Weather Climate Research Articles

The role of ascent timescales for warm conveyor belt (WCB) moisture transport into the upper troposphere and lower stratosphere (UTLS)

Abstract. Warm conveyor belts (WCBs) are coherent ascending airstreams in extratropical cyclones. They are a major source of moisture for the extratropical upper troposphere and lower stratosphere (UTLS), where moisture acts as a potent greenhouse gas and WCB-associated cirrus clouds contribute to cloud radiative forcing. However, the processes controlling WCB moisture transport and cloud properties are poorly characterised. Furthermore, recent studies have revealed (embedded) convection as a ubiquitous feature of WCBs, highlighting the importance of understanding their updraught and microphysical structure. We present a Lagrangian investigation of WCB moisture transport for a case from the WISE (Wave-driven ISentropic Exchange) campaign based on a convection-permitting simulation. Lagrangian non-dimensional metrics of the moisture budget suggest that the ascent timescale (τ600) strongly controls the end-of-ascent total moisture content, which is largest for slowly ascending trajectories (τ600≥20 h, 30 % of all WCB trajectories). This is due to relatively warm end-of-ascent temperatures and the strong temperature control on transported water vapour. Deviations from equilibrium water vapour condensate partitioning are largest for slow trajectories due to faster glaciation and lower ice crystal numbers. A local moisture transport minimum at intermediate τ600 results from a shift towards a riming-dominated precipitation formation pathway and decreasing outflow temperatures with decreasing τ600. The fastest trajectories (τ600≤5 h, 5 % of all WCB trajectories) transport the largest condensate mass to the UTLS due to less efficient condensate loss and produce the longest-lived outflow cirrus clouds. Models that parameterise convection may under-represent these processes, potentially impacting weather forecasts and climate predictions.

Assessing the Impact of Observations on the Brazilian Global Atmospheric Model (BAM) Using Gridpoint Statistical Interpolation (GSI) System

This article describes the main features of the impacts of global observations on the reduction of errors in the data assimilation (DA) cycle carried out in the Brazilian Global Atmospheric Model (BAM) at Center for Weather Forecast and Climate Studies [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)] at the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)]. These results show the importance of studying and evaluating the contribution of each observation to the DA system, therefore, two experiments (exp1/exp2) were performed with different configurations of the BAM model, with exp2 presenting the best fit between the Gridpoint Statistical Interpolation (GSI) and BAM systems. The BAM model was validated by the statistical metrics of root mean-square error and correlation anomaly, but this validation is not explored in this paper. A metric was applied that does not depend on the adjoint-based method, but only on the residuals that are made available in the GSI system for the observation space, given by the total impact, the fractional impact and the fractional beneficial impact. In general, the average daily showed that the observations of the global system that contribute most to the reduction of errors in the DA cycle are from the pilot balloon data (−3.54/−3.45 J kg−1)and the profilers (−2.13/−1.97 J kg−1), and the smallest contributions came from the land (−0.28/−0.29 J kg−1) and sea (−0.44/−0.44 J kg−1) surfaces. The same pattern was observed for the synoptic times presented. However, when verifying the fraction of the impact by each type of observation, it was found that the radiance data (64.88/30.30%), followed by radiosondes (14.85/27.42%) and satellite winds (11.03/22.70%), are the most important fractions for both experiments. These results show that the DA system is working to generate the best analyses at the research center and that the deficiencies found in some observations can be adjusted to improve the development of the GSI and the BAM model, since together, the entire database used is evaluated, as well as the forecast model itself, indicating the relationship between the assertiveness of the atmospheric model and the DA system used at the research center.

