Seasonal Climate Research Articles

Effect of access to natural shade on scrotal thermoregulatory capacity, integrity of the testicular parenchyma and sperm morphology of Nelore (Bos indicus) and Canchim (Bos taurus x Bos indicus) bulls.

The objective of this work was to evaluate the effect of using naturally shaded pastures on scrotal thermoregulatory capacity, testicular echotexture, and sperm morphology of Nelore (Bos indicus) and Canchim (5/8 Bos taurus x 3/8 Bos indicus) bulls in a tropical climate region. Sixty-four adult Nelore and Canchim bulls were used, equally allocated in Full Sun (FS, n = 32) or Crop-Livestock-Forestry (CLF, n = 32) pasture systems. During five consecutive climate seasons, the bulls underwent monthly breeding soundness evaluations and the biometeorological variables in the systems were continuously monitored. Microclimate was significantly different between systems. CLF system had lower BGHI than FS throughout the experimental period. No triple interaction (Season x Breed x Treatment, P > 0.05) was observed for any of the variables. Animals in CLF showed lower body temperature in Summer (FS:39.41 ± 0.05 vs. CLF:39.30 ± 0.05°C; P = 0.005) and in Autumn (FS:39.54 ± 0.05 vs. CLF:39.35 ± 0.05°C; P = 0.005). Access to shading did not determine differences in the evolution of scrotal biometry, temperatures, and scrotal thermal gradients (P > 0.05). Regardless of breed, animals in CLF showed greater right testicular volume (FS:247.5 ± 5.7 vs. CLF:259.0 ± 5.7cm³; P < 0.05), more suitable parenchyma echotexture, and fewer microlithiasis spots in the Spring and Summer. Testosterone concentration was higher in FS (FS:2.6 ± 0.2 vs. CLF:2.1 ± 0.2 ng/mL; P = 0.035). Canchim bulls presented higher total sperm defects during the Autumn and Winter (P = 0.010), but the total defects levels for Canchim and Nelore bulls were in normal range for adult bulls. Thus, the natural shade in CLF system was effective in improving the microclimate of pastures and minimizing adverse environmental effects on some reproductive features of interest in beef cattle.

Weed phytosociology survey in a Brazilian native pineapple, variety Turiaçu

The native pineapple variety Turiaçu holds significant market value and substantial export potential in comparison to other traditional pineapple varieties. Despite its economic importance, there is limited research on weed management for pineapple Turiaçu. Weed interference poses a threat to various crops at different phenological stages. This study had two primary objectives: (1) to assess the dynamics of weeds in a Turiaçu pineapple orchard across different climatic seasons and (2) to determine the critical control phase for weed management. We conducted weed phytosociological surveys during the dry, transition, and rainy seasons. Conventional methods were employed in 20 samples per season, evaluating relative density, relative frequency, relative abundance, importance value index, and Soresen's similarity index. Our findings revealed the presence of 32 weed species, spanning 24 genera and 12 botanical families, predominantly eudicotyledonous (78%), with an annual life cycle (50%) and sexual reproduction (78%). Seasonal variations significantly influenced the weed community, with a 500% increase in the number of species during the rainy season compared to the dry season. Based on our observations, we recommend implementing soil cover methods for controlling the weed seed bank. This approach is crucial in all seasons, with particular emphasis on the transition season, characterized by a relatively short and latent weed community that is more easily manageable.

Coccidioidomycosis seasonality in California: a longitudinal surveillance study of the climate determinants and spatiotemporal variability of seasonal dynamics, 2000–2021

Coccidioidomycosis, an emerging fungal disease in the western USA, exhibits seasonal patterns that are poorly understood, including periods of strong cyclicity, aseasonal intervals, and variation in seasonal timing that have been minimally characterized, and unexplained as to their causal factors. Coccidioidomycosis incidence has increased markedly in recent years, and our limited understanding of intra- and inter-annual seasonality has hindered the identification of important drivers of disease transmission, including climate conditions. In this study, we aim to characterize coccidioidomycosis seasonality in endemic regions of California and to estimate the relationship between drought conditions and coccidioidomycosis seasonal periodicity and timing. We analysed data on all reported incident cases of coccidioidomycosis in California from 2000 to 2021 to characterize seasonal patterns in incidence, and conducted wavelet analyses to assess the dominant periodicity, power, and timing of incidence for 17 counties with consistently high incidence rates. We assessed associations between seasonality parameters and measures of drought in California using a distributed lag nonlinear modelling framework. All counties exhibited annual cyclicity in incidence (i.e., a dominant wavelet periodicity of 12 months), but there was considerable heterogeneity in seasonal strength and timing across regions and years. On average, 12-month periodicity was most pronounced in the Southern San Joaquin Valley and Central Coast. Further, the annual seasonal cycles in the Southern San Joaquin Valley and the Southern Inland regions occurred earlier than those in coastal and northern counties, yet the timing of annual cycles became more aligned among counties by the end of the study period. Drought conditions were associated with a strong attenuation of the annual seasonal cycle, and seasonal peaks became more pronounced in the 1-2 years after a drought ended. We conclude that drought conditions do not increase the risk of coccidioidomycosis onset uniformly across the year, but instead promote increased risk concentrated within a specific calendar period (September to December). The findings have important implications for public health preparedness, and for how future shifts in seasonal climate patterns and extreme events may impact spatial and temporal coccidioidomycosis risk. National Institutes of Health.

