Strong Variations Research Articles

The Unique Role of the Intraseasonal Zonal Wind in March Over the Equatorial Western Pacific Contributes to Shaping the Subsequent ENSO Development

AbstractThe predictability of the El Niño‐Southern Oscillation (ENSO) is significantly limited by the spring predictability barrier (SPB). Our observational analysis suggests that considering high‐frequency wind components during the spring season may mitigate the SPB impact on ENSO predictability. The intraseasonal zonal wind in March over the equatorial western Pacific initially perturbs the sea surface temperature (SST). It contributes nearly 40% of the east‐west SST gradient after 2–3 months, ranking first among other calendar months. This significant contribution causes March to become the earliest month to effectively indicate the following ENSO direction due to the active eastward‐propagating Madden‐Julian oscillation (MJO) activity. Through the obvious variation of the eastward‐propagating MJO speed, it also shows the possible close relationship between the mean SST state and the interdecadal variability in intraseasonal zonal wind. Additionally, the current strong variability of intraseasonal zonal wind suggests the important role of atmospheric information in recent ENSO development.

Present Variability and Future Change in Onset and Cessation of the Rainy Season Over Peru

ABSTRACTChanges in patterns of accumulated rainfall, as well as the rainy season onset, cessation and duration, can impact the availability of water resources and sectors such as agriculture, affecting the livelihoods of the population. The knowledge of these changes is crucial for regions driven by strong precipitation variability such as the Andean countries. Therefore, the aim of this work is to determine the present and future spatio‐temporal patterns of the onset, cessation and duration of the rainy season in Peru. For this purpose, we analysed in a first step the present variability and trends in 11 homogeneous regions using data from 377 ground stations for the period 1981–2019. The results showed significant trends (1981–2019) of earlier onset and increased duration only in the Southern Peruvian Amazon (Madre de Dios River basin). Furthermore, the accumulated rainfall has significant trends of increases in North East Andes, Northern and Southern Amazon. In a second step, we assessed future changes of the rainy season from an ensemble of statistically downscaled CMIP6 climate scenarios. A two‐tailed Student t‐test was used to evaluate the significance of changes. Two future time slices (2031–2060 and 2071–2100) relative to the reference period (1981–2010) were analysed. Future changes of the rainy season showed significant delays in the onset for the Central East Andes, South West Andes and Amazon regions in the period 2071–2100. Likewise, the rainy season duration presents future significant reductions in regions of the central and southern Andes under the SSP2‐4.5 scenario. Moreover, the accumulated precipitation is projected to increase significantly in the Pacific slope and Andes regions, mainly under the SSP5‐8.5 scenario. These findings are particularly important for sectors like agriculture, energy and water resources management.

Unveiling the dynamics of climate change narratives: A Google Trends analysis

This study explores public interest and misinformation surrounding climate change through an analysis of Google Trends data. It focuses on online search behaviors related to “climate”, “climate change”, “climate hoax”, and “HAARP” throughout 2023. By tracking search patterns, we aim to understand public perceptions, concerns, and the influence of misinformation on climate change discourse. Our findings indicate a strong temporal and regional variability in search interest, which spikes notably during significant events like Earth Day and in response to public statements by influential figures. The results demonstrate high engagement in regions with direct climate impact, such as Africa and parts of Asia, contrasted with varied engagement in Western nations. Notably, the data reveals significant public intrigue following conspiracy narratives during specific periods, which emphasizes the role of misinformation in shaping public discourse. This analysis underscores the need for targeted communication strategies to effectively address public misconceptions and enhance the understanding of climate-related issues. This research highlights the utility of Google Trends as a tool for real-time monitoring of public interest and misinformation and offers valuable insights for policymakers, educators, and activists to tailor their approaches to combat climate change more effectively. Through its comprehensive analysis, the study contributes to the broader understanding of how digital tools can aid in the ongoing efforts to engage the public in meaningful climate action discussions.

