Weather Index Research Articles

Physical and Geochemical Characterisation of Pongo Pitti and Bossambo Farm Clays (Littoral Region, Cameroon) for use in Ceramics

The main objective of this work is to discern the physico-chemical and mechanical behavior of the Pongo Pitti and Bossambo Farm clay formations, located on either side of the Sanaga river in the littoral region of Cameroon. To achieve this, two field campaigns and laboratory works were carried out. Four clay materials were collected and brought back to the laboratory. The laboratory investigations involved physico-mechanical and geochemical analyses, followed by ceramic testing. The materials studied have a clayey texture, with clay percentages ranging from 26.89% to 80.3%, silt from 13.2% to 57.6% and sand from 1.58% to 47.26%. The plasticity index ranges from 11.9% to 24.2%. From a geochemical standpoint, the materials studied show a predominance of silica (49.6% to 63.5%), alumina (20.5% to 27.1%) and iron (2.39% to 8.6%), with the SiO2/Al2O3 ratio varying from 1.96 to 3.1. The Al2O3/Fe2O3 ratio is 8.4 for Pongo Pitti clays and 2.9 for Bossambo Farm clays. The geochemical weathering indices (ICV, CIA, PIA, CIW, CIW) show a high degree of weathering, with values in excess of 90%. Alkali and alkaline-earth proportions are lower, and the K2O/Na2O ratio varies between 8.45 and 14.24, showing an enrichment of potassium in relation to sodium. Lantane is the most abundant rare-earth element (323.84 ppm). The REE study shows a predominance of light REEs over heavy REEs (LREE/HREE~4.51) and a negative Europium anomaly. Infrared spectroscopy showed poor kaolinite crystallinity. Mechanically, the specimens studied, fired at temperatures ranging from 950℃ to 1050℃ showed good cohesion and metallic soundness. Linear shrinkage was 4.8%. Mass loss increases with temperature. Its value is 12.5% for Pongo Pitti materials and 8.5% for Bossambo Farm materials. Water absorption decreases with temperature and is 20.27% for Pongo Pitti samples and 15.79% for Bossambo Farm samples. Bulk density increases with temperature (2.11 at 950℃ to 2.51 at 1050℃). Flexural strength increases with temperature and is 6.66 MPa for materials from Pongo Pitti and 8.89 MPa for those from Bossambo Farm. These materials can be used to make fired bricks and roof tiles, and Pongo Pitti materials can be used to make refractory ceramics

Climate change induced heat and drought stress hamper climate change mitigation in German cereal production

ContextAgricultural production and climate change strongly influence each other and there are significant efforts to minimize negative impacts in both directions. In particular, breeding progress has succeeded in reducing the carbon footprint (CFP) of cereals over time. However, there is widespread certainty that climate change-related weather extremes have led to stagnation of cereal yields in many global production regions. Research questionWe assume that climate change-related yield stagnation is also evident in variety trials in Germany, which has to date only been shown for on-farm yields. Furthermore, we expect that the stagnation in yields also leads to a stagnation in the downward trend of CFP, and that heat and drought stress in particular increase the CFP of cereals. In addition, we hypothesize that the site-specific soil quality largely determines stress induced increases in CFP. MethodsWe conduct a partial life cycle assessment (LCA) with German variety trial data from 1993 to 2021 and determine the greenhouse gas emissions per unit of land (GHGL), as well as the CFP of winter wheat, winter rye, and winter barley. Further, we evaluate the time trends of yield, GHGL, and CFP using linear and quadratic plateau models. In addition, we calculate spatio-dynamic weather indices (WIs) for moderate, severe and extreme heat and drought stress. Using mixed models, we estimate the explanatory power and effect size of heat and drought WIs on the CFP. Finally, we present the spatial differences of heat and drought on the CFP at different soil qualities. ResultsWe show yield plateaus in all crops and stagnating GHGL trends, resulting in a stagnation of the downward trend of CFP, especially for rye and barley. We highlight that heat and drought increase the CFP of all crops. However, the impact of heat and drought on the CFP varies greatly with soil quality across all crops. ConclusionsWe conclude that climate change-induced weather extremes are major challenges not only for cereal production and food security but also for climate change mitigation in the agricultural sector, highlighting the importance of high-yield locations, alongside variety selection and resource-efficient management, for climate change mitigation. SignificanceThis study is the first that proves significant yield stagnation in German variety trials. Moreover, this study is the first to analyze the impact of heat and drought stress on cereal CFP, with novel results that proof that climate adaptation will become a crucial aspect of climate change mitigation in field crops.

