Literature DB >> 27789838

Using climate models to estimate the quality of global observational data sets.

François Massonnet1, Omar Bellprat2, Virginie Guemas3, Francisco J Doblas-Reyes4.   

Abstract

Observational estimates of the climate system are essential to monitoring and understanding ongoing climate change and to assessing the quality of climate models used to produce near- and long-term climate information. This study poses the dual and unconventional question: Can climate models be used to assess the quality of observational references? We show that this question not only rests on solid theoretical grounds but also offers insightful applications in practice. By comparing four observational products of sea surface temperature with a large multimodel climate forecast ensemble, we find compelling evidence that models systematically score better against the most recent, advanced, but also most independent product. These results call for generalized procedures of model-observation comparison and provide guidance for a more objective observational data set selection.
Copyright © 2016, American Association for the Advancement of Science.

Year:  2016        PMID: 27789838     DOI: 10.1126/science.aaf6369

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  4 in total

1.  Spatiotemporal Error in Rainfall Data: Consequences for Epidemiologic Analysis of Waterborne Diseases.

Authors:  Morgan C Levy; Philip A Collender; Elizabeth J Carlton; Howard H Chang; Matthew J Strickland; Joseph N S Eisenberg; Justin V Remais
Journal:  Am J Epidemiol       Date:  2019-05-01       Impact factor: 4.897

2.  Addressing rainfall data selection uncertainty using connections between rainfall and streamflow.

Authors:  Morgan C Levy; Avery Cohn; Alan Vaz Lopes; Sally E Thompson
Journal:  Sci Rep       Date:  2017-03-16       Impact factor: 4.379

Review 3.  Should Sea-Ice Modeling Tools Designed for Climate Research Be Used for Short-Term Forecasting?

Authors:  Elizabeth Hunke; Richard Allard; Philippe Blain; Ed Blockley; Daniel Feltham; Thierry Fichefet; Gilles Garric; Robert Grumbine; Jean-François Lemieux; Till Rasmussen; Mads Ribergaard; Andrew Roberts; Axel Schweiger; Steffen Tietsche; Bruno Tremblay; Martin Vancoppenolle; Jinlun Zhang
Journal:  Curr Clim Change Rep       Date:  2020-09-26

4.  Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting.

Authors:  Maria Myrto Villia; Grigorios Tsagkatakis; Mahta Moghaddam; Panagiotis Tsakalides
Journal:  Sensors (Basel)       Date:  2022-02-26       Impact factor: 3.576

  4 in total

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