Untersuchung der Rolle der Ozon-Klimawechselwirkungen für die dekadische und langfristige Klimavorhersage mithilfe des Klima-Chemie-Modells EMAC mit schneller stratosphärischer Ozonchemie

Die stratosphärische Ozonchemie übt durch ihre Kopplung an Strahlung und Dynamik der Atmosphäre einen markanten Einfluss auf den Zustand der Stratosphäre und der Troposphäre aus. Dieser Einfluss bleibt im Großteil der für dekadische und Klimavorhersagen genutzten Modelle jedoch unberücksichtigt. Klimamodelle, die die Ozonchemie mit komplexen interaktiven Chemieschemata berücksichtigen, benötigen deutlich mehr Rechenzeit als Klimamodelle, die Ozonmischungsverhältnisse als externe Felder vorschreiben. Eine alternative Methode, mit der die atmosphärische Chemie in Klimamodellen berücksichtigt werden kann, ist das schnelle stratosphärische Ozonschema SWIFT, das die heterogene Ozonchemie im stratosphärischen Polarwirbel mit minimalem Zuwachs der Rechenzeit bestimmen kann. In dieser Arbeit wurde die SWIFT-Chemie in das Klima-Chemiemodell ECHAM/MESSy Atmospheric Chemistry (EMAC) eingebaut und validiert. Es wird gezeigt, dass EMAC mit SWIFT die chemische Entwicklung von Spurenstoffen im Polarwirbel, die durch heterogene Prozesse verursacht wird, im Vergleich zu EMAC mit komplexer, interaktiver Chemie, gut wiedergeben kann. Weiterhin wird gezeigt, dass SWIFT auch für multidekadische Simulationen mit sich ändernden Konzentrationen relevanter atmosphärischer Spurenstoffe genutzt werden kann. Mit SWIFT in EMAC wurde ein Ensemble mit 10 Mitgliedern für das RCP6.0-Szenario berechnet und mit einer Simulation mit komplexer interaktiver Chemie und mit einer Simulation mit vorgeschriebenen Ozonmischungsverhältnissen verglichen. Es wird gezeigt, dass der Einfluss der internen Variabilität auf die Anzahl und saisonale Verteilung Großer Stratosphärenerwärmungen (SSWs) stärker ist als die Unterschiede durch der Berücksichtigung der Ozonchemie. Außerdem wird die Stärke des Einflusses von Änderungen in der polaren unteren Stratosphäre auf den Strahlstrom in den mittleren Breiten in Simulationen mit vorgeschriebenen Ozonfeldern unterschätzt. Es wird gezeigt, dass die äquatorwärtige Verschiebung des Strahlstroms in der 1. Hälfte des 21. Jahrhunderts auf der Wirkung der abnehmenden ozonzerstörenden Substanzen beruht, die polwärtige Verschiebung des Strahlstroms in der 2. Hälfte des 21. Jahrhunderts jedoch auf dem Treibhausgasanstieg. Aspekte der Stratosphären-Troposphärenkopplung, die durch die Verteilung von polarem Ozon beeinflusst werden, kann EMAC mit SWIFT gut wiedergeben. Mit SWIFT in EMAC ist es möglich multidekadische Simulationen mit interaktiver Chemie unter Berücksichtigung der internen Variabilität rechenzeitgünstig durchzuführen. Durch die so verbesserte Berücksichtigung der Stratosphären-Troposphärenkopplung können die Vorhersagen für dekadische und multidekadische Modellsysteme verbessert werden

Elevated stratopause events in the current and a future climate: A chemistry-climate model study

The characteristics and driving mechanisms of Elevated Stratopause Events (ESEs) are examined in simulations of the ECHAM/MESSy Atmospheric Chemistry (EMAC) chemistry-climate model under present and projected climate conditions. ESEs develop after sudden stratospheric warmings (SSWs) in boreal winter. While the stratopause descends during SSWs, it is reformed at higher altitudes after the SSWs, leading to ESEs in years with a particularly high new stratopause. EMAC reproduces well the frequency and main characteristics of observed ESEs. ESEs occur in 24% of the winters, mostly after major SSWs. They develop in stable polar vortices due to a persistent tropospheric wave forcing leading to a prolonged zonal wind reversal in the lower stratosphere. By wave filtering, this enables a faster re-establishment of the mesospheric westerly jet, polar downwelling and a higher stratopause. We find the presence of a westward-propagating wavenumber-1 planetary wave in the mesosphere following the onset, consistent with in-situ generation by large-scale instability. By the end of the 21st century, the number of ESEs is projected to increase, mainly due to a sinking of the original stratopause after strong tropospheric wave forcing and planetary wave dissipation at lower levels. Future ESEs develop preferably in more intense and cold polar vortices, and tend to be shorter. While in the current climate, planetary wavenumber-2 contributes to the forcing of ESEs, future wave forcing is dominated by wavenumber-1 activity as a result of climate change. Hence, a persistent wave forcing seems to be more relevant for the development of an ESE than the wavenumber decomposition of the forcing

Elevated stratopause events in the current and a future climate: a chemistry-climate model study

