Uncertainties in Atmospheric River Lifecycles by Detection Algorithms: Climatology and Variability

Yang, Zhiyong; O’Brien, T. A.; Ullrich, P. A.; Collins, William D.; Patricola, Christina M.; Rhoades, Alan M.

doi:10.1029/2020jd033711

Cited by 36 publications

(42 citation statements)

References 93 publications

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“…The agreement between these two results is reassuring and confirms that the AR tracker employed in this section is consistent with other trackers. In the lower figure we see that the AR contribution to poleward transport around 45 • N and 45 • S is indeed close to the 90 % value reported by Zhu and Newell (1998), although this contribution then decays precipitously at more poleward latitudes. Note, however, that this is in part because AR moisture transport is almost always poleward, whereas non-AR transport is a mix of both poleward and equatorward contributions.…”

Section: Results From Calculation Of Northward Vapor Transport From Ar and Non-ar Pointssupporting

confidence: 77%

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TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

et al. 2021

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Abstract. TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear or unstructured/native grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal (i.e., pointwise) and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, including algorithms for editing pointwise trajectory files, composition of fields around nodal features, generation of areal masks via thresholding and nodal features, and tracking of areal features in time. Several examples are provided of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes. These examples include analyzing the fraction of precipitation from tropical cyclones, compositing meteorological fields around extratropical cyclones, calculating fractional contribution to poleward vapor transport from atmospheric rivers, and building a climatology of atmospheric blocks.

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Section: Results From Calculation Of Northward Vapor Transport From Ar and Non-ar Pointssupporting

confidence: 77%

“…In Sect. 3.5, a novel atmospheric river detection algorithm was developed using TE and validated against meridional moisture transport put forward in Zhu and Newell (1998). Finally, in Sect.…”

Section: Discussionmentioning

confidence: 99%

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

et al. 2021

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“…There are many other types of more detailed analyses that others could take on. For example, this study has not considered the temporal characteristics of ARs, since relatively few existing ARDTs track ARs as they propagate in time; a recent study by Zhou et al (2021) uses a common temporal tracking algorithm on multiple ARDTs, and such an approach could be applied to the Tier 2 data set. We encourage others in the research community to utilize this data set for research on future ARs and climate change (see data availability statement in Acknowledgments).…”

Section: Discussionmentioning

confidence: 99%

Increases in Future AR Count and Size: Overview of the ARTMIP Tier 2 CMIP5/6 Experiment

et al. 2022

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The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tier 2 experimental design and initial results using the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 multi‐model ensembles. We show that AR statistics from a given ARDT in CMIP5/6 historical simulations compare remarkably well with the MERRA‐2 reanalysis. In CMIP5/6 future simulations, most ARDTs project a global increase in AR frequency, counts, and sizes, especially along the western coastlines of the Pacific and Atlantic oceans. We find that the choice of ARDT is the dominant contributor to the uncertainty in projected AR frequency when compared with model choice. These results imply that new projects investigating future changes in ARs should explicitly consider ARDT uncertainty as a core part of the experimental design.

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“…It is important to consider the methodology in terms of ARTMIP as only a single AR detection/tracking algorithm (Guan & Waliser, 2019; GW 19 ) is used in this study. While GW 19 has been validated and continuously updated (Guan & Waliser 2015, 2017, 2019; Guan et al., 2018) and has good agreement of AR genesis with other AR lifecycle algorithms (Shearer et al., 2020; Zhou & Kim, 2019; Zhou et al., 2021), it is only one of a variety of published algorithms which could be used in this methodology.…”

Section: Methodsmentioning

confidence: 99%

Genesis Locations of the Costliest Atmospheric Rivers Impacting the Western United States

Prince

Gibson

DeFlorio

et al. 2021

Geophysical Research Letters

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Atmospheric rivers (ARs) are filamentary corridors of enhanced atmospheric water vapor transport that can produce extreme precipitation in mid-latitude and polar regions, particularly when an AR undergoes topographic ascent (Nash et al., 2018;Neiman et al., 2008;Zhu & Newell, 1998). The magnitude and duration of the moisture flux directly relates to the intensity of precipitation with the highest precipitation rates being associated with strong, prolonged ARs (Eiras- Barca et al., 2021;Konrad & Dettinger, 2017;Prince et al., 2021;Ralph et al., 2019). Given the association between ARs and precipitation, the occurrence of ARs brings the potential for substantial environmental and socioeconomic impacts (Corringham et al., 2019). On the West Coast of the U.S., landfalling ARs are the primary cause of flooding with ∼90% of all floods occurring during ARs (Dettinger et al., 2011;Paltan et al., 2017). The occurrence of these hydrological extremes often results in damage to property and infrastructure, a noteworthy event being the damage to the Oroville Dam in northern California resulting in mass evacuations and financial damages exceeding USD$1 billion

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Uncertainties in Atmospheric River Lifecycles by Detection Algorithms: Climatology and Variability

Cited by 36 publications

References 93 publications

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

Increases in Future AR Count and Size: Overview of the ARTMIP Tier 2 CMIP5/6 Experiment

Genesis Locations of the Costliest Atmospheric Rivers Impacting the Western United States

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