Review of aging mechanisms, mechanical properties, and prediction models of fiber‐reinforced composites in natural environments

AbstractFiber‐reinforced composites are widely used in civil engineering, aerospace, automotive, and medical sectors. However, these composites undergo aging due to factors such as temperature, humidity, load, and so on, during their usage. As a result, their performance deteriorates, and predicting their durability under real service conditions is a significant challenge. In order to better understand the durability of fiber‐reinforced composites, this article summarizes their microscopic aging mechanism under natural aging conditions and analyzes the changing rules of tensile, bending, and shear properties of composites under seven typical climate types, including tropical desert climate, temperate continental climate, temperate oceanic climate, Mediterranean climate, seasonal climate, tropical rainforest climate, and seawater immersion. This article reviews various durability prediction models, such as the Arrhenius model, prediction models based on residual modulus of elasticity and residual strength, and median strength regression analysis. To enhance the accuracy of aging life prediction during the natural aging process of composites, it is important to consider the influence of loads under real service conditions, incorporate different climatic types, utilize comprehensive mechanical property indices, establish an equivalent conversion relationship between natural aging and accelerated aging, and create a database with unified test standards.Highlights The aging mechanisms of FRP after exposure in natural aging environments are discussed. The tensile, bending, and shear properties of FRP after exposure under seven climate types are discussed. The predictive models are proposed for the FRP used in natural aging. The future research needs are proposed for the FRP used in natural aging.

Weather Research and Forecasting Model (WRF) Sensitivity to Choice of Parameterization Options over Ethiopia

Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs’ more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate model setup for a particular purpose over a given region through numerical experiments. Thus, this sensitivity study was aimed at identifying an optimum configuration in the Weather, Research, and Forecasting (WRF) model over Ethiopia. A total of 35 WRF simulations with different combinations of parameterization schemes for cumulus (CU), planetary boundary layer (PBL), cloud microphysics (MP), longwave (LW), and shortwave (SW) radiation were tested during the summer (June to August, JJA) season of 2002. The WRF simulations used a two-domain configuration with a 12 km nested domain covering Ethiopia. The initial and boundary forcing data for WRF were from the Climate Forecast System Reanalysis (CFSR). The simulations were compared with station and gridded observations to evaluate their ability to reproduce different aspects of JJA rainfall. An objective ranking method using an aggregate score of several statistics was used to select the best-performing model configuration. The JJA rainfall was found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All the simulations captured the spatial distribution of JJA rainfall with the pattern correlation coefficient (PCC) ranging from 0.89 to 0.94. However, all the simulations overestimated the JJA rainfall amount and the number of rainy days. Out of the 35 simulations, one that used the Grell CU, ACM2 PBL, LIN MP, RRTM LW, and Dudhia SW schemes performed the best in reproducing the amount and spatio-temporal distribution of JJA rainfall and was selected for downscaling the CFSv2 operational forecast.

A new data-driven map predicts substantial undocumented peatland areas in Amazonia

Abstract Tropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025–1700 000 km2) and below-ground carbon storage (105–288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km2 (95th percentile confidence interval: 128 671–373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin.

Can teleconnections help to improve the seasonal prediction over the Southern African Development Community Region?

