Detection Uncertainty Matters for Understanding Atmospheric Rivers

O’Brien, T. A.; Payne, Ashley E.; Shields, Christine A.; Rutz, Jonathan J.; Brands, Swen; Castellano, Christopher M.; Chen, Jiayi; Cleveland, William S.; DeFlorio, Michael J.; Goldenson, Naomi

doi:10.31223/osf.io/ftwgm

Cited by 17 publications

(25 citation statements)

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“…Our second major improvement responds to the call by the third ARTMIP workshop (Brien et al, 2020) to identify different “flavors” of ARs that are controlled by diverse physical mechanisms and weather systems, such as subtropical high, subpolar low, TC‐like features, ECs, monsoon, and localized perturbation (Hu et al, 2017; Kamae, Mei, Xie, Naoi, & Ueda, 2017; Xiong et al, 2019; Zhang et al, 2018). Our experience strongly suggest that one AR event might possess multiple “flavors” carried by different AR segments, that is, one AR event might encounter a number of systems that modify its segmental geometric and dynamic features.…”

Section: Panlu20 and Ea Ar Catalogmentioning

confidence: 90%

“…First, notwithstanding some progress in distinguishing TC‐like features (Mundhenk et al, 2016; Pan & Lu, 2019), failures are still reported when the TC‐like feature occurs as a partition of AR. Second, a robust method distinguishing TMFs from ARs is still lacking, which is critical for studies covering the tropics (Brien et al, 2020). Third, as a large‐scale phenomenon, partitions of an AR are likely affected by different systems and processes (e.g., monsoon, extratropical cyclones [ECs], TC‐like features, and localized perturbation) and have distinct features (e.g., varying IVT intensity and IVT coherence), especially for ARs with varying shape, orientation, and curvature.…”

Section: Introductionmentioning

confidence: 99%

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East Asia Atmospheric River catalog: Annual Cycle, Transition Mechanism, and Precipitation

Pan

2020

Geophysical Research Letters

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An all‐season East Asia Atmospheric River (EA AR) catalog is constructed based on PanLu2.0. The annual cycle of EA AR exhibits eight stages, synchronizing with the seasonal‐to‐subseasonal variation of precipitation and their associated atmospheric steering. Particularly, extension of the western North Pacific subtropical high (WNPSH) to south China sea, due to the springtime east‐west thermal contrast, steers ARs over Southeast China (SEC), inducing spring precipitation. During the Asian summer monsoon season, the spatial variation of the AR main route echoes the northward displacement of WNPSH, coincides with the prevailing monsoonal wind onset and stepwise propagation of rain belt. The warm moisture‐laden air of the wintertime ARs confronts the dry and cold air over SEC, contributing to the winter precipitation. ARs offer substantial potential predictability of (heavy) precipitation days over the midlatitude from the late spring to early summer, and over SEC and south Japan from late winter to early spring.

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Section: Panlu20 and Ea Ar Catalogmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

East Asia Atmospheric River catalog: Annual Cycle, Transition Mechanism, and Precipitation

Pan

2020

Geophysical Research Letters

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“…The American Meteorological Society (AMS) definition for AR contains little quantitative guidance (AMS, 2019), allowing for flexibility in ARDT development but also contributing to differences across ARDTs. These differences, which include variations in terms of detection variable (e.g., IWV and IVT), thresholds on the intensity of detection variables, geometry, event persistence, and/or other detection considerations, can ultimately affect conclusions about AR characteristics and impacts (O'Brien et al, 2020; Payne et al, 2020; Rutz et al, 2019; Shields et al, 2018). While some ARDTs rely on relative moisture thresholds derived from climatology (e.g., Guan & Waliser, 2015; Lavers et al, 2012), others use absolute thresholds for either IWV (Ralph et al, 2004; Wick et al, 2013) or IVT (Equation ) (Leung & Qian, 2009; Rutz et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

Sensitivity of Atmospheric River Vapor Transport and Precipitation to Uniform Sea Surface Temperature Increases

2020

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Filaments of intense vapor transport called atmospheric rivers (ARs) are responsible for the majority of poleward vapor transport in the midlatitudes. Despite their importance to the hydrologic cycle, there remain many unanswered questions about changes to ARs in a warming climate. In this study we perform a series of escalating uniform SST increases (+2, +4, and +6K, respectively) in the Community Atmosphere Model version 5 in an aquaplanet configuration to evaluate the thermodynamic and dynamical response of AR vapor content, transport, and precipitation to warming SSTs. We find that AR column integrated water vapor (IWV) is especially sensitive to SST and increases by 6.3-9.7% per degree warming despite decreasing relative humidity through much of the column. Further analysis provides a more nuanced view of AR IWV changes: Since SST warming is modest compared to that in the midtroposphere, computing fractional changes in IWV with respect to SST results in finding spuriously large increases. Meanwhile, results here show that AR IWV transport increases relatively uniformly with temperature and at consistently lower rates than IWV, as modulated by systematically decreasing low-level wind speeds. Similarly, changes in AR precipitation are related to a compensatory relationship between enhanced near-surface moisture and damped vertical motions.

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“…Studies based on ARTMIP have started to explore uncertainty with respect to AR detection, and the uncertainty is larger than many in the community had anticipated (Shields et al, 2018(Shields et al, , 2019bChen et al, 2018;Rutz et al, 2019;Shields et al, 2019a;Chen et al, 2019;Ralph et al, 2019b;Payne et al, 2020). Preliminary results from the ARTMIP Tier 2 experiments suggest that AR detection uncertainty may be comparable to model uncertainty in future climate simulations (O'Brien et al, 2020b), which implies that ongoing AR research would benefit from consideration of AR detection uncertainty. TECA BARD v1.0.1 offers an efficient way for future studies to quantify AR detection uncertainty in situ.…”

Section: The Importance Of Uncertainty In Feature Detectionmentioning

confidence: 99%

Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1

et al. 2020

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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 following 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 eight 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 v1.0.1, to the MERRA-2 reanalysis and show that the sign of the correlation between global AR count and El Niño–Southern Oscillation depends on the set of parameters used.

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Detection Uncertainty Matters for Understanding Atmospheric Rivers

Abstract: The 3rd ARTMIP WorkshopWhat: Over 30 participants from multiple universities and research insititutionsmet to discuss new results from the Atmospheric River TrackingMethod Intercomparison Project.Where: Lawrence Berkeley National Lab, Berkeley, CA, USAWhen: 16-18 October 2019

Cited by 17 publications

References 10 publications

East Asia Atmospheric River catalog: Annual Cycle, Transition Mechanism, and Precipitation

East Asia Atmospheric River catalog: Annual Cycle, Transition Mechanism, and Precipitation

Sensitivity of Atmospheric River Vapor Transport and Precipitation to Uniform Sea Surface Temperature Increases

Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1

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