Drought conditions disrupt atmospheric carbon uptake in a Mediterranean saline lake

Inland saline lakes play a key role in the global carbon cycle, acting as dynamic zones for atmospheric carbon exchange and storage. Given the global decline of saline lakes and the expected increase of periods of drought in a climate change scenario, changes in their potential capacity to uptake or emit atmospheric carbon are expected. Here, we conducted continuous measurements of CO2 and CH4 fluxes at the ecosystem scale in an endorheic saline lake of the Mediterranean region over nearly 2 years. Our focus was on determining net CO2 and CH4 exchanges with the atmosphere under both dry and flooded conditions, using the eddy covariance (EC) method. We coupled greenhouse gas flux measurements with water storage and analysed meteorological variables like air temperature and radiation, known to influence carbon fluxes in lakes. This extensive data integration enabled the projection of the net carbon flux over time, accounting for both dry and wet conditions on an interannual scale. We found that the system acts as a substantial carbon sink by absorbing atmospheric CO2 under wet conditions. In years with prolonged water storage, it is predicted that the lake's CO2 assimilation capacity can surpass 0.7 kg C m2 annually. Conversely, during extended drought years, a reduction in CO2 uptake capacity of more than 80 % is expected. Regarding CH4, we measured uptake rates that exceeded those of well-aerated soils such as forest soils or grasslands, reaching values of 0.2 µmol m−2 s−1. Additionally, we observed that CH4 uptake during dry conditions was nearly double that of wet conditions. However, the absence of continuous data prevented us from correlating CH4 uptake processes with potential environmental predictors. Our study challenges the widespread notion that wetlands are universally greenhouse gas emitters, highlighting the significant role that endorheic saline lakes can play as a natural sink of atmospheric carbon. However, our work also underscores the vulnerability of these ecosystem services in the current climate change scenario, where drought episodes are expected to become more frequent and intense in the coming years.</p

Non-biting midges (Chironomidae) as a proxy for summer temperatures during the post-Holsteinian (MIS 11b) &ndash; a central European perspective

Climatic and environmental changes during past interglacial periods can be investigated to improve our understanding of mechanisms governing the changes which are currently observed. Numerous proxies might be utilised to reconstruct various environmental parameters. For instance, pollen analysis indicates changes in vegetation as well as winter temperature fluctuations, while Chironomidae larvae head capsules are widely used to recreate summer thermal conditions. Non-biting midges remains indicate trophy and pH of water bodies as well. Nevertheless, they have been used mostly in the studies of the Holocene with hardly any Chironomid-inferred temperature reconstructions conducted for MIS 11 period. In this study we present the first quantitative summer temperature reconstruction for the post-Holsteinian (Marine Isotope Stage &ndash; MIS 11b) in Central Europe based on the analysis of fossil chironomid remains preserved in palaeolake sediments recovered at Krępa, southeastern Poland. The stratigraphic context for the chironomid-based summer temperature reconstruction is provided by pollen data, together allowing to compare our results in the context of climate development at the end of the Holsteinian Interglacial. Chironomidae assemblages at the Krępa site consist mainly of oligotrophic and mesotrophic species (e.g Corynocera ambigua-type, Chironomus anthracinus-type) with lower abundance of eutrophic species (e.g. Chironomus plumosus-type). The chironomid-based summer temperature reconstruction indicates July temperature ranging between 15,3 ॰C and 20,1 ॰C during the early post-Holsteinian. Temperature changes during the first stadial after the Holstein Interglacial period are also reflected by the pollen data, which, however, show a certain delay compared to the chironomids. In any case, results from Krępa prove that conducting Chironomidae analysis is even feasible for periods as early as the mid-Pleistocene, enhancing our understanding of the mechanisms that control present-day climatic and environmental changes. The additional element of this research is indicating sites within the Polish borders that were investigated so far &ndash; mostly on the basis of pollen analysis, occasionally Cladocera, isotopes, etc. &ndash; and might be new objects of studies based on Chironomid-inferred temperature reconstructions. However, bringing Chironomid analysis with particular emphasis of challenges of conducting it with the use of sediments older than Holocene is the primary aim of this publication. Data from the MIS 11 complex are unique. There are only 4 sites with pre-Late Glacial chironomid-based summer temperature reconstructions in Europe

