Annual Weather Research Articles

Integrating experiments and monitoring reveals extreme sensitivity of invasive winter annuals to precipitation.

In arid and semiarid systems of western North America, the most damaging invasive plants are winter annuals. These plants are destroying wildlife habitat, reducing livestock production, and increasing wildfires. Monitoring these plants for lasting population changes is challenging because their abundances vary widely from year to year. Some of this variation is due to weather, and quantifying effects of weather is important for distinguishing transcient from lasting population changes and understanding effects of climate change. Fall and spring weather affect germination and seed production of the current generation of plants and, therefore, impact population sizes of subsequent generations of plants. Extensive data are required to estimate effects of fall and spring weather on multiple generations of plants. We used Bayesian statistics to integrate experimental and long-term (31 years) monitoring data and quantify invasive annual grass [downy brome (Bromus tectorum L.) and Japanese brome (Bromus japonicus Thunb.)]responses to weather. Bromes ranged from nearly absent to comprising half of total biomass depending on three previous years of weather. Brome biomass increased with precipitation one, two, and three falls prior to measurement. Fall precipitation is projected to increase, and a mere 6.5 mm increase, which is just 2% of mean annual precipitation, would increase brome biomass 40% (28%, 54%) (mean [95% CI]) according to our model. Increased fall precipitation could favor many invasive winter annual grasses and forbs. Dry spring conditions reduced brome biomass the current year but increased brome biomass one and likely two (p = 0.08) years later, perhaps because dry conditions weakened perennial competitors. This finding casts doubt on several one-year precipitation experiments that concluded drier spring weather would reduce brome abundances. Integrating short-term experiments and long-term monitoring is useful for estimating invasive plant responses to the weather and characterizing their responses to climate change. Our research provides predictions of brome abundances that could improve monitoring efforts by helping land managers interpret population dynamics in the context of seasonal precipitation patterns.

Evaluation of influential factors on energy system optimisation

Energy system analysis is gaining more importance in political decisions, science, and society, thus, the basis of interpreting the results is increasingly being questioned. Different approaches have been used in energy system models: Scenario variations, changing base assumptions or sensitivity analyses. A direct comparison of these approaches aims to show that the influence on the results is comparable for all and thus needs to be considered alongside each other. Therefore, this paper uses energy system optimisation within a case study on Germany. It confirms that all approaches greatly influence the results and are thus suitable for discussing uncertainties. Among other things, the sole change of the reference weather year can have a comparable influence on the emissions just like the specification of an entirely different mobility scenario (+44% and +56% respectively). However, the target function (total costs) is significantly more influenced by scenarios. Among the evaluated parameters, the investment costs of renewable technologies and the potential of renewable feedstock affect the system the most. In summary, all three methods provide important insights into the optimisation model and should therefore be used together for validation, discussion and design of energy system models.

Effects of Rainfall Variability on Maize Production in Migori County, Kenya

Maize is the main staple food crop in Kenya and is of vital concern to agricultural policy decisions, food security, and the overall development of the sector and the economy. It is also the dominant staple food crop in Migori-County. However, there has been a declining trend in maize production among farmers in the study area threatening household food security. This study was conducted in Migori County using cross sectional survey research design. A sample size of 384 households was selected through stratified and systematic sampling techniques though just 310 households accepted to be interviewed. Data was collected using a structured questionnaire. Validated secondary weather data for the period 2011-2023 was obtained from the Migori Meteorological Station. The collected data was entered into SPSS software, version 20, and analysed using both descriptive and inferential statistical methods. The results indicated that 60% of households have an adequate food supply, while 40% face food shortages. Further, there was a strong positive and significant relationship between maize production (yield) and annual weather conditions from 2018 to 2023 (r = 0.845, p = 0.05). This indicates that changes in weather patterns, such as rainfall and temperature, had a significant influence on maize yields during the study period. Hence, farmers should be encouraged to use income from cash crops to purchase food stocks and engage in non-farm activities as alternative income sources to mitigate the effects of rainfall variability and climate change on food security.