Wavelet analysis of discharge and sediment load for the Subarnarekha, Brahmani, and Mahanadi rivers of India

ABSTRACT This study aims to identify distinct small-scale temporary patterns between the river discharge and suspended sediment load for the main tropical rivers using continuous wavelet transform. Strong annual and irregular semi-annual variabilities, with few quarterly and interdecadal variabilities, are observed in river discharge and sediment load. The wavelet coherence reveals that the strongest in-phase coherence relationships exist between total suspended sediment and streamflow. The methodology for identifying the primary climate factors that integrate the partial wavelet coherence (PWC), multiple wavelet coherence (MWC), and quadruple wavelet coherence (QMWC) techniques is presented. The results show that the El-Nino/Southern Oscillation (ENSO) is the major regulator of streamflow followed by Indian Ocean Dipole (IOD) and Pacific Decadal Oscillation (PDO). The chosen rivers show a statistically significant ( p ≤ 0.05 ) descending trend in sediment load but no significant positive or negative trend in river discharge. This could be due to regional sediment storage and large-scale stream events affecting the sediment transport dynamics.

Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism

Accurate and reliable daily reservoir inflow forecast plays an essential role in several applications involving the management and planning of water resources, such as hydroelectric generation, flood control, water supply, and basin ecological dispatching. Runoff usually exhibits strong non-linearity, high uncertainty, and spatial and temporal variability. Existing techniques fail to capture complete dynamics change processes effectively. A data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on a multi-head attention mechanism was developed, referred to as the GWOCS-VMD-CNN-Transformer (GCVCT). First, the model utilize Grey Wolf Optimizer coupled with Cuckoo Search (GWO-CS) algorithms to optimize parameters in variational mode decomposition model (VMD). This approach helps obtain highly correlated intrinsic mode function (IMF) components, enhancing the frequency resolution of the input dataset. The proposed method overcomes the bottleneck of other available methods by decomposing the time series to capture the main long-term and short-term properties of hydrological processes. Second, the convolution neural network and Transformer (CNN-Transformer) are based on a multi-head attention mechanism as the objective predictive method. Finally, six evaluation indicators verify the performance of the proposed approach. The approach’s reliability was evaluated using the historical daily reservoir inflow data from the Xiluodu (XLD) and Wudongde (WDD) reservoirs in the Jinsha River Basin, China. Several single and hybrid models were developed for comparative analysis. The results indicate that the proposed ensemble approach fits better than other developed model methods. The GCVCT model showed excellent performance in forecasting the inflows of XLD and WDD reservoirs, with NSE values of 0.985 and 0.984, respectively. Furthermore, the GCVCT framework forecast capacity for peak inflow was further verified through discussion and analysis of the 48 peak flows during the validation period, consistently outperforming other models in predicting peak flow for both study reservoirs. This framework provides an effective method for the scientific optimal scheduling of hydropower reservoirs, enabling more sustainable and efficient management practices. It also demonstrates the potential of powerful deep-learning models in intelligent hydrological forecasting.

Spatial and Seasonal Variability of Remote and Urban Speciated Fine Particulate Matter in the United States.

The spatial and seasonal variability in the composition of major PM2.5 (particles with aerodynamic diameters less than 2.5μm) aerosol species in the United States were characterized using data from ground-based aerosol monitoring networks. The IMPROVE (Interagency Monitoring of Protected Visual Environments) network and the Chemical Speciation Network (CSN) operate in mostly rural/remote or urban/suburban sites, respectively. The networks have similar sampling schedules and analysis methods. Regional, monthly, and annual mean concentrations from 2019 to 2022 were calculated for ammonium sulfate (AS), ammonium nitrate (AN), particulate organic matter (POM), elemental carbon (EC), fine dust (FD), and sea salt (SS), as well as their relative contributions to reconstructed PM2.5 mass (RCFM). Organic aerosols were the largest contributor to RCFM across the United States (>40% annually, up to 80% monthly), with significant impacts from biomass smoke on POM and EC concentrations, contributions, and seasonality. AS concentrations and contributions were similar in urban and rural regions and contributed <20% annually to RCFM, considerably less than two decades ago. In general, urban concentrations were greater for AN, POM, and EC, suggesting additional urban sources. Some species, such as POM, FD, and AN, exhibited strong seasonal variability due to episodic source impacts or seasonal formation conditions. Evaluating the urban and rural monthly variability of major aerosol species is necessary for understanding the impacts of emission sources, regional transport, and atmospheric processes governing aerosol concentrations in the atmosphere.