The characteristics and driving mechanisms of Elevated Stratopause Events (ESEs) are examined in simulations of the ECHAM/MESSy Atmospheric Chemistry (EMAC) chemistry-climate model under present and projected climate conditions. ESEs develop after sudden stratospheric warmings (SSWs) in boreal winter. While the stratopause descends during SSWs, it is reformed at higher altitudes after the SSWs, leading to ESEs in years with a particularly high new stratopause. EMAC reproduces well the frequency and main characteristics of observed ESEs. ESEs occur in 24% of the winters, mostly after major SSWs. They develop in stable polar vortices due to a persistent tropospheric wave forcing leading to a prolonged zonal wind reversal in the lower stratosphere. By wave filtering, this enables a faster re-establishment of the mesospheric westerly jet, polar downwelling and a higher stratopause. We find the presence of a westward-propagating wavenumber-1 planetary wave in the mesosphere following the onset, consistent with in-situ generation by large-scale instability. By the end of the 21st century, the number of ESEs is projected to increase, mainly due to a sinking of the original stratopause after strong tropospheric wave forcing and planetary wave dissipation at lower levels. Future ESEs develop preferably in more intense and cold polar vortices, and tend to be shorter. While in the current climate, planetary wavenumber-2 contributes to the forcing of ESEs, future wave forcing is dominated by wavenumber-1 activity as a result of climate change. Hence, a persistent wave forcing seems to be more relevant for the development of an ESE than the wavenumber decomposition of the forcing

Paramagnetic lanthanide chelates for multicontrast MRI

[Abstract] The preparation of a paramagnetic chelator that serves as a platform for multicontrast MRI, and can be utilized either as a T1-weighted, paraCEST or 19F MRI contrast agent is reported. Its europium(III) complex exhibits an extremely slow water exchange rate which is optimal for the use in CEST MRI. The potential of this platform was demonstrated through a series of MRI studies on tube phantoms and animals.Ministerio de Economía y Competitividad; CTQ2013-43243-PMinisterio de Economía y Competitividad; CTQ2015-71211-RED

UKCEH at the Edinburgh Climate Festival, 14th Aug 2021, Leith Links, Edinburgh

In total over 200 people engaged with the UKCEH Team between noon and 6 pm on the 14th Aug 2021 at the Edinburgh Climate Festival. The stand fulfilled its aim to raise awareness of publicly funded research conducted at UKCEH Edinburgh, including the national capability project UK-SCAPE, as evidenced by the number of people attracted into the stand, the remarks made in conversations with the team members and written in the answers to the poster quiz. Three activities were offered to target different age groups: • Carbon Game – target children- duration typically 2-4 min, estimated 100 children and adults participated • Intergenerational trend in CO2 concentration – target all age ranges – duration typically 1 to 5 min, estimated 80 children and adults participated • Poster quiz – target adults - duration typically 5-20 min, 42 primarily adults participated. A wide range of conversations were noted by the UKCEH team members primarily focused on: • the role of carbon in the environment and link to climate change • the steep rise in CO2 concentration in the lifetime of the people present • the range of science conducted at a local institution • the variety of options that people could make to their life choices that could improve the environment • routes for a career in STEM subjects

Naturbasierte Lösungen zur Stärkung der Resilienz in Städten

Chapter 10 (in German). Als naturbasierte Lösungen (NBL) werden auf EU-Ebene naturbezogene Ansätze bezeichnet, die als Instrumente zur Bewältigung gesellschaftlicher Herausforderungen hinsichtlich des Klimawandels dienen. Eine Stadt erhöht ihre Resilienz, wenn ihre NBL-Ansätze auch soziale Probleme und das Wohlergehen der Bürger*innen ansprechen. Dieser Beitrag ist der beispielhaften Anwendung von NBL in drei europäischen Städten unterschiedlicher Größe gewidmet: einer Megacity (Region Paris, Frankreich), einer mittelgroßen Stadt (Aarhus, Dänemark) und einer Kleinstadt (Velika Gorica, Kroatien). Dabei wird untersucht, welche Herausforderungen und Chancen bei der Anwendung von NBL in verschiedenen sozialen und ökologischen Systemen auftreten und inwiefern NBL ein Schlüssel zur städtischen Resilienz sind

An adaptable integrated modelling platform to support rapidly evolving agricultural and environmental policy

The utility of integrated models for informing policy has been criticised due to limited stakeholder engagement, model opaqueness, inadequate transparency in assumptions, lack of model flexibility and lack of communication of uncertainty that, together, lead to a lack of trust in model outputs. We address these criticisms by presenting the ERAMMP Integrated Modelling Platform (IMP), developed to support the design of new “business-critical” policies focused on agriculture, land-use and natural resource management. We demonstrate how the long-term (>5 years), iterative, two-way and continuously evolving participatory process led to the co-creation of the IMP with government, building trust and understanding in a complex integrated model. This is supported by a customisable modelling framework that is sufficiently flexible to adapt to changing policy needs in near real-time. We discuss how these attributes have facilitated cultural change within the Welsh Government where the IMP is being actively used to explore, test and iterate policy ideas prior to final policy design and implementation

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p