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1. Drift bottle data hint at large-scale ocean circulation changes

Author

Natasha A. Ridenour, Liam Buchart, Eddy Carmack, Inge Deschepper, Eric Galbraith, Geoff Green, Juliana M. Marson, Clark Pennelly, Tahya Weiss-Gibbons, and Paul G. Myers

Subjects
ocean modelling, drift bottles, North Atlantic, citizen science, subpolar gyre, Science, General. Including nature conservation, geographical distribution, QH1-199.5
Abstract

Over the last two decades, in an effort to engage youth in polar science, the Students On Ice (SOI; https://studentsonice.com/) project has become a platform for youth to partake in scientific expeditions around the globe. Among the various activities offered, youth are able to join cruises in the North Atlantic or Arctic, and drop sealed glass bottles into the ocean. Of the thousands that have been deployed, 5% of bottles have been recovered and reported back to SOI with details on when and where they were found. Here, we compare the observational bottle data with virtual particle trajectories from a high resolution regional ocean model. Although modelling results indicate a higher likelihood of bottles reaching the shores of the western Atlantic, the majority of recovered bottles were found on the eastern side of the Atlantic. We attribute this disparity to differences in population density in Canada and Europe, biasing the recovery rates. Despite this bias, we find that changes in recovery locations over time are consistent with changes in the main ocean currents associated with the contraction and expansion of the North Atlantic Subpolar Gyre, as simulated in our ocean model. In 2007, a large number of bottles were found in Norway, coinciding with a contracted North Atlantic Subpolar Gyre during 2004-2008. While between 2012-2016, the majority of bottles were recovered on the British Isles, during a time of gyre expansion. These results underline the importance of large scale oceanic cycles for tracking marine debris and pollution, and show how even simple data collection methods, such as drift bottles, can provide clues to the changes in the large scale ocean circulation.

Published
2023
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2. Model Evaluation of Short-Lived Climate Forcers for the Arctic Monitoring and Assessment Programme: A Multi-Species, Multi-Model Study

Author

Cynthia H Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory S Faluvegi, Mark Flanner, Joshua S Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kuhn, Joakim Langner, Kathy S Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A Thomas, Rita Traversi, Konstantinos Tsigaridis, Svetlana Tsyro, Steven T Turnock, Vito Vitale, Kaley A Walker, Minqi Wang, Duncan Watson Parris, and Tahya Weiss-Gibbons

Subjects
Meteorology And Climatology
Abstract

While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008-2009 and 2014-2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH4, 03, BC, and SO(sup 2-)(sub 4)), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for C"4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.

Published
2022
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3. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study

Author

Wanmin Gong, Sangeeta Sharma, Fumikazu Taketani, Duncan Watson-Parris, Knut von Salzen, Tahya Weiss-Gibbons, Sujay Damani, Kostas Tsigaridis, Henrik Skov, Tatsuo Onishi, Jean-Christophe Raut, Sabine Eckhardt, Barbara Winter, Ulas Im, S. R. Beagley, Laura Saunders, Jesper H. Christensen, Joshua Fu, Lin Huang, Fabio Giardi, Yugo Kanaya, Nikolaos Evangeliou, Stephen R. Arnold, Srinath Krishnan, Naga Oshima, Minqi Wang, Mark Flanner, Julia Schmale, Yiran Peng, Rashed Mahmood, Gregory Faluvegi, Joakim Langner, Cynthia H. Whaley, Andreas Massling, David A. Plummer, Kaley A. Walker, Luca Pozzoli, Michael Gauss, Vito Vitale, Steven Turnock, Dirk Jan Leo Oliviè, Rong-You Chien, Maria Sand, Manu Anna Thomas, Silvia Becagli, Louis Marelle, Rita Traversi, Olga Popovicheva, Svetlana Tsyro, Zbigniew Klimont, Jens Hjorth, Thomas Kuhn, Konstantinos Eleftheriadis, Kathy S. Law, Canadian Centre for Climate Modelling and Analysis (CCCma), Environment and Climate Change Canada, Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Department of Geography [Montréal], McGill University = Université McGill [Montréal, Canada], Norwegian Institute for Air Research (NILU), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, Climate Chemistry Measurements and Research, Norwegian Meteorological Institute [Oslo] (MET), The University of Tennessee [Knoxville], Department of Environmental Science [Roskilde] (ENVS), Aarhus University [Aarhus], Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety (INRASTES), National Center for Scientific Research 'Demokritos' (NCSR), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], University of Michigan [Ann Arbor], University of Michigan System, Dipartimento di Chimica 'Ugo schifo', Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Center for International Climate and Environmental Research [Oslo] (CICERO), University of Oslo (UiO), International Institute for Applied Systems Analysis [Laxenburg] (IIASA), Department of Applied Physics [Kuopio], University of Kuopio, Atmospheric Research Centre of Eastern Finland, Finnish Meteorological Institute (FMI), Swedish Meteorological and Hydrological Institute (SMHI), TROPO - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Meteorological Research Institute [Tsukuba] (MRI), Japan Meteorological Agency (JMA), Center for Earth System Science [Beijing] (CESS), Tsinghua University [Beijing] (THU), Lomonosov Moscow State University (MSU), European Commission - Joint Research Centre [Ispra] (JRC), University of Toronto, Extreme Environments Research Laboratory (EERL), Ecole Polytechnique Fédérale de Lausanne (EPFL), Met Office Hadley Centre for Climate Change (MOHC), United Kingdom Met Office [Exeter], Department of Atmospheric, Oceanic and Planetary Physics [Oxford] (AOPP), University of Oxford [Oxford], and European Project: iCUPE