The limited skill of seasonal climate predictions in some regions of the Southern African Development Community (SADC) restricts their potential application to the development of climate services. This study explores the feasibility of improving the quality of these predictions by using the observed relationship between Essential Climate Variables (ECVs) and large-scale teleconnection indices, namely Niño3.4, the Atlantic Niño and the Indian Ocean Dipole. The underlying hypothesis is that, for certain areas, the empirical observed teleconnections could improve the predictions offered by the seasonal forecasting systems. This is achieved by implementing linear regression models between each index and ECV, for up to 12 months into the past, and for each season and grid point. After obtaining the index-derived estimates of the variables, the correlation coefficients and fair Ranked Probability Skill Scores (fRPSS) are computed and compared to those of the ECMWF SEAS5 (SEAS5) predictions for different lead times. The results show that 10–25 % of the entire domain exhibits improved correlations for the index-derived precipitation in all seasons. In the case of temperature, though, higher correlations could be observed only in six seasons (and solely for Niño3.4). Regarding fRPSS, up to 7 % of the entire area shows an improvement when using Niño3.4 to estimate temperature (in four seasons). Conversely, for precipitation there is no detected enhancement. In future work, it would be worth investigating whether a combined multi-index regression can further raise the observed increase in performance.

Morphology, DNA barcoding and seasonal occurrence of Ergasilus lizae Krøyer, 1863 (Copepoda: Ergasilidae) parasitizing mullets from northwestern Mexico

Ergasilus lizae Krøyer, 1863 is a parasitic copepod known to infect mullets (Mugilidae) in different parts of the world. It was originally reported from the east coast of North America, but the original description lacks enough detail, making identification with this information difficult. In this study, we provide a redescription of E. lizae found on Mugil curema Valenciennes and M. cephalus Linnaeus, caught in two coastal lagoons of northwestern Mexico during two climatic seasons: warm/rainy and cold/dry. The prevalence of this parasite was higher in the warm season than in the cold season. To facilitate the species identification, new sequences of the barcoding gene (COI mtDNA) of E. lizae were generated and compared against unpublished sequences of E. lizae available in the Barcode of Life Database (BOLD). Our results suggest that the sequences of BOLD possibly belong to a species misidentified as E. lizae.

A hybrid reanalysis-forecast meteorological forcing data for advancing climate adaptation in agriculture

Climate variability in the growing season is well suited for testing adaptation measures. Adaptation to adverse events, such as heatwaves and droughts, increases the capacity of players in agri-food systems, not only producers but also transporters and food manufacturers, to prepare for production disruptions due to seasonal extremes and climate change. Climate impact models (e.g., crop models) can be used to develop adaptation responses. To run these models, historical records and climate forecasts need to be combined as a single daily time series. We introduce the daily 0.5° global hybrid reanalysis-forecast meteorological forcing dataset from 2010 to 2021. The dataset consists of the Japanese 55-yr Reanalysis (JRA55) and the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 2 (JMA/MRI-CPS2) 5-member ensemble forecast. Both are bias-corrected using the Delta method and integrated with a baseline climatology derived from the Environmental Research and Technology Development Fund’s Strategic Research 14 Meteorological Forcing Dataset (S14FD). The dataset is called JCDS (JRA55-CPS2-Delta-S14FD) and offers a framework for monitoring and forecasting applications towards adaptation.

Seasonally dry tropical forests in the late Pleistocene of Mesopotamia, Argentina and their relationship to environmental changes during the Last Interglacial

ABSTRACTThe concept of seasonal forests groups structural types of vegetation that are related to climatic seasonality in the tropics of South America. Consequently, this determines the physiognomy of the vegetation, from semi‐deciduous to strongly deciduous. The strongest link between seasonally dry tropical forests (SDTFs) is their floristic composition, where Leguminosae and Anacardiaceae dominate the woody flora. The fossil records of the Neogene of northwestern Argentina reveal a list of species found in various locations and formations of the Miocene–Lower Pleistocene obtained from studies of pollen, woods (logs), cuticles, impressions of leaves and fruits. The analysis of sediments and woody structures from the Tapebicuá, Toropí/Yupoí and El Palmar Formations (Upper Pleistocene) of the Mesopotamia region allowed us to identify several pollen taxa and silicified wood fragments (mineralized). The woody and shrubby association whose current relatives characterize the SDTF is composed of the 16 fossil species described here belonging to seven families. In our samples, Anadenanthera colubrina and Myracrodruon balansae are the most significant members of the families Leguminosae and Anacardiaceae, which are dominant in the SDTF. The paleobotanical species described in this study confirm the extension of the SDTF to the province of Corrientes, coinciding with various climatic events (dry subtropical, semi‐desert and warm‐humid climate) that would have favored the development of these forests during the Pleistocene in this region. The absolute dates obtained for the Toropí/Yupoí and Tapebicuá Formations confirm their synchronicity and correlation to Marine Isotope Stage 5. The palynological analysis, the presence of Menendoxylon and the sedimentological data allow us to infer the existence of a seasonally dry humid paleoclimate in northeastern Argentina during the Late Pleistocene favorable to the development of the SDTF.