Capturing the Variability of the Nocturnal Boundary Layer through Localized Perturbation Modeling

Abstract A single-column model is used to investigate regime transitions within the stable atmospheric boundary layer, focusing on the role of small-scale fluctuations in wind and temperature dynamics and of turbulence intermittency as triggers for these transitions. Previous studies revealed abrupt near-surface temperature inversion transitions within a limited wind speed range. However, representing these transitions in numerical weather prediction (NWP) and climate models is a known difficulty. To shed light on boundary layer processes that explain these abrupt transitions, the Ekman layer height and its correlation with regime shifts are analyzed. A sensitivity study is performed with several types of perturbations of the wind and temperature tendencies, as well as with the inclusion of intermittent turbulent mixing through a stochastic stability equation. The effect of small fluctuations of the dynamics on regime transitions is thereby quantified. The combined results for all tested perturbation types indicate that small-scale phenomena can drive persistent regime transitions from very to weakly stable regimes, but for the opposite direction, no evidence of persistent regime transitions was found. The inclusion of intermittency prevents the model from getting trapped in the very stable regime, thus preventing the so-called ”runaway cooling”, an issue for commonly used short-tail stability functions. The findings suggest that using stochastic parameterizations of boundary layer processes, either through stochastically perturbed tendencies or parameters, is an effective approach to represent sharp transitions in the boundary layer regimes and is, therefore, a promising avenue to improve the representation of stable boundary layers in NWP and climate models.

Weather- and climate-driven power supply and demand time series for power and energy system analyses

Reaching net-zero carbon emissions requires large shares of intermittent renewable energy and the electrification of end-use consumption, such as heating, making the future energy system highly dependent on weather variability and climate change. Weather exhibits fluctuations on temporal scales ranging from sub-hourly to yearly while climate variations occur on decadal scales. To investigate the intricate interplay between weather patterns, climate variations, and power systems, we developed a database of time series of wind and solar power generation, hydropower inflow, heating and cooling demand using an internally consistent modeling framework. Here we focused on the European continent and generated country level time series extending between 1940 and 2100. Our database can be used for analyses aimed at understanding and addressing the challenges posed by the evolving energy landscape in the face of deep decarbonization and climate change.

Impacts of Climate Change on Energy-Saving Sensitivity of Residential Building Envelope Design Parameters in Three Hot-Dry Cities

Warming climatic conditions are projected to intensify extreme heat events, especially in hot climates, affecting the energy-saving effectiveness of various building envelope design parameters. However, limited studies exist on the energy-saving sensitivity of building envelope design parameters for residential buildings in hot and dry climates under future climate change. This study aims to fill this gap by examining typical residential buildings in three cities in Iraq, classified as hot desert, hot semiarid, and Mediterranean. First, the most common type of residential building was selected as a case study, and its energy modelling was validated using measured energy bills. Second, future climatic weather files were constructed using the latest general circulation models to assess their impacts on building energy consumption. Third, the energy-saving sensitivity of applicable building envelope design parameters under different future climatic conditions was obtained through local and global sensitivity analysis methods. The results of global sensitivity analysis indicate that the solar heat gain coefficient of windows, the specific heat of exterior walls, and the projection of side fins are the most important factors, with standardized regression coefficients (SRC) ranging from -0.92 to 0.62. Notably, the SRC of windows' solar heat gain coefficient is expected to increase under future climate scenarios. In contrast, the significance of the specific heat of exterior walls depends on local climates. Although this study only included envelope design parameters and excluded economic analysis, it provides insights for policymakers and architects to prioritize building envelope design strategies that enhance the climate resilience of residential buildings.

Soybean Yield Losses Related to Drought Events in Brazil: Spatial–Temporal Trends over Five Decades and Management Strategies

