State Legislative Update

As the use of collaborative law increases, the need for uniform laws to help facilitate this process across state lines grew. In February 2007, the Uniform Law Commission (ULC) began drafting an act to address this need. At the July 2009 meeting, the Uniform Collaborative Law Act (UCLA) was unanimously approved by the Commission and was subsequently submitted to the American Bar Association (ABA) House of Delegates for approval. In March 2010, the house approved the amended act after the ULC made a few small changes per the house\u27s recommendation. Since receiving ABA approval, the UCLA has been passed in eight states, most recently Alabama, and introduced this year in five more

The MOSAiC ice floe: Sediment-laden survivor from the Siberian shelf

In September 2019, the research icebreaker Polarstern started the largest multidisciplinary Arctic expedition to date, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) drift experiment. Being moored to an ice floe for a whole year, thus including the winter season, the declared goal of the expedition is to better understand and quantify relevant processes within the atmosphere-ice-ocean system that impact the sea ice mass and energy budget, ultimately leading to much improved climate models. Satellite observations, atmospheric reanalysis data, and readings from a nearby meteorological station indicate that the interplay of high ice export in late winter and exceptionally high air temperatures resulted in the longest ice-free summer period since reliable instrumental records began. We show, using a Lagrangian tracking tool and a thermodynamic sea ice model, that the MOSAiC floe carrying the Central Observatory (CO) formed in a polynya event north of the New Siberian Islands at the beginning of December 2018. The results further indicate that sea ice in the vicinity of the CO ( \u3c 40 km distance) was younger and 36 % thinner than the surrounding ice with potential consequences for ice dynamics and momentum and heat transfer between ocean and atmosphere. Sea ice surveys carried out on various reference floes in autumn 2019 verify this gradient in ice thickness, and sediments discovered in ice cores (so-called dirty sea ice) around the CO confirm contact with shallow waters in an early phase of growth, consistent with the tracking analysis. Since less and less ice from the Siberian shelves survives its first summer (Krumpen et al., 2019), the MOSAiC experiment provides the unique opportunity to study the role of sea ice as a transport medium for gases, macronutrients, iron, organic matter, sediments and pollutants from shelf areas to the central Arctic Ocean and beyond. Compared to data for the past 26 years, the sea ice encountered at the end of September 2019 can already be classified as exceptionally thin, and further predicted changes towards a seasonally ice-free ocean will likely cut off the long-range transport of ice-rafted materials by the Transpolar Drift in the future. A reduced long-range transport of sea ice would have strong implications for the redistribution of biogeochemical matter in the central Arctic Ocean, with consequences for the balance of climate-relevant trace gases, primary production and biodiversity in the Arctic Ocean

The Electron Capture in 163^{163} Ho Experiment - a Short Update

The definition of the absolute neutrino mass scale is one of the main goals of the Particle Physics today. The study of the end-point regions of the β- and electron capture (EC) spectrum offers a possibility to determine the effective electron (anti-)neutrino mass in a completely model independent way, as it only relies on the energy and momentum conservation. The ECHo (Electron Capture in 163Ho) experiment has been designed in the attempt to measure the effective mass of the electron neutrino by performing high statistics and high energy resolution measurements of the 163 Ho electron capture spectrum. To achieve this goal, large arrays of low temperature metallic magnetic calorimeters (MMCs) implanted with with 163Ho are used. Here we report on the structure and the status of the experiment

The MOSAiC ice floe: sediment-laden survivor from the Siberian shelf

In September 2019, the research icebreaker Polarstern started the largest multidisciplinary Arctic expedition to date, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) drift experiment. Being moored to an ice floe for a whole year, thus including the winter season, the declared goal of the expedition is to better understand and quantify relevant processes within the atmosphere–ice–ocean system that impact the sea ice mass and energy budget, ultimately leading to much improved climate models. Satellite observations, atmospheric reanalysis data, and readings from a nearby meteorological station indicate that the interplay of high ice export in late winter and exceptionally high air temperatures resulted in the longest ice-free summer period since reliable instrumental records began. We show, using a Lagrangian tracking tool and a thermodynamic sea ice model, that the MOSAiC floe carrying the Central Observatory (CO) formed in a polynya event north of the New Siberian Islands at the beginning of December 2018. The results further indicate that sea ice in the vicinity of the CO (<40 km distance) was younger and 36 % thinner than the surrounding ice with potential consequences for ice dynamics and momentum and heat transfer between ocean and atmosphere. Sea ice surveys carried out on various reference floes in autumn 2019 verify this gradient in ice thickness, and sediments discovered in ice cores (so-called dirty sea ice) around the CO confirm contact with shallow waters in an early phase of growth, consistent with the tracking analysis. Since less and less ice from the Siberian shelves survives its first summer (Krumpen et al., 2019), the MOSAiC experiment provides the unique opportunity to study the role of sea ice as a transport medium for gases, macronutrients, iron, organic matter, sediments and pollutants from shelf areas to the central Arctic Ocean and beyond. Compared to data for the past 26 years, the sea ice encountered at the end of September 2019 can already be classified as exceptionally thin, and further predicted changes towards a seasonally ice-free ocean will likely cut off the long-range transport of ice-rafted materials by the Transpolar Drift in the future. A reduced long-range transport of sea ice would have strong implications for the redistribution of biogeochemical matter in the central Arctic Ocean, with consequences for the balance of climate-relevant trace gases, primary production and biodiversity in the Arctic Ocean

