Annual Weather Research Articles

‘Unscrambling’ the drivers of egg production in Agassiz’s desert tortoise: climate and individual attributes predict reproductive output

The ‘bet hedging’ life history strategy of long-lived iteroparous species reduces short-term reproductive output to minimize the risk of reproductive failure over a lifetime. For desert-dwelling ectotherms living in variable and unpredictable environments, reproductive output is further influenced by precipitation and temperature via effects on food availability and limits on activity. We assembled multiple (n = 12) data sets on egg production for the threatened Agassiz’s desert tortoiseGopherus agassiziiacross its range and used these data to build a range-wide predictive model of annual reproductive output as a function of annual weather variation and individual-level attributes (body size and prior-year reproductive status). Climate variables were more robust predictors of reproductive output than individual-level attributes, with overall reproductive output positively related to prior-year precipitation and an earlier start to the spring activity season, and negatively related to spring temperature extremes (monthly temperature range in March-April). Reproductive output was highest for individuals with larger body sizes that reproduced in the previous year. Expected annual reproductive output from 1990-2018 varied from 2-5 to 6-12 eggs female-1yr-1, with a weak decline in expected reproductive output over this time (p = 0.02). Climate-driven environmental variation in expected reproductive output was highly correlated across all 5 Recovery Units for this species (Pearson’s r > 0.9). Overall, our model suggests that climate change could strongly impact the reproductive output of Agassiz’s desert tortoise, and could have a negative population-level effect if precipitation is significantly reduced across the species’ range as predicted under some climate models.

Sustainable development of China's smart energy industry based on artificial intelligence and low‐carbon economy

AbstractWith the continuous advancement of urbanization, the contradiction between urban development and environmental resources has become increasingly prominent, and environmental pollution has become increasingly serious. To fundamentally solve the problems of environment, energy, and low carbon, we must rely on the intelligence of energy. This paper aims to study the sustainable development of China's intelligent energy industry based on artificial intelligence and low‐carbon economy. In view of the problems existing in the optimization of power generation industry, this paper uses the annual load, electricity price, weather, and climate data of a southern power grid, uses the statistical variation particle swarm optimization algorithm, uses the historical runoff and rainfall data to optimize it, and studies the analysis methods, characteristics and laws of short‐term load, electricity price and runoff, as well as the uncertain factors affecting their changes. The experimental results show that the predicted price is close to the actual price, and the median error of each period is <1% in statistical analysis, so the forecast value can be used to replace the actual value for scheduling. This method makes full use of the adaptive mutation in the late stage of particle optimization, and introduces the mechanism of particle size selection, which fully ensures the diversity of particles and improves the search ability of particles.

Economic and environmental consequences of nitrogen application rates, timing and methods on corn in Ontario

Large scale adoption of nitrogen (N) management practices, including the 4Rs, could lead to beneficial environmental and economic outcomes, but only if they improve agricultural profit or are driven by policy and legislation. This study uses a spatial economic maximization and environmental simulation model to examine the economic and environmental tradeoffs of adopting three different 4R practices, specifically N rate reduction, timing (sidedress versus at-planting) and placement methods (broadcast versus injection) in Ontario corn production, under alternative annual weather conditions. The study shows that the use of optimized agronomic, based on meeting crop N requirements specific to each fertilizer management practice, rather than historic, N application increases annual average Ontario-wide farm profit from $124 million to $173 million (39.5%), decreases N application from 122 to 97 kt (20.5%), decreases nitrous oxide (N2O) emissions from 3.4 to 2.4 kt (29.4%), decreases ammonia (NH3) volatilization from 12.4 to 1.2 kt (90.3%) and decreases nitrate (NO3−) leaching from 47 to 13 kt (72.3%), over 30-years of weather data. Injection and sidedress appear to be more N use efficient than broadcast, with sidedress becoming increasingly important as N prices and constraints increase. N2O emissions appear to be more difficult to mitigate compared to NH3 emissions and NO3− leaching. Regarding the 4Rs, right placement and right timing play a role under specific spatial conditions, while right rate appears to be the most effective means to mitigate the environmental impacts of corn N application and may be mutually beneficial to farmers and society.

