting regional and seasonal variations, with stronger correlations in winter along the West Coast and in summer in the Southeast, consistent with known climatological patterns. Overall, our findings establish a framework for building parsimonious stochastic descriptions, useful when addressing spatial heterogeneity in the memory structure of precipitation dynamics and support further advances in real-time, data-driven forecasting schemes.

The digitization of academic publications and newspapers from the 1800s has permitted identification of several authoritative sources that credit Dr. Joseph W. Gleitsmann with establishing the first successful tuberculosis sanitarium in the United States in Asheville, North Carolina, in 1875, antedating by 9 years the Trudeau Sanatorium in Saranac Lake, New York. The facility used German climatological methods and a defined medical treatment program. Gleitsmann's Mountain Sanitarium for Pulmonary Diseases had a 30-bed occupancy and published outcomes data from 5 years of clinical experience by 1880. By 1910, Asheville had become a tuberculosis care "colony," with 25 private tuberculosis sanitaria with a national referral base. Asheville was a key driver of the development of climatotherapy in the treatment of tuberculosis and other respiratory ailments in the preantibiotic era. From 1870 to 1930, medical, mental health, and wellness tourism largely drove the population growth (1500 to 50&#x2009;000) of Asheville, a previously remote Appalachian town. The stigmatization of tuberculosis sufferers is illustrated by restrictive municipal regulations that led to the demolition of almost all tuberculosis sanitaria within Asheville city limits by the 1920s. The Von Ruck Research Laboratory for Tuberculosis produced more than 50 papers from 1890 to 1930, published mostly in the Journal of the American Medical Association and the Journal of Immunology. These included pioneering immunotherapy studies with tuberculin variants and the first robust description of the antigenic profile of Mycobacterium tuberculosis. Tuberculosis was both incurable and a leading cause of death, and thus perseverance with fractionated tubercle bacillus products and subunits by so many is understandable in the context of the times. By analogy, public health now seems more ready to accept disease-specific immunotherapy agents and vaccines that save lives even if they are substantially less than 100% effective.

Incidence of coccidioidomycosis (Valley fever), a fungal infection caused by inhalation of Coccidioides species spores, has increased substantially across the southwestern United States in association with increasing aridity, warming temperatures, and precipitation volatility. Arizona and California report >95% of U.S. coccidioidomycosis cases, and incidence in Arizona has increased statewide. Patterns within Arizona's distinct climatological regions have not been characterized, especially in regions outside the known zone of persistently high levels of disease occurrence (hyperendemicity) in the southwest Sonoran Desert region. In this study, surveillance data reported to the Arizona Department of Health Services since 2005 were used to calculate coccidioidomycosis incidence within six ecological regions. During 2005-2022, annual incidence approximately doubled in Arizona, with >95% of cases reported from the Sonoran Desert region. Although the Plateaus and Mojave Desert regions (in the northern parts of the state) reported <1.5% of Arizona cases during this period, these regions experienced the highest relative increases in incidence from the 2005-2007 period to the 2020-2022 period. During 2020-2022, coccidioidomycosis incidence in the Plateaus region was 6.61 times the incidence during 2005-2007 (95% CI&#xa0;=&#xa0;4.22-10.30), and in the Mojave Desert region, incidence was 4.50 times that during 2005-2007 (95% CI&#xa0;=&#xa0;3.45-5.89). The Plateau and Mojave regions also reported the highest relative increases in incidence from the 2014-2016 period to the 2020-2022 period. Large relative incidence increases in northern regions, including cooler and wetter regions generally considered less suitable for Coccidioides species establishment and transmission, necessitate targeted public health messaging in these areas and support ongoing investigation into the causes of these increases.

Amazonian floodplain lakes rank among the most dynamic aquatic ecosystems globally, yet their monitoring is hampered by extreme hydrological variability, high turbidity, and optical complexity. We investigated chlorophyll-a (Chl-a) dynamics in Lago Grande de Curuai, a large floodplain lake hydraulically connected to the Amazon River, by integrating multi-campaign in situ measurements with two decades of MODIS surface reflectance (2001-2024) within a machine learning and Explainable Artificial Intelligence (XAI) framework. Among tested regressors, an optimized Support Vector Regression (SVR_Optuna) consistently outperformed Random Forest, XGBoost, LightGBM, Partial Least Squares, and linear models, achieving R2&#xa0;=&#xa0;0.855 in log1p space and RMSE&#xa0;=&#xa0;12.5&#xa0;mg&#xa0;m-3 after back-transformation in holdout validation. Applied to MODIS monthly composites, the model reconstructed 24&#xa0;years of Chl-a variability, revealing recurrent seasonal maxima during receding and low-water phases of the Amazon flood pulse. Spatial analyses highlighted enhanced concentrations in semi-isolated basins and stronger dilution near riverine connections. Pixel-wise uncertainty metrics-derived from temporal variability, bootstrap resampling, and climatological anomalies-indicated robust retrievals in pelagic zones but higher uncertainty along river-lake interfaces. SHAP-based XAI confirmed the dominant role of red and red-edge spectral features (665-709&#xa0;nm) in Chl-a prediction, consistent with bio-optical theory and strengthening model interpretability. Beyond Curuai, this scalable approach enables systematic monitoring of floodplain lakes across the Amazon Basin, where hydrological connectivity, extreme droughts, and intensifying human pressures increasingly affect water quality.