ALBERT

Description: This julia package implements various tools for working with simple, non-marked, one dimensional point processes. In special it implements a bootstrap-based goodness-of-fit test for parametric models.

Description: To better understand possible reasons for the diverse modeling results and large discrepancies of the detected solar fingerprints, we took one step back and assessed the "initial" solar signals in the middle atmosphere based on large ensemble simulations with multiple climate models — FOCI, EMAC, and MPI-ESM-HR. Consistent with previous work, we find that the 11-year solar cycle signals in the short wave heating rate (SWHR) and ozone anomalies are robust and statistically significant in all three models. These "initial" solar cycle signals in SWHR, ozone, and temperature anomalies are sensitive to the strength of the solar forcing. Correlation coefficients of the solar cycle with the SWHR, ozone, and temperature anomalies linearly increase along with the enhancement of the solar cycle amplitude, and this reliance becomes more complex when the solar cycle amplitude exceeds a certain threshold. In addition, the cold bias in the tropical stratopause of EMAC dampens the subsequent results of the "initial" solar signal. The warm pole bias in MPI-ESM-HR leads to a weak polar night jet (PNJ), which may limit the top-down propagation of the initial solar signal. Although FOCI simulated a so-called top-down response as revealed in previous studies in a period with large solar cycle amplitudes, its warm bias in the tropical upper stratosphere results in a positive bias in PNJ and can lead to a "reversed" response in some extreme cases. We suggest a careful interpretation of the single model result and further re-examination of the solar signal based on more climate models.

Description: The mesopelagic or ocean twilight zone (OTZ) in the ocean contains huge numbers of fish in a relatively pristine environment and may therefore attract interest as a commercial fishery. In this study we evaluate in economic terms, the likely trade-offs between the different services provided by the mesopelagic layer in the Bay of Biscay and the societal benefits of its commercial exploitation. Benefits arise mainly from the likely use of this group of species as raw material for producing fishmeal and fish oil. Costs are derived from the loss in climate regulating and cultural, services, but also from the loss in the provisioning service of other commercial species. To do so we compare the current non-exploited status with a situation in where mesopelagic fishes are harvested at levels capable of producing the Maximum Sustainable Yield. Results suggest that if mesopelagic fishes are harvested, a mean value of 1.2 million Euro loss in a year will be created in the Bay of Biscay, although in a range between 42 million Euro loss and 48 Euro million benefits. This uncertainty comes, mainly, from the limited existing knowledge of the mesopelagic fishes’ biomass but also from the uncertainty on the biomass of the rest of the species of the studied ecosystem. The large range indicates that a better understanding of the mesopelagic ecosystem is needed, however, results also show that ecosystem services under no exploitation provided by the OTZ could be more valuable than the fishmeal and fish oil that potentially could be obtained from the fishes harvested in this sea layer.

Description: In the framework of a changing climate, it is useful to devise methods capable of effectively assessing and monitoring the changing landscape of air-sea CO2 fluxes. In this study, we developed an integrated machine learning tool to objectively classify and track marine carbon biomes under seasonally and interannually changing environmental conditions. The tool was applied to the monthly output of a global ocean biogeochemistry model at 0.25° resolution run under atmospheric forcing for the period 1958–2018. Carbon biomes are defined as regions having consistent relations between surface CO2 fugacity (fCO2) and its main drivers (temperature, dissolved inorganic carbon, alkalinity). We detected carbon biomes by using an agglomerative hierarchical clustering (HC) methodology applied to spatial target-driver relationships, whereby a novel adaptive approach to cut the HC dendrogram based on the compactness and similarity of the clusters was employed. Based only on the spatial variability of the target-driver relationships and with no prior knowledge on the cluster location, we were able to detect well-defined and geographically meaningful carbon biomes. A deep learning model was constructed to track the seasonal and interannual evolution of the carbon biomes, wherein a feed-forward neural network was trained to assign labels to detected biomes. We find that the area covered by the carbon biomes responds robustly to seasonal variations in environmental conditions. A seasonal alternation between different biomes is observed over the North Atlantic and Southern Ocean. Long-term trends in biome coverage over the 1958–2018 period, namely a 10 % expansion of the subtropical biome in the North Atlantic and a 10 % expansion of the subpolar biome in the Southern Ocean, are suggestive of long-term climate shifts. Our approach thus provides a framework that can facilitate the monitoring of the impacts of climate change on the ocean carbon cycle and the evaluation of carbon cycle projections across Earth System Models.