Investigating the Potential Impact of Future Climate Change on UK Supermarket Building Performance

The large-scale shifts in weather patterns and an unprecedented change in climate have given rise to the interest in how climate change will affect the carbon emissions of supermarkets. This study investigates the implications of future climatic conditions on the operation of supermarkets in the UK. The investigation was conducted by performing a series of energy modelling simulations on a LIDL supermarket model in London, based on the UK Climate Projections (UKCP09) future weather years provided by the Chartered Institution of Building Services Engineers (CIBSE). Computational fluid dynamic (CFD) simulations were used to perform the experiment, and the baseline model was validated against the actual data. This investigation ascertains and quantifies the annual energy consumption, carbon emissions, and cooling and heating demand of the supermarket under different climatic projections, which further validate the scientific theory of annual temperature rise as a result of long-term climatic variation. The maximum percentage increase for the annual energy consumption for current and future weather data sets observed was 7.01 and 6.45 for the 2050s medium emissions scenario, (90th) percentile and high emissions scenario, (90th) percentile, respectively, and 11.05, 14.07, and 17.68 for the 2080s low emissions scenario, (90th) percentile, medium (90th) percentile and high emissions scenario (90th) percentile, respectively. A similar inclining trend in the case of annual CO2 emissions was observed where the peak increase percentage was 6.80 and 6.24 for the 2050s medium emissions scenario, (90th) percentile and high (90th) percentile, respectively and 10.84, 13.84, and 17.45 for the 2080s low emissions scenario, (90th) percentile, medium emissions scenario (90th) percentile and high emissions scenario (90th) percentile, respectively. The study also analyses the future heating and cooling demands of the three warmest months and three coldest months of the year, respectively, to determine future variance in their relative values.

Analysis of Climate Change Trend in the Lower Kaski District of Nepal

Climate change is considered as the most critical global challenge of the century. Unusual precipitation pattern and outflanking of hydraulic structures frequently reported these days in the Kaski district of Nepal. This research aimed to analyze the trend of climate change in the lower Kaski using annual and seasonal weather data (2001-2017) of five meteorological stations out of which three on the upper tropical region, one on the sub-tropical region and the other one on the temperate region. Individual trend analysis was performed for rainfall and temperature patterns using Mann-Kendell test. Trend analysis for seasonal average precipitation shows an increasing trend for winter and pre-monsoon seasons and a decreasing trend for monsoon and post-monsoon seasons. In contrast, overall annual precipitation shows a decreasing trend. An increasing trend was found for maximum seasonal temperature for all seasons except pre-monsoon season. The seasonal minimum temperature shows a growing trend and overall annual trends for both the maximum and minimum temperatures were also increasing. All of these trends are the evidence of climatic changes that are happening over time. Additionally, a comparative study was conducted between a meteorological station and a locally established manual station. The differences in the numeric values of cumulative rainfall with comparable precipitation readings suggest the need to take into account local station data for design, construction, and planning of hydraulic structures.

Typical-year and multi-year building energy simulation approaches: A critical comparison

Building Energy Simulation (BES) tools usually make use of single Typical Weather Years (TWYs) but the increased calculation capacity now allows for multi-year simulations based on Actual Weather Years (AWYs). This research analyzes how the use of a TWY affects the prediction of the energy demand and the peak load in a residential and an office building located in Catania (Italy), if compared to a multi-year approach. Different TWYs are considered, either developed by the authors according to standard international procedures and based on recent measurements from a local weather station, or already available on the internet. Results show that the adoption of a TWY is useful to assess the mean long-term energy demand, with a preference for the IWEC procedure (mean bias error below 2%). However, TWYs would underestimate the peak heating load by 12.5% and the peak cooling load by around 18% in the residential building, while in the office building the underestimation can be even higher. In the absence of specific design weather files when the aim of the simulations consists in sizing the mechanical systems, the peak values of the AWYs distribution based on the last 10–15 years of measurement should be preferred to TWYs.