Multi‐cohort survival of northern red oak seedlings at a northern hardwood forest transition

AbstractGlobal change has created less stable forest systems and given urgency to understanding limitations to the establishment of tree seedlings beyond current range boundaries. We quantified trends in 13 years of annual northern red oak (QURU) seedling survival data for 1733 marked individuals at a local species distribution boundary within the northern hardwood forest in New Hampshire, USA. Over the study period, the median distance of seedlings into the valley did not change, although there was a net gain of 89 plots (5 m2) occupied. For a subset of seedlings that were marked in their year of birth (N = 937), we examined relationships among terrain, vegetation community, and initial individual seedling traits, and evaluated their effects on time to seedling mortality using a parametric accelerated failure time model. The year of seedling germination had the largest effect on survival with increasing mortality rates for seedlings from more recent cohorts. Seedlings had longer survival times where oak seedling densities were lower, shrub cover was higher, and when the acorn remained attached. Additionally, survival time was increased in higher elevation plots, which were also located further into the valley. Interannual seedling survival (N = 1580) was strongly impacted by seedling condition in the previous year, particularly leaf number and amount of leaf damage. Most seedling deaths occurred over winter, and seedlings failed to break bud the following spring. Interannual variation in seasonal climate, particularly deep, heavy snowpack in 2019 followed by drought conditions in 2020, coincided with recent elevated mortality. Overall, the median survival time of 3–4 years and the rapid turnover of the oak seedling population currently limit ability for expansion, although the net gain of occupied plots and increase in survival at higher elevation plots with lower QURU densities present some mechanisms that could promote expansion if the current suboptimal understory conditions shift to favor QURU.

Broad-scale seasonal climate tracking is a consequence, not a driver, of avian migratory connectivity.

Tracking climatic conditions throughout the year is often assumed to be an adaptive behaviour underlying seasonal migration patterns in animal populations. We investigate this hypothesis using genetic markers data to map migratory connectivity for 27 genetically distinct bird populations from 7 species. We found that the variation in seasonal climate tracking across our suite of populations at a continental scale is more likely a consequence, rather than a direct driver, of migratory connectivity, which is primarily shaped by energy efficiency-i.e., optimizing the balance between accessing available resources and movement costs. However, our results also suggest that regional-scale seasonal precipitation tracking affects population migration destinations, thus revealing a potential scale dependency of ecological processes driving migration. Our results have implications for the conservation of these migratory species under climate change, as populations tracking climate seasonally are potentially at higher risk if they adapt to a narrow range of climatic conditions.

How Well Do Seasonal Climate Anomalies Match Expected El Niño–Southern Oscillation (ENSO) Impacts?

Abstract Did the strong 2023–24 El Niño live up to the hype? While climate prediction is inherently probabilistic, many users compare El Niño events against a deterministic map of expected impacts (e.g., wetter or drier regions). Here, using this event as a guide, we show that no El Niño perfectly matches the ideal image and that observed anomalies will only partially match what was anticipated. In fact, the degree to which the climate anomalies match the expected ENSO impacts tends to scale with the strength of the event. The 2023–24 event generally matched well with ENSO expectations around the United States. However, this will not always be the case, as the analysis shows larger deviations from the historical ENSO pattern of impacts are commonplace, with some climate variables more prone to inconsistencies (e.g., temperature) than others (e.g., precipitation). Users should incorporate this inherent uncertainty in their risk and decision-making analysis.

A novel green learning artificial intelligence model for regional electrical load prediction

Load prediction is crucial to unit commitment and scheduling in electricity systems; however, load prediction models consume considerable electricity. Most conventional prediction methods are based on deep learning (DL) models, such as long short-term memory and gated recurrent units. The nonlinear activation functions and backpropagation mechanisms used in these methods result in high computational complexity and correspondingly high energy consumption. This paper proposes a prediction model based on a green learning (GL) framework aimed at reducing energy consumption. The proposed GL model replaces the activation functions conventionally used for feature extraction with a hybrid scheme combining categorical and numerical features, such as seasonal climate features and multi-autoregression terms. The proposed GL model also eliminates the backpropagation methods used to optimize hyperparameters by employing seasonal centroids and the quantile auto-regression forest algorithm for classification/regression. In a case study, the proposed GL model significantly outperformed conventional DL models in terms of energy consumption without compromising accuracy. The energy savings of the proposed GL model are equivalent to the annual electricity consumption of 211 ∼ 565 households, increasing with model penetration. This paper demonstrates the importance of energy-saving models in mitigating the linkage effect of industrial carbon emissions. It also explores policy implications for the implementation of GL models.