Provenance, Intensity of Weathering, and Economic Potential of Placer-bearing Sediments on the Shelf off Quilon, Southwestern Coast, India

ABSTRACT In this study, the mineralogy and geochemistry of sediments of the continental shelf off Quilon, southwest coast, India is used as proxies to constrain their provenance, the intensity of weathering at source, and economic potential. The weathering index calculations were carried out on the sediments and the results showed that Chemical Alteration Index (CIA) (Average = 72.88), Chemical Index of Weathering (CIW) (Average = 92.72), and Plagioclase Alteration Index (PIA) (Average = 89.74) are high, indicating moderate to intense source area weathering. High source area weathering is also supported by high Th/U ratio (Average = 5.68) which is above the average crustal Th/U ratio of 3.8. The sediments are first-cycle sediments derived from the nearby source area, as evidenced by the identification of unaltered heavy minerals and rock-forming minerals, along with a high Average Composition Variability Index (ICV) (Average = 1.20). The high K2O/Na2O ratios (Average = 3.88), and LREE/HREE ratios (Average = 20), plotting of samples in the felsic field of various binary plots (SiO2 vs. Al2O3/TiO2, Zr vs. TiO2, Ce vs. La/Yb and La/Th vs. Hf binary plots) and normalized REE patterns with LREE enrichment and negative Eu anomaly indicate the sediments were primarily derived from Khondalites of the Trivandrum block and were transported by the Kallada and Ithikkara rivers. Average Σ REE, Th, and U concentrations are 222 ppm, 15.2 ppm, and 2.58 ppm respectively in the bulk sediments. The heavy mineral separates showed significantly increased concentrations of Σ REE (Average = 3009 ppm), Th (Average = 478 ppm), and U (Average = 30 ppm) due to the presence of monazite. Due to high Σ REE, Th, and U concentrations in the sediments due to monazite, present study recommend for further exploration of these sediments, which could contribute significantly to the resource base of the nation.

Statistical and machine learning models for location-specific crop yield prediction using weather indices.

Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors.

A series of regression models to predict the weathering index of tropical granite rock mass

In the recent past, several weathering indicators have been developed to describe its state of weathering. The state of rock weathering is a useful indicator to estimate the integrity of tropically weathered rock material and mass which weatherability plays an important role in a tropical region. Through a ground assessment tool, the strength and durability of the rock mass could be estimated and complex or adopted to simplify the early prediction of the complex engineering parameter. This paper presents several models of the Weathering Index (WI) using selected significant parameters using statistical analysis. For this purpose, several sites have been chosen to represent granitic rock mass. Forty (40) numbers of samples were collected and tested comprising from four (4) sites in Malaysia. Several laboratory tests have been conducted such as Point Load Index (Is(50)), dry density, Slake Durability 1 (SD1), Slake Durability 2 (SD2) and moisture content. The field and laboratory data sets are used to determine the WI by using simple regression and MLR analysis Significant parameters found to be useful in determining the WI are selected namely SD1, dry density, Is(50), and block volume. These parameters were selected based on stepwise analysis using Statistical Package for the Social Sciences (SPSS). Following the models’ implementation, the models were evaluated and the best prediction model was selected after considering statistical coefficients, such as coefficient of determination (R2), variance account for (VAF), and root mean squared error (RMSE), as well as utilizing a straightforward ranking approach. The findings of this study could contribute to the more accurate prediction of WI using a more simplistic field and laboratory parameters. Therefore, the WI is useful during the initial stages and planning of rock excavation work and provides a good description of weathering grade and rock mass properties, which will affect excavatability in granitic areas.