The Value of Inter-Regional Coordination and Transmission in Decarbonizing the US Electricity System

Summary Preventing global warming in excess of 1.5°C–2°C requires a transition to zero-carbon electricity systems by midcentury along with the widespread electrification of other sectors. Current state-level renewable portfolio standards and regional transmission arrangements do not capture the benefits of inter-regional transmission or coordination of planning and dispatch for renewable-energy integration. Here, using a co-optimized capacity-planning and dispatch model over 7 years of hourly operation, we show that inter-state coordination and transmission expansion reduce the system cost of electricity in a 100%-renewable US power system by 46% compared with a state-by-state approach, from 135 $/MWh to 73 $/MWh. Sensitivity analyses show that reductions in the cost of photovoltaics, wind, and lithium-ion batteries lead to the lowest electricity costs for systems in which transmission expansion is allowed, while cost reductions for nuclear power or long-duration energy storage lead to greater electricity cost reductions for isolated systems.

Resilience of an Integrated Crop–Livestock System to Climate Change: A Simulation Analysis of Cover Crop Grazing in Southern Brazil

Integrated crop–livestock systems are a form of sustainable intensification of agriculture that rely on synergistic relationships between plant and animal system elements to bolster critical agroecosystem processes, with potential impacts on resilience to weather anomalies. We simulated productivity dynamics in an integrated cover crop grazing agroecosystem typical of southern Brazil to gain a better understanding of the impacts of livestock integration on system performance, including future productivity and resilience under climate change. Long-term historical simulations in APSIM showed that the integrated system resulted in greater system-wide productivity than a specialized control system in 77% of simulated years. Although soybean yields were typically lower in the integrated system, the additional forage and livestock production increased total system outputs. Under simulated future climate conditions [representative concentration pathway 8.5 (RCP8.5) scenario from 2020 to 2060], integrated system productivity exceeded specialized system productivity in 95% of years despite declines in average soybean yield and aboveground cover crop biomass production. While the integrated system provided a productivity buffer against chronic climate stress, its resilience to annual weather anomalies depended on disturbance type and timing. This study demonstrates the utility of process-based models for exploring biophysical proxies for resilience, as well as the potential advantages of livestock integration into cropland as a sustainable intensification strategy.

Stronger together: Multi-annual variability of hydrogen production supported by wind power in Sweden

Hydrogen produced from renewable electricity will play an important role in deep decarbonisation of industry. However, adding large electrolyser capacities to a low-carbon electricity system also increases the need for additional electricity generation from variable renewable energies. This will require hydrogen production to be variable unless other sources provide sufficient flexibility. Existing sources of flexibility in hydro-thermal systems are hydropower and thermal generation, which are both associated with sustainability concerns. In this work, we use a dispatch model for the case of Sweden to assess the power system operation with large-scale electrolysers, assuming that additional wind power generation matches the electricity demand of hydrogen production on average. We evaluate different scenarios for restricting the flexibility of hydropower and thermal generation and include 29 different weather years to test the impact of variable weather regimes. We show that (a) in all scenarios electrolyser utilisation is above 60% on average, (b) the inter-annual variability of hydrogen production is substantial if thermal power is not dispatched for electrolysis, and (c) this problem is aggravated if hydropower flexibility is also restricted. Therefore, either long-term storage of hydrogen or backup hydrogen sources may be necessary to guarantee continuous hydrogen flows. Large-scale dispatch of electrolysis capacity supported by wind power makes the system more stable, if electrolysers ramp down in rare hours of extreme events with low renewable generation. The need for additional backup capacities in a fully renewable electricity system will thus be reduced if wind power and electrolyser operation are combined in the system.

