Inter-annual Weather Variability Research Articles

Designing a sector-coupled European energy system robust to 60 years of historical weather data

As energy systems transform to rely on renewable energy and electrification to mitigate climate change, they encounter stronger year-to-year variability in energy supply and demand. Yet, most infrastructure planning relies on a single weather year, risking a potential lack of robustness. In this paper, we optimize capacity layouts for a European energy system under net-zero CO2 emissions for 62 different weather years. Subsequently, we fix the layouts and optimize their operation in every other weather year to assess resource adequacy and CO2 emissions. Our analysis shows a variation of ± 10% in total system costs across weather years. Layouts designed for years with compound weather events prove more robust, achieving resource adequacy of 99.9% and net-negative CO2 emissions of −0.5% per year relative to 1990 levels. CO2-emitting backup generation regulated by a CO2 tax offers a cost-effective measure to enhance robustness. It increases emissions only marginally, keeping average emissions below 1% of 1990 levels over all layouts. Our findings underscore the need for policymakers and energy stakeholders to account for interannual weather variability in future infrastructure planning.

Modeling cheatgrass distribution, abundance, and response to climate change as a function of soil microclimate.

Exotic annual grass invasions in water-limited systems cause degradation of native plant and animal communities and increased fire risk. The life history of invasive annual grasses allows for high sensitivity to interannual variability in weather. Current distribution and abundance models derived from remote sensing, however, provide only a coarse understanding of how species respond to weather, making it difficult to anticipate how climate change will affect vulnerability to invasion. Here, we derived germination covariates (rate sums) from mechanistic germination and soil microclimate models to quantify the favorability of soil microclimate for cheatgrass (Bromus tectorum L.) establishment and growth across 30 years at 2662 sites across the sagebrush steppe system in the western United States. Our approach, using four bioclimatic covariates alone, predicted cheatgrass distribution with accuracy comparable to previous models fit using many years of remotely-sensed imagery. Accuracy metrics from our out-of-sample testing dataset indicate that our model predicted distribution well (72% overall accuracy) but explained patterns of abundance poorly (R2 = 0.22). Climatic suitability for cheatgrass presence depended on both spatial (mean) and temporal (annual anomaly) variation of fall and spring rate sums. Sites that on average have warm and wet fall soils and warm and wet spring soils (high rate sums during these periods) were predicted to have a high abundance of cheatgrass. Interannual variation in fall soil conditions had a greater impact on cheatgrass presence and abundance than spring conditions. Our model predicts that climate change has already affected cheatgrass distribution with suitable microclimatic conditions expanding 10%-17% from 1989 to 2019 across all aspects at low- to mid-elevation sites, while high- elevation sites (>2100 m) remain unfavorable for cheatgrass due to cold spring and fall soils.

The LTAR Cropland Common Experiment at Upper Mississippi River Basin-St. Paul.

The Soil and Water Management Research Unit of the USDA-Agricultural Research Service is located in St. Paul, MN, and conducts long-term research at the University of Minnesota Research and Outreach Center located at Rosemount, MN. As part of USDA's Long-Term Agroecosystem Research (LTAR) network, the croplands common experiment (CCE) at this location is focused on integration of a kura clover (Trifolium ambiguum M. Bieb.) living mulch (KCLM) system into the prevailing 2-year rotation of corn (Zea mays L.) and soybean (Glycine max L.) that is typical of the midwestern Corn Belt. The LTAR-CCE conducted at Rosemount, MN, aims to compare the long-term environmental and agronomic performance of KCLM while identifying challenges and developing management strategies for this alternative practice. The use of a living mulch for this region is advantageous because, once established, it does not require additional time for fall field operations typically associated with winter cover crops. Results from LTAR-CCE studies at this site show that KCLM results in a substantial increase in soil field-saturated hydraulic conductivity and decreases in leaching of nitrate-nitrogen (NO3 --N). Disadvantages of the KCLM system include potential for increased emissions of nitrous oxide (N2O) and reduced crop yields, particularly during drought. Also, the optimal approach for crop row establishment in the spring remains uncertain. Ongoing LTAR-CCE research with KCLM aims to better understand and quantify both benefits and risks across conditions of interannual weather variability and changing climate to develop guidance for suitable adoption and management of this alternative practice.