Liming acidic soils creates profits, land use options but often more emissions

Context Soil acidity constrains crop production in Australia. The practice of liming can reduce soil acidity but produces greenhouse gas emissions. Aims By examining land use sequences over three decades at a range of locations in Western Australia, this study aims to identify firstly where and when liming might boost farm profits and secondly, what emissions and land use management flexibilities are generated by liming. Methods Bioeconomic simulation modelling is used to identify the gross margins and emissions associated with liming in land use sequences at 14 locations in Western Australia. Three intensities of cropping and three different rotational sequences are considered. The simulations account for price and weather–year variations across a 30-year period of analysis. Key results Liming is profitable at almost all locations and across all rotation sequences examined. Where problematic soil acidity is a feature or is poised to soon become a problem at a location, liming is a profitable ameliorative practice that enables greater diversity in land use. For most situations assessed, liming increases emissions. The exceptions are at locations where liming prevents a switch away from a crop-dominant system, due to soil acidity reducing crop yields, into additional sheep production that increases emissions. Conclusions Liming is profitable in most acidic soil situations and preserves land use flexibility, although additional greenhouse gas emissions are often generated. Implications Liming acidic soils bolsters land use profitability and helps sustain biologically diverse land use sequences, despite often increasing greenhouse gas emissions.

The Pivotal Role of Evaporation in Lake Water Isotopic Variability Across Space and Time in a High Arctic Periglacial Landscape

AbstractRapidly changing climate is disrupting the High Arctic's water systems. As tracers of hydrological processes, stable water isotopes can be used for high quality monitoring of Arctic waters to better reconstruct past changes and assess future environmental threats. However, logistical challenges typically limit the length and scope of isotopic monitoring in High Arctic landscapes. Here, we present a comprehensive isotopic survey of 535 water samples taken in 2018 and 2019 of the lakes and other surface waters of the periglacial Pituffik Peninsula in far northwest Greenland. The δ18O, δ2H, and deuterium‐excess values of these samples, representing 196 unique sites, grant unprecedented insight into the environmental drivers of the regional hydrology and water isotopic variability. We find that the spatial variability of lake water isotopes can best be explained through evaporation and the hydrological ability of a lake to replace evaporative water losses with precipitation and snowmelt. Temporally, summer‐long evaporation can drive lake water isotopes beyond the isotopic range observed in precipitation, and wide interannual changes in lake water isotopes reflect annual weather differences that influenced evaporation. Following this, water isotope samples taken at individual times or sites in similar periglacial landscapes may have limited regional representativeness, and increasing the spatiotemporal extent of isotopic sampling is critical to producing accurate and informative High Arctic paleoclimate reconstructions. Overall, our survey highlights the diversity of isotopic compositions in Pituffik surface waters, and our complete isotopic and geospatial database provides a strong foundation for future researchers to study hydrological changes at Pituffik and across the Arctic.

Survival and reproduction tests using springtails reveal weathered petroleum hydrocarbon soil toxicity in boreal ecozone.

Survival and reproduction tests were conducted using two native springtail (subclass: Collembola) species to determine the toxicity of a fine-grained (< 0.005 - 0.425 mm) soil from an industrial site located in the Canadian boreal ecozone. Accidental petroleum hydrocarbon (PHC) release continuously occurred at this site until 1998, resulting in a total hydrocarbon concentration of 12,800mg/kg (soil dry weight). Subfractions of the PHC-contaminated soil were characterized using Canadian Council of Ministers of the Environment Fractions, which are based on effective carbon numbers (nC). Fraction 2 (> nC10 to nC16) was measured at 8400mg/kg and Fraction 3 (> nC16 to nC34) at 4250mg/kg in the contaminated soil. Age-synchronized colonies of Folsomia candida and Proisotoma minuta were subject to 0%, 25%, 50%, 75%, and 100% relative contamination mixtures of the PHC-contaminated and background site soil (< 100mg/kg total PHCs) for 28 and 21days, respectively. Survival and reproduction decreased significantly (Kruskal-Wallis Tests: p < 0.05, df = 4.0) in treatments of the contaminated site soil compared to the background soil. In both species, the most significant decline in survival and reproduction occurred between the 0% and 25% contaminated soil. Toxicity responses in the two springtails were ascribed to the standardized test design, short lifespans, and high fecundity in both species. This study showed that 25 + years of soil weathering has not eliminated the toxicity of fine-grained PHC-contaminated soil on two native terrestrial springtail species. Adverse effects to springtail health were attributed to exposure to soils dominated by genotoxic PHC Fraction 2 compounds and slow weathering processes due to the cold climate at the site.