A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt

Crop yield maps estimated from satellite data increasingly are used to understand drivers of yield trends and variability, yet satellite-derived regional maps are rarely compared with location-specific yields due to the difficulty of acquiring sub-field ground truth data at scale. In commercial agricultural systems, combine harvesters with onboard yield monitors collect real-time yield data during harvest with high spatial resolution, generating a large ground dataset. Here, we leveraged a yield monitor dataset of over one million maize field observations across the United States Corn Belt from 2008 to 2018 to evaluate the Scalable Crop Yield Mapper (SCYM). SCYM uses region-specific crop model simulations and climate data to interpret vegetation indices from satellite observations, thus enabling efficient sub-field yield estimation across large regions and multiple years without reliance on ground data calibration. We used the ground dataset to compare alternative SCYM model implementations, define minimum required satellite observation criteria, and evaluate the sensitivity of satellite-based yield estimates to on-the-ground variation in management, soil, and annual weather. We found that smoothing annual time series data with harmonic regression increased 30 m pixel-level accuracy from r2 = 0.31 to 0.40 and reduced dependency on specific satellite observation timing, enabling robust yield estimation on 97% of annual maize area using only Landsat data. Agreement improved as the assessment was aggregated to field-level (r2 = 0.45) and county-level (r2 = 0.69) scales, demonstrating the need for fine-resolution ground truth data to better assess sub-field level accuracy in high resolution products. We found that SCYM and ground data showed a similar yield response to management and environmental variation, particularly capturing linear and nonlinear responses to sowing density, soil water holding capacity, and growing season precipitation. However, sensitivity to factors like soil quality and planting date was muted for SCYM estimates compared to ground-based yields. Random forest models were able to match SCYM performance when trained on at least 1000 ground observations, but performed poorly when tested on years and locations not represented in the training data. Our results demonstrate that satellite yield maps can provide much-needed information on multidecadal yield trends and inform yield gap analyses.

Short-term reaction of European beech stem taper due to weather extremes

This study analyses a tree’s short-term allocation pattern under varying weather conditions. Based on a sample of 311 stem discs of the stem boles from 92 European beech (Fagus sylvatica L.) trees, the annual stem taper was calculated retrospectively over multiple decades and linked to the annual weather conditions. The bole shape results from long-term local density and stand structure. A general understanding of short-term tree allometry due to weather conditions is even more relevant as future climate developments are assumed to cause drought stress in Central-Europe more frequently.Taper was computed by linear regression analysis and normalized with a medium-stiff spline that considers mid-term impacts of thinning, other disturbances such as insect attacks and age, but neglected short-term impacts of weather conditions. Subtracting the standardized taper by one, the percentage change of taper was confronted with the temperature and precipitation of the vegetation period.Compared to years of normal weather conditions, median taper changed by +0.090% in favorable and −0.080% in unfavorable years. In particular, rainfall influenced significantly annual taper change by +4.15 × 10−4% mm−1, i.e. larger amount of precipitation during the vegetation period accelerated bole taper.Bole taper sums up many past aspects of tree environment like competition situation and growing conditions over time. Even when the annual weather impact on taper demonstrated in this study was small, it relates to tree stability and timber quality, such as fiber deviation in boards.

Small-scale renewable polygeneration system for off-grid applications: Desalination, power generation and space cooling

A stand-alone small-scale renewable heat powered polygeneration system to provide cold for space cooling, electricity, and seawater desalination was proposed and numerically modelled. The system is based on Permeate/Conductive-gap MD modules (P/CGMD) and an ammonia/water absorption power-refrigeration system driven by heat from an evacuated flat plate solar collector field with a biomass-fired backup boiler. Energy and exergy performance of the system was analysed using climate conditions of a typical location well-endowed with solar irradiation and characterized by a potential shortage of freshwater supply and high cooling demand. In the base-case, the system provided 130 kW of cooling capacity at 11 °C, 6.4 kW of net electrical power, and 41.4 m3/day of desalinated water based on annual average weather conditions of Almería (Spain). The overall system's resource utilization efficiency and exergy efficiency were estimated at 44.2% and 6.9% respectively. The use of CGMD reduced the required specific membrane surface area by 53% compared to the PGMD. The results of this study show that a thermally coupled absorption refrigeration system and MD process can be implemented (i) to increase the efficiency of the entire thermal energy conversion process and (ii) to cost-effectively utilize the solar thermal installation, particularly in regions where desalination is a necessity.

Changing environmental conditions and structure of a breeding population of the threatened Lesser White-fronted Goose (Anser erythropus L.)