Impact of Nitrogen and Water on Barley Grain Yield and Malting Quality

AbstractBarley is among the most important crops in northern latitudes especially for malting and distilling. Inter-annual weather variability in terms of rainfall and temperature patterns can impact crop uptake of soil water and nitrogen, which influences the crop growth and development. The present study shows the effects of nitrogen and water applied on: (i) specific grain quality traits necessary for distilling; (ii) plant biomass, nitrogen, and yield; and (iii) farmer’s marginal net return. The experiment was conducted during the growing seasons of 2018 and 2019 at the James Hutton Institute (UK) with two nitrogen fertilizers and two irrigation levels. During the growing season soil mineral nitrogen and soil water content and plant biomass and nitrogen were measured. At harvest yield, yield component, and grain quality traits were determined.2018 was a very dry growing season, as opposed to the wetter 2019 respect to the long-term growing season rainfall (1974–2017). Grain yield in 2018 was higher for the irrigated treatment, but in 2019 the irrigation, due to high rainfall, had lower yield. Environmental conditions impacted grain quality, and the patterns of soil water and mineral N affecting the final quality traits. Despite variable weather conditions the grain quality requirements from the industry of either beer or whisky are met.

Climatological Aspects of Notable Tornado Events in Chile

Abstract Tornadoes in Chile seem to develop in what are called “high-shear, low-CAPE” (HSLC) environments. An analysis of convective parameters from the ERA5 reanalysis during 16 notable tornadoes in Chile showed that several increased markedly before the time of the reports. The significant tornado parameter (STP) was able to discriminate the timing and location of the tornadoes, even though it was not created with that goal. We established thresholds for the severe hazards in environments with reduced buoyancy (SHERBE) parameter (≥1) and the STP (≤−0.3) to further identify days favorable for tornado activity in Chile. The SHERBE and STP parameters were then used to conduct a climatological analysis from 1959 to 2021 of the seasonal, interannual, and latitudinal variations of the environments that might favor tornadoes. Both parameters were found to have a strong annual cycle. The largest magnitudes of STP were found to be generally confined to south-central Chile, in agreement with the (sparse) tornado record. The probability of a day with both SHERBE and STP values beyond their thresholds was greatest between May and August, which aligns with the months with the most tornado reports. The number of days with both SHERBE and STP beyond their respective thresholds was found to fluctuate interannually. This result warrants further study given the known interannual variability of synoptic and mesoscale weather in Chile. The results of this study extend our understanding of tornado environments in Chile and provide insight into their spatiotemporal variability.

Ranking genotype, environment, management effects on the optimum nitrogen rate for maize: A cropping system modeling analysis

AbstractRanking the contribution of genotype, environment, and management (G × E × M) on maize's economic optimum nitrogen fertilizer rate (EONR) variability could improve understanding and predictability of EONR. We performed a simulation experiment using the Agricultural Production Systems sIMulator model with the objectives to (1) rank the effects of 24 individual G × E × M factors on the magnitude and interannual variability of the EONR across the US Midwest and (2) investigate the impact of G × M factors on the EONR variability under present and future climate scenarios. Results indicate that genetics (27%), management (31%), and environmental conditions (41%) each influence the EONR variability. Within these broad categories, the top three individual factors impacting the EONR were interannual weather variability, crop radiation use efficiency, and the soil inorganic N carryover from the previous year. The G × E × M factors influenced the yield response to N fertilizer in different ways. Soil‐related factors (e.g., organic matter and residual nitrate) influenced grain yields at the low N rates, while management factors (e.g., planting date and density) influenced yield at all N rates. Combining increases in plant density and changes in genetics synergistically increased the EONR by 15% from baseline. Future climate scenarios without adaptation decreased the EONR and yield loss, but crop adaptation was buffered against the negative climate change impacts. We concluded that 59% of the annual EONR variability is manageable (due to genetics and management) and that G × M factors could buffer climate change's negative effects on crop production. Present results can inform experimental research on N fertilizer and N rate decisions.

