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- ItemA review of coarse mineral dust in the Earth system(Amsterdam [u.a.] : Elsevier, 2022) Adebiyi, Adeyemi; Kok, Jasper F.; Murray, Benjamin J.; Ryder, Claire L.; Stuut, Jan-Berend W.; Kahn, Ralph A.; Knippertz, Peter; Formenti, Paola; Mahowald, Natalie M.; Pérez García-Pando, Carlos; Klose, Martina; Ansmann, Albert; Samset, Bjørn H.; Ito, Akinori; Balkanski, Yves; Di Biagio, Claudia; Romanias, Manolis N.; Huang, Yue; Meng, JunMineral dust particles suspended in the atmosphere span more than three orders of magnitude in diameter, from <0.1 µm to more than 100 µm. This wide size range makes dust a unique aerosol species with the ability to interact with many aspects of the Earth system, including radiation, clouds, hydrology, atmospheric chemistry, and biogeochemistry. This review focuses on coarse and super-coarse dust aerosols, which we respectively define as dust particles with a diameter of 2.5–10 µm and 10–62.5 µm. We review several lines of observational evidence indicating that coarse and super-coarse dust particles are transported farther than previously expected and that the abundance of these particles is substantially underestimated in current global models. We synthesize previous studies that used observations, theories, and model simulations to highlight the impacts of coarse and super-coarse dust aerosols on the Earth system, including their effects on dust-radiation interactions, dust-cloud interactions, atmospheric chemistry, and biogeochemistry. Specifically, coarse and super-coarse dust aerosols produce a net positive direct radiative effect (warming) at the top of the atmosphere and can modify temperature and water vapor profiles, influencing the distribution of clouds and precipitation. In addition, coarse and super-coarse dust aerosols contribute a substantial fraction of ice-nucleating particles, especially at temperatures above –23 °C. They also contribute a substantial fraction to the available reactive surfaces for atmospheric processing and the dust deposition flux that impacts land and ocean biogeochemistry by supplying important nutrients such as iron and phosphorus. Furthermore, we examine several limitations in the representation of coarse and super-coarse dust aerosols in current model simulations and remote-sensing retrievals. Because these limitations substantially contribute to the uncertainties in simulating the abundance and impacts of coarse and super-coarse dust aerosols, we offer some recommendations to facilitate future studies. Overall, we conclude that an accurate representation of coarse and super-coarse properties is critical in understanding the impacts of dust aerosols on the Earth system.
- ItemTeleconnections among tipping elements in the Earth system(London : Nature Publ. Group, 2023) Liu, Teng; Chen, Dean; Yang, Lan; Meng, Jun; Wang, Zanchenling; Ludescher, Josef; Fan, Jingfang; Yang, Saini; Chen, Deliang; Kurths, Jürgen; Chen, Xiaosong; Havlin, Shlomo; Schellnhuber, Hans JoachimTipping elements are components of the Earth system that may shift abruptly and irreversibly from one state to another at specific thresholds. It is not well understood to what degree tipping of one system can influence other regions or tipping elements. Here, we propose a climate network approach to analyse the global impacts of a prominent tipping element, the Amazon Rainforest Area (ARA). We find that the ARA exhibits strong correlations with regions such as the Tibetan Plateau (TP) and West Antarctic ice sheet. Models show that the identified teleconnection propagation path between the ARA and the TP is robust under climate change. In addition, we detect that TP snow cover extent has been losing stability since 2008. We further uncover that various climate extremes between the ARA and the TP are synchronized under climate change. Our framework highlights that tipping elements can be linked and also the potential predictability of cascading tipping dynamics.
- ItemTopology of products similarity network for market forecasting([Cham] : Springer International Publishing, 2019) Fan, Jingfang; Cohen, Keren; Shekhtman, Louis M.; Liu, Sibo; Meng, Jun; Louzoun, Yoram; Havlin, ShlomoThe detection and prediction of risk in financial markets is one of the main challenges of economic forecasting, and draws much attention from the scientific community. An even more challenging task is the prediction of the future relative gain of companies. We here develop a novel combination of product text analysis, network theory and topological based machine learning to study the future performance of companies in financial markets. Our network links are based on the similarity of firms’ products and constructed using the Securities Exchange Commission (SEC) filings of US listed firms. We find that several topological features of this network can serve as good precursors of risks or future gain of companies. We then apply machine learning to network attributes vectors for each node to predict successful and failing firms. The resulting accuracies are much better than current state of the art techniques. The framework presented here not only facilitates the prediction of financial markets but also provides insight and demonstrates the power of combining network theory and topology based machine learning.
- ItemStatistical physics approaches to the complex Earth system(Amsterdam [u.a.] : Elsevier Science, North-Holland, 2020) Fan, Jingfang; Meng, Jun; Ludescher, Josef; Chen, Xiaosong; Ashkenazy, Yosef; Kurths, Jürgen; Havlin, Shlomo; Schellnhuber, Hans JoachimGlobal warming, extreme climate events, earthquakes and their accompanying socioeconomic disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, multiple interactions and complex structures of the Earth system, the understanding and, in particular, the prediction of such disruptive events represent formidable challenges to both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge of the Earth system, including climate extreme events, earthquakes and geological relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as critical phenomena, network theory, percolation, tipping points analysis, and entropy can be applied to complex Earth systems. Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics concepts and theories can be useful in the field of Earth system science.