17 research outputs found

    Volume variability diagnostic for 4D datasets

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95249/1/grl28020.pd

    Three dimensional adaptive mesh refinement on a spherical shell for atmospheric models with lagrangian coordinates

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    One of the most important advances needed in global climate models is the development of atmospheric General Circulation Models (GCMs) that can reliably treat convection. Such GCMs require high resolution in local convectively active regions, both in the horizontal and vertical directions. During previous research we have developed an Adaptive Mesh Refinement (AMR) dynamical core that can adapt its grid resolution horizontally. Our approach utilizes a finite volume numerical representation of the partial differential equations with floating Lagrangian vertical coordinates and requires resolving dynamical processes on small spatial scales. For the latter it uses a newly developed general-purpose library, which facilitates 3D block-structured AMR on spherical grids. The library manages neighbor information as the blocks adapt, and handles the parallel communication and load balancing, freeing the user to concentrate on the scientific modeling aspects of their code. In particular, this library defines and manages adaptive blocks on the sphere, provides user interfaces for interpolation routines and supports the communication and load-balancing aspects for parallel applications. We have successfully tested the library in a 2-D (longitude-latitude) implementation. During the past year, we have extended the library to treat adaptive mesh refinement in the vertical direction. Preliminary results are discussed. This research project is characterized by an interdisciplinary approach involving atmospheric science, computer science and mathematical/numerical aspects. The work is done in close collaboration between the Atmospheric Science, Computer Science and Aerospace Engineering Departments at the University of Michigan and NOAA GFDL.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58181/2/jpconf7_78_012072.pd

    1 The Concept of Climate Sensitivity: History and Development

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    The concept of climate sensitivity has more than a century of history. According to this concept, a change in the global near-surface air temperature of the Earth, �T, due to external disturbance of the Earth’s energy balance, can be linearly related to a change in the net radiation at some level in the atmosphere, �F. Thus, �T ���F, where � is the climate sensitivity. By doubling the pre-industrial atmospheric carbon dioxide concentration, estimates of climate sensitivity have been made using a variety of methods, ranging from “back of the envelope ” to sophisticated mathematical models. Thus, �T 2x has become a surrogate for � and has played a central role throughout the history of the assessment reports of the Intergovernmental Panel on Climate Change (IPCC) in interpreting the output of numerical models, evaluating future climate changes from various scenarios, and in attributing the causes of the observed temperature changes. Estimates of �T 2x have been published in all three IPCC assessment reports, and since 1979 the accepted range for the climate sensitivity, from 1.5�C to 4.5�C, has stayed unchanged. Recent studies, however, have shown there is a significant likelihood that �T 2x greatly exceeds 4.5�C. This means that different policy strategies in climate-change mitigation and adaptation need to be considered. Prior to the IPCC Fourth Assessment Report (AR4), the concept of climate sensitivity has been intensively examined by several recent scientific workshops. Here we briefly overview the concept of climate sensitivity and show possible ways of its future development. 2 1

    A reagent-driven visual method for analyzing chemical reaction data

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    The increasing use of machine learning and artificial intelligence in chemical reaction studies demands high-quality reaction data, necessitating specialized tools enabling data understanding and cura- tion. Our work introduces a novel methodology for reaction data exploration centered on reagents — essential molecules in reactions that do not contribute atoms to products. We propose an intu- itive tool for creating interactive reagent space maps using distributed vector representations, akin to word2vec in Natural Language Processing, capturing the statistics of reagent usage within datasets. Our approach enables swift assessment of reaction type distributions, identification of alternative reagents, and detection of errors, which we demonstrate using the USPTO dataset. Our contributions include an open-source web application for visual reagent pattern analysis and a table cataloging around six hundred of the most frequent reagents in USPTO annotated with detailed roles. Accessible via GitHub at https://github.com/Academich/reagent_emb_vis, our method supports organic chemists and cheminformatics experts in navigating extensive reaction datasets efficiently
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