Future scenario generation and reduction methods for solar photovoltaic electricity production analysis

Abstract A morphing-clustering-based method of generation and reduction future scenario for solar photovoltaic electricity production analysis is studied. Present historical annual weather data are transformed into future scenarios and timeframes by a morphing method with a general circulation model. The Copula methodology is applied to model the forthcoming decade’s annual meteorological data and generate abundant intra-day hourly scenario samples. The comparative analysis of the photovoltaic power output variations between future and present in reduction typical scenarios is conducted by the K-means clustering method with appropriate clustering parameter optimization. The simulation results show that the annual mean variations in global horizontal radiation, dry bulb temperature, and wind speed are projected to increase by approximately from 20.1 to 36.5 W/m2, 1.6 to 2.3°C, and 0.1 to 0.6 m/s, respectively in the forthcoming scenario of shared socioeconomic pathways 2-4.5. It is seen that solar photovoltaic electricity production anticipates a positive increase from 2.5% to 17.9%.

Reconstructing 120 years of climate change impacts on Joshua tree flowering.

Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (Yucca brevifolia and Y. jaegeriana), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo-referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records and reveals a slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years-but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity.

Scenarios on future electricity storage requirements in the Austrian electricity system with high shares of variable renewables

This paper presents three scenarios (policy, renewables and electrification and efficiency) for transitioning to a 100 % renewable electricity sector in Austria, based predominantly on wind and photovoltaics, alongside sector-specific electrification. Considering renewable expansion targets and three distinctive weather years from an overall system perspective, the core objective is to minimize variable costs of electricity storage and dispatchable power plants. The model developed determines their optimal dispatch for meeting the underlying electricity demand each hour. Within the scenarios for renewable expansion, a special focus lies on integrating short-duration (batteries), medium-duration (pumped storage hydro) and long-duration (hydrogen) energy storage. Our analysis reveals the significant impact of weather patterns on renewable electricity generation, particularly the differences between winter and summer generation quantities. This necessitates seasonal balancing and the mitigation of extremes like low wind power events, which require corresponding backup capacities. This contrast is particularly evident when comparing the years 2030–2050, wherein in the latter, certain dispatchable generators are only utilized in one of the three underlying weather years during extreme weather conditions. In our paper, we demonstrate how, especially for hydrogen production and storage, weather conditions influence production levels and the re-electrification demand. The results indicate the feasibility of achieving a fully decarbonized energy system in Austria through suitable policy measures and expanded renewable generation, with long-duration storage playing a crucial role in seasonal balance and compensating for the absence of fossil fuel generation. Strategic planning is essential to aligning the expansion of renewable energy generation with the necessary flexibility.

Integration of ten years of daily weather, traffic, and air pollution data from Norway’s six largest cities

This study integrates ten years of daily weather, traffic, and air pollution data across the six largest Norwegian cities, utilizing data from the Norwegian Public Roads Administration, the Norwegian Institute of Air Research, and the Norwegian Meteorological Institute. The compilation of this dataset involved detailed selection and verification of monitoring stations to ensure consistency and accuracy. Initial data collection focused on the top ten most populous cities in Norway, with the subsequent examination of traffic and air pollution monitoring sites. Weather variables were then matched to the selected sites, resulting in a comprehensive dataset from 2009 to 2018. The resulting dataset encompasses extensive information, including harmful pollutants such as Nitric oxide (NO), Nitrogen dioxide (NO2), Nitrogen oxides (NOx), Particulate Matter less than 2.5 micrometers in diameter (PM2.5), and Particulate Matter less than 10 micrometers in diameter (PM10). The dataset’s potential for further analysis and its utility in informing policy decisions underscore its significance. This integrated dataset is a valuable resource for researchers and policymakers alike, facilitating comprehensive studies on the intersection of weather, traffic, and air pollution in urban environments.