Evaluating the consistency of forest disturbance datasets in continental USA

Forests play a crucial role in the Earth System, providing essential ecosystem services and sustaining biological diversity. However, forest ecosystems are increasingly impacted by disturbances, which are often integral to their dynamics but have been exacerbated by climate change. Despite the growing concern, there is currently a lack of globally consistent and temporally continuous data on forest disturbances to characterize changes in disturbance regimes. This gap hinders our ability to accurately assess and respond to these changes. In this study, we focus on the continental United States and compare four datasets on forest disturbances to evaluate their consistency and reliability regarding their spatial and temporal characteristics and driven agents, when available. Our analysis reveals a moderate agreement across the datasets, with inventory-based comparisons demonstrating the highest level of consistency. In contrast, comparisons involving remote sensing data show lower alignment and a delayed detection of disturbances by satellite observations compared to ground-based inventories. Additionally, discrepancies were observed in the identification of disturbance agents in overlapping areas. Our findings underscore the importance of careful data quality assessment and consideration of their inherent uncertainty when utilizing them for further applications. This study highlights the need for improved data integration and accuracy to advance the understanding of forest disturbances

The effect of group size and laying month on the quality, IgG, and corticosterone levels of goose eggs

Environmental stress is known to negatively affect poultry health, production, and egg quality. Our study aimed to evaluate the effects of two different group sizes and the laying month on egg quality characteristics as well as the effect of group size on corticosterone and IgG levels in goose eggs. The research was conducted on a semi-free-range goose breeder farm in Hajdú–Bihar county, Hungary. The eggs included in the analysis were produced by 4-year-old geese of the Grimaud breed. Two group sizes were constructed; the large group contained 850 birds; and there were three small groups, each containing 50 geese as replicates. The effect of the laying month and group size on egg quality parameters and the effect of group size on egg IgG and corticosterone contents were investigated. Eggs laid in January at the peak of production and at the end of February (low-production period) were involved in the study. Regarding the effect of months, we noticed a decrease in egg width (from 6.12 to 5.98 cm), shell thickness (from 0.76 to 0.61 mm at the blunt end, from 0.69 to 0.61 mm at the equator, and from 0.65 to 0.56 mm at the pointed end), shell weight (from 19.56 to 18.19 g), yolk weight (from 69.05 to 62.35 g), yolk ratio (from 36.45 % to 34.43 %), yolk diameter (from 7.09 to 6.59 cm), and yolk colour with fan (from 12.58 to 11.83) and b∗ (from 54.57 to 49.91) (P ≤ 0.05). The albumen ratio and yolk pH increased from 53.24 % to 55.51 % and from 6.18 to 6.29 from January to February, respectively. Regarding group size, the albumen pH (8.77 vs. 8.67), IgG (4955 vs. 3823 ng mL−1), and corticosterone (187.26 vs. 76.24 ng mL−1) levels were higher in the small groups (P ≤ 0.05).</p

Characterization of non-Gaussianity in the snow distributions of various landscapes

Seasonal snowpack is an important predictor of the water resources available in the following spring and early-summer melt season. Total basin snow water equivalent (SWE) estimation usually requires a form of statistical analysis that is implicitly built upon the Gaussian framework. However, it is important to characterize the non-Gaussian properties of snow distribution for accurate large-scale SWE estimation based on remotely sensed or sparse ground-based observations. This study quantified non-Gaussianity using sample negentropy; the Kullback–Leibler divergence from the Gaussian distribution for field-observed snow depth data from the North Slope, Alaska; and three representative SWE distributions in the western USA from the Airborne Snow Observatory (ASO). Snowdrifts around lakeshore cliffs and deep gullies can bring moderate non-Gaussianity in the open, lowland tundra of North Slope, Alaska, while the ASO dataset suggests that subalpine forests may effectively suppress the non-Gaussianity of snow distribution. Thus, non-Gaussianity is found in areas with partial snow cover and wind-induced snowdrifts around topographic breaks on slopes and on other steep terrain features. The snowpacks may be considered weakly Gaussian in coastal regions with open tundra in Alaska and alpine and subalpine terrains in the western USA if the land is completely covered by snow. The wind-induced snowdrift effect can potentially be partitioned from the observed snow spatial distribution guided by its Gaussianity.</p

Using the FY-3E satellite hyperspectral infrared atmospheric sounder to quantitatively monitor volcanic SO2