Geochemistry and U–Pb geochronology of detrital zircon grains in beach sediments from the northwestern gulf of Mexico, Tamaulipas, Mexico: Implication for provenance

The mineralogy, bulk geochemical composition, chemistry and U–Pb ages of detrital zircons in sediments from the La Pesca (LP) and Tesoro Altamira (TA) beaches, NW Gulf of Mexico are analyzed. The aim of this study is to infer the weathering history and provenance of sediments, and to identify the potential source terranes that are contributing sediments to the LP and TA beach areas. The beach sediments are rich in quartz and aluminosilicates. The Chemical Index of Weathering (CIW') and trace elements such as Th, Sr, U, and Ba reveal high weathering intensity for the LP and TA beach sediments. Major-element based diagrams and trace elemental ratios based on Co, Cr, Sc, La, and Th contents in sediments indicate felsic provenance, which is further supported by a negative europium anomaly and rare earth element (REE) patterns. The Th/U ratios (>0.3) together with positive cerium and negative europium anomalies of zircons in the LP and TA beaches indicate igneous origin. The comparison of zircon U–Pb ages of this study with ages reported from the adjacent terranes revealed that the Proterozoic (Paleoproterozoic: 1607.07–1943.45 Ma and Mesoproterozoic: 1021.19–1586.52 Ma) zircons are derived from the Oaxaquia, Mesa Central, Sierra Madre Oriental Provinces of Mexico and Mazatzal–Yavapai Province of the USA. On the other hand, Mesozoic (Jurassic 146.49–199.43 Ma and Cretaceous 68.46–136.82 Ma) and Cenozoic (Eocene 33.97–51.46 Ma and Oligocene 23.39–33.86 Ma) zircons are contributed by the Mexican volcanic rocks, Mesa Central and Sierra Madre Oriental Provinces in Mexico, and Mogollon-Datil Volcanic field and Colorado Plateau in USA. The rivers and their tributaries draining from the source areas are considered as a carrier and agent of distributing sediments along the northwestern Gulf of Mexico coastal areas, which are subsequently mixed by littoral currents.

Middle Pleistocene weakening of the Indian summer monsoon driven by global cooling

The Indian Monsoon profoundly impacts the climate, agriculture, and economy of the Indian subcontinent and southwestern China. However, the long-term variability of the Indian summer monsoon (ISM) and its forcing mechanism in the context of the stepwise Pleistocene cooling trend are poorly understood, due to the lack of high-quality terrestrial paleoclimate records. Quaternary loess and paleosol deposits in SW China provide the opportunity to study the long-term evolution of the ISM. Based on analyses of magnetic properties and chemical weathering indices, we found that decreased chemical weathering and pedogenic intensity occurred in SW China since the Middle Pleistocene. Comparison of our results with published paleoclimate records from the Indian Ocean and SW China suggest that substantial ISM weakening occurred after ∼0.7–0.6 Ma, coincident with the Mid-Pleistocene climatic transition. We conclude that decreasing global surface temperatures associated with the major growth of Northern Hemisphere ice sheets caused the Middle Pleistocene weakening of the ISM, leading to changes in precipitation regimes in the southern Asian continent and SW China. Our results suggest that global cooling is strongly linked with the South Asian monsoon climate system, and they provide clues for predicting future climate changes in its area of influence.

Hydrogeochemical factors influencing the dynamics of groundwater characteristics in eco-sensitive areas of the Southern Western Ghats, India