Responses of winter wheat yield and soil organic carbon to long-term (1990–2021) fertilization regimes under inter-annual weather variation in the Loess Plateau

Understanding the long-term responses of crop yield and soil organic carbon to fertilization is necessary to sustain food supply in a context of global change. The time-course of winter wheat yield and soil organic carbon were established in a 31-year long experiment with four fertilization regimes: unfertilized control; fertilized with mineral nitrogen and phosphorus, NP; fertilized with mineral N, P and K, NPK; and fertilized with manure and mineral NPK, MNPK. Over the time series, mean temperature increased at 0.054 ℃ year−1 (i.e. 1.67 degrees over the course of the experiment) and annual precipitation increased at 8.9 mm year−1. Yield did not show trends in unfertilized controls, increased at 210 kg ha−1 year−1 in the first 20 years and decreased slightly afterwards in the NP and NPK treatments, and increased at 310 kg ha−1 in the MNPK treatment until 2006, and leveled off afterwards. Soil organic carbon increased linearly with rates from 0.05 g kg−1 year−1 in unfertilized controls to 0.24 g kg−1 year−1 in MNPK. In fertilized crops, yield correlated nonlinearly with annual precipitation, and linearly with average temperature during the growing season. Across fertilizer treatments, yield correlated nonlinearly with soil organic carbon and the thresholds of soil organic carbon for peak yield increased with water supply. It is concluded that the combination of organic and inorganic fertilizers contributed to yield and soil organic carbon against the background of a warmer and wetter climate.

Dietary flexibility of the greater bamboo lemur (Prolemur simus), a specialized feeder, in eastern Madagascar.

The degree of dietary flexibility in primates is species specific; some incorporate a wider array of resources than others. Extreme interannual weather variability in Madagascar results in seasonal resource scarcity which has been linked to specialized behaviors in lemurs. Prolemur simus, for example, has been considered an obligate specialist on large culm bamboo with >60% of its diet composed of woody bamboos requiring morphological and physiological adaptations to process. Recent studies reported an ever-expanding list of dietary items, suggesting that this species may not be an obligate specialist. However, long-term quantitative feeding data are unavailable across this species' range. To explore the dietary flexibility of P. simus, we collected data at two northern sites, Ambalafary and Sahavola, and one southern site, Vatovavy, from September 2010 to January 2016 and May 2017 to September 2018, respectively. In total, we recorded 4022 h of behavioral data using instantaneous sampling of adult males and females from one group in Ambalafary, and two groups each in Sahavola and Vatovavy. We recorded 45 plant species eaten by P. simus over 7 years. We also observed significant differences in seasonal dietary composition between study sites. In Ambalafary, bamboo was the most frequently observed resource consumed (92.2%); however, non-bamboo resources comprised nearly one-third of the diet of P. simus in Sahavola and over 60% in Vatovavy. Consumption of all bamboo resources increased during the dry season at Ambalafary and during the wet season at Vatovavy, but never exceeded non-bamboo feeding at the latter. Culm pith feeding was only observed at Ambalafary, where it was more common during the dry season. We identify P. simus as a bamboo facultative specialist capable of adjusting its feeding behavior to its environment, indicating greater dietary flexibility than previously documented, which may enable the species to survive in increasingly degraded habitats.

Annual relative performance degradation in photovoltaic solar plants

This study aims to assess annual relative performance degradation σ to be used in the computations of the Levelised Cost of Energy (LCOE) of new plants based on the standard year for weather and solar resource. The assessment is based on the experimental data of power generation, weather, and resource for different years, and simulations of power generation by using the National Renewable Energy Laboratory (NREL) System Advisor Model (SAM) software. The raw data of power generation for 53 different plants spanning about one decade show a σ assessed at <0.29 %. This makes it reasonable to assume in SAM σ = 0 % to σ = 0.29 %. This is less than the default σ = 0.5 % currently used in SAM based on limited outdated data. The updated performance degradation factor diminished to σ = 0–0.29 % reduces the computed LCOE for typical new projects in the United States from 2.86 to 2.74–2.80 ¢/kWh. Correction of the σ trends for interannual variability of weather and resource is unnecessary, providing enough years of operation are covered, given the inaccuracies in the model and the supporting data, the mitigation by management of the performance changes, and the complex phenomena correlated to the change of weather and irradiance affecting the power output in different directions. The reduced σ is the result of a significant product improvement over the last decades, especially for large power plants, compared to the plant which provided data for the prior correlation of performance degradation, and much better management and maintenance of the plants.