Gekoppelte Simulationen des arktischen Klimasystems: Sensitive und unsichere Prozessbeschreibungen

Das arktische Klimasystem wird ganz entscheidend durch das Vorhandensein eineseisbedeckten Ozeans geprägt, sodass eine realistische Darstellung des arktischenMeereises in gekoppelten Klimamodellen eine wichtige Voraussetzung für glaubhafteSimulationen des arktischen Klimas ist. Allerdings weisen gekoppelte Modellesowohl große Unterschiede untereinander als auch im Vergleich zu Beobachtungenauf. Sensitivitätsstudien mit dem gekoppelten regionalen AtmosphärenOzeanEisModell HIRHAMNAOSIM ergaben, dass die Fähigkeit des gekoppelten Modells,den beobachteten Eisrückzug während des Sommers zu reproduzieren, stark voneinem annähernd realistischen Eisvolumen zu Beginn der Schmelzperiode abhängt,welches durch das Verhältnis von Eiswachstum im Winter und Eisverlust im Sommerbestimmt wird. Während der sommerliche Eisverlust im Modell sehr stark vonder Eisalbedo-Parametrisierung bestimmt wird, hängt das winterliche Eiswachstumsignifikant von der Parametrisierung des seitlichen Gefrierens von Meereis ab.Unsicherheiten in den atmosphärischen Energieflüssen können bis zu einem gewissenGrade durch eine Feinabstimmung empirischer Modellparameter kompensiert werden,doch potenzielle grundlegende Schwächen des Modells können auf diese Weisenicht beseitigt werden. Da das seitliche Gefrieren von Meereis auch die Eiskonzentrationim Winter bestimmt, und somit den Wärmeverlust des Ozeans und die oberflächennahenLufttemperaturen, sind die Möglichkeiten, empirische Modellparameterfrei zu wählen, begrenzt. Eine große Unsicherheit im Modell ist die Simulationder langwelligen Strahlung, höchstwahrscheinlich infolge einer Überschätzung derWolkenbedeckung im Winter. Die Ergebnisse weisen darauf hin, dass Unsicherheitenin den Beschreibungen arktischer Wolken, der Schnee- und Eisalbedo, des seitlichenGefrierens und Schmelzens von Meereis, einschließlich der Behandlung von Schnee,für die großen Abweichungen in der Simulation des arktischen Klimas mit gekoppeltenModellen verantwortlich sind. Verbesserte Beschreibungen dieser Prozesse sindnotwendig, um Modellschwächen zu reduzieren und die Glaubwürdigkeit von Projektionendes zukünftigen Klimas zu erhöhen

Uncertain descriptions of Arctic climate processes in coupled models and their impact on the simulation of Arctic sea ice

The presence of an ice covered Arctic Ocean plays an important role inthe Arctic climate system by its influence on the exchange betweenatmosphere and ocean. The interactions between atmosphere, ocean, andsea ice are determined by a couple of feedback processes which are notyet completely understood. For this reason, coupled climate modelshave still difficulties in reproducing present-day sea-ice conditionsand their variability close to reality. The outcome of this is a largescatter in the simulated sea-ice cover and thickness among differentmodels, which is further amplified when applying the models to climatechange scenarios where an Arctic amplification of global warming isexpected as a result of diminishing sea-ice cover.Sensitivity experiments with the coupled regional climate modelHIRHAM-NAOSIM have shown that uncertainties in the description forArctic clouds, snow and sea-ice albedo, lateral freezing and meltingof sea ice, and a snow cover on sea ice are responsible for largedeviations in the simulation of Arctic sea ice. While moresophisticated schemes for the albedo, the treatment of lateralfreezing and melting, and the snow cover have already beensuccessfully introduced into the model, the parameterization of cloudsis still an open issue. Currently the model overestimates the cloudcover during winter associated with a warm temperature bias and toolow ice growth during the cold season. The outcome of this is athinner ice cover at the beginning of the melting period which tendsto disappear more quickly than observed. This has strong consequencesfor the model performance at large, since feedback processes mayfurther amplify biases in the coupled model system