Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling

Detection Uncertainty Matters for Understanding Atmospheric Rivers

TitleDetection Uncertainty Matters for Understanding Atmospheric Rivers
Publication TypeJournal Article
Year of Publication2020
JournalBulletin of the American Meteorological Society
Abstract / Summary

Atmospheric rivers (ARs) are increasingly recognized globally as an important weather phenomenon associated with extreme precipitation. There is a substantial body of literature indicating that ARs are responsible for a large fraction of wet-season precipitation on western coasts (Rutz et al. 2019) and that they can cause large changes in snowpack (both positive and negative; Guan et al. 2010; Chen et al. 2019). Individual ARs and collections of ARs can bring large amounts of precipitation that drives floods and other storm-related hazards (Ralph et al. 2006, 2019a). ARs are a significant factor for water and associated water systems in the vicinity of western coasts (Gao et al. 2016; Ralph et al. 2019b). It is increasingly evident that they have major impacts on the energy and water budgets of the cryosphere: including mountains (Chen et al. 2019) and high latitude regions (Gorodetskaya et al. 2014). These research advances hinge on technical advances in tracking ARs in observations, reanalyses, and climate model simulations and on understanding uncertainties associated with different tracking methods. In parallel with the recent increase in research activity around ARs, an increasing number of research groups have developed unique methods for tracking ARs (Shields et al. 2019). The Atmospheric River Tracking Intercomparison Project (ARTMIP) was created to design a set of experiments that could quantify the uncertainty associated with AR tracking (Shields et al. 2018; Rutz et al. 2019). The concept of a multi-tiered experimental approach, based on tracking ARs across common datasets, resulted from the 1st ARTMIP workshop in 2017. The Tier 1 experiment is focused on tracking ARs in a modern reanalysis (MERRA2). The 2nd ARTMIP workshop (Shields et al. 2019) was oriented around discussion of Tier 1 results and around designing and planning the first set of Tier 2 experiments: the Tier 2 C20C+ experiment and the Tier 2 CMIP5/6 experiment. Both initial Tier 2 experiments are focused on understanding the effects of climate change on AR characteristics, with the C20C+ experiment focusing on a set of high-resolution atmosphere-only simulations, and the CMIP5/6 experiment focusing on a multimodel collection of fully-coupled simulations from the Coupled Model Intercomparison Project. Following the 2nd ARTMIP Workshop, two separate developments motivated the need for developing a large dataset of hand-labeled ARs. Discussions following the 2nd ARTMIP Workshop suggested that differences among AR tracking algorithms might reflect differences in expert opinion about what constitutes the boundary of ARs; resolving this question would require experts to hand-label ARs. Unrelated, but concurrent, advances in Computational Climate Science have demonstrated the utility of modern machine learning methods for tracking weather phenomena (Mudigonda et al. 2017; Muszynski et al. 2019; Kurth et al. 2018). These developments also high light the need for high-quality data to train machine learning methods: expert-labeled datasets. Emerging results from the Tier 1 and 2 experiments, along with the recently identified nto develop a high-quality, hand-labeled dataset of ARs, motivated the ARTMIP Committee to convene the 3rd ARTMIP Workshop1, held at Lawrence Berkeley Lab on October 16-18, 2019. The meeting included a substantial virtual component, with 25% of attendees attending virtually; the meeting included several presentations from remote attendees. The 3rd ARTMIP Workshop was organized around:
* presentation of results from recent and ongoing ARTMIP research: Tier 1 and beyond (with a focus on Tier 2);
* working discussion of current and future ARTMIP experiments and papers; and
* solicitation of expert identification of atmospheric rivers and other weather phenomena for machine learning.

Initial Tier 2 results presented at the workshop show that, while most methods agree, qualitative conclusions about the effect of climate change on ARs can depend on tracking algorithm. These results further motivate exploration of the role of AR tracking uncertainty on other aspects of AR science. Specifications and timelines for three new Tier 2 experiments were defined: Tier 2 Reanalysis, Tier 2 High-Latitude, and Tier 2 paleo-ARTMIP. A future Tier 2 experiment was also discussed, and specifications and a timeline will be developed in future ARTMIP interactions (e.g., teleconferences): Tier 2 MPAS-ENSO. Group and breakout discussions during the workshop identified numerous gaps in understanding and associated research priorities. These gaps and research priorities are a key outcome for the ARTMIP workshop. Those interested in more information about the workshop should refer to the full workshop report, which is available at the Department of Energy website.