Simultaneously mapping the 3D distributions of multiple heavy metals in an industrial site using deep learning and multisource auxiliary data

Three-dimensional (3D) distributions of multiple soil pollutants in industrial site are crucial for risk assessment and remediation. Yet, their 3D prediction accuracies are often low because of the strong variability of pollutants and availability of 3D covariate data. This study proposed a patch-based multi-task convolution neural network (MT-CNN) model for simultaneously predicting the 3D distributions of Zn, Pb, Ni, and Cu at an industrial site. By integrating neighborhood patches from multisource covariates, the MT-CNN model captured both horizontal and vertical pollution information, and outperformed the widely-used methods such as random forest (RF), ordinary Kriging (OK), and inverse distance weighting (IDW) for all the 4 heavy metals, with R2 values of 0.58, 0.56, 0.29 and 0.23 for Zn, Pb, Ni and Cu, respectively. Besides, the MT-CNN model achieved more stable predictions with reasonable accuracy, in comparison with the single-task CNN model. These results highlighted the potential of the proposed MT-CNN in simultaneously mapping the 3D distributions of multiple pollutants, while balancing the model training, maintaining and accuracy for low-cost rapid assessment of soil pollution at industrial sites.

Global Terrestrial Water–Energy Coupling Across Scales

ABSTRACTTerrestrial water–energy coupling (WEC), in the form of a non‐linear relationship between Soil Moisture (SM) and evaporative fraction (EF, ratio of actual and potential evapotranspiration), controls critical ecohydrological processes. We investigate and parameterize the evolution of global SM–EF coupling from the field to remote‐sensing (RS)‐pixel. The field‐scale EF and SM were obtained from eddy covariance (EC) and SM sensors at the global FLUXNET and Texas Water Observatory sites. RS‐pixel‐scale EF and SM estimates were obtained from Moderate‐resolution Imaging Spectroradiometer (MODIS) and Soil Moisture Active Passive (SMAP) sensors, respectively. We estimate the effective thresholds of the WEC regimes from both EC and satellite datasets to highlight the influence of sub‐pixel‐scale heterogeneity, and, scaling and observational constraints on the evolution of WEC regimes from the field to RS‐pixel scale. We argue that the changes in land surface conditions add a temporal variability in the critical thresholds of terrestrial WEC at RS pixel scale. We compare the critical WEC thresholds of the water‐ and energy‐limited regimes with a SM drydown‐based approach and highlight the similarities between both methods in partitioning dominant WEC regimes. EF and SM are strongly coupled in dryland arid and semi‐arid regions compared to humid climates. WEC regimes and thresholds have strong interseason variability due to dynamic interactions between soil, vegetation and atmosphere at the RS‐pixel scale. In contrast, field‐scale SM‐EF coupling is influenced predominantly by soil conditions and land‐use/management practices. Hence, future development of Earth‐system/Land‐surface models must account for the inter‐scale differences in the coupling between terrestrial water and energy fluxes representative of the ‘effective’ processes at large spatial scales.

Entropy-informed multi-stage sampling design for soil pollution mapping in heavy metal contaminated areas