Subjects
[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], Atmospheric models, Global warming, Northern Hemisphere, Particulates, Atmospheric sciences, Earth system science, Atmosphere, chemistry.chemical_compound, Arctic, chemistry, 13. Climate action, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, Environmental science, Tropospheric ozone
Abstract

The Arctic atmosphere is warming rapidly and its relatively pristine environment is sensitive to the long-range transport of atmospheric pollutants. While carbon dioxide is the main cause for global warming, short-lived climate forcers (SLCFs) such as methane, ozone, and particles also play a role in Arctic climate on near-term time scales. Atmospheric modelling is critical for understanding the abundance and distribution of SLCFs throughout the Arctic atmosphere, and is used as a tool towards determining SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models, assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over four years (2008–2009 and 2014–2015) conducted for the 2021 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship and aircraft-based observations. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean was able to represent the general features of SLCFs in the Arctic, though vertical mixing, long-range transport, deposition, and wildfire emissions remain highly uncertain processes. These need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment.

Published
2022

4. Supplementary material to 'Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study'

Author

Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay M. Damani, Kostas Eleftheriadis, Nikolaos Evangeliou, Gregory S. Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons

Published
2021

5. CloudScheduler V2: Distributed Cloud Computing in the 21st century

Author

Colin Roy Leavett-Brown, Colson Driemel, Danika MacDonell, Fernando Fernandez Galindo, Frank Berghaus, Marcus Ebert, Michael Paterson, Randall Sobie, Rolf Seuster, Shaelyn Tolkamp, and Tahya Weiss-Gibbons

Abstract

The cloudscheduler VM provisioning service has been running production jobs for ATLAS and Belle II for many years using commercial and private clouds in Europe, North America and Australia. Initially released in 2009, version 1 is a single Python 2 module implementing multiple threads to poll resources and jobs, and to create and destroy virtual machine. The code is difficult to scale, maintain or extend and lacks many desirable features, such as status displays, multiple user/project management, robust error analysis and handling, and time series plotting, to name just a few examples. To address these shortcomings, our team has re-engineered the cloudscheduler VM provisioning service from the ground up. The new version, dubbed cloudscheduler version 2 or CSV2, is written in Python 3 runs on any modern Linux distribution, and uses current supporting applications and libraries. The system is composed of multiple, independent Python 3 modules communicating with each other through a central MariaDB (version 10) database. It features both graphical (web browser), and command line user interfaces and supports multiples users/projects with ease. Users have the ability to manage and monitor their own cloud resources without the intervention of a system administrator. The system is scalable, extensible, and maintainable. It is also far easier to use and is more flexible than its predecessor. We present the design, highlight the development process which utilizes unit tests, and show encouraging results from our operational experience with thousands of jobs and workernodes. We also present our experience with containers for running workloads, code development and software distribution.

Published
2019
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6. Sim@P1: Using Cloudscheduler for offline processing on the ATLAS HLT farm

Author

Kevin Casteels, Tahya Weiss-Gibbons, Rolf Seuster, Christopher Jon Lee, Franco Brasolin, Marcus Ebert, Randall Sobie, Michael Paterson, Frank Berghaus, Colson Driemel, Diana Scannicchio, Peter Love, and Colin Leavatt-Brown

Subjects
Large Hadron Collider, SIMPLE (military communications protocol), Database, 010308 nuclear & particles physics, Atlas (topology), Physics, QC1-999, ATLAS experiment, Provisioning, computer.software_genre, 01 natural sciences, Computing and Computers, Data acquisition, Resource (project management), Virtual machine, 0103 physical sciences, 010306 general physics, computer
Abstract

The Simulation at Point1 (Sim@P1) project was established in 2013 to take advantage of the Trigger and Data Acquisition High Level Trigger (HLT) farm of the ATLAS experiment at the LHC. The HLT farm is a significant compute resource, which is critical to ATLAS during data taking. This large compute resource is used to generate and process simulation data for the experiment when ATLAS is not recording data. The Sim@P1 system uses virtual machines, deployed by OpenStack, in order to isolate the resources from the ATLAS technical and control network. During the upcoming long shutdown in 2019 (LS2), the HLT farm including the Sim@P1 infrastructure will be upgraded. A previous paper on the project emphasized the need for “simple, reliable, and efficient tools” to quickly switch between data acquisition operation and offline processing. In this contribution we assess various options for updating and simplifying the provisional tools. Cloudscheduler is a tool for provisioning cloud resources for batch computing that has been managing cloud resources in HEP offline computing since 2012. We present the argument for choosing Cloudscheduler, and describe technical details regarding optimal utilization of the Sim@P1 re-sources.

Published
2019

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