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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2020-55
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-55
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 17 Apr 2020

Submitted as: model description paper | 17 Apr 2020

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A revised version of this preprint is currently under review for the journal GMD.

Detection of Atmospheric Rivers with Inline Uncertainty Quantification: TECA-BARD v1.0

Travis A. O'Brien1,2, Mark D. Risser2, Burlen Loring3, Abdelrahman A. Elbashandy3, Harinarayan Krishnan3, Jeffrey Johnson4, Christina M. Patricola2, John P. O'Brien2,5, Ankur Mahesh2, Prabhat6, Sarahí Arriaga Ramirez2,7, Alan M. Rhoades2, Alexander Charn2,8, Héctor Inda Díaz2,7, and William D. Collins2,8 Travis A. O'Brien et al.
  • 1Dept. of Earth and Atmospheric Sciences, Indiana University, Bloomington, Indiana, USA
  • 2Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
  • 3Computational Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
  • 4Cohere Consulting LLC, Seattle, WA, USA
  • 5Dept. of Earth and Planetary Science, University of California, Santa Cruz, California, USA
  • 6National Energy Research Scientific Computing Center, Lawrence Berkeley National Lab, Berkeley, California, USA
  • 7Dept. of Land, Air and Water Resources, University of California, Davis, California, USA
  • 8Dept. of Earth and Planetary Science, University of California, Berkeley, California, USA

Abstract. It has become increasingly common for researchers to utilize methods that identify weather features in climate models. There is an increasing recognition that the uncertainty associated with choice of detection method may affect our scientific understanding. For example, results from the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) indicate that there are a broad range of plausible atmospheric river (AR) detectors, and that scientific results can depend on the algorithm used. There are similar examples from the literature on extratropical cyclones and tropical cyclones. It is therefore imperative to develop detection techniques that explicitly quantify the uncertainty associated with the detection of events. We seek to answer the question: given a ‘plausible’ AR detector, how does uncertainty in the detector quantitatively impact scientific results? We develop a large dataset of global AR counts, manually identified by a set of 8 researchers with expertise in atmospheric science, which we use to constrain parameters in a novel AR detection method. We use a Bayesian framework to sample from the set of AR detector parameters that yield AR counts similar to the expert database of AR counts; this yields a set of 'plausible' AR detectors from which we can assess quantitative uncertainty. This probabilistic AR detector has been implemented in the Toolkit for Extreme Climate Analysis (TECA), which allows for efficient processing of petabyte-scale datasets. We apply the TECA Bayesian AR Detector, TECA-BARD v1.0, to the MERRA2 reanalysis and show that the sign of the correlation between global AR count and El Nino Southern Oscillation depends on the set of parameters used.

Travis A. O'Brien et al.

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Travis A. O'Brien et al.

Data sets

TECA BARD Parameters T. A. O'Brien, M. D. Risser, B. Loring, A. A. Elbashandy, H. Krishnan, J. Johnson, C. M. Patricola, J. P. O'Brien, A. Mahesh, Prabhat, S. Arriaga Ramirez, A. M. Rhoades, A. Charn, H.Inda Díaz, and W. D. Collins https://doi.org/10.5281/zenodo.3677542

Model code and software

Toolkit for Extreme Climate Analysis B. Loring, T. A. O'Brien, A. E. Elbashandy, J. N. Johnson, H. Krishnan, K. Harinarayan, N. Keen, and Prabhat https://doi.org/10.20358/C8C651

Travis A. O'Brien et al.

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Latest update: 28 Sep 2020
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Short summary
Researchers utilize various algorithms to identify extreme weather features in climate data, and we seek to answer the question: given a 'plausible' weather event detector, how does uncertainty in the detector impact scientific results? We generate a suite of statistical models that emulate expert identification of weather features. We find that the connection between El Niño and atmospheric rivers--a specific extreme weather type--depends systematically on the design of the detector.
Researchers utilize various algorithms to identify extreme weather features in climate data, and...
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