The accuracy of soil heavy metal pollution mapping is heavily reliant on the sampling strategies utilized in both the preliminary and detailed survey stages of site investigations. This study introduces an entropy-informed multi-stage sampling design (EIMSD) method that leverages preliminary survey data as background information and utilizes relative entropy to progressively select sampling points in detailed surveys. Results indicate that the EIMSD method outperforms the grid sampling design (GSD) and conventional sampling design (CSD) methods across both hypothetical and real-world study areas. This superiority is evidenced by a notable rise in R2 values, ranging from 6.4% to 60.4% and a decrease in RMSE values from 16.7% to 54.0% relative to GSD, and a similar trend with an increase in R2 values from 6.7% to 44.1% and a reduction in RMSE values from 16.5% to 39.7% when compared to CSD. This study also investigates the optimal configurations for EIMSD, focusing on the number of detailed sampling points per stage (Nadd) and the ratio of preliminary-to-detailed survey sample sizes (Np/Nd) when the sum of Np and Nd is held constant. Our findings highlight that adding one detailed sampling point per stage (Nadd = 1) is the most effective. For areas with strong spatial variability, a larger Np/Nd value of approximately 3/2 is recommended, whereas a ratio close to 1 is apt for areas with moderate variability. Conversely, for areas with weak variability, a smaller Np/Nd value of about 2/3 is advised. EIMSD provides a more detailed and accurate map of soil heavy metal contamination, facilitating more targeted and effective remediation strategies.

A Climatological Study of the Frequency Spectra of Vertical Winds From MU Radar Data (1987–2022)

AbstractIn this paper, we present a statistical analysis of the frequency spectra of vertical velocity W based on data collected by the VHF middle and upper atmosphere (MU) radar from 2.025 to 19.875 km altitude during several days per month over 36 years (1987–2022). We analyzed the mean spectral slopes in the band [0.5–6 hr], which is in the core of the period range for internal gravity waves at the MU radar latitude (∼36° N). First, we tested the performance of several spectral estimators when data are missing to minimize estimation bias on and variance. Second, we analyzed the properties of . We found a dependence on radar echo power aspect ratio and power imbalance between oblique beams maybe due to contaminations of measurements by the horizontal wind . After corrections, we found that: (a) in the troposphere ( is almost seasonally invariant (∼−0.8) despite the strong variability of and (b) is highly variable in the stratosphere ( from ∼0 to −1.2 and shows a clear exponential dependence on at least up to . An empirical expression of the apparent frequency spectra in the stratosphere is with, for example, and . From numerical simulations, we show that Doppler shift effects cannot explain the dependence on . Therefore, the variation of with may represent a real change in the shape of the intrinsic frequency spectrum, which is not considered in the theoretical models.

Canonical Correlation of Milk Composition Parameters and Blood Biomarkers in High-Producing Dairy Cows During Different Lactation Stages.

This study explores milk composition and blood markers in cows across lactation stages. Holstein cows were divided into four groups: beginning of lactation (BL; n = 21), peak of lactation (PL; n = 21), middle of lactation (ML; n = 21), and end of lactation (EL; n = 20). Blood (1 × 15 mL) and milk samples (1 × 100 mL) were collected for biomarker analysis. Blood chemistry profiles were determined using a clinical chemistry analyser, and milk lactose, fat, and protein levels (%) were determined using an infrared absorbance analyser. Minerals (Ca, P, and Mg) in milk were determined by atomic absorption spectrometry after mineralizing the samples. Glucose was higher in the EL group than in the BL group (p < 0.01), whereas D-beta-hydroxybutyrate (D-BHB) was higher in the BL group than in the PL and ML groups (p < 0.001). Cholesterol was higher in the PL, ML, and EL groups than in the BL group (p < 0.001). Gamma-glutamyl transferase was increased in the PL group compared to the BL group. Phosphorus levels were lower in the PL than in the BL group, whereas protein levels were higher in the EL than in the PL group. Spearman and partial correlation analysis showed several significant associations between the observed variables. Using canonical correlation analysis were identified three significant correlations (rc1 = 0.853; rc2 = 0.823; rc3 = 0.739). The main canonical correlation identified blood TG and milk urea as the strongest variables. According to the canonical loading, the biomarkers TG, Mg, urea, cholesterol, and alkaline phosphatase (U1) are the primary variables associated with milk parameters (V1), specifically with milk urea, milk Mg and P, protein, and lactose.

Methodological insights into soil elemental nickel in typical Karst areas: comprehensive analysis of geochemical characteristics, source determination, and influencing factors.

