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Facility for Weather and Climate Assessments (FACTS) In Box Facility for Weather and Climate Assessments (FACTS) A Community Resource for Assessing Weather and Climate Variability Donald Murray, Andrew Hoell, Martin Hoerling, Judith Perlwitz, Xiao-Wei Quan, Dave Allured, Tao Zhang, Jon Eischeid, Catherine A. Smith, Joseph Barsugli, Jeff McWhirter, Chris Kreutzer, and Robert S. Webb ABSTRACT: The Facility for Weather and Climate Assessments (FACTS) developed at the NOAA Physical Sciences Laboratory is a freely available resource that provides the science commu- nity with analysis tools; multimodel, multiforcing climate model ensembles; and observational/ reanalysis datasets for addressing a wide class of problems on weather and climate variability and its causes. In this paper, an overview of the datasets, the visualization capabilities, and data dissemination techniques of FACTS is presented. In addition, two examples are given that show the use of the interactive analysis and visualization feature of FACTS to explore questions related to climate variability and trends. Furthermore, we provide examples from published studies that have used data downloaded from FACTS to illustrate the types of research that can be pursued with its unique collection of datasets. https://doi.org/10.1175/BAMS-D-19-0224.1 Corresponding author: Andrew Hoell, [email protected] In final form 18 April 2020 ©2020 American Meteorological Society For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy. AMERICAN METEOROLOGICAL SOCIETY Brought to you by NOAA Central Library | UnauthenticatedJULY | 2020DownloadedE1214 07/12/21 03:00 PM UTC AFFILIATIONS: Murray, Quan, Allured, Zhang, Eischeid, Smith, and Barsugli—Cooperative Institute for Research in the Environmental Sciences, University of Colorado Boulder, and NOAA/Physical Sciences Laboratory, Boulder, Colorado; Hoell, Hoerling, Perlwitz, and Webb—NOAA/Physical Sciences Labora- tory, Boulder, Colorado; McWhirter—Geode Systems, Boulder, Colorado; Kreutzer—Cherokee Services Group, Fort Collins, Colorado A facility for assessing weather and climate conditions Droughts, heat waves, floods, and other extreme weather and climate events can have costly and devastating effects. The basis for anticipating such events more than a few weeks in ad- vance lies in understanding their causal relationship with extreme subseasonal to interannual climate states, decadal variability, and long-term trends. While observations are essential to characterize these events and to place them in historical context, observations alone are not sufficient to fully establish these causal relationships. Climate model experiments can provide the framework necessary to disentangle the various factors that are responsible for weather and climate variability on these time scales, including extremes. The Facility for Weather and Climate Assessments (FACTS) (www.psl.noaa.gov/repository/facts) developed at the NOAA Physical Sciences Laboratory (PSL) is a freely available resource that provides the science community with analysis tools; multimodel, multiforcing climate model ensembles; and observational/reanalysis datasets for addressing a wide class of problems on weather and climate variability and its causes. Researchers can use the FACTS website to quickly probe science questions through interactive analysis and visualizations and, if desired, download model and observational data for additional analysis. Educators can use the site for illustration of basic concepts of weather and climate variability. While other facili- ties provide data and tools for analyzing climate model simulations such as “KNMI Climate Explorer” (KNMI 2020) and “Climate Reanalyzer” (University of Maine 2020), FACTS offers unique datasets and analysis tools for the understanding of weather and climate variability and for extreme event attribution. The FACTS data archive and website The FACTS archive contains data from the Atmospheric Model Intercomparison Project (AMIP; Gates et al. 1999) and Coupled Model Intercomparison Project (CMIP; Hibbard et al. 2007) styles of climate model experiments (Table 1). For each experiment, many ensemble members (typically 20–50 realizations depending on the model) are available in the archive. These large ensembles are particularly useful for estimating the predict- able signal and comparing that to the climate system’s internal variability. The signal is obtained through the average across all ensemble members, and the so-called noise of internal variability can be estimated from the standard deviation across those same realizations. In more advanced applications, the probability density functions (PDFs) can be compared in their entirety. FACTS also contains parallel suites of AMIP-style model experiments that can