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; Gorodetskaya, Irina; Díaz, Héctor Inda; Kashinath, Karthik; Kawzenuk, Brian; Kim, Sol; Krinitskiy, Mikhail; Lora, Juan M.; McClenny, Beth; Michaelis, A.; O’Brien, John P.; Patricola, Christina M.; Ramos, Alexandre M.; Shearer, E. J.; Tung, Wen‐wen; Ullrich, P. A.; Wehner, Michael; Yang, Kevin; Zhang, Rudong; Zhang, Zhenhai; Yang, Zhiyong

doi:10.1175/bams-d-19-0348.1

Cited by 33 publications

(26 citation statements)

References 12 publications

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“…We argue that the differences in correlation coefficient between the restrictive and permissive EGIDs likely result from differences in the degree to which tropical moisture anomalies are filtered among the EGIDs. Patricola et al (2020) show that strong El Niño events are associated with positive IVT anomalies in much of the tropics and a separate band of positive anomalies in the midlatitudes (around 30 • latitude; their Fig. 11).…”

Section: Uncertainty In the Relationship Between Enso And Ar Countmentioning

confidence: 88%

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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Section: Uncertainty In the Relationship Between Enso And Ar Countmentioning

confidence: 88%

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

et al. 2020

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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 1) (Leung & Qian, 2009;Rutz et al, 2013).…”

Section: Ar Detectionmentioning

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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“…Even though ARDTs are often initially designed with different purposes in mind, Payne et al (2020) demonstrate that there is overlap in what they are ultimately used to study. The community has recently started to recognize that uncertainty associated with the numerical definition of ARs may have important implications for our understanding of ARs and their changes in a future warmer world (Guan et al, 2018;Huning et al, 2017;Lora et al, 2020;Newman et al, 2012;O'Brien, Payne, et al, 2020;O'Brien, Risser, et al, 2020;Ralph, Wilson, et al, 2019;Rutz et al, 2019;Shields et al, 2018;Shields, Rosenbloom, et al, 2019;Shields, Rutz, et al, 2019) The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) was launched by members of the AR research community in order to systematically assess the impact of this uncertainty on our scientific understanding (Shields et al, 2018). The First ARTMIP Workshop (Shields, Rutz, et al, 2019) defined a multitier experimental design focusing on uncertainty in the observational record (Tier 1; Rutz et al, 2019), and uncertainty in AR variability and change (Tier 2).…”

Section: Introductionmentioning

confidence: 99%

“…( 2020 ) demonstrate that there is overlap in what they are ultimately used to study. The community has recently started to recognize that uncertainty associated with the numerical definition of ARs may have important implications for our understanding of ARs and their changes in a future warmer world (Guan et al., 2018 ; Huning et al., 2017 ; Lora et al., 2020 ; Newman et al., 2012 ; O’Brien, Payne, et al., 2020 ; O’Brien, Risser, et al., 2020 ; Ralph, Wilson, et al., 2019 ; Rutz et al., 2019 ; Shields et al., 2018 ; Shields, Rosenbloom, et al., 2019 ; Shields, Rutz, et al., 2019 )…”

Section: Introductionmentioning

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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Detection Uncertainty Matters for Understanding Atmospheric Rivers

Cited by 33 publications

References 12 publications

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

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

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

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

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