Excessive levels of Nickel in the soil can compromise the security of agricultural products, posing a threat to health of human beings; therefore, the repair and treatment of Nickel exceeding the standard levels in soil are particularly critical. Although it is crucial that the potential restoration of Nickel in ensuring the security of both soil and farm produce within karst regions., few studies have been conducted on the potential restoration of large-scale Nickel-contaminated soils. In this study, the soil in Wuming, Guangxi, a typical karst area, was comprehensively studied. 12,547 surface soil samples, 134 deep soil samples and 60 soil profiles were collected systematically. The results showed that the Nickel background value of the surface soil was 34.9mg/kg, indicating strong background characteristics and high variability. Principal component analysis showed that soil Nickel was primarily derived from natural sources in the geological background and partly derived from agricultural sources. Analysis of variance showed that the Nickel content of the soil was affected by the parent rock, soil type, soil use type, and topography. In addition, the distribution of Nickel in the soil profile increased exponentially with depth. Therefore, the exponential model and multiple integrals were used to derive the formula for the Nickel potential restoration amount at different depth ranges, and the potential restoration amount of soil Nickel was calculated based on different parent material, soil, and land use types. The formula is reasonable and representative and can provide a theoretical basis for the remediation and treatment of Nickel-polluted soil in karst areas.

Inferring the distribution of the ionising photon escape fraction

The escape fraction of ionising photons from galaxies ($f_ esc $) is a key parameter for understanding how intergalactic hydrogen became reionised, but it remains mostly unconstrained. Measurements have been limited to the average value in galaxy ensembles and to handfuls of individual detections. To help understand which mechanisms govern ionising photon escape, here we infer the distribution of $f_ esc We developed a hierarchical Bayesian inference technique to estimate the population distribution of $f_ esc $ from the ratio of Lyman continuum to non-ionising UV flux measured from broadband photometry. We applied it to a sample of 148 $z 3.5$ star-forming galaxies from the VANDELS spectroscopic survey. We explored four physically motivated distributions: constant, log-normal, exponential, and bimodal, and recovered $ f_ esc for most models. We find the observations are best described by an exponential $f_ esc $ distribution with scale factor $ $. This indicates most galaxies in our sample exhibit very low escape fractions, while predicting substantial ionising photon leakage for only a few galaxies, implying a range of optical depths in the interstellar medium and/or time variability in ionising photon escape. We rule out a bimodal distribution at high significance, indicating that a purely bimodal model of ionising photon escape (due to very strong sightline and/or time variability) is not favoured. We compare our recovered exponential distribution with the SPHINX simulations and find that, while the simulation also predicts an exponential distribution, it significantly underpredicts our inferred mean. The distribution of $f_ esc $ can be a vital test for simulations in understanding ionising photon leakage, and is important to consider to gain a complete picture of reionisation.

Improved nitrogen fertilizer management reduces nitrous oxide emissions in a northern Prairie cropland

Arable croplands are a significant source of nitrous oxide (N2O) emissions, largely due to nitrogen (N) fertilizer applications to support crop production. Nevertheless, there is limited research on the N2O dynamics from canola-wheat rotations in the semi-arid northern Prairies, an important agricultural region. Here, we present micrometeorological N2O fluxes measured from January 2021 to April 2024 in Saskatchewan, Canada, to evaluate the impact of N fertilizer management on the year-round N2O emissions from a canola-wheat rotation. A combination of two 4R (Right Source, Right Rate, Right Time, Right Place) N management practices – a reduced N rate and an enhanced efficiency N fertilizer source – was compared to common fertilizer management practices for the region. Two periods at high risk for N2O flux events were identified, after N fertilizer applications and the following spring thaw, with the magnitude of emissions varying over the multi-year period. As for cumulative emissions, the growing season (GS) N2O emissions were 50 % of annual emissions, presenting an opportunity to mitigate N2O emissions through improved N fertilizer management. Indeed, the improved 4R N management reduced N2O emissions by 57 % over the entire study period without impacting yields. The reduction in GS N2O emissions resulted from the 4R N management lowering mean N2O flux at times of high WFPS (>50 %). The non-growing season (NGS) N2O accounted for 11–67 % of annual emissions. Fall soil nitrate levels were a strong explanatory variable of NGS emissions (r2 = 0.69, r2 = 0.39), but the rate of change and magnitude of NGS emissions depended on thawing conditions – lower for drier thaws, higher for wetter thaws. Ultimately, better N fertilizer management reduces cumulative N2O emissions from cropping systems when practiced for several years.