The inter-ionic relationships of groundwater present in a region as well as various chemical and physical factors all have an impact on the geochemistry of groundwater in an aquifer. To assess the factors influencing the geochemical composition of groundwater in the eco-sensitive area of Western Ghats, Kerala, India, various weathering, and ionic indices were analyzed. Results show groundwater ranges from soft to extremely hard and acidic to alkaline, with high Mn and Fe levels. WQI analysis found 7% of samples unfit for drinking due to Fe/Mn contamination in the southeast part of the study area. Main water types are CaHCO3 (46%) and CaMgCl. Geochemical modeling indicates silicate and carbonate weathering, cation exchange, and reverse ion processes influence the aquifer. Groundwater is often supersaturated with iron minerals, saturated with carbonate minerals, and undersaturated with sulfate and chloride minerals. Cluster analysis (CA) revealed that NO3− and K+ are derived from anthropogenic sources. Principal component analysis (PCA) resulted in three factors. Factor 1 is for geogenic processes, while Factors 2 and 3 imply the anthropogenic and weathering of iron-rich minerals. Hierarchical cluster analysis defines anthropogenic input, silicate and carbonate weathering, and different patterns of mineralization in the groundwater. The study underscores the need for comprehensive management to protect groundwater quality, considering the complex interplay of natural processes and human factors.

Data-driven determination of plant growth stages for improved weather index insurance design

PurposeThis paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.Design/methodology/approachUsing the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.FindingsOur results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.Originality/valueThis research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

The Role of Field Measurements of Fine Dead Fuel Moisture Content in the Canadian Fire Weather Index System—A Study Case in the Central Region of Portugal

The Canadian Fire Weather Index System (CFWIS), empirically developed for forests in Canada, estimates the fuel moisture content (mf) at different depths and loads through meteorological parameters. While it is often suggested that adapting an existing fire danger rating system like CFWIS for a new environment requires developing new relationships or modifying existing ones, it is worth considering if such adaptations are always necessary. Based on a dataset of field measurements for surface litter (Pinus pinaster) carried out in the central region of Portugal (2014–2023), we propose a correction of mf based on the Fine Fuel Moisture Code (FFMC) of the CFWIS. This moisture correction was used to determine the Initial Spread Index (ISI) directly and, subsequently, the Fire Weather Index (FWI). Fire records from the study region were used to analyze the performance of the corrected indices. We found that the moisture correction led to higher values and potentially more accurate indices under dry conditions but did not provide a significant improvement in predicting the number of fires and burned areas compared to the original indices. The results suggest that, in relation to fire activity, the CFWIS is sufficiently robust to variations in the fuel moisture content in the study region.

Controlling factors of wildfires in Australia and their changes under global warming

Abstract This study investigates a fire weather index (FWI) and its associated components in Australia using the downscaled projects for the Coupled Model Intercomparison Project phase 6 (CMIP6) dataset, aiming to understand how they respond to global warming, particularly associated with different phases of the El Niño-Southern Oscillation (ENSO). In the historical simulation, multimodel mean composite results show positive anomalies of FWI during El Niño and negative anomalies during La Niña over most of Australia relative to the neutral year. At the end of the 21st century under the Shared Socioeconomic Pathways (SSP585 scenario), FWI anomalies increased across Australia; however, ENSO wildfire teleconnections weakened (-4.4%) during El Niño but strengthened (+6.0%) during La Niña, especially in northern Australia. Further examination of the contribution from individual environmental variables that enter the FWI shows that increased temperature and drought conditions with warming in La Niña strengthen positive FWI anomalies, thus making fire more favorable in north and central Australia. The impacts of relative humidity and wind speed anomaly changes also favor fire activity toward the north. These results suggest a more robust modulation of FWI in northern Australia by ENSO in a warmer climate; future efforts to predict wildfire will depend on the model’s ability to predict local climate conditions.