By the end of the decade, the world population is expected to increase by nearly one billion people, posing challenges to meeting global food demand. In this scenario, soybean production is projected to increase by 18% within this decade. Despite being the largest soybean producer, responsible for over 40% of soybeans produced worldwide, drought events often impair Brazilian production. The goals of the present research were to quantify soybean yield losses related to drought in Brazil from 1973 to 2023 at national, state, and municipal levels and to assess the spatial distribution of losses across the production areas. The hypothesis investigated is that year-to-year variations in soybean yield are closely related to water availability, considering that crop management practices are constant from year to year, while increments in soybean yield across time (more than five years) relate tightly to better crop management practices and breeding improvements. Thus, quantifying year-to-year yield losses might demonstrate the effects of water availability on soybean yield. Yield data from the 1976/1977 to 2022/2023 crop seasons from the 26 states and the Federal District came from the National Supply Company, while the Brazilian Institute of Geography and Statistics supplied yield data for the 1973/1974 to 2020/2021 crop seasons from 1998 municipalities with more than 14 crop seasons. Soybean drought yield losses were calculated for each cropping season individually at the municipal, state, and national levels, based on the deviation in the observed yield to the corresponding maximum yield in the five-year window, considering that crop management practices and genetics represent a regular increment in soybean yield, which means that production practices improved over time and deviations from year to year are mainly related to drought occurrence. Annual soybean yield loss (expressed in tons, USD, and percentage), frequency of yield loss, and severity of yield loss were calculated at national, state, and municipal levels for each cropping season. The Standardized Precipitation Index (SPI), acquired from the Brazilian Weather Forecast and Climate Studies Center at the National Space Research Institute, was used as a qualitative indicator to corroborate the assessed soybean yield losses related to drought. The results demonstrate yield losses in more than 50% of crop seasons at the national level, with a similar frequency across the five decades, albeit with lower severities in the last 30 years. The Central–West region was more stable than the South region, with yield losses of up to 74%. In five decades, yield losses related to drought events stand at 11.65%, corresponding to 280 million tons or USD 152 billion (considering the average soybean price in 2022 at the Chicago Board of Trade). At the municipal level, analogous behavior was observed across time and space. The outcomes from the present research might subsidize public and corporative policies related to agricultural zoning, farm loan programs, crop insurance contracts, and food security, contributing to higher agricultural, environmental, economic, and social sustainability.

Research on Extreme Weather Risk Management Based on Gray Prediction and Multi-Criteria Evaluation Models

This study investigates the growing challenges faced by the insurance industry due to extreme weather events and climate change, with a focus on the widening global insurance gap. We propose a novel dual-model approach to address these complex issues. First, we develop a Gray Prediction-Based Risk Assessment Framework (GPRAF) that integrates multidimensional data on natural disasters, economic development, population dynamics, and agricultural trends. This framework enables insurance companies to make more informed underwriting decisions in the context of increasing climate-related risks. Through case studies in Japan and Mexico, we demonstrate the practical applications and implications of our approach. Our research contributes to the growing body of literature on climate risk management in the insurance sector and provides valuable insights for policymakers and industry practitioners. By bridging the gap between risk assessment and community preservation needs, this study offers a holistic perspective on adapting insurance strategies to the challenges of climate change while preserving community values.

Extreme Weather, agricultural insurance and farmer's climate adaptation technologies adoption in China

We explore how farmers adjust their climate adaptation behaviors and participation in agricultural insurance in response to extreme weather events. A random effects model is constructed to clarify the relationships between extreme weather events, agricultural insurance and climate adaptation technologies (CAT). Using survey data from 622 farmers in China, Probit models and instrumental variable analysis show that extreme weather events significantly increase farmers' adoption of CAT and their participation in agricultural insurance. Furthermore, agricultural insurance significantly promotes farmers' adoption of CAT. Agricultural insurance influences the adoption of CAT through both substitutive and complementary effects, the complementary effects are more pronounced. Therefore, we recommended the continued promotion of CAT and agricultural insurance to strengthen farmers' resilience to extreme weather events.