The Hyperspectral Infrared Atmospheric Sounder Type II (HIRAS-II) aboard the Fengyun 3E (FY-3E) satellite provides valuable data on the vertical distribution of atmospheric states. However, effectively extracting quantitative atmospheric information from the observations is challenging due to the large number of hyperspectral sensor channels, inter-channel correlations, associated observational errors, and susceptibility of the results to influence by trace gases. This study explores the potential of FY-3E/HIRAS-II to atmospheric loadings of SO2 from volcanic eruption. A methodology for selecting SO2 sensitive channels from the large number of hyperspectral channels recorded by FY-3E/HIRAS-II is presented. The methodology allows for the selection of SO2-sensitive channels that contain similar information on variations in atmospheric temperature and water vapor for minimizing the influence of atmospheric water vapor and temperature to SO2. A sensitivity study shows that the difference in brightness temperature between the experimentally selected SO2 sensitive channels and the background channels effectively removes interference signals from surface temperature, atmospheric temperature, and water vapor during SO2 detection and inversion. A positive difference between near-surface atmospheric temperature and surface temperature enables the infrared band to capture more SO2 information in the lower and middle layers. The efficacy of FY-3E/HIRAS-II SO2 sensitive channels in quantitively monitor volcanic SO2 is demonstrated using data from the 29 April 2024 eruption of Mount Ruang in Indonesia. Using FY-3E/HIRAS-II measurements, the spatial distribution and quantitatively information of volcanic SO2 are easily observed. The channel selection can significantly enhance the computation efficiency while maintain the accuracy of SO2 detection and retrieval, despite the large volume of data

Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5

The rivers of High-mountain Asia provide freshwater to around 1.9 billion people. However, precipitation, the main driver of river flow, is still poorly understood due to limited in situ measurements in this area. Existing tools to interpolate these measurements or downscale and bias-correct precipitation models have several limitations. To overcome these challenges, this paper uses a probabilistic machine learning approach called multi-fidelity Gaussian processes (MFGPs) to downscale the fifth ECMWF climate reanalysis (ERA5). The method is first validated by downscaling ERA5 precipitation data over data-rich Europe and then data-sparse upper Beas and Sutlej river basins in the Himalayas. We find that MFGPs are simpler to implement and more applicable to smaller datasets than other state-of-the-art machine learning methods. MFGPs are also able to quantify and narrow the uncertainty associated with the precipitation estimates, which is especially needed over ungauged areas and can be used to estimate the likelihood of extreme events that lead to floods or droughts. Over the upper Beas and Sutlej river basins, the precipitation estimates from the MFGP model are similar to or more accurate than available gridded precipitation products (APHRODITE, TRMM, CRU TS, and bias-corrected WRF). The MFGP model and APHRODITE annual mean precipitation estimates generally agree with each other for this region, with the MFGP model predicting slightly higher average precipitation and variance. However, more significant spatial deviations between the MFGP model and APHRODITE over this region appear during the summer monsoon. The MFGP model also presents a more effective resolution, generating more structure at finer spatial scales than ERA5 and APHRODITE. MFGP precipitation estimates for the upper Beas and Sutlej basins between 1980 and 2012 at a 0.0625° resolution (approx. 7 km) are jointly published with this paper.</p

Generalised drought index: a novel multi-scale daily approach for drought assessment

Drought is a complex climatic phenomenon characterised by water scarcity and is recognised as the most widespread and insidious natural hazard, posing significant challenges to ecosystems and human society. In this study, we propose a new daily based index for characterising droughts, which involves standardising precipitation and/or precipitation minus potential evapotranspiration (PET) data. The new index proposed here, the generalised drought index (GDI), is computed for the entire period available from the Iberian Gridded Dataset (1971 to 2015). Comparative assessments are conducted against the daily Standardised Precipitation Index (SPI), the Standardised Precipitation Evapotranspiration Index (SPEI), and a simple Z-Score standardisation of climatic variables. Seven different accumulation periods are considered (7, 15, 30, 90, 180, 360, and 720 d) with three drought levels: moderate, severe, and extreme. The evaluation focuses mainly on the direct comparison amongst indices in terms of their ability to conform to the standard normal distribution, added value assessment using the distribution added value (DAV), and a simple bias difference for drought characteristics. Results reveal that the GDI, together with the SPI and SPEI, follows the standard normal distribution. In contrast, the Z-Score index depends on the original distribution of the data. The daily time step of all indices allows the characterisation of flash droughts, with the GDI demonstrating added value when compared to the SPI and SPEI for the shorter and longer accumulations, with a positive DAV up to 35 %. Compared to the Z-Score, the GDI shows expected greater gains, particularly at lower accumulation periods, with the DAV reaching 100 %. Furthermore, the spatial extent of drought for the 2004–2005 event is assessed. All three indices generally provide similar representations, except for the Z-Score, which exhibits limitations in capturing extreme drought events at lower accumulation periods. Overall, the findings suggest that the new index offers improved performance and comparatively adds value to similar indices with a daily time step.</p

