Climate change over the high-mountain versus plain areas: Effects on the land surface hydrologic budget in the Alpine area and northern Italy

Climate change may intensify during the second half of the current century. Changes in temperature and precipitation can exert a significant impact on the regional hydrologic cycle. Because the land surface serves as the hub of interactions among the variables constituting the energy and water cycles, evaluating the land surface processes is essential to detail the future climate. In this study, we employ a trusted soil–vegetation–atmosphere transfer scheme, called the University of Torino model of land Processes Interaction with Atmosphere (UTOPIA), in offline simulations to quantify the changes in hydrologic components in the Alpine area and northern Italy, between the period of 1961–1990 and 2071–2100. The regional climate projections are obtained by the Regional Climate Model version 3 (RegCM3) via two emission scenarios – A2 and B2 from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. The hydroclimate projections, especially from A2, indicate that evapotranspiration generally increases, especially over the plain areas, and consequently the surface soil moisture decreases during summer, falling below the wilting point threshold for an extra month. In the high-mountain areas, due to the earlier snowmelt, the land surface becomes snowless for an additional month. The annual mean number of dry (wet) days increases remarkably (slightly), thus increasing the risk of severe droughts, and slightly increasing the risk of floods coincidently. Our results have serious implications for human life, including agricultural production, water sustainability, and general infrastructures, over the Alpine and adjacent plain areas and can be used to plan the managements of water resources, floods, irrigation, forestry, hydropower, and many other relevant activities

Impact of COVID-19 measures on electricity consumption

As COVID-19 spreads worldwide, governments have been implementing a wide range of measures to contain it, from movement restrictions to economy-wide shutdowns. Understanding their impacts is essential to support better policies for countries still experiencing outbreaks or in case of emergence of second pandemic waves. Here we show that the cumulative decline in electricity consumption within the four months following the stay-home orders ranges between 4-13% in the most affected EU countries and USA states, except Florida that shows no significant impact. Whereas the studied USA states have recovered baseline levels, electricity consumption remains lower in the European countries. These results illustrate the severity of the crisis across countries and can support further research on the effect of specific measures, evolution of economic activity or relationship with other high-frequency indicators

Linear Magnetoelectric Phase in Ultrathin MnPS₃ Probed by Optical Second Harmonic Generation

The transition metal thiophosphates MPS₃ (M=Mn, Fe, Ni) are a class of van der Waals stacked insulating antiferromagnets that can be exfoliated down to the ultrathin limit. MnPS₃ is particularly interesting because its Néel ordered state breaks both spatial-inversion and time-reversal symmetries, allowing for a linear magnetoelectric phase that is rare among van der Waals materials. However, it is unknown whether this unique magnetic structure of bulk MnPS₃ remains stable in the ultrathin limit. Using optical second harmonic generation rotational anisotropy, we show that long-range linear magnetoelectric type Néel order in MnPS₃ persists down to at least 5.3 nm thickness. However an unusual mirror symmetry breaking develops in ultrathin samples on SiO₂ substrates that is absent in bulk materials, which is likely related to substrate induced strain

Artificial intelligence for climate prediction of extremes: state of the art, challenges, and future perspectives

Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are particularly challenging to predict accurately due to their rarity and chaotic nature, and because of model limitations. However, recent studies have shown that there might be systemic predictability that is not being leveraged, whose exploitation could meet the need for reliable predictions of aggregated extreme weather measures on timescales from weeks to decades ahead. Recently, numerous studies have been devoted to the use of artificial intelligence (AI) to study predictability and make climate predictions. AI techniques have shown great potential to improve the prediction of extreme events and uncover their links to large‐scale and local drivers. Machine and deep learning have been explored to enhance prediction, while causal discovery and explainable AI have been tested to improve our understanding of the processes underlying predictability. Hybrid predictions combining AI, which can reveal unknown spatiotemporal connections from data, with climate models that provide the theoretical foundation and interpretability of the physical world, have shown that improving prediction skills of extremes on climate‐relevant timescales is possible. However, numerous challenges persist in various aspects, including data curation, model uncertainty, generalizability, reproducibility of methods, and workflows. This review aims at overviewing achievements and challenges in the use of AI techniques to improve the prediction of extremes at the subseasonal to decadal timescale. A few best practices are identified to increase trust in these novel techniques, and future perspectives are envisaged for further scientific development