Quartz luminescence sensitivity enhanced by residence time in the critical zone

The emerging use of quartz luminescence properties to characterize Earth-surface processes shows promise, with optically stimulated luminescence (OSL) sensitivity proposed as a valuable tool for provenance or sediment history tracing. However, the geologic processes that lead to quartz sensitization remain unclear. Here we study the impact of source rock and surface processes on the luminescence properties of quartz sand from bedrock and modern and Late Pleistocene alluvium generated from a mountainous catchment in northern Utah, USA. Continuous wave and linear modulated OSL are used to characterize the luminescence sensitivity and intensity of the fast-decay component. We compare the OSL sensitivity with sand-grain provenance and with proxies for surface processes such as topographic metrics, cosmogenic 10Be-derived erosion rates, chemical weathering indices, and magnetic susceptibility. Late Pleistocene sediment has low OSL sensitivity and a weak fast-decay component, similar to bedrock samples from the source area. In contrast, modern alluvium is dominated by the fast-decay component and has higher and more variable OSL sensitivity, with no clear relationship to upstream bedrock source. There is, however, an inverse relationship between OSL sensitivity and catchment-averaged erosion rates and a positive relationship with chemical weathering indices and magnetic susceptibility. These metrics suggest that the modern alluvium has experienced increased residence time in the shallow critical zone compared to the Late Pleistocene sediments. We suggest that changes in hillslope processes between the effectively wetter, cooler Pleistocene and the dryer, warmer conditions of the Holocene enhanced the luminescence properties. The results suggest that climatic controls on rates and processes of chemical and mechanical weathering and sediment transport and residence within the critical zone are encoded in the luminescence properties of quartz sand.

Investigating FWI Moisture Codes in Relation to Satellite-Derived Soil Moisture Data across Varied Resolutions

In the Mediterranean region, particularly in Antalya, southern Türkiye, rising forest fire risks due to climate change threaten ecosystems, property, and lives. Reduced soil moisture during the growing season is a key factor increasing fire risk by stressing plants and lowering fuel moisture content. This study assessed soil moisture and fuel moisture content (FMC) in ten fires (2019–2021) affecting over 50 hectares. The Fire Weather Index (FWI) and its components (FFMC, DMC, DC) were calculated using data from the General Directorate of Meteorology, EFFIS (8 km), and ERA5 (≈28 km) satellite sources. Relationships between FMCs, satellite-based soil moisture datasets (SMAP, SMOS), and land surface temperature (LST) data (MODIS, Landsat 8) were analyzed. Strong correlations were found between FWI codes and satellite soil moisture, particularly with SMAP. Positive correlations were observed between LST and FWIs, while negative correlations were evident with soil moisture. Statistical models integrating in situ soil moisture and EFFIS FWI (R: −0.86, −0.84, −0.83 for FFMC, DMC, DC) predicted soil moisture levels during extended fire events effectively, with model accuracy assessed through RMSE (0.60–3.64%). The SMAP surface (0–5 cm) dataset yielded a lower RMSE of 0.60–2.08%, aligning with its higher correlation. This study underlines the critical role of soil moisture in comprehensive fire risk assessments and highlights the necessity of incorporating modeled soil moisture data in fire management strategies, particularly in regions lacking comprehensive in situ monitoring.

Impacts of fire prevention strategies in a changing climate: an assessment for Portugal

Abstract Climate change poses a formidable strain on societies worldwide, demanding viable and timely adaptation measures to ensure future prosperity while avoiding the impact of more frequent and intense extreme events, like wildfires, that affect all continents and biomes, leaving authorities grappling to respond effectively. Here, we focus on mainland Portugal that is inserted in the Mediterranean climate change hotspot and investigate the impact of different adaptation strategies on wildfire risk. Relying on an ensemble of regional climate models from the EURO-CORDEX initiative, we project Fire Weather Index (FWI) and Fire Radiative Power (FRP) for various Representative Concentration Pathways (RCPs).
Our findings reveal that very energetic fires, with energy release exceeding 1000 MW, may increase up to more than three-fold, depending on the RCP. Even under strong mitigation scenarios, the likelihood of having megafires increases by 1.5-fold. This underscores the need for proactive adaptation regardless of mitigation efforts.
We present three different ignition avoidance strategies under different climate change scenarios. For all cases results indicate that a reduction between 20 and 60% is achievable for intense wildfires (above 1000 MW).