Multi-Global Navigation Satellite System (GNSS) real-time tropospheric delay retrieval based on state-space representation (SSR) products from different analysis centers

Abstract. The troposphere plays an important role in a range of weather and various climate changes. With the development of the Global Navigation Satellite System (GNSS), the zenith tropospheric delay (ZTD) retrieval using GNSS technology has become a popular method. Research on ZTD accuracies of state-space representation (SSR) corrections from different analysis centers derived from real-time precise point positioning (RT-PPP) is important for Earth observation correction, meteorological disaster forecasting, and warning with the increasing abundance of state-space representation (SSR) products obtained by the International GNSS Service (IGS) analysis center. Therefore, accuracies and availability of real-time orbits and clock errors obtained by the Chinese Academy of Sciences (CAS), GMV Aerospace and Defense (GMV), Centre National d'Etudes Spatiales (CNE), and Wuhan University (WHU) are evaluated, and the RT positioning performance and ZTD accuracies are analyzed for Global Positioning System (GPS), Galileo (GAL), and BeiDou Navigation Satellite System-3 (BDS3) satellites. The results indicate that CAS has the higher satellite availability, providing SSR corrections for 82 GPS, Galileo, and BDS3 satellites. The accuracies of GPS, Galileo, and BDS3 orbits are best at WHU, CAS, and WHU with values of 5.57, 5.91, and 11.77 cm, respectively; the standard deviations (SDs) of clock error are all better than 0.22, 0.19, and 0.55 ns, and the root mean square errors (RMSEs) are better than 0.54, 0.32, and 1.46 ns. CAS has the best signal-in-space ranging errors (SISREs) followed by WHU, while CNE and GMV are worse. In the RT-PPP test, convergence times for CAS and WHU are 14.9 and 14.4 min, respectively, with 3D positioning accuracy for both of around 3.3 cm, which is better than for CNE and GMV. Among them, WHU SSR has the higher accuracy of RT-PPP-derived ZTD, with an RMSE of 6.06 mm and desirable availability with a completeness rate of 89 %.

Research on National Short-Term Precipitation Forecast Method Based on Machine Learning

Abstract. Climate change is closely related to human lives. With the development of information technology, meteorological data gradually show the characteristics of big data. The purpose of this study is to compare the application effects of different machine learning models in short-term precipitation prediction, to improve the accuracy and efficiency of prediction. Based on the annual average precipitation data of prefecture-level cities in China from 1990 to 2022, this paper uses Random Forest, eXtreme Gradient Boosting and Neural Networks to construct prediction models, and comprehensively evaluate them. In this study, the data is preprocessed to ensure the data quality of the input model. Next, the data is fed into three machine learning algorithm models, Random Forest, eXtreme Gradient Boosting and Neural Networks. Finally, the prediction performance of each model is evaluated by various indexes. In this paper, it is found that the Random Forest model has the best performance, and its prediction accuracy is higher than the other two models, which has great application potential in the field of short-term precipitation prediction. This study shows that a reasonable selection of machine learning methods and optimization of model parameters can effectively improve the accuracy of short-term precipitation prediction. This paper provides some empirical evidence for precipitation prediction, which will help to make more effective decisions in dealing with extreme weather events and climate change challenges in the future.

Research on National Short-Term Precipitation Forecast Method Based on Machine Learning

Abstract. Climate change is closely related to human lives. With the development of information technology, meteorological data gradually show the characteristics of big data. The purpose of this study is to compare the application effects of different machine learning models in short-term precipitation prediction, to improve the accuracy and efficiency of prediction. Based on the annual average precipitation data of prefecture-level cities in China from 1990 to 2022, this paper uses Random Forest, eXtreme Gradient Boosting and Neural Networks to construct prediction models, and comprehensively evaluate them. In this study, the data is preprocessed to ensure the data quality of the input model. Next, the data is fed into three machine learning algorithm models, Random Forest, eXtreme Gradient Boosting and Neural Networks. Finally, the prediction performance of each model is evaluated by various indexes. In this paper, it is found that the Random Forest model has the best performance, and its prediction accuracy is higher than the other two models, which has great application potential in the field of short-term precipitation prediction. This study shows that a reasonable selection of machine learning methods and optimization of model parameters can effectively improve the accuracy of short-term precipitation prediction. This paper provides some empirical evidence for precipitation prediction, which will help to make more effective decisions in dealing with extreme weather events and climate change challenges in the future.