Impact of interannual weather variation on ammonia emissions and concentrations in Germany

Ammonia is one of the most impactful pollutants emitted from agricultural activities, harming human health and contributing to biodiversity loss. In ammonia emission inventories, the spatial distribution of annual emissions is mostly approximated by constant empirical emission fractions, which do not account for spatial variability, nor for temporal variability within a year or between years caused by weather variations. Besides, factors like manure properties, soil properties, and manure application techniques also lead to differences in the amount of ammonia emitted into the atmosphere. By not or only partly accounting for these factors, significant uncertainties are introduced into ammonia emission estimates at regional and national scales. In this study, we applied the empirical ALFAM2 model to derive spatially explicit slurry application emission fractions from cropland for use in the large-scale INTEGRATOR model, using the information on slurry properties (dry matter content and pH), manure application rate, application technique, incorporation time, air temperature, wind speed, and rainfall rate. In addition, the impact of weather on the ammonia emissions from animal housing and manure storage systems was included through a temperature-dependent scaling. We applied the method to investigate the year-to-year spatio-temporal variabilities of ammonia emissions and modeled concentrations across Germany from 2015 to 2018. Through the comparison with in situ measurements and satellite-derived observations, we studied how surface concentrations and total columns relate to local meteorology. We found that the spatio-temporal variability in emission fractions improves the ability to reproduce the interannual variability observed in ammonia concentration and total column measurements. This study shows that the developed approach to derive spatially explicit emission fractions can significantly improve ammonia emission modeling and is of great importance for studying the temporal variability between years.

Estimating the potential drivers of dispersal outcomes for juvenile gopher frogs (Rana capito) using agent-based models

Among mobile terrestrial animals, movement among microsites can allow individuals to behaviorally moderate their body temperatures and rates of water loss, which can have important consequences for activity times, growth, fecundity, and survival. Ground-layer vegetation can change the availability and variability of microclimates; however, gaps in our understanding of how individuals interact with the microclimates created by vegetation limit our ability to inform management actions for wildlife. Amphibians can simultaneously balance operant body temperatures and water loss and the availability of heterogeneous microclimates should moderate how effectively they are able to do so. However, relatively few studies have attempted to mechanistically demonstrate how ground vegetation-driven effects on microclimatic variation may affect amphibian performance and survival. Agent-based modeling (ABM) can incorporate behavior and other mechanisms to understand how animals interact with their environments to result in larger scale patterns. They are effective for exploring alternative scenarios and representing the uncertainty in systems. Here, we use ABMs to integrate field and laboratory measurements of movement behavior, physiology, and plant effects on near-ground microclimate to explore how ground vegetation and the availability of terrestrial refugia may affect the survival and terrestrial distributions of juvenile gopher frogs (Rana capito) under two weather regimes. We also examine how assumptions regarding micro-scale movement (&amp;lt; 1 m2) affect the influence of ground vegetation on survival and settlement within refugia. While all variables affected settlement and survival, our models predict that inter-annual variation in weather and the density and spatial distribution of permanent refugia likely have the greatest influence on juvenile survival. The benefit of increased ground vegetation was dependent on the reasonable assumption that gopher frogs exhibit microclimate habitat selection throughout the day and night to limit water loss. Our models suggest that vegetation would be most beneficial to amphibians under warmer weather regimes provided there is sufficient rainfall.

Weather potential for high-quality still wine from Chardonnay viticulture in different regions of the UK with climate change