The effects of seasonal climate change on the safflower genotypes productivity under Central Anatolian conditions

The effects of seasonal climate change on the safflower genotypes productivity under <scp>Central Anatolian</scp> conditions

Functional ventilation building envelope integrated photovoltaic modules and phrase change material in subtropical climate: An in-depth numerical investigation

Most of existing solar ventilation envelope systems adopt a single structure including a glass cover a massive wall, which possesses the drawbacks of low thermal efficiency and poor stability. In recent decades, auxiliary power generation devices, led by photovoltaic (PV), have made epochal achievements in integrated building applications, while the superior thermal regulation capability of thermal storage materials, represented by phrase change materials (PCM), has further helped to improve the thermal environment of buildings, and the above two technologies have demonstrated great energy conservation potential. In order to explore the application and synergistic effect of the above two energy-conservation technologies in passive ventilation envelopes, a novel functional ventilation building envelope (FVBE) integrated with PV panel and PCM is proposed in this paper. A comprehensive exploration of the influence of various key parameters on the thermal performance of the FVBE-PV/PCM system. The electrical and thermal performance of the system is explored based on different seasonal climates. Specifically, effects of thickness as well as the melting temperature of PCM and the air channel width has been provided in this study. Taking the inner wall surface temperature (Tinner_wall), equivalent indoor load (Qload) and electrical efficiency (ηe) as evaluation indexes, the thermal efficiency, electrical performance and the coupling mechanism was assessed. Specifically, the coupling mechanism between the thermal performance and electrical performance of the FVBE-PV/PCM system was analyzed from the perspective of combined heat and power, revealing the functional characteristics of the system. Meanwhile, the article conducted a comparative study on the thermal and electrical performance of a typical building integrated PV (BIPV), FVBE-PV system FVBE-PV/PCM system. In particular, energy and exergy analysis is carried out to characterize the energy utilization of the FVBE-PV/PCM system in different structural systems. The research found that increasing the thickness of both the PCM and air channel leads to lower indoor temperature and heat flux, with different optimal phase transition temperatures for summer and winter. Although the FVBE-PV system exhibited a maximum 3.12 % reduction in power generation efficiency compared to the BIPV system due to increased PV temperature caused by air layer insulation, incorporating PCM can reduce exergy losses by an average of 31.731 % to mitigate this drawback and improve energy efficiency. Additionally, the analysis revealed varying coupling correlations between thermal performance and power generation based on two key parameters. This study could contribute to the functional improvement of the building envelope and simultaneous indoor thermal regulation.

Qualitative Production of Mixture Silage within a Sustainable Concept

Climate change and seasonality in forage production have caused alterations in animal feed. Thus, this study evaluated the composition of silages from soybean (Glycine max (L.) Merrill) mixed with tropical grasses. The experimental design was randomized blocks with four replications. Treatments were silage from soybeans, silage from soybeans with Aruana Guinea grass (Megathyrsus maximus cv. Aruana), and silage from soybeans with Congo grass (Urochloa ruziziensis cv. Comum). Silos were stored for 60 days in the laboratory at room temperature. The silage from soybeans with Aruana Guinea grass showed the highest contents of dry matter, crude fiber, neutral detergent fiber, insoluble nitrogen in neutral detergent fiber, and insoluble nitrogen in acid detergent fiber but the lowest levels of crude protein and ether extract. The highest content of mineral material and hemicellulose was observed in the silage from soybeans with Congo grass. The silages from soybeans and soybeans with Congo grass showed no significant differences for acid detergent fiber and lignin. In conclusion, the use of tropical grasses as a component to improve the quality of silage from soybeans is an alternative for forage conservation in ruminant production systems, especially at the dry season.