Description: Urban heat island (UHI) not only reflects the environmental thermal comfort and energy consumption, but also affects the urban meso‑scale climate. There are many researches related with UHI mainly focusing on urban and rural area, while neglecting dynamic rural–urban transition especially in a rapid urbanization in China. Beijing and Zhengzhou are studied by using city clustering algorithm (CCA) and boundary generation algorithm (BGA) to delineate the urban, peri‑urban and rural boundaries from 2000 to 2023 within three stages. Fourier transform model was used to identify the UHI patterns. Results show: 1) Two cities have undergone obvious expansions in 20 years, with a consistent mean LST decrease from urban to peri‑urban and rural areas in three stages. 2) The distribution of UHII was more consistent in Beijing, while it varied more in Zhengzhou across seasons. 3) The UHI patterns notably differ, with Zhengzhou experiencing variable patterns and Beijing consistently showing oblate patterns. 4) The profiles of UHII and NDVI in two cities varied seasonally and reflected urban expansions in terms of longitude and latitude. Understanding the long-term changes and patterns of urban heat islands in different cities will provide information for formulating adaptive policies for urban sustainability.

Description: Transportation and mobility patterns contribute to greenhouse gas emissions. Understanding the drivers of these emissions, particularly for high emitters, is key to designing appropriate climate and mobility policies. In this article, we study the distribution of emissions from mobility in Germany and their drivers. We use a 2017 nation-wide mobility survey to calculate the carbon footprint of individuals associated with day-to-day and long-distance travels. We use quantile regression to investigate both socio-economic and attitudinal drivers of emissions across different categories of emitters, and for different mobility types. We discuss our results with respect to previous findings in the literature. Overall, we find that the top 10% of emitters are responsible for 51% of total emissions, and for 80% of emissions from long-distance travel. The statistical analysis reveals strong differences regarding the contribution of socio-economic drivers such as income or location at different levels of emissions. Attitudes towards different transportation modes also strongly correlate with differences in mobility behaviors.

Description: Climate change heavily threatens forest ecosystems worldwide and there is urgent need to understand what controls tree survival and forests stability. There is evidence that biodiversity can enhance ecosystem stability (Loreau and de Mazancourt in Ecol Lett 16:106–115, 2013; McCann in Nature 405:228–233, 2000), however it remains largely unclear whether this also holds for climate change and what aspects of biodiversity might be most important. Here we apply machine learning to outputs of a flexible-trait Dynamic Global Vegetation Model to unravel the effects of enhanced functional tree trait diversity and its sub-components on climate-change resistance of temperate forests (http://www.pik-potsdam.de/~billing/video/Forest_Resistance_LPJmLFIT.mp4). We find that functional tree trait diversity enhances forest resistance. We explain this with 1. stronger complementarity effects (~ 25% importance) especially improving the survival of trees in the understorey of up to + 16.8% (± 1.6%) and 2. environmental and competitive filtering of trees better adapted to future climate (40–87% importance). We conclude that forests containing functionally diverse trees better resist and adapt to future conditions. In this context, we especially highlight the role of functionally diverse understorey trees as they provide the fundament for better survival of young trees and filtering of resistant tree individuals in the future.

Description: Background: Inherited blood disorders affect 7% of the population worldwide, with higher prevalences in countries in the “thalassemia belt,” which includes Bangladesh. Clinical management options for severely affected individuals are expensive; thus, targeted government policies are needed to support prevention and treatment programs. In Bangladesh, there is a lack of data, in particular community-based estimates, to determine population prevalence. This study aims to estimate the prevalence of a wide range of hemoglobinopathies and their associations with anemia in a community-based sample of women and young children in rural Sylhet, Bangladesh. - Methods: Capillary blood samples from 900 reproductive-aged women and 395 children (aged 6–37 months) participating in the Food and Agricultural Approaches to Reducing Malnutrition (FAARM) trial in two sub-districts of Habiganj, Sylhet Division, Bangladesh were analyzed for alpha thalassemia, beta thalassemia, and other hemoglobinopathies. We examined the association of each inherited blood disorder with hemoglobin concentration and anemia using linear and logistic regression. - Results: We identified at least one inherited blood disorder in 11% of women and 10% of children. Alpha thalassemia was most prevalent, identified in 7% of women and 5% of children, followed by beta thalassemia and hemoglobin E in 2–3%. We also identified cases of hemoglobin S and hemoglobin D in this population. Having any of the identified inherited blood disorders was associated with lower hemoglobin values among non-pregnant women, largely driven by alpha and beta thalassemia. Pregnant women with beta thalassemia were also more likely to have lower hemoglobin concentrations. Among children, we found weak evidence for a relationship between hemoglobinopathy and lower hemoglobin concentrations. - Conclusions: We found a high prevalence of alpha thalassemia among both women and children in rural Sylhet, Bangladesh–higher than all other identified hemoglobinopathies combined. Community-based estimates of alpha thalassemia prevalence in Bangladesh are scarce, yet our findings suggest that alpha thalassemia may comprise the majority of inherited blood disorders in some regions of the country. We recommend that future research on inherited blood disorders in Bangladesh include estimates of alpha thalassemia in their reporting for public health awareness and to facilitate couples counseling.