Financial Inclusion and Climate Resilience: The Role for an AI-Enhanced Digital Wallet in Caribbean SIDS

Caribbean Small Island Developing States (SIDS) are highly vulnerable to extreme weather events and climate change. Caribbean SIDS climate vulnerability is worsened by their high level of financial exclusion. Many people do not have bank accounts and access to electronic fund transfer (EFT). As such, they cannot electronically receive funds before or after a natural disaster to cope with the effects. The financial exclusion problem can be addressed through a digital wallet. A digital wallet is a financial transaction application that securely stores a user’s banking and payment information on a cloud interface and allows the user to perform a transaction while hiding their banking information from a vendor. The biggest concern of users with regards to the use of digital wallets are its convenience and security. While digital wallets offer outstanding convenience of purchasing goods and services, data privacy and fraud risks deter people from adopting mobile payment. Potential fraud risk to digital wallets can be identified with anomaly detection techniques. The research problem investigated in this study is how the implementation of an artificial intelligence (AI) enhanced digital wallet can facilitate financial inclusion in the Caribbean, particularly in the context of disaster preparedness and recovery. Since security is an important aspect of a digital wallet, a sub-objective of this study is to derive an appropriate model for anomaly detection in financial transactions in a digital wallet. This study modifies Liu et al. [1] Gated Transformer Networks (GTN) architecture to allow for univariate time series classification. The corresponding new model is referred to as the Univariate Gated Transformer Network (UGTN). The UGTN is used for anomaly detection in financial data from a digital wallet. This study also provides policy recommendations for the implementation of a digital wallet to facilitate financial inclusion and climate resilience in Caribbean SIDS.

Wildfire aerosols and their impact on weather: A case study of the August 2021 fires in Greece using the WRF‐Chem model

AbstractWildfires are significant contributors to atmospheric gases and aerosols, impacting air quality and composition. This pollution from fires also affects radiative forcing, influencing short‐term weather patterns and climate dynamics. Our research employs the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) to investigate the repercussions of wildfires on aerosol abundances and associated immediate weather responses. We examine the summer season of 2021, a period marked by severe wildfire events in the country during a heatwave period. We conducted sensitivity experiments including and excluding wildfire emissions to measure their effects on aerosol optical depth (AOD), radiative forcing, and weather features such as temperature, humidity, clouds, and atmospheric circulation. Our findings demonstrate that the radiative impacts of wildfires negatively influence the local temperature over the fire smoke plume‐affected areas. Conversely, neighbouring areas of continental Greece experience increases in temperature due to remote effects of wildfire emissions, caused by meteorological feedbacks that reduce atmospheric humidity. Crucially, including fire emissions significantly improves the simulated surface temperatures predicted by the model over the Greek domain. Our work demonstrates that wildfire‐generated aerosols can significantly impact weather conditions and highlights the importance of including both local radiative effects and remote feedback for achieving more accurate weather prediction.

Correlation Analysis of Vertical Ground Movement and Climate Using Sentinel-1 InSAR