Associations of weather and air pollution with objective physical activity and sedentary time before and after bariatric surgery: a secondary analysis of a prospective cohort study

Purpose. Identifying factors that influence moderate-to-vigorous intensity physical activity (MVPA) and sedentary time in metabolic and bariatric surgery (MBS) patients is necessary to inform the development of interventions. Weather/environmental factors may be especially important considering rapid climate change and the vulnerability of people with obesity to heat and pollution. Our study aimed to examine the associations of weather (maximal, average and Wet Bulb Globe Temperatures), and air pollution indices (air quality index [AQI]) with daily physical activity (PA) of both light (LPA) and MVPA and sedentary time before and after MBS. Materials and methods. Participants (n = 77) wore an accelerometer at pre- and 3, 6, and 12-months post-MBS to assess LPA/MVPA/ sedentary time (min/d). These data were combined with participants’ local (Boston, MA or Providence, RI, USA) daily weather and AQI data (extracted from federal weather and environmental websites). Results. Multilevel generalized additive models showed inverted U-shaped associations between weather indices and MVPA, with a marked reduction in MVPA for daily maximal temperatures ≥20 °C. Sensitivity analysis showed a less marked decrease of MVPA (min/d) during higher temperatures after versus before MBS. Both MVPA before and after MBS and sedentary time before MBS were negatively impacted by higher AQI levels. Conclusion. This study is the first to show that weather and air pollution indices, even in locations with good AQI and moderate temperatures, are related to variability in activity behaviors, particularly MVPA, during pre- and post-MBS. Weather/environmental conditions should be considered in MVPA prescription/strategies for adults who have undergone MBS.

Palaeoenvironmental and palaeoclimatic study of the Olazagutia (Spain) and Ten Mile Creek-Arbor Park (USA) sections during the Coniacian–Santonian interval

The mechanisms and palaeoenvironmental conditions leading to and occurring through the Coniacian–Santonian Ocean Anoxic Event 3 (OAE3) are poorly known, particularly with regard to the marine phosphorus cycle and the climatic conditions in general. To explore these issues further, two famous locations have been studied, Olazagutia (Spain) and Ten Mile Creek–Arbor Park (TX, USA), which are located in different palaeogeographical areas and deposited at different palaeodepths. The study of these two locations is based on mineralogy (bulk and clay), geochemistry (stable isotopes, organic matter, phosphorus and major element) and high-resolution biostratigraphy (nannofossils) to infer changes in climate and primary productivity across OAE3. The investigated sections were both previously proposed as candidates for the base Santonian global boundary stratotype section and point (GSSP). The Olazagutia section was ratified in 2013, where the base of the Santonian was defined by the first appearance datum of the inoceramid Platyceramus undulatoplicatus . Both sections, deposited in oxygenated conditions, record the δ 13 C patterns which characterized the Coniacian–Santonian OAE3 interval, in particular the positive carbon-isotope excursions comprising the K2, Michel Dean and Bedwell events. New nannofossil biostratigraphy for both sections is presented including new occurrences. Significant diachronism of marker taxa is observed between the Spanish and Texan sections. Based on a weathering index and mineralogy, similar climate changes are observed in all sections. The climate shifted synchronously from relatively drier to warmer and wetter conditions above the Coniacian–Santonian boundary from the Michel Dean event to above the Bedwell event during the early Santonian. Fluctuations in total phosphorus contents appear mainly to have been driven by changes in detrital input and consequently by climate change in Spain and Texas. Several bentonite layers were observed in Texas close to the Coniacian–Santonian boundary, but only one located 7 m above the Coniacian–Santonian boundary provided sufficient well-preserved zircon minerals, and gives an age of 86.24 ± 0.12 Ma based on U–Pb geochronology, consistent with an early Santonian age.

The fire weather in Europe: large-scale trends towards higher danger

Abstract The climate over Europe has been recorded to be hotter, drier, and more fire-prone over the last decade than ever before, leading to concerns about how climate change will alter fire weather in the future. A typical measure to estimate fire weather severity based on climate is the Canadian fire weather index (FWI). In this study, we used high-resolution, bias-corrected climate model output (∼9 km) from six CMIP6 climate models and four shared socio-economic pathway projections (SSPs) to calculate consistent and comparable daily FWI datasets for Europe from 1950 to 2080. Our study aims to identify regional and large-scale shifts in fire weather severity and its predictability over time to support adaptive planning. We show that irrespective of the future SSP, fire weather will become more severe, but the increase is much stronger under high greenhouse gas emissions. This leads to new areas being exposed to severe fire weather, such as central Europe and rapidly warming mountainous areas. Already fire-prone regions in southern Europe will experience more extreme conditions. We conclude that only the low-emission SSP1-2.6 pathway can prevent strong increases in fire weather beyond the 2050s. Fire surveillance and management will become more important, even in areas and in seasons where they have not been in the focus so far.