Migratory birds breeding at high latitudes face challenges in relation to timing of breeding vs. annual weather, climate change, and predator abundance. Hunting pressure along migration routes and wintering quarters forms an additional challenge. We studied population structure and interaction with environmental factors in a small population of threatened Lesser White-fronted Geese Anser erythropus, living in sub-arctic zone in Lapland in 1989–1996. Thereafter the population disappeared. The population comprised 2–15 breeding pairs plus 0–12 non-breeders, which left in June to moult elsewhere. 30 broods were observed (0–8 annually) with an average number of 2.9 goslings. Of the 3 satellite tagged plus 7 ringed geese at least 3 were shot and altogether 4 killed during the first year.

Warming weather changes the chemical composition of oat hulls

The current threats of climate change are driving attention away from the petrochemical industry towards more sustainable and bio‐based production processes for fuels and speciality chemicals. These processes require suitable low‐cost starting material. One potential material assessed here is the oat hull. Its overall chemical composition has so far not been fully characterized. Furthermore, it is not known how it is affected by extreme weather events.Oat hulls (Kerstin and Galant varieties) grown during ‘normal’ weather years (2016 and 2017) are compared to the harvest of the warmer and drier year (2018). Standard methods for determination of plant chemical composition, with focus on carbohydrate composition, are utilized.Oat hulls grown in ‘normal’ weather conditions (2017) are rich in lignocellulose (84%), consisting of 35% hemicellulose, 25% lignin and 23% cellulose. Arabinoxylan was found to be the major biopolymer (32%). However, this composition is greatly influenced by weather variations during the oat growth phase. A lignocellulose reduction of 25% was recorded in the warmer and drier 2018 harvest. Additionally, a 6.6‐fold increase in starch content, a four‐fold increase in protein content and a 60% decrease in phenolic content was noted.Due to its high lignocellulose composition, with an exceptionally large hemicellulose fraction, the chemical composition of oat hulls is unique among agricultural by‐products. However, this characteristic is significantly reduced when grown in warmer and drier weather, which could compromise its suitability for use in a successful biorefinery.

A data-driven simulation platform to predict cultivars\u2019 performances under uncertain weather conditions

In most crops, genetic and environmental factors interact in complex ways giving rise to substantial genotype-by-environment interactions (G×E). We propose that computer simulations leveraging field trial data, DNA sequences, and historical weather records can be used to tackle the longstanding problem of predicting cultivars’ future performances under largely uncertain weather conditions. We present a computer simulation platform that uses Monte Carlo methods to integrate uncertainty about future weather conditions and model parameters. We use extensive experimental wheat yield data (n = 25,841) to learn G×E patterns and validate, using left-trial-out cross-validation, the predictive performance of the model. Subsequently, we use the fitted model to generate circa 143 million grain yield data points for 28 wheat genotypes in 16 locations in France, over 16 years of historical weather records. The phenotypes generated by the simulation platform have multiple downstream uses; we illustrate this by predicting the distribution of expected yield at 448 cultivar-location combinations and performing means-stability analyses.

Updated Typical Weather Years for the Energy Simulation of Buildings in Mediterranean Climate. A Case Study for Sicily

Building energy simulations are normally run through Typical Weather Years (TWYs) that reflect the average trend of local long-term weather data. This paper presents a research aimed at generating updated typical weather files for the city of Catania (Italy), based on 18 years of records (2002–2019) from a local weather station. The paper reports on the statistical analysis of the main recorded variables, and discusses the difference with the data included in a weather file currently available for the same location based on measurements taken before the 1970s but still used in dynamic energy simulation tools. The discussion also includes a further weather file, made available by the Italian Thermotechnical Committee (CTI) in 2015 and built upon the data registered by the same weather station but covering a much shorter period. Three new TWYs are then developed starting from the recent data, according to well-established procedures reported by ASHRAE and ISO standards. The paper discusses the influence of the updated TWYs on the results of building energy simulations for a typical residential building, showing that the cooling and heating demand can differ by 50% or even 65% from the simulations based on the outdated weather file.