Structural characteristics and physicomechanical properties of bamboo scrimber composite during natural weathering

Bamboo-based composite materials used outdoors are exposed to diverse natural climates, necessitating an understanding of their surface, physical, and mechanical properties for long-term use. This study evaluated changes in chemical composition, surface color, microstructure, and physicomechanical attributes of bamboo scrimber composite (BSC) manufactured from heat-treated moso bamboo bundles and phenol formaldehyde resin. Over two years of weathering, photodegradation affects bamboo and phenol formaldehyde resin, especially lignin, causing surface discoloration and cracking. Initial weathering stages reduced the width swelling rate, thickness swelling rate, and shear strength of BSC. Despite this, two-year weathered BSC meets the Chinese GB/T 30364-2013 standard for outdoor flooring and exhibits strong weather resistance.

Second-best electricity pricing in France: Effectiveness of existing rates in evolving power markets

The adoption of time-varying pricing in the French electricity market has been historically low despite their availability to consumers well before the deployment of smart metering. However, as the share of variable renewable electricity increases and carbon prices grow, the demand-side response will become increasingly important to achieve efficiency gains. Relying on the historical hourly consumption of French electricity consumers and multiple prospective weather years, we study the gain allowed by the broader adoption of time-varying electricity retail rates in low-carbon power systems. We develop a four-stage methodology to assess the efficiency and stability of the electricity supply cost component of the electricity bill in France for historical and prospective years. Our analysis demonstrates that peak pricing schemes have increasing interest in the context of further deployment of renewable capacity, capturing 25% to 50% of the welfare gain reached by real-time pricing schemes. The corresponding deadweight loss of peak pricing schemes represents 0.4 to 1 bn EUR per year in 2030 compared to real-time prices (RTP) schemes. Conversely, deadweight loss of current time-of-use (ToU) rates represents 1 to 1.2 bn EUR per year in 2025 and increases over time. The current design of TOU rates does not provide adequate incentives in future power systems. Besides the tariff efficiency, the results underlined an increasing price difference between on-peak and off-peak rates. This questions the social acceptance of time-varying retail rates for consumers unable to hedge against peak prices. By analysing the effectiveness and limitations of time-varying rates, our findings underline the importance and value of price-based DR in future power systems in general. Our results highlight the need to revise the end-user tariff design in the French power system, notably with regard to the conveyed price signals.

Hymenoscyphus fraxineus persistence in the ash litter

AbstractHymenoscyphus fraxineus causes ash dieback in Europe. It overwinters on ash leaf residue (rachis) within the forest litter. In late spring, the fungus produces apothecia on the rachises and releases ascospores to infect leaves. Previous studies reported that H. fraxineus was able to produce apothecia on the rachis for 5 years after the leaf infection under artificial conditions. However, ash litter is known to decompose rapidly in situ. We therefore monitored the decomposition kinetics of ash leaf debris and the persistence of the pathogen, as well as its ability to produce apothecia in the forest litter. For this, leaves shed in autumn in stands affected by ash dieback were placed in mesh bags and left in the forest litter for 6, 18 and 30 months. At each sampling period, litter mass loss and level of colonization of the rachises by H. fraxineus were measured, as was the pathogen's ability to produce apothecia on them. Despite high fragmentation, about 14% of the rachis dry weight remained in the mesh bags after 30 months, and the pathogen retained the ability to produce apothecia on these rachises. A simulation estimating the age composition of the colonized rachises present in the litter during the fruiting period was developed from these results. It shows that the persistence of H. fraxineus in old rachises of the litter represents a reservoir of inoculum that could compensate for poor colonization of autumn leaves and revive local outbreaks after years of unfavourable weather for the development of the pathogen.