Toward more accurate and reliable precipitation data

Im Zuge der globalen Erwärmung ändern sich die Niederschlagsmuster. Zudem erfolgen Änderungen von Starkniederschlägen räumlich nicht gleichförmig. Präzise, hochauflösende und langzeitig verfügbare Beobachtungsdaten für Niederschlag sind rar, aber dort wo sie vorhanden sind, stellen sie wertvolle Information zu Verfügung und dienen als Basis zur Bestimmung des aktuellen Starkniederschlags und zur Beobachtung von Änderungen über die Zeit. Diese Dissertation beschäftigt sich mit zwei hochauflösenden NiederschlagsdatensätzenRegenmessungen der meteorologischen Stationen des WegenerNet (WEGN) und Niederschlagsschätzungen des Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG). Im WEGN können „wahre“ Werte von lokalen Niederschlagsereignissen (im südöstlichen Österreich) beobachtet werden, während IMERG zur Beobachtung von Niederschlagssystemen von kontinentaler bis globaler Skala geeignet ist. Beide stellen Daten mit einer besseren Auflösung als andere bestehende Plattformen bereit. Die Forschung dieser Dissertation gliedert sich in zwei Teile. Im ersten Teil wird die Exaktheit von WEGN und IMERG evaluiert, wodurch auf Erfordernisse von DatennutzerInnen und AlgorithmenentwicklerInnen eingegangen wird. Systematische Fehler in den Regenmessungen wurden berechnet und korrigiert (WEGN), beziehungsweise wurden mögliche Quellen der systematischen Fehler diskutiert (IMERG). Im zweiten Teil wird die Eignung der Datensätze zur Beobachtung von Starkregenereignissen einerseits auf lokaler Skala und andererseits auf kontinentaler Skala untersucht. Unter Benutzung der hochaufgelösten WEGN Starkregenmessungen, wurden Informationen über die Wahrscheinlichkeit der Unterschätzung durch (niedriger aufgelöste) operationelle Meßstationen hergeleitet. Für IMERG wurde Sommerregen über den Vereinigten Staaten evaluiert. Obwohl IMERG Regenmengenschätzungen liefert, die mit denen von Bodenradarstationen vergleichbar sind, wurde herausgefunden, dass erhebliche Möglichkeiten zur Verbesserung bestehen. Daraus ergibt sich, dass die Resultate dieser Arbeit zu den fortlaufenden Bestrebungen beitragen, genauere und zuverlässigere Niederschlagsdaten zu erlangen. WEGN stellt Meßdaten von mehr als 10 Jahren zur Verfügung und wird dies auch in den kommenden Jahren tun. IMERG startet mit seinen Beobachtungen Anfang 2014, wobei die Daten bis rückwirkend bis in die Ära der Vorgängermission TRMM (1998) prozessiert werden. In diesem Zusammenhang zeigt die Dissertation die mögliche Nutzung von WEGN und IMGERG zur Untersuchung von Langzeittrends von Starkniederschlag von lokalen bis kontinentalen Skalen, indem sie deren Genauigkeit sowie die Fähigkeit zur Erfassung von Starkregencharakteristika darlegt.Global warming is altering precipitation patterns. Moreover, the change in heavy precipitation is not spatiallyuniform. Accurate, long-term, and high-resolution observational precipitation data are rare, but where availablethey provide valuable information, which serves as a baseline to define the current status of heavy precipitationand to monitor its change over time. This thesis selects two high-resolution precipitation data setsrain gaugemeasurements from theWegenerNet dense network (WEGN) and satellite precipitation estimates from the IntegratedMulti-satellitE Retrievals for Global Precipitation Measurement (IMERG). WEGN can observe “true” valuesof local-scale precipitation events (in southeastern Austria), while IMERG is capable of observing continentalto-global-scale precipitation systems, both with a higher resolution than other existing platforms. The researchcarried out in this thesis consists of two parts. The first part evaluated the accuracy of the WEGN and IMERGin order to address the needs of data users and algorithm developers. Biases in the rainfall data were computedand corrected (WEGN), or possible sources of the biases were discussed (IMERG). The second part assessed thecapacity of the data sets in observing heavy rainfall events at local and continental scales, respectively. Using thefine-scale extreme convective rainfall measurements by WEGN, information on the probability of underestimationby (lower-density) operational gauges was derived. For IMERG, summertime rainfall events over the United Stateswere evaluated. Although IMERG provides comparable rainfall estimates to those obtained from ground-basedradars, it was found that there is still considerable scope for improvement. The findings of this thesis contribute tothe ongoing efforts to obtain more accurate and reliable precipitation data. WEGN already collected more than 10-year measurements data and will continue its operation for years to come. IMERG started its observation from theearly 2014, however, the data will be retrospectively processed back to the era of its predecessor TRMM (1998).In this context, this thesis proves the potential use of WEGN and IMERG for studying long-term trends in heavyprecipitation and their relation with global warming, across spatial scales, by demonstrating their accuracy andcapacity to capture characteristics of heavy precipitation events.vorgelegt von Sungmin OKumulative Dissertation aus 4 ArtikelnZusammenfassungen in Deutsch und EnglischKarl-Franzens-Universität Graz, Dissertation, 2018OeBB(VLID)258136

Global ecosystem responses to flash droughts are modulated by background climate and vegetation conditions

Abstract Flash droughts and their physical processes have received increasing attention in recent years due to concerns about the potential of flash droughts to affect water resources and ecosystems. Yet to date, the response of ecosystems during flash drought events, particularly on a large scale, and the determinants of the ecosystem responses to flash droughts have been underexplored. Here we analyse temporal variations in vegetation anomalies during flash drought events at a global scale between 2001 and 2020 using observation-based leaf area index, gross primary productivity, and solar-induced chlorophyll fluorescence data. We identify divergent ecosystem responses in terms of the timing and intensification of drought-induced vegetation stress across different regions around the world. Furthermore, we find that these regional differences are largely modulated by background climate and vegetation conditions, rather than meteorological conditions, with ecosystems being subjected to more rapidly developing and greater degrees of vegetation stress in arid and short vegetation-dominated regions as compared to humid forests. Our results highlight the spatially heterogeneous ecological impacts of flash droughts, implying the need to comprehensively integrate aspects of both atmospheric and bioclimatic properties in flash drought monitoring and forecasting systems to improve our ability to track their evolution and impacts