Seasonal vertical ground movement (SVGM), which refers to the periodic vertical displacement of the Earth’s surface, has significant implications for infrastructure stability, agricultural productivity, and environmental sustainability. Understanding how SVGM correlates with climatic conditions—such as temperatures and drought—is essential in managing risks posed by land subsidence or uplift, particularly in regions prone to extreme weather events and climate variability. The correlation of periodic SVGM with climatic data from Earth observation was investigated in this work. The European Ground Motion Service (EGMS) vertical ground movement measurements, provided from 2018 to 2022, were compared with temperature and precipitation data from MODIS and CHIRP datasets, respectively. Measurement points (MP) from the EGMS over Italy provided a value for ground vertical movement approximately every 6 days. The precipitation and temperature datasets were processed to provide drought code (DC) maps calculated ad hoc for this study at a 1 km spatial resolution and daily temporal resolution. Seasonal patterns were analyzed to assess correlations with Spearman’s rank correlation coefficient (ρ) between this measure and the DCs from the Copernicus Emergency Management Service (DCCEMS), from MODIS + CHIRP (DC1km) and from the temperature. The results over the considered area (Italy) showed that 0.46% of all MPs (32,826 MPs out of 7,193,676 MPs) had a ρ greater than 0.7; 12,142 of these had a positive correlation, and 20,684 had a negative correlation. DC1km was the climatic factor that provided the highest number of correlated MPs, roughly giving +59% more correlated MPs than DCCEMS and +300% than the temperature data. If a ρ greater than 0.8 was considered, the number of MPs dropped by a factor of 10: from 12,142 to 1275 for positive correlations and from 20,684 to 2594 for negative correlations between the DC1km values and SVGM measurements. Correlations that lagged in time resulted in most of the correlated MPs being within a window of ±6 days (a single satellite overpass time). Because the DC and temperature are strongly co-linear, further analysis to assess which was superior in explaining the seasonality of the MPs was carried out, resulting in DC1km significantly explaining more variance in the SVGM than the temperature for the inversely correlated points rather than the directly correlated points. The spatial distribution of the correlated MPs showed that they were unevenly distributed in clusters across the Italian territory. This work will lead to further investigation both at a local scale and at a pan-European scale. An interactive WebGIS application that is open to the public is available for data consultation. This article is a revised and expanded version of a paper entitled “Detection and correlation analysis of seasonal vertical ground movement measured from SAR and drought condition” which was accepted and presented at the ISPRS Mid-Term Symposium, Belem, Brasil, 8–12 November 2024. Data are shared in a public repository for the replication of the method.

Climate change and emerging infections: Risks, adaptation and response

Abstract Background Weather and changing climate are key drivers of the emergence, re-emergence and spread of infectious diseases globally. However, approaches to emerging infections and climate strategies are still largely being developed separately missing the opportunities for integrated climate adaptation actions to address infectious diseases. Description To inform the development of the UK national emerging infectious diseases plan, a literature review of the current and projected risks from extreme weather events and climate change on emerging infections in the UK is currently underway. This will support further work examining and co-designing adaptation options in the context of preparedness and response to emerging infections. Results Key preliminary findings from the literature review have indicated risks predominantly from evolving patterns of vector borne diseases as climatic conditions and habitat suitability of pathogens change. Changing temperatures and hotter summer temperatures have been associated with changing food-borne infection patterns. Further risks driven by extreme weather, including flooding and droughts, could perpetuate changes in water-borne infection patterns. Adaptation actions focussed on these infectious disease groups are currently under review and will cover risk assessment, data and surveillance, preparedness and response. Lessons This work integrates climate action with action on emerging infectious diseases. By examining the evidence on climate sensitive infections and co-designing solutions across teams, awareness is being raised and relationships and infrastructures built to better prepare and respond to both fundamental global public health challenges.

The first kind of predictability problem of El Niño predictions in a multivariate coupled data‐driven model