Opposite Trends in the Northern Hemisphere Stratosphere Between Mid‐Winter and Early Spring Linked to Surface Temperature Anomalies

AbstractTrends in the coupled stratosphere‐troposphere system during the 1979–2022 period are investigated in the Northern Hemisphere using reanalysis datasets. More upward planetary wave propagation in December is shown to precede the deceleration of the stratospheric polar vortex in January. This deceleration prevents the waves from continuing to propagate upward in February and favors an acceleration of the stratospheric polar vortex in March. This is associated with an increased Northern Hemisphere annular mode in March in the stratosphere and the troposphere. Trends show a moderate significance level because of strong interannual variability. Recent seasons whose anomalies project onto the trends are those for which wave‐1 anomaly constructively interferes with wave‐1 climatology in December, which occurs when there is warming in an area extending from Eastern Canada to Greenland and slight cooling over Eurasia. It shows the potential for predicting the springtime stratospheric polar vortex from wintertime wave‐1 anomalies.

Strengthening of the Equatorial Pacific Upper‐Ocean Circulation Over the Past Three Decades

AbstractThirty years (1993–2022) of concurrent satellite and in‐situ observations show a long‐term strengthening of the equatorial Pacific upper‐ocean circulation. Enhanced southeasterly and cross‐equatorial winds have caused an annual mean, basin‐wide acceleration of the equatorial westward near‐surface currents by ∼20% and an acceleration of poleward flow north (south) of the equator by ∼60% (∼20%). Additional moored velocity data reveal a deepening of the Equatorial Undercurrent (EUC) core at 170°W and significant shoaling at 140°W and 110°W, but no significant changes in EUC core velocity. The strongest subsurface zonal velocity trends are observed above the EUC core in the central to eastern Pacific and occur before and after the seasonal maximum of EUC core velocity, causing enhanced upper‐ocean vertical current shear. Consistent with trends of the 20°C isotherm depth along the equatorial Pacific, a significant basin‐wide steepening of the equatorial thermocline is observed. Both the accelerating equatorial current system and the enhanced thermocline slope are consistent with an observed steepening of the zonal sea surface height gradient due to increased wind‐driven westward mass transport at the surface. During February‐March, both surface and subsurface currents along the equator show eastward velocity trends, in contrast to westward near‐surface current trends during the remainder of the year. The trend reversal is attributed to both a long‐term shift in equatorial Kelvin wave activity and to the impact of strong interannual variability due to El Niño Southern Oscillation and other modes of natural variability on decadal to multidecadal time scales.

A wiggling filamentary jet at the origin of the blazar multi-wavelength behaviour

Blazars are beamed active galactic nuclei (AGNs) known for their strong multi-wavelength variability on timescales ranging from years down to minutes. Many different models have been proposed to explain this variability. We aim to investigate the suitability of the twisting jet model presented in previous works to explain the multi-wavelength behaviour of BL Lacertae, the prototype of one of the blazar classes. According to this model, the jet is inhomogeneous, curved, and twisting, and the long-term variability is due to changes in the Doppler factor due to variations in the orientation of the jet-emitting regions. We analysed optical data of the source obtained during monitoring campaigns organised by the Whole Earth Blazar Telescope (WEBT) in 2019--2022, together with radio data from the WEBT and other teams, and gamma -ray data from the Fermi satellite. In this period, BL Lacertae underwent an extraordinary activity phase, reaching its historical optical and gamma -ray brightness maxima. The application of the twisting jet model to the source light curves allows us to infer the wiggling motion of the optical, radio, and gamma -ray jet-emitting regions. The optical-radio correlation shows that the changes in the radio viewing angle follow those in the optical viewing angle by about 120 days, and it suggests that the jet is composed of plasma filaments, which is in agreement with some radio high-resolution observations of other sources. The gamma -ray emitting region is found to be co-spatial with the optical one, and the analysis of the gamma -optical correlation is consistent with both the geometric interpretation and a synchrotron self-Compton (SSC) origin of the high-energy photons. We propose a geometric scenario where the jet is made up of a pair of emitting plasma filaments in a sort of double-helix curved rotating structure, whose wiggling motion produces changes in the Doppler beaming and can thus explain the observed multi-wavelength long-term variability.