UK viticulture is benefitting from climate change with an increase in vineyard area and a move towards French grapevine varieties, primarily Chardonnay and Pinot noir, to produce sparkling wine. Doubt remains, however, as to how good UK still wine can be from these varieties. The simple Chablis vintage model uses only three climatic indices: mean temperature from April to September, mean minimum temperature in September (cool night index) and total rainfall from June to September. It was applied to the UK for the periods 1981–2000, 2010–2019 and, with climate change projections, to 2040–2059 to locate sites in the UK with the climate potential to produce high-quality Chardonnay still wine. Weather data for 1981–2000 and 2010–2019 were taken from the Met Office’s HadUK-Grid at a resolution of 5 × 5 km, and climate projections for 2040–2059 were derived from UKCP18, using intermediate emission scenario RCP 4.5 at the 5th, 50th and 95th percentile probabilities. Recent and current climatic conditions throughout most of the UK were unsuitable for sustainable production of high-quality still Chardonnay wine (only 0.2 to 1.8 % of UK land area suitable), but model scores corresponded with high-quality Chardonnay still wine production observed in some regions of England in 2018. Under the 5th percentile RCP 4.5 projection for 2040–2059, climatic conditions are similar to 2010–2019 and generally unsuitable for sustainable, high-quality still Chardonnay wine production. Under the median and 95th percentile projections for 2040–2059, however, South East England and East of England have the potential for high-quality still Chardonnay wine production in an average year, and Central England also with the 95th percentile projection. Overall, climate change is expected to benefit the production of high-quality still Chardonnay wine in the medium term, with up to 42.4 % of UK land possibly climatically (but not necessarily agronomically) suitable by mid-century. The model does not account for extreme events, however, and there is uncertainty over future inter-annual weather variability, and so the sustainability of high-quality still wine production. Planting Chardonnay clones suitable for both sparkling and still wines in the most-suitable areas of England would provide flexibility and so resilience.

Climate adaptive rice planting strategies diverge across environmental gradients in the Indo-Gangetic Plains

Abstract The timing of rice planting has a profound influence on the productivity of the rice-wheat cropping pattern in the Indo-Gangetic Plains (IGP), a system that provides the foundation for food security in South Asia. Nevertheless, strategies for adaptive rice planting in a rapidly changing climate are not well established. In this ex-ante analysis, regional gridded crop model simulations are deployed to investigate the impact of different rice planting strategies on system level productivity, resilience, and environmental benefits. Our results suggest that synchronizing rice planting dates with the monsoon onset substantially outperforms farmer practice (+41%) and static state recommendations in the Eastern IGP. However, planting long-duration rice with the monsoon onset is ineffective in the Northwestern IGP since the later arrival of the monsoon increases the probability of cold damage to rice and terminal heat stress in wheat. Here, fixed planting dates (+12.5%) or planting medium duration varieties at monsoon onset (+18%) performed best. We conclude that resilient and productive rice planting strategies must account for interannual weather variability and divergent climate conditions across sub-regions in the IGP.

Influence of short and long term processes on SAR11 communities in open ocean and coastal systems

SAR11 bacteria dominate the surface ocean and are major players in converting fixed carbon back to atmospheric carbon dioxide. The SAR11 clade is comprised of niche-specialized ecotypes that display distinctive spatiotemporal transitions. We analyzed SAR11 ecotype seasonality in two long-term 16S rRNA amplicon time series representing different North Atlantic regimes: the Sargasso Sea (subtropical ocean-gyre; BATS) and the temperate coastal Western English Channel (WEC). Using phylogenetically resolved amplicon sequence variants (ASVs), we evaluated seasonal environmental constraints on SAR11 ecotype periodicity. Despite large differences in temperature and nutrient availability between the two sites, at both SAR11 succession was defined by summer and winter clusters of ASVs. The summer cluster was dominated by ecotype Ia.3 in both sites. Winter clusters were dominated by ecotypes Ib and IIa.A at BATS and Ia.1 and IIa.B at WEC. A 2-year weekly analysis within the WEC time series showed that the response of SAR11 communities to short-term environmental fluctuations was variable. In 2016, community shifts were abrupt and synchronized to environmental shifts. However, in 2015, changes were gradual and decoupled from environmental fluctuations, likely due to increased mixing from strong winds. We demonstrate that interannual weather variability disturb the pace of SAR11 seasonal progression.