Towards a Global Spatial Machine Learning Model for Seasonal Groundwater Level Predictions in Germany

Reliable predictions of groundwater levels are crucial for a sustainable groundwater resource management, which needs to balance diverse water needs and to address potential ecological consequences of groundwater depletion. Machine Learning (ML) approaches for time series prediction, in particular, have shown promising predictive accuracy for groundwater level prediction and have scalability advantages over traditional numerical methods when sufficient data is available. Global ML architectures enable predictions across numerous monitoring wells concurrently using a single model, allowing predictions for monitoring wells over a broad range of hydrogeological and meteorological conditions and simplifying model management. In this contribution, groundwater levels were predicted up to 12 weeks for 5,288 monitoring wells across Germany using two state-of-the-art ML approaches, the Temporal Fusion Transformer (TFT) and the Neural Hierarchical Forecasting for Time Series (N-HiTS) algorithm. The models were provided with historical groundwater levels, meteorological features and a wide range of static features describing hydrogeological and soil properties at the wells. To determine the conditions under which the model achieves good performance and whether it aligns with hydrogeological system understanding, the model&rsquo;s performance was evaluated spatially and correlations with both static input features and time-series features from hydrograph data were examined. The N-HiTS model outperformed the TFT model, achieving a median NSE of 0.5 for the 12-week prediction over all 5,288 monitoring wells. Performance varied widely: 25 % of wells achieved an NSE &gt; 0.68, while 15 % had an NSE &lt; 0 with the best N-HiTS model. A tendency for better predictions in areas with high data density was observed. Moreover, the models achieved higher performance in lowland areas with distinct seasonal groundwater dynamics, in monitoring wells located in porous aquifers, and at sites with moderate permeabilities, which aligns with theoretical expectations. Overall, the findings highlight that global ML models can facilitate accurate seasonal groundwater predictions over large, hydrogeological diverse areas, potentially informing future groundwater management practices at a national scale

Enhanced emission of intermediate/semi-volatile organic matters in both gas and particle phases from ship exhausts with low-sulfur fuels

The widespread utilization of low-sulfur fuels in compliance with global sulfur limit regulations has significantly mitigated the emissions of sulfur dioxide (SO2) and particulate matter (PM) on ships. However, significant uncertainties still persist regarding the impact on intermediate/semi-volatile organic compounds (I/SVOCs). Therefore, on-board test of I/SVOCs from three ocean-going vessels (OGVs) and four inland cargo ships (ICSs) with low-sulfur fuels (&lt; 0.50 % m/m) in China were carried out in this study. Results showed that the emission factors of total I/SVOCs were 881 &plusmn; 487, 1181 &plusmn; 421 and 1834 &plusmn; 667 mg (kg fuel)-1 for OGVs with heavy fuel oil (HFO), marine gas oil (MGO) and ICSs with 0# diesel, respectively. The transition from low-sulfur content (&lt; 0.50 % m/m) to ultra-low-sulfur content (&lt; 0.10 % m/m) fuels had evidently enhanced the emission factor of I/SVOCs, with non-ignorable contribution from particle-phase I/SVOCs, thereby further amplifying the secondary organic aerosol formation potential (SOAFP). Fuel type, engine type, and operating conditions comprehensively influenced the emission factor level, composition, and volatility distribution of I/SVOCs. Notably, a substantial proportion of fatty acids had been identified in ship exhausts, necessitating heightened attention. Furthermore, organic diagnostic markers of hopanes, in conjunction with the C18:0 to C14:0 acid ratio, could be considered as potential markers for HFO exhausts. The findings suggest that there is a necessity to optimize the implementation of a global policy on ultra-low-sulfur oil in the near future