Harnessing Curtailed Wind-Generated Electricity via Electrical Water Heating Aggregation to Alleviate Energy Poverty: A Use Case in Ireland

Ireland experiences high energy poverty rates alongside surplus wind energy resources. With 77% of Irish households equipped with electrical immersion heaters for domestic hot water (DHW) generation, this study proposes an Electrical Water Heating Aggregation (EWHA) scheme. The scheme allocates surplus wind-generated electricity to provide DHW to fuel-poor households, thereby alleviating energy poverty through harnessing curtailed wind energy. Through a developed wind-generated electricity allocation model and half-hourly data analysis for a weather year, this research assesses the feasibility and economic viability of the EWHA scheme, focusing on the householder as the primary benefactor from the scheme (as opposed to ancillary grid service provision). The results suggest an optimal aggregation size where maximum curtailment and carbon offset coincide with maximum benefits for participants. The findings indicate that fuel-poor households in Ireland could receive a full DHW tank every three weeks using surplus wind energy, harnessing 89% of overnight curtailed wind energy and offsetting 33 MkgCO2 annually. Moreover, the scheme could potentially save the Irish state approximately EUR 4 million by 2030, increasing to EUR 11 million by 2050, in carbon costs. Overall, this research demonstrates the potential of EWHA schemes to alleviate energy poverty, optimise wind energy utilisation, and contribute significantly to carbon emission reduction targets.

Typical and extreme weather datasets for studying the resilience of buildings to climate change and heatwaves

We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001–2020), future mid-term (2041–2060), and future long-term (2081–2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 “Resilient Cooling for Buildings”, are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.

Near-, medium- and long-term impacts of climate change on the thermal energy consumption of buildings in Finland under RCP climate scenarios

The building sector is critical in climate change mitigation by reducing its energy consumption. At the same time, warming climate requires adaptation measures from buildings, making it necessary to study the future thermal energy demand of buildings. This paper studies the impact of climate change on the thermal energy demand of buildings in a heating dominated climate of Finland, under “representative concentration pathway” (RCP) climate scenarios in near- (2030), medium- (2050) and long-term (2080) horizon. Additionally, the impact of the thermal performance level of the buildings is investigated. The thermal energy demand calculation is based on overarching standards from the European “energy performance of buildings” directive. The weather scenarios present both typical and extreme weather years, applied through morphing and future weather data from the Finnish Meteorological Institute. The results show that annual thermal energy demand in Finland in 2080 can decrease up to 74.6kWh/m2. In addition, the decrease in heating demand will be higher than the increase in cooling demand. Cooling can become the dominant peak in long-term evaluations, especially in South Finland (Vantaa) with passive buildings under RCP8.5 climate scenario. Renovation is the most efficient action to decrease energy intensity, the earliest it is done the better.

Natural Hazards in a Changing World: Methods for Analyzing Trends and Non‐Linear Changes

AbstractEstimating the frequency and magnitude of natural hazards largely hinges on stationary models, which do not account for changes in the climatological, hydrological, and geophysical baseline conditions. Using five diverse case studies encompassing various natural hazard types, we present advanced statistical and machine learning methods to analyze and model transient states from long‐term inventory data. A novel storminess metric reveals increasing European winter windstorm severity from 1950 to 2010. Non‐stationary extreme value models quantify trends, seasonal shifts, and regional differences in extreme precipitation for Germany between 1941 and 2021. Utilizing quantile sampling and empirical mode decomposition on 148 years of daily weather and discharge data in the European Alps, we assess the impacts of changing snow cover, precipitation, and anthropogenic river network modifications on river runoff. Moreover, a probabilistic framework estimates return periods of glacier lake outburst floods in the Himalayas, demonstrating large differences in 100‐year flood levels. Utilizing a Bayesian change point algorithm, we track the onset of increased seismicity in the southern central United States and find correlation with wastewater injections into deep wells. In conclusion, data science reveals transient states for very different natural hazard types, characterized by diverse forms of change, ranging from gradual trends to sudden change points and from altered seasonality to overall intensity variations. In synergy with the physical understanding of Earth science, we gain important new insights into the dynamics of the studied hazards and their possible mechanisms.