be inter- compared to assess how, and by how much, events or conditions were influenced by climate change. One set of experiments consists of a set of factual runs, where historical forcings of sea surface temperature and atmospheric composition are used. A second set consists of AMERICAN METEOROLOGICAL SOCIETY Brought to you by NOAA Central Library | UnauthenticatedJULY | 2020DownloadedE1215 07/12/21 03:00 PM UTC counterfactual runs, where the ocean boundary forcings and atmospheric composition are modified in the experiment by removing the estimated effects of long-term change, thereby retaining only an estimate of the natural variability (Stone et al. 2019). Comparison of factual and counterfactual experiments is an emerging approach (e.g., Sun et al. 2018) that has been increasingly used to inform extreme event attribution [e.g., see papers appearing in the BAMS special supplement “Explaining Extreme Events of 2017 from a Climate Perspective” (Herring et al. 2019)]. FACTS also includes a variety of atmospheric reanalysis and observational datasets that can be compared with the model simulations (see Table 1 for a representative list). Several of these datasets span the last century, including station-based analyses from the University of Delaware (UDEL 5.01; Willmott and Matsuura 2001) and the Global Historical Climate Net- work (GHCN CAMS; Fan and van den Dool 2008) as well as the Twentieth Century Reanalysis version 3 (20CR; Slivin- ski et al. 2019). Table 1. Summary of the principal types of datasets in the FACTS archive. Time period is inclusive of all datasets per type, some dataset periods are shorter than All data are stored in others. A more detailed listing of the available datasets, time periods and number netCDF format at their of ensembles is available on the FACTS website (www.psl.noaa.gov/repository/factsdocs). native resolution and Data type Purpose Examples Time period are regridded only when Diagnose the response to observed boundary CAM5 (Neale et conditions like SST, sea ice, and atmospheric com- al. 2010) necessary during anal- AMIP 1901–present ysis (e.g., comparing position in the presence of chaotic atmospheric ECHAM5 (Roeck- variability. data having different ner et al. 2003) Diagnose the response to boundary conditions resolutions). To inves- CAM5 (Neale et with an estimate of their long-term changes al. 2010) tigate and better un- Counterfac- removed. Comparing this experiment to the 1979–present tual AMIP AMIP experiments allows one to estimate the derstand high-impact ECHAM5 (Roeck- effect of long-term changes on the behavior of events like droughts, ner et al. 2003) weather and climate. heat waves and extreme CESM1 (Kay et al. precipitation, the ar- Ensembles 2015) Historical simulations and future projections chive of model experi- of coupled CanESM2 (Arora to diagnose internal coupled variability and 1920–2100 climate et al. 2011) ments includes a selec- sensitivity to external radiative forcing. tion of many relevant models CM3 (Donner et surface parameters (e.g., al. 2011) precipitation, 2-m tem- 20CR (Slivinski et al. 2019) perature, soil moisture, ECMWF reanaly- and runoff) and pres- ses (ERA5, CERA- sure level data (wind 20C; Hersbach et Estimate observed conditions in the Earth Reanalyses al. 2018; Laloyaux 1836–present components, geopoten- system. et al. 2018) tial heights). Monthly Japanese 55-year data are available for Reanalysis (JRA- all models, reanalyses, 55; Kobayashi et and observations. The al. 2015) capabilities to analyze UDEL temperature and visualize daily data and precipitation (Willmott and for some datasets is in Matsuura 2001) development. GHCN CAMS Observa- Estimate observed precipitation and temperature (Fan The FACTS website is 1880–present built on RAMADDA (Re- tions temperature. and van den Dool 2008) pository for Archiving, GISS Surface Tem- Managing and Access- perature Analysis ing Diverse Data; https:// (GISTEMP; Lens- ramadda.org), a freely sen et al. 2019) AMERICAN METEOROLOGICAL SOCIETY Brought to you by NOAA Central Library | UnauthenticatedJULY | 2020DownloadedE1216 07/12/21 03:00 PM UTC Fig. 1. FACTS tool web page showing the common features among the analysis and visualization components. See text for details. available, FAIR (Findable, Accessible, Interoperable, and Reusable) data (Wilkinson et al. 2016) compatible content management system for geospatial (and other) data. The software was designed to be portable so that FACTS servers can be set up at other intuitions for their datasets. The modular design of the software enables reuse of analysis components in dif- ferent contexts. The goal is to provide a similar user experience for each of the analysis tools. Analysis and plotting capabilities are provided through freely available
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