Experimentally increased snow depth affects high Arctic microarthropods inconsistently over two consecutive winters

Climate change induced alterations to winter conditions may affect decomposer organisms controlling the vast carbon stores in northern soils. Soil microarthropods are particularly abundant decomposers in Arctic ecosystems. We studied whether increased snow depth affected microarthropods, and if effects were consistent over two consecutive winters. We sampled Collembola and soil mites from a snow accumulation experiment at Svalbard in early summer and used soil microclimatic data to explore to which aspects of winter climate microarthropods are most sensitive. Community densities differed substantially between years and increased snow depth had inconsistent effects. Deeper snow hardly affected microarthropods in 2015, but decreased densities and altered relative abundances of microarthropods and Collembola species after a milder winter in 2016. Although increased snow depth increased soil temperatures by 3.2 °C throughout the snow cover periods, the best microclimatic predictors of microarthropod density changes were spring soil temperature and snowmelt day. Our study shows that extrapolation of observations of decomposer responses to altered winter climate conditions to future scenarios should be avoided when communities are only sampled on a single occasion, since effects of longer-term gradual changes in winter climate may be obscured by inter-annual weather variability and natural variability in population sizes.

Multi-year evolution of 75 snow and ice deposits in Schachtdolines and Shafts of recently deglaciated karst terrain: Observations from Mount Canin-Kanin, Julian Alps, Europe

Dolines and shafts are common geomorphologies in karst landscapes. When affected also by glacial processes, they are considered an example of glaciokarst landform. In high alpine karst, their typical vertical shape lends itself particularly well to the formation of snow and ice accumulations that can survive the ablation season, potentially becoming perennial. This study analyses the 12-years changes of several permanent snow-ice deposits in Schachtdolines and Shafts (SIDS) and compares them with the mass balance of surrounding external ice bodies. According to recent studies and in contrast with the rest of the Alpine glaciers, ice masses of the area are resilient to climate change and show a slightly positive mass balance in the last decade. The presence of a long term mass balance monitoring program provides an excellent basis for a robust comparison with 75 selected SIDS, all located in the Mount Canin-Kanin massif, in the Julian Alps. Six airborne LiDAR surveys performed from 2006 to 2018 at the end of the ablation season allowed accurate calculation of thickness and volume changes. The measured differences show a general increase in thickness of the SIDS with more than one meter gain, although multi-annual observed variability between the sites is high. The observed 2006–2018 positive mass balance is in agreement with the external cryosphere although SIDS appear even more resilient than the external ice patches to both interannual weather variability and climate forcing.

Climate change projections for UK viticulture to 2040: a focus on improving suitability for Pinot Noir

Between 1981–2000 and 1999–2018, growing season average temperatures (GST) in the main UK viticulture regions have warmed ~1.0 °C and are now more reliably &gt;14.0 °C GST. This warming has underpinned the rapid expansion of the UK viticulture sector and its current focus on growing grape varieties for sparkling wine. Near-term (2021–2040) climate change may condition opportunities for further variety and/or wine style changes. Using the latest high-resolution (5 km) ensemble (×12) of downscaled climate change models for the UK (UK Climate Projections; UKCP18) under Representative Concentration Pathway (RCP) 8.5, we calculate near-term trends and variability in bioclimatic indices (BCIs). We simulate the projected repetition of the UK’s highest yielding season—2018—and use an analogue approach to model the 1999–2018 mean growing season temperatures from Pinot Noir producing areas of Champagne (France), Burgundy (France) and Baden (Germany) over the UK during 2021–2040. We also project, across the UK for the 2021–2040 period, BCI values of recent high-quality vintage years from Champagne and Burgundy. GST are projected to increase from a 1999–2018 spatial range of 13.0 (minimum threshold) –15.7 °C to a future (2021–2040) range of 13.0–17.0 °C, and Growing Degree Days (GDD) from 850 (minimum threshold) –1267 to 850–1515. Growing season precipitation (GSP) is projected to decline in some UK viticulture areas but is not modelled as a limiting viticulture factor. High inter-annual weather variability is simulated to remain a feature of the UK viticulture climate and early season frost risk is likely to occur earlier. Large areas of the UK are projected to have &gt;50 % of years within the bioclimatic ranges experienced during the 2018 growing season, indicating potential higher yields in the future. The 1999–2018 mean Champagne, Burgundy and Baden GST and GDD are projected for much of England and some areas in the far south and south-east of Wales during 2021–2040, with significant areas projected to have &gt;25 % of years within the BCI ranges of top Champagne vintages. These results indicate greater potential for Pinot Noir for sparkling wines and shifting suitability to still red wine production. Accounting for changes in variety suitability and wine styles will be essential to maximise opportunities and build resilience within this rapidly expanding wine region.