The performance of solvent-based direct air capture across geospatial and temporal climate regimes

IntroductionLiquid-solvent direct air capture (DAC) is a prominent approach for carbon dioxide removal but knowing where to site these systems is challenging because it requires considering a multitude of interrelated geospatial factors. Two of the most pressing factors are: (1) how should DAC be powered to provide the greatest net removal of CO2 and (2) how does weather impact its performance?.MethodsTo investigate these questions, this study develops a process-level model of a liquid-solvent DAC system and couples it to a 20-year dataset of temperature and humidity conditions at a ~9km resolution across the contiguous US.Results and discussionWe find that the amount of CO2 sequestered could be 30% to 50% greater than the amount of CO2 removed from the atmosphere if natural gas is burned on site to power DAC, but that the optimal way to power DAC is independent of capture rate (i.e., weather), depending solely on the upstream GHG intensity of electricity and natural gas. Regardless of how it is powered, air temperature and humidity conditions can change the performance of DAC by up to ~3x and can also vary substantially across weather years. Across the continuous US, we find that southern states (e.g., Gulf Coast) are preferrable locations for a variety of reasons, including higher and less variable air temperature and relative humidity. Lastly, we also find the performance of liquid-solvent DAC calculated with monthly means is within 2% of the estimated performance calculated with hourly data for more than a third of the country, including in the states with weather most favorable for liquid-solvent DAC.

Long-term “memory” of extraordinary climatic seasons in the hysteretic seepage of an unsaturated infinite slope

This paper presents a study of the hydraulic response of an infinite unsaturated slope exposed to a perturbation of the ordinary seasonal climatic cycle. The ground flow is modelled via a simplified one-dimensional finite difference scheme by decomposing the two-dimensional slope seepage into antisymmetric and symmetric parts. The numerical scheme incorporates two distinct hysteretic and non-hysteretic soil water retention laws, whose parameters have been selected after a preliminary sensitivity analysis. Results indicate that, in the hysteretic case, the “memory” of the perturbation takes a long time to fade, and the ordinary soil saturation cycle is only restored after several years of normal weather. Instead, in the non-hysteretic case, the recovery of the ordinary saturation regime is almost immediate after the perturbation. In contrast with the markedly different predictions of degree of saturation, both hysteretic and non-hysteretic slope models predict virtually identical evolutions of negative pore water pressures, with an almost immediate restoration of the ordinary cycle after the perturbation.

Convection‐Permitting ICON‐LAM Simulations for Renewable Energy Potential Estimates Over Southern Africa

AbstractRenewable energy is recognized in Africa as a means for climate change mitigation, but also to provide access to electricity in sub‐Saharan Africa, where three‐quarters of the global population without electricity resides. Reliable and highly resolved renewable energy potential (REP) information is indispensable to support power plants expansion. Existing atmospheric data sets over Africa that are used for REP estimates are often characterized by data gaps, or coarse resolution. With the aim to overcome these challenges, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON‐NWP) model in its Limited Area Mode (ICON‐LAM) is implemented and run over southern Africa in a hindcast dynamical downscaling setup at a convection‐permitting 3.3 km horizontal resolution. The simulation time span covers contrasting solar and wind weather years from 2017 to 2019. To assess the suitability of the novel simulations for REP estimates, the simulated hourly 10 m wind speed (sfcWind) and hourly surface solar irradiance (rsds) are extensively evaluated against a large compilation of in situ observations, satellite, and composite data products. ICON‐LAM reproduces the spatial patterns, temporal evolution, the variability, and absolute values of sfcWind sufficiently well, albeit with a slight overestimation and a mean bias (mean error (ME)) of 1.12 m s−1 over land. Likewise the simulated rsds with an ME of 50 W m−2 well resembles the observations. This new ICON simulation data product will be the basis for ensuing REP estimates that will be compared with existing lower resolution data sets.