Seasonality in cave dripwater and air properties – implications for speleothem palaeoclimatology, Nova Grgosova Cave (Croatia)

Due to the wealth of climate-sensitive properties, speleothems have been propelled into prominence as one of the most powerful continental archives of past climate changes. However, the multitude of processes that operate in the unsaturated karst zone and in the cave atmosphere can make palaeoclimate interpretations of the speleothem proxies challenging and site specific. Hence, to better understand the climate-proxy relationship, cave monitoring studies are usually undertaken. Here, we present the first results of an ongoing cave monitoring study in the Nova Grgosova Cave in Croatia covering an eighteen-month long monitoring period. The driving mechanisms for Mg/Ca and Sr/Ca variability in dripwater samples that feed ten stalagmites are discussed. The results reveal high variability in infiltration among the monitored sites as well as strong seasonal variability in cave air carbon dioxide (CO2) concentrations. A strong positive correlation between dripwater Mg/Ca and Sr/Ca suggests that prior calcite precipitation (PCP) is taking place at this site affecting the chemical composition of dripwater. Principal component analysis furthermore reveals that dripwater Mg/Ca and Sr/Ca are in strong negative correlation with cave air CO2 concentrations, while there is a weak correlation with dripwater quantity. Cave ventilation is a primary process leading to the PCP at the Nova Grgosova Cave. The seasonality revealed in this study suggests the possibility that the Mg/Ca and Sr/Ca ratio in the speleothems from this cave site can be used to aid seasonal reconstructions of past climate conditions in central Croatia and beyond.

Strong Subseasonal Variability of Oxic Methane Production Challenges Methane Budgeting in Freshwater Lakes.

Methane (CH4) accumulation in the well-oxygenated lake epilimnion enhances the diffusive atmospheric CH4 emission. Both lateral transport and in situ oxic methane production (OMP) have been suggested as potential sources. While the latter has been recently supported by increasing evidence, quantifying the exact contribution of OMP to atmospheric emissions remains challenging. Based on a large high-resolution field data set collected during 2019-2020 in the deep stratified Lake Stechlin and on three-dimensional hydrodynamic modeling, we improved existing CH4 budgets by resolving each component of the mass balance model at a seasonal scale and therefore better constrained the residual OMP. All terms in our model showed a large temporal variability at scales from intraday to seasonal, and the modeled OMP was most sensitive to the surface CH4 flux estimates. Future efforts are needed to reduce the uncertainties in estimating OMP rates using the mass balance approach by increasing the frequency of atmospheric CH4 flux measurements.

Hydrokinetic energy site selection under high seasonality: The i-IHE index

A novel index is developed to select sites for hydrokinetic energy exploitation in coastal areas with high resource seasonality – the typical case being estuaries whose hydrodynamics are highly influenced by variable river discharges. The intra-annual Integrated Hydrokinetic Energy (i-IHE) index is constructed by means of a new penalty function that modifies the Integrated Hydrokinetic Energy (IHE) index. The i-IHE index thus defined is applied to a case study: the Miño Estuary (NW Iberian Peninsula), with seasonal variations of up to 1000 % in the riverine discharge. The hydrodynamics are modelled by the state-of-the-art Delft3D-FLOW code using a high-resolution 2DH grid. The results confirm a strong variability in the resource, with time periods shorter than seasons. The results obtained for the penalty function throughout the Miño Estuary, with values ranging between 0.5 and 0.75, show the relevance of the i-IHE index in order to not overestimate the potential of coastal areas subject to large intra-annual variability. Compared to the conventional IHE index, the novel i-IHE index highlights smaller areas with greater energy potential. The larger exploitable energy in these areas, along with the lower costs and fewer restrictions for hydrokinetic energy operation, will be of interest to developers and stakeholders.