Seasonal weather effects on offspring survival differ between reproductive stages in a long-lived neotropical seabird.

Weather conditions can profoundly affect avian reproduction. It is known that weather conditions prior to and after the onset of reproduction can affect the breeding success of birds. However, little is known about how seasonal weather variability can affect birds' breeding performance, particularly for species with a slow pace of life. Long-term studies are key to understanding how weather variability can affect a population's dynamics, especially when extreme weather events are expected to increase with climate change. Using a 32-year population study of the Blue-footed booby (Sula nebouxii) in Mexico, we show that seasonal variation in weather conditions, predominantly during the incubation stage, affects offspring survival and body condition at independence. During most of the incubation period, warm sea surface temperatures were correlated with low hatching success, while rainfall in the middle of the incubation stage was correlated with high fledging success. In addition, chicks from nests that experienced warm sea surface temperatures from the pre-laying stage to near-fledging had lower body condition at 70days of age. Finally, we show that variable annual SST conditions before and during the incubation stage can impair breeding performance. Our results provide insight into how seasonal and interannual weather variation during key reproductive stages can affect hatching success, fledging success, and fledgling body condition in a long-lived neotropical seabird.

Nitrate losses across 29 Iowa watersheds: Measuring long-term trends in the context of interannual variability.

In the U.S. Corn Belt, annual croplands are the primary source of nitrate loading to waterways. Long periods of fallow cause most nitrate loss, but there is extreme interannual variability in the magnitude of nitrate loss due to weather. Using mean annual (2001-2018) flow-weighted nitrate-N concentration (FWNC; mg NO3 - -N L-1 ), load (kg NO3 - -N), and yield (kg NO3 - -N ha-1 cropland) for 29 watersheds, our objectives were (a) to quantify the magnitude and interannual variability of 5-yr moving average FWNC, load, and yield; (2) to estimate the probability of measuring 41% reductions in nitrate loss after isolating the effect of weather on nitrate loss by quantifying the interannual variability of nitrate loss in watersheds where there was no trend in 5-yr moving average nitrate loss (Iowa targets a 41% nitrate loss reduction from croplands); and (c) to identify factors that, in the absence of long-term trends in nitrate loss, best explain the interannual variability in nitrate loss. Averaged across all watersheds, the mean probability of measuring a statistically significant 41% reduction in FWNC across 15 yr, should it occur, was 96%. However, the probabilities of measuring 41% reductions in nitrate load and yield were only 44 and 32%. Across watersheds, soil organic matter, tile drainage, interannual variability of precipitation, and watershed area accounted for interannual variability in these nitrate loss indices. Our results have important implications for setting realistic timelines to measure nitrate loss reductions against the background of interannual weather variation and can help to target monitoring intensity across diverse watersheds.

Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N2O Emissions, and Seed Yield.

Limited knowledge about how nitrogen (N) dynamics are affected by climate change, weather variability, and crop management is a major barrier to improving the productivity and environmental performance of soybean-based cropping systems. To fill this knowledge gap, we created a systems understanding of agroecosystem N dynamics and quantified the impact of controllable (management) and uncontrollable (weather, climate) factors on N fluxes and soybean yields. We performed a simulation experiment across 10 soybean production environments in the United States using the Agricultural Production Systems sIMulator (APSIM) model and future climate projections from five global circulation models. Climate change (2020–2080) increased N mineralization (24%) and N2O emissions (19%) but decreased N fixation (32%), seed N (20%), and yields (19%). Soil and crop management practices altered N fluxes at a similar magnitude as climate change but in many different directions, revealing opportunities to improve soybean systems’ performance. Among many practices explored, we identified two solutions with great potential: improved residue management (short-term) and water management (long-term). Inter-annual weather variability and management practices affected soybean yield less than N fluxes, which creates opportunities to manage N fluxes without compromising yields, especially in regions with adequate to excess soil moisture. This work provides actionable results (tradeoffs, synergies, directions) to inform decision-making for adapting crop management in a changing climate to improve soybean production systems.