AbstractEl Niño‐Southern Oscillation (ENSO) is the dominant atmosphere–ocean coupled mode of year‐to‐year variations in the tropical Pacific. It shows diverse spatiotemporal characteristics and casts major influences on seasonal predictions of global weather–climate extrema. Despite numerous dynamical and statistical models for ENSO prediction and predictability studies, they are commonly subjected to one‐to‐three issues among less skillful simulation of El Niño diversity, huge requirements of computational resources and a low robustness in statistics. Here, an efficient deep‐learning model involving nonlinear coupling of multiple variables is independently developed to study the predictability of two types of El Niño events related to initial uncertainty, which is the first kind of predictability problem. The model can skillfully simulate statistically robust features of observed El Niño diversity in terms of periodicity, amplitude, and seasonal phase‐locking. Using this model, we have revealed mathematically several new types of fastest‐growing initial errors in two types of El Niño predictions based on a novel concept of conditional nonlinear optimal perturbation (CNOP), especially including one that can strengthen central Pacific types of events, which is rarely investigated before. Moreover, CNOPs are superimposed into a numerical model, GFDL CM2p1, for comprehensive validation and growth mechanism mining, which demonstrates the consistent dynamical evolution of initial errors in both numerical and AI models. Our study represents the first attempt to explore the first kind of ENSO predictability problem from perspectives of nonlinear error‐evolving dynamics using a data‐driven model. This is of great importance as it offers us sufficient confidence to perform ENSO‐related (such as the Madden–Julian Oscillation, etc.) mechanisms and predictability studies in the future without strongly relying on dynamical numerical models.

Decision Support Framework for Water Quality Management in Reservoirs Integrating Artificial Intelligence and Statistical Approaches

Planning, managing and optimising surface water quality is a complex and multifaceted process, influenced by the effects of both climate uncertainties and anthropogenic activities. Developing an innovative and robust decision support framework (DSF) is essential for effective and efficient water quality management, so it can provide essential information on water quality and assist policy makers and water resource managers to identify potential causes of water quality deterioration. This framework is crucial for implementing actions such as infrastructure development, legislative compliance and environmental initiatives. Recent advancements in computational domains have created opportunities for employing artificial intelligence (AI), advanced statistics and mathematical methods for use in improved water quality management. This study proposed a comprehensive conceptual DSF to minimise the adverse effects of extreme weather events and climate change on water quality. The framework utilises machine learning (ML), deep learning (DL), geographical information system (GIS) and advanced statistical and mathematical techniques for water quality management. The foundation of this framework is the outcomes from our three studies, where we examined the application of ML and DL models for predicting water quality index (WQI) in reservoirs, utilising statistical and mathematical methods to find the seasonal trend of rainfall and water quality, exploring the potential connection between streamflow, rainfall and water quality, and employing GIS to show the spatial and temporal variability of hydrological parameters and WQI. Three potable water supply reservoirs in the Toowoomba region of Australia were taken as the study area for practical implementation of the proposed DSF. This framework can serve as a comprehensive mechanism to identify distinct seasonal characteristics and understand correlations between rainfall, streamflow and water quality. This will enable policy makers and water resource managers to enhance their decision making processes by selecting the management priorities to safeguard water quality in the face of future climate variability, including prolonged droughts and flooding.

Seasonal environmental fluctuations alter the transcriptome dynamics of oocytes and granulosa cells in beef cows

