Constraining and Characterizing the Size of Atmospheric Rivers: A Perspective Independent From the Detection Algorithm

Díaz, Héctor Alejandro Inda; O’Brien, T. A.; Yang, Zhiyong; Collins, William D.

doi:10.1029/2020jd033746

Cited by 10 publications

(14 citation statements)

References 52 publications

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“…The uncertainty in AR detection is key to answering this question. It has been discussed the possibility that there is more than one type of dynamical phenomenon that produces AR-like objects and that different definitions for these processes could help in future studies (Inda- Díaz et al, 2021;. This gains particular relevance for the study of future ARs in CWM, because, in general, different "types" of AR-like phenomena (including CWM landfalling tropical ARs) could have different responses to climate change.…”

Section: Discussionmentioning

confidence: 99%

Relationship Between Atmospheric Rivers and the Dry Season Extreme Precipitation in Central-Western Mexico

Díaz

O’Brien

2023

Preprint

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Atmospheric rivers (AR) are long, narrow jets of moisture transport responsible for over 90% of moisture transport from the tropics to higher latitudes, covering only between 2% and 10% of the earth’s surface. ARs have a significant impact on the hydrological cycle of midlatitudes and polar regions, which has resulted in a large effort to study ARs and their impacts on these regions. It is not until recently that ARs in tropical latitudes are starting to generate interest within the scientific AR community.We use the ERA-20C reanalysis and the Bayesian AR detector TECA-BARD to show the relationship between extreme precipitation and atmospheric rivers in central-western Mexico (CWM) during the dry seasons (November-March) in the 1900-2010 period.We find that more than 25% of extreme precipitation amount and frequency are associated with ARs, with a maximum of 60%-80% during December and January near the coast of Sinaloa (107.5W,25N). Composites of the mean meteorological state show “ideal” conditions for orographic precipitation due to landfalling ARs: high horizontal vapor transport perpendicular to the Sierra Madre. We observe a tropospheric wave pattern in vertical velocity, surface pressure, and geopotential height associated with these events. The nature and evolution of these waves need to be further studied. Our results suggest that TECA-BARD provides a reasonable estimation for AR presence in CWM. Nevertheless, we recommend using multiple AR detectors and one tuned explicitly for tropical latitudes. This will allow investigation of the response of CWM landfalling ARs and the region’s hydroclimatology under future climate scenarios.

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Section: Discussionmentioning

confidence: 99%

Relationship Between Atmospheric Rivers and the Dry Season Extreme Precipitation in Central-Western Mexico

Díaz

O’Brien

2023

Preprint

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“…The wide variety of accepted detection algorithms in the AR research community creates a dilemma in determining the truth to compare this algorithm to for verification. Due to the lack of agreement between ARTMIP algorithms and the types of algorithms used to calculate ARs (Inda‐Díaz et al., 2021; Zhang et al., 2021), it is necessary to compare CG‐Climate to additional common tracking methods to best understand its reliability. We compare the output of CG‐Climate to eight different ARTMIP algorithms (Table 1).…”

Section: Methodsmentioning

confidence: 99%

“…This is a common theme given that all AR detection algorithms have their own unique spatial biases. The training set used in this study used AR masks that were particularly larger than ARs detected from other studies (Inda‐Díaz et al., 2021). An extreme example of this happened on 3 January 2006 at 18Z (Figure 4) and serves as a useful case study to demonstrate this phenomenon.…”