URLhttp://dx.doi.org/10.31223/osf.io/ftwgm
DOI10.31223/osf.io/ftwgm
Journal: Bulletin of the American Meteorological Society
Year of Publication: 2020
Publication Date: 01/2020

Atmospheric rivers (ARs) are increasingly recognized globally as an important weather phenomenon associated with extreme precipitation. There is a substantial body of literature indicating that ARs are responsible for a large fraction of wet-season precipitation on western coasts (Rutz et al. 2019) and that they can cause large changes in snowpack (both positive and negative; Guan et al. 2010; Chen et al. 2019). Individual ARs and collections of ARs can bring large amounts of precipitation that drives floods and other storm-related hazards (Ralph et al. 2006, 2019a). ARs are a significant factor for water and associated water systems in the vicinity of western coasts (Gao et al. 2016; Ralph et al. 2019b). It is increasingly evident that they have major impacts on the energy and water budgets of the cryosphere: including mountains (Chen et al. 2019) and high latitude regions (Gorodetskaya et al. 2014). These research advances hinge on technical advances in tracking ARs in observations, reanalyses, and climate model simulations and on understanding uncertainties associated with different tracking methods. In parallel with the recent increase in research activity around ARs, an increasing number of research groups have developed unique methods for tracking ARs (Shields et al. 2019). The Atmospheric River Tracking Intercomparison Project (ARTMIP) was created to design a set of experiments that could quantify the uncertainty associated with AR tracking (Shields et al. 2018; Rutz et al. 2019). The concept of a multi-tiered experimental approach, based on tracking ARs across common datasets, resulted from the 1st ARTMIP workshop in 2017. The Tier 1 experiment is focused on tracking ARs in a modern reanalysis (MERRA2). The 2nd ARTMIP workshop (Shields et al. 2019) was oriented around discussion of Tier 1 results and around designing and planning the first set of Tier 2 experiments: the Tier 2 C20C+ experiment and the Tier 2 CMIP5/6 experiment. Both initial Tier 2 experiments are focused on understanding the effects of climate change on AR characteristics, with the C20C+ experiment focusing on a set of high-resolution atmosphere-only simulations, and the CMIP5/6 experiment focusing on a multimodel collection of fully-coupled simulations from the Coupled Model Intercomparison Project. Following the 2nd ARTMIP Workshop, two separate developments motivated the need for developing a large dataset of hand-labeled ARs. Discussions following the 2nd ARTMIP Workshop suggested that differences among AR tracking algorithms might reflect differences in expert opinion about what constitutes the boundary of ARs; resolving this question would require experts to hand-label ARs. Unrelated, but concurrent, advances in Computational Climate Science have demonstrated the utility of modern machine learning methods for tracking weather phenomena (Mudigonda et al. 2017; Muszynski et al. 2019; Kurth et al. 2018). These developments also high light the need for high-quality data to train machine learning methods: expert-labeled datasets. Emerging results from the Tier 1 and 2 experiments, along with the recently identified nto develop a high-quality, hand-labeled dataset of ARs, motivated the ARTMIP Committee to convene the 3rd ARTMIP Workshop1, held at Lawrence Berkeley Lab on October 16-18, 2019. The meeting included a substantial virtual component, with 25% of attendees attending virtually; the meeting included several presentations from remote attendees. The 3rd ARTMIP Workshop was organized around:
* presentation of results from recent and ongoing ARTMIP research: Tier 1 and beyond (with a focus on Tier 2);
* working discussion of current and future ARTMIP experiments and papers; and
* solicitation of expert identification of atmospheric rivers and other weather phenomena for machine learning.

Initial Tier 2 results presented at the workshop show that, while most methods agree, qualitative conclusions about the effect of climate change on ARs can depend on tracking algorithm. These results further motivate exploration of the role of AR tracking uncertainty on other aspects of AR science. Specifications and timelines for three new Tier 2 experiments were defined: Tier 2 Reanalysis, Tier 2 High-Latitude, and Tier 2 paleo-ARTMIP. A future Tier 2 experiment was also discussed, and specifications and a timeline will be developed in future ARTMIP interactions (e.g., teleconferences): Tier 2 MPAS-ENSO. Group and breakout discussions during the workshop identified numerous gaps in understanding and associated research priorities. These gaps and research priorities are a key outcome for the ARTMIP workshop. Those interested in more information about the workshop should refer to the full workshop report, which is available at the Department of Energy website.

DOI: 10.31223/osf.io/ftwgm
Citation:
O'Brien, T, A Payne, C Shields, J Rutz, S Brands, C Castellano, J Chen, et al.  2020.  "Detection Uncertainty Matters for Understanding Atmospheric Rivers."  Bulletin of the American Meteorological Society.  https://doi.org/10.31223/osf.io/ftwgm.