BackgroundExamining the mechanistic cellular responses to heat stress could aid in addressing the increasing prevalence of decreased fertility due to elevated ambient temperatures. Here, we aimed to study the differential responses of oocytes and granulosa cells to thermal fluctuations due to seasonal differences. Dry beef cows (n = 10) were housed together, synchronized and subjected to a stimulation protocol to induce follicular growth before ovum pick-up (OPU). Two OPU’s were conducted (summer and winter) to collect cumulus-oocyte-complexes (COCs) and granulosa cells. In addition, rectal temperatures and circulating blood samples were collected during OPU. Oocytes were separated from the adherent cumulus cells, and granulosa cells were isolated from the collected OPU fluid. RNA was extracted from pools of oocytes and granulosa cells, followed by library preparation and RNA-sequencing. Blood samples were further processed for the isolation of plasma and leukocytes. The transcript abundance of HSP70 and HSP90 in leukocytes was evaluated using RT-qPCR, and plasma cortisol levels were evaluated by immunoassay. Environmental data were collected daily for three weeks before each OPU session. Data were analyzed using MIXED, Glimmix or GENMOD procedures of SAS, according to each variable distribution.ResultsAir temperatures (27.5 °C vs. 11.5 °C), average max air temperatures (33.7 °C vs. 16.9 °C), and temperature-humidity indexes, THI (79.16 vs. 53.39) were shown to contrast significantly comparing both the summer and winter seasons, respectively. Rectal temperatures (Summer: 39.2 ± 0.2 °C; Winter: 38.8 ± 0.2 °C) and leukocyte HSP70 transcript abundance (Summer: 4.18 ± 0.47 arbitrary units; Winter: 2.69 ± 0.66 arbitrary units) were shown to increase in the summer compared to the winter. No visual differences persisted in HSP90 transcript abundance in leukocytes and plasma cortisol concentrations during seasonal changes. Additionally, during the summer, 446 and 940 transcripts were up and downregulated in oocytes, while 1083 and 1126 transcripts were up and downregulated in the corresponding granulosa cells, respectively (Fold Change ≤ -2 or ≥ 2 and FDR ≤ 0.05). Downregulated transcripts in the oocytes were found to be involved in ECM-receptor interaction and focal adhesion pathways, while the upregulated transcripts were involved in protein digestion and absorption, ABC transporters, and oocyte meiosis pathways. Downregulated transcripts in the granulosa cells were shown to be involved in cell adhesion molecules, chemokine signaling, and cytokine-cytokine receptor interaction pathways, while those upregulated transcripts were involved in protein processing and metabolic pathways.ConclusionIn conclusion, seasonal changes dramatically alter the gene expression profiles of oocytes and granulosa cells in beef cows, which may in part explain the seasonal discrepancies in pregnancy success rates during diverging climatic weather conditions.

Fusarium Species Shifts in Maize Grain as a Response to Climatic Changes in Poland

Maize, along with wheat and rice, is the most important crop for food security. Ear rots caused by Fusarium species are among the most important diseases of maize. The distribution of Fusarium species provides essential epidemiological information for disease management. The effect of weather conditions, climate change and geographic localization on the Fusarium population in Poland was evaluated between 2015 and 2018. Grain samples (n = 233) were collected from hybrids at 16 locations (L1–L16). The differences in altitude between locations ranged from 39 to 243 m above sea level, longitude varied between 15°55′ and 23°12′ E, and latitude spanned from 50°12′ to 54°01′ N. Isolates were identified using molecular techniques. The highest Fusarium species frequency was recorded for 2016 (30.70%) and 2017 (28.18%), and the lowest for 2018 (5.36%). F. verticillioides and F. temperatum were the most frequent. Altitude has an effect on F. vericillioides frequency: F. graminearum showed a negative correlation with both latitude and longitude. In Polish conditions, from silking to harvesting, the number of days with higher precipitation and lower temperatures is associated with an increased frequency of F. verticillioides, F. temperatum, F. graminearum and F. avenaceum. This suggests that the Fusarium presence in Poland is significantly influenced not only by climate change but also by extreme weather changes.

Enhanced Performance Analysis of 120 Gbps Single‐Channel IQ Modulation Based PDM‐FSO Transmission System Using 64‐QAM Signals

ABSTRACTThe ever‐augmenting demand of data channel‐bandwidth by the end users has resulted in the exploration of new transmission techniques for wireless transmission networks. In this work, we have demonstrated the modeling of a single‐channel free space optics (FSO) transmission system by employing polarization‐division‐multiplexing (PDM) technique. Further, in‐phase‐quadrature modulation (IQM) has been used to increase the transmission efficiency of the system. To decrease the baud‐rate of the system, we have proposed the use of 64‐level quadrature amplitude modulation (QAM) signals. The net transmission rate of the system is 120 Gbps with a baud‐rate of 10 Gbps. Furthermore, the overcome the signal degradation of the 120 Gbps signal propagating through atmospheric channel, we have reported the use of advanced digital signal processing (DSP) algorithms. The system's performance is investigated for adverse external climate weather conditions and the results reported show reliable 120 Gbps data transmission along range between 870 m and 16.25 km with good performance of BER and EVM 7.5%.