Multi-model ensembles for regional and national wheat yield forecasts in Argentina

Abstract While multi-model ensembles (MMEs) of seasonal climate models (SCMs) have been used for crop yield forecasting, there has not been a systematic attempt to select the most skillful SCMs to optimize the performance of a MME and improve in-season yield forecasts. Here, we propose a statistical model to forecast regional and national wheat yield variability from 1993–2016 over the main wheat production area in Argentina. Monthly mean temperature and precipitation from the four months (August–November) before harvest were used as features. The model was validated for end-of-season estimation in December using reanalysis data (ERA) from the European Centre for Medium-Range Weather Forecasts (ECMWF) as well as for in-season forecasts from June to November using a MME of three SCMs from 10 SCMs analyzed. A benchmark model for end-of-season yield estimation using ERA data achieved a R 2 of 0.33, a root-mean-square error (RMSE) of 9.8% and a receiver operating characteristic (ROC) score of 0.8 on national level. On regional level, the model demonstrated the best estimation accuracy in the northern sub-humid Pampas with a R 2 of 0.5, a RMSE of 12.6% and a ROC score of 0.9. Across all months of initialization, SCMs from the National Centers for Environmental Prediction, the National Center for Atmospheric Research and the Geophysical Fluid Dynamics Laboratory had the highest mean absolute error of forecasted features compared to ERA data. The most skillful in-season wheat yield forecasts were possible with a 3-member-MME, combining data from the SCMs of the ECMWF, the National Aeronautics and Space Administration and the French national meteorological service. This MME forecasted wheat yield on national level at the beginning of November, one month before harvest, with a R 2 of 0.32, a RMSE of 9.9% and a ROC score of 0.7. This approach can be applied to other crops and regions.

Seasonal sonic patterns reveal phenological phases (sonophases) associated with climate change in subarctic Alaska

Given that ecosystems are composed of sounds created by geophysical events (e.g., wind, rain), animal behaviors (e.g., dawn songbird chorus), and human activities (e.g., tourism) that depend on seasonal climate conditions, the phenological patterns of a soundscape could be coupled with long-term weather station data as a complimentary ecological indicator of climate change. We tested whether the seasonality of the soundscape coincided with common weather variables used to monitor climate. We recorded ambient sounds hourly for five minutes (01 January–30 June) over three years (2019–2021) near a weather station in a subarctic ecosystem in south-central Alaska. We quantified sonic information using the Acoustic Complexity Index (ACItf), coupled with weather data, and used machine learning (TreeNet) to identify sonic-climate relationships. We grouped ACItf according to time periods of prominent seasonal events (e.g., days with temperatures &amp;gt;0°C, no snow cover, green up, dawn biophony, and road-based tourism) and identified distinct sonic phenophases (sonophases) for groups with non-overlapping 95% confidence intervals. In general, sonic activity increased dramatically as winter transitioned to spring and summer. We identified two winter sonophases, a spring sonophase, and a summer sonophase, each coinciding with hours of daylight, temperature, precipitation, snow cover, and the prevalence of animal and human activities. We discuss how sonophases and weather data combined serve as a multi-dimensional, systems-based approach to understanding the ecological effects of climate change in subarctic environments.

Two‐way feedback between the Madden–Julian Oscillation and diurnal warm layers in a coupled ocean–atmosphere model

AbstractDiurnal warm layers develop in the upper ocean on sunny days with low surface wind speeds. They rectify intraseasonal sea‐surface temperatures (SSTs), potentially impacting intraseasonal weather patterns such as the Madden–Julian Oscillation (MJO). Here we analyse 15‐lead‐day forecast composites of coupled ocean–atmosphere and atmosphere‐only numerical weather prediction (NWP) models of the UK Met Office to reveal that the presence of diurnal warming of SST (dSST) leads to a faster MJO propagation in the coupled model compared with the atmosphere‐only model. To test the feedback between the MJO and the dSST, we designed a set of experiments with instantaneous vertical mixing over the top 5 or of the ocean component of the coupled model. Weaker dSST in the mixing experiments leads to a slower MJO over 15 lead days. The dSST produces a increase in the MJO phase speed between the coupled and the atmosphere‐only model. An additional increase is found for other coupling effects, unrelated to the dSST. A two‐way feedback manifests in the coupled model over the 15 lead days of the forecast between the MJO and the dSST. The MJO regime dictates the strength of the dSST and the dSST rectifies the intraseasonal anomalies of SST in the coupled model. Stronger dSST in the coupled model leads to stronger intraseasonal anomalies of SST. The MJO convection responds to these SSTs on a seven‐lead‐day timescale, and feeds back into the SST anomalies within the next three lead days. Overall, this study demonstrates the importance of high vertical resolution in the upper ocean for predicting the eastward propagation of the MJO in an NWP setting, which is potentially impactful for seasonal predictions and climate projections, should this feedback be unrepresented in the models.