Using a deep temporal convolutional network as a building energy surrogate model that spans multiple climate zones

Surrogate models can emulate physics-based building energy simulation with a machine learning model trained on simulation input and output data. The trained model is extremely fast to run, allowing us to estimate simulation outcomes for thousands of different building designs in seconds. Recent studies have shown the diverse benefits for sustainable building design. Surrogates were applied to provide rapid feedback at the early design stage, to accelerate sensitivity analysis, uncertainty analysis and design optimization, or to improve building model calibration.However, the current process of surrogate modelling offers much room for improvement. In particular, a surrogate model is bound to the specific building design problem it has been trained for. This includes a specific site, requiring time-intensive retraining if the building performance at another location is to be analysed.In this paper, we develop a single surrogate model that spans arbitrarily many locations. For that purpose, we are among the first to use a deep temporal convolutional neural network to process annual multivariate weather data with hourly resolution (≈150,000 inputs). The network learns features relevant to estimate heating or cooling demand. We combine these location-specific weather features with building design parameters to serve as input to a single surrogate model (feed-forward neural network). In a case study with 569 weather files from locations in Canada, we show that the surrogate model deviates by less than 3% when predicting annual heating demand for new building designs at locations outside of the training data set.

How to model European electricity load profiles using artificial neural networks

We present a method to create synthetic, weather-dependent, annual electricity load profiles for European countries in hourly resolution using artificial neural networks as a necessary basis for long-term forecasts. To this end, we train fully connected dense artificial neural networks with 5 hidden layers and 1,024 hidden nodes per layer using historic data for Germany from 2006 to 2015. Input parameters used in the model comprise calendrical information, annual peak loads and weather data. We benchmark our results against the current state-of-the-art method to generate synthetic load profiles used in mid-term adequacy forecasts published by the European Network of Transmission System Operators (entso-e). For validation year 2016, our approach shows a mean absolute percentage error of 2.8%, whereas the method as used by entso-e shows an average error of 4.8%. We then conduct forecasts for Germany, Sweden, Spain, and France using our synthetic load profiles for scenario year 2025 to demonstrate their pan-European applicability. Finally, we assess parameter variations that demonstrate high influences of outdoor temperatures and wind speed on the electricity load. Our approach can help to increase prediction accuracy of future electricity loads as electricity load profiles are a necessary input for these forecasts.

Frequency and duration of low-wind-power events in Germany

In the transition to a renewable energy system, the occurrence of low-wind-power events receives increasing attention. We analyze the frequency and duration of such events for onshore wind power in Germany, based on 40 years of reanalysis data and open software. We find that low-wind-power events are less frequent in winter than in summer, but the maximum duration is distributed more evenly between months. While short events are frequent, very long events are much rarer. Every year, a period of around five consecutive days with an average wind capacity factor below 10% occurs, and every ten years a respective period of nearly eight days. These durations decrease if only winter months are considered. The longest event in the data lasts nearly ten days. We conclude that public concerns about low-wind-power events in winter may be overrated, but recommend that modeling studies consider multiple weather years to properly account for such events.

Weather-driven infiltration and interzonal airflow in a multifamily high-rise building: Dwelling infiltration distribution

As multifamily high-rise buildings have various infiltration and interzonal airflow distributions caused by wind and stack interactions, dwelling infiltration from the outdoor air and interzonal airflow varies depending on the floor and direction. Thus, the different infiltration is one of major factors on the indoor air or energy demand loads of dwelling units in the buildings. This study characterizes dwelling infiltration distributions in multifamily high-rise buildings that accounts for infiltration and interzonal airflow, which are caused by the interactions of stack and wind effects. To do so, typical reference choices were determined for the building, annual weather conditions, leakage area levels, and analysis methods. The infiltration and interzonal airflow were characterized by season, floor, household (or direction), and leakage level. While past studies largely ignored the contribution of interzonal airflow, this paper demonstrates here that this contribution is predominant (about 80%) and exhibits significant variation between floors. Finally, this study finds that infiltration rates by household and interzonal airflow differences by floor should be addressed more carefully for annual dwelling infiltration distributions. In addition, three leakage relationships are defined by air driving forces (stack and/or wind effects) and household locations in terms of the annual dwelling infiltration distribution. Considering the leakage area of household entrance door is emphasized for dwelling infiltration. Based on the leakage level of household entrance, interesting airflow features, such as wind-driven interzonal airflow or wind-driven infiltration on upper floors under the strong stack effect, are observed.