Section: Methodsmentioning

confidence: 99%

Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set

Higgins

Subramanian

Graubner

et al. 2023

J Adv Model Earth Syst

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Atmospheric rivers (ARs) are elongated corridors of water vapor in the lower troposphere that cause extreme precipitation over many coastal regions around the globe. They play a vital role in the water cycle in the western US, fueling the most extreme west coast precipitation and sometimes accounting for more than 50% of total annual west coast precipitation (Gershunov et al., 2017). Severe ARs are associated with extreme flooding and damages while weak ARs are typically more beneficial to our society as they bring much needed drought relief (Ralph et al., 2019). Future climate projections show an increase in US west coast precipitation variability caused by AR precipitation increasing and non-AR precipitation decreasing (Gershunov et al., 2019). From 2012 to 2016, California experienced a historic drought, which was followed by the state's second wettest year on record. 2020 and 2021 are two of the driest years on record over much of the western US (Williams et al., 2022). The extreme interannual variability in western US precipitation in recent years coinciding with climate change projections of increased precipitation variability is a serious cause for concern over how patterns may change in the coming decades (Polade et al., 2017;Shields & Kiehl, 2016).A necessary step in understanding changing patterns in ARs as a function of climate change is employing an AR detection method. There are a variety of different algorithms used to track ARs due to their relatively diverse definitions (Shields et al., 2018). The Atmospheric River Tracking Intercomparison Project (ARTMIP) organizes and provides information on all the widely accepted algorithms that exist. Nearly all the algorithms included in ARTMIP rely on absolute and relative numerical thresholds. Absolute thresholds are static constraints that are required for an AR to exist, typically coming in the form of length, width, minimum inte-Abstract There is currently large uncertainty over the impacts of climate change on precipitation trends over the US west coast. Atmospheric rivers (ARs) are a significant source of US west coast precipitation and trends in ARs can provide insight into future precipitation trends. There are already a variety of different methods used to identify ARs, but many are used in contexts that are often difficult to apply to large climate datasets due to their computational cost and requirement of integrated vapor transport as an input variable, which can be expensive to compute in climate models at high temporal frequencies. Using deep learning (DL) to track ARs is a unique approach that can alleviate some of the computational challenges that exist in more traditional methods. However, some questions still remain regarding its flexibility and robustness. This research investigates the consistency of a DL methodology of tracking ARs with more established algorithms to demonstrate its high-level performance for future studies.Plain Language Summary Atmospheric rivers (ARs) are long corridors of water vapor in the lower atmosphere that are...

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“…How the relative dominance of wind and moisture will change through the lifecycle of ARs remains unanswered. Previous studies showed that landfalling ARs in different regions have various impact levels and distinct characteristics including genesis locations and AR sizes, and are connected to different large‐scale patterns (Inda‐Díaz et al., 2021; Prince et al., 2021; Zhou & Kim, 2019). Therefore, it is important to extend the discussion of AR flavors to the perspective of AR lifecycles and investigate whether the dominance of wind or moisture is constant throughout the lifecycle.…”

Section: Introductionmentioning

confidence: 99%

Characteristics and Variability of Winter Northern Pacific Atmospheric River Flavors

et al. 2022

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Atmospheric rivers (ARs) are intensive poleward moisture transport events that are essential to the global hydrological cycle and are often linked to extreme weather events. We categorize the winter North Pacific ARs into two “flavors”: wind‐dominated (windy ARs) and moisture‐dominated (wet ARs) using 40 years of hourly data from fifth generation of the European Centre for Medium‐Range Weather Forecasts Interim Reanalysis. We compare the differences between windy ARs and wet ARs including the lifecycle characteristics (such as genesis locations and changes of meteorological elements through the lifecycle), overall AR frequency, landfall impacts, and variability. The windy ARs are more likely to occur in the midlatitudes, while wet ARs are more active in the subtropics. Windy ARs are associated with intensive surface pressure lows, where the strong pressure gradient can support the strong wind within ARs. Due to larger size and longer lifetime, wet ARs are more likely to produce more precipitation over a lifecycle. By scaling the landfalling ARs, we show that wet ARs dominate the high‐category ARs (Category 4 and 5) with higher spatial frequency and more precipitation, and windy ARs have higher contributions in the lower AR categories especially over British Columbia. Windy ARs are modulated by El Niño Southern Oscillation (ENSO) teleconnections via the anomalous geopotential height and extended subtropical jet. Wet ARs are affected by the anomalous sea surface temperature over the midlatitudes related to ENSO. Sensitivity analysis with an alternate AR detection algorithm shows consistent results on AR flavors but with disagreement on the amplitude.

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Constraining and Characterizing the Size of Atmospheric Rivers: A Perspective Independent From the Detection Algorithm

Cited by 10 publications

References 52 publications

Relationship Between Atmospheric Rivers and the Dry Season Extreme Precipitation in Central-Western Mexico

Relationship Between Atmospheric Rivers and the Dry Season Extreme Precipitation in Central-Western Mexico

Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set

Characteristics and Variability of Winter Northern Pacific Atmospheric River Flavors

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