Indian Agriculture Under Climate Change: The Competing Effect of Temperature and Rainfall Anomalies

AbstractThe latest generation of global climate models robustly projects that monsoon rainfall anomalies in India will significantly increase in the 21st century due to global warming. This raises the question of the impact of these changes on the agricultural yield. Based on annual district data for the years 1966-2014, we estimate the relationship between weather indices (amount of seasonal rainfall, number of wet days, average temperature) and the most widely grown kharif crops, including rice, in a flexible non-parametric way. We use the empirical relationship in order to predict district-specific crop yield based on the climate projections of eight evaluated state-of-the-art climate models under two global warming scenarios for the years 2021-2100. We find that the loss in rice yield by the end of the 21st century lies on average between 3 - 22% depending on the underlying emission scenario. For the sustainable scenario impacts range from an increase of 3.2% to a decrease of 12.1% for individual districts. In the worst-case scenario, all districts are negatively affected, with a predicted decrease in rice yield ranging from 34% to a decrease of 11.5% in the long run. Potential gains due to increasing rainfall are more than offset by the negative impacts of increasing temperature. Adaptation efforts in the worst-case global warming scenario would need to cut the negative impacts of temperature by 50% in order to reach the outcome of the sustainable scenario.

Differences in the intensity of past forest fire events inferred from stable oxygen isotope analysis of charred bark

Understanding past fire regimes requires reliable proxy data that record fire conditions and preserve them over time. The objective of this study was to determine if the oxygen isotope composition of charred bark samples (pyrogenic organic matter) could be used as proxy data to differentiate wildfires based on burn intensity. We collected charred and uncharred bark samples from three fire sites in northern Ontario, Canada that represented a gradient of fire intensity as depicted by Fire Weather Index (FWI) data. We hypothesized that the mean Δ18Obark-char (the difference between δ18O of uncharred bark and a charred sample) would be greater for fires with higher intensities. Analysis of variance of Δ18Obark-char indicated a significant effect of fire event ( F = 73.6, p < 0.001), which explained 57.0% of the variance. A prescribed surface fire treatment (mean FWI = 9.5) had significantly lower Δ18Obark-char than two natural crown fires (FWI = 21 and 27). These results demonstrate that Δ18Obark-char differentiated moderate from high intensity fires in a similar manner to the FWI data.

Multiscale Interactions between Local Short- and Long-Term Spatio-Temporal Mechanisms and Their Impact on California Wildfire Dynamics

California has experienced a surge in wildfires, prompting research into contributing factors, including weather and climate conditions. This study investigates the complex, multiscale interactions between large-scale climate patterns, such as the Boreal Summer Intraseasonal Oscillation (BSISO), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) and their influence on moisture and temperature fluctuations, and wildfire dynamics in California. The combined impacts of PDO and BSISO on intraseasonal fire weather changes; the interplay between fire weather index (FWI), relative humidity, vapor pressure deficit (VPD), and temperature in assessing wildfire risks; and geographical variations in the relationship between the FWI and climatic factors within California are examined. The study employs a multi-pronged approach, analyzing wildfire frequency and burned areas alongside climate patterns and atmospheric conditions. The findings reveal significant variability in wildfire activity across different climate conditions, with heightened risks during specific BSISO phases, La-Niña, and cool PDO. The influence of BSISO varies depending on its interaction with PDO. Temperature, relative humidity, and VPD show strong predictive significance for wildfire risks, with significant relationships between FWI and temperature in elevated regions (correlation, r > 0.7, p ≤ 0.05) and FWI and relative humidity along the Sierra Nevada Mountains (r ≤ −0.7, p ≤ 0.05).