In Search of the Optimal Atmospheric River Index for US Precipitation: A Multifactorial Analysis

Zhang, Chen; Tung, Wen‐wen; Cleveland, William S.

doi:10.1029/2020jd033667

Cited by 3 publications

(9 citation statements)

References 61 publications

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“…More restrictive climate thresholds, such as the 95th percentile, emphasized extreme AR events with limited occurrences, smaller affected areas, and shorter duration in the Arctic. These results were mostly consistent with the conclusions derived from the ARs making landfall on the US continents (C. Zhang et al, 2021). Furthermore, even though ERA5 had more AR events than MERRA-2.…”

Section: Ar Event Statisticssupporting

confidence: 89%

“…The AR detection and tracking algorithm followed a similar multifactorial design as C. Zhang et al (2021), in which we found that the choices of moisture fields and climate thresholds strongly dictated the summary statistics of the identified ARs and ARrelated surface hydrometeorological effects. In this work, we created six Arctic AR la-bels with each reanalysis dataset from January 1980 to December 2019.…”

Section: Ar Detection and Tracking Algorithmmentioning

confidence: 99%

“…In probing these questions, we created twelve sets of Arctic AR labels with ERA5 and MERRA-2 reanalysis from 1980 to 2019. Essentially, the Arctic AR detection and tracking algorithm was designed to be consistent with that developed in the mid-latitudes (C. Zhang et al, 2021) since the AR labels were used to study the moisture transported by the ARs into the Arctic, while there is no physical boundary between the Arctic and the lower latitudes.…”

Section: Introductionmentioning

confidence: 99%

“…Henceforth, we detected and tracked Arctic AR events that might be associated with significant heat and moisture transports or distinctly accompanied by extensive radiative effects using the multifactorial approach in C. Zhang et al (2021). We then examined and discussed the AR climatology, trends, and decade-by-decade changes in the Pan-Arctic region (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Climatology and Decadal Changes of Arctic Atmospheric Rivers Based on ERA5 and MERRA-2

Zhang¹,

Tung

Cleveland

2023

Preprint

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We present {the} Arctic atmospheric river (AR) climatology based on twelve {sets of} labels derived from ERA5 and MERRA-2 reanalyses for 1980–2019. The ARs were identified and tracked in the 3-hourly reanalysis data with a multifactorial approach based on either atmospheric column-integrated water vapor ($IWV$) or integrated water vapor transport ($IVT$) exceeding one of the three climate thresholds (75th, 85th, and 95th percentiles). Time series analysis of the AR event counts from the AR labels showed overall upward trends from the mid-1990s to 2019. The 75th $IVT$- and $IWV$-based labels, as well as the 85th $IWV$-based labels, are likely more sensitive to Arctic surface warming, therefore, detected some broadening of AR-affected areas over time, while the rest of the labels did not. Spatial exploratory analysis of these labels revealed that the AR frequency of occurrence maxima shifted poleward from over-land in 1980–1999 to over the Arctic Ocean and its outlying Seas in 2000–2019. Regions across the Atlantic, the Arctic, to the Pacific Oceans trended higher AR occurrence, surface temperature, and column-integrated moisture. Meanwhile, ARs were increasingly responsible for the rising moisture transport into the Arctic. Even though the increase of Arctic AR occurrence was primarily associated with long-term Arctic surface warming and moistening, the effects of changing atmospheric circulation could stand out locally, such as on the Pacific side over the Chukchi Sea. The changing teleconnection patterns strongly modulated AR activities in time and space, with prominent anomalies in the Arctic-Pacific sector during the latest decade. Besides, the extreme events identified by the 95th-percentile labels displayed the most significant changes and were most influenced by the teleconnection patterns. The twelve Arctic AR labels and the detailed graphics in the atlas can help navigate the uncertainty of detecting and quantifying Arctic ARs and their associated effects in current and future studies.

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Section: Ar Event Statisticssupporting

confidence: 89%

Section: Ar Detection and Tracking Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Climatology and Decadal Changes of Arctic Atmospheric Rivers Based on ERA5 and MERRA-2

Zhang¹,

Tung

Cleveland

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%

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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Climatology and decadal changes of Arctic atmospheric rivers based on ERA5 and MERRA-2

Zhang

Tung

Cleveland

2023

Environ. Res.: Climate

Self Cite

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We present the Arctic atmospheric river (AR) climatology based on twelve sets of labels derived from ERA5 and MERRA-2 reanalyses for 1980--2019. The ARs were identified and tracked in the 3-hourly reanalysis data with a multifactorial approach based on either atmospheric column-integrated water vapor ($IWV$) or integrated water vapor transport ($IVT$) exceeding one of the three climate thresholds (75th, 85th, and 95th percentiles). Time series analysis of the AR event counts from the AR labels showed overall upward trends from the mid-1990s to 2019. The 75th $IVT$- and $IWV$-based labels, as well as the 85th $IWV$-based labels, are likely more sensitive to Arctic surface warming, therefore, detected some broadening of AR-affected areas over time, while the rest of the labels did not. Spatial exploratory analysis of these labels revealed that the AR frequency of occurrence maxima shifted poleward from over-land in 1980--1999 to over the Arctic Ocean and its outlying Seas in 2000--2019. Regions across the Atlantic, the Arctic, to the Pacific Oceans trended higher AR occurrence, surface temperature, and column-integrated moisture. Meanwhile, ARs were increasingly responsible for the rising moisture transport into the Arctic. Even though the increase of Arctic AR occurrence was primarily associated with long-term Arctic surface warming and moistening, the effects of changing atmospheric circulation could stand out locally, such as on the Pacific side over the Chukchi Sea. The changing teleconnection patterns strongly modulated AR activities in time and space, with prominent anomalies in the Arctic-Pacific sector during the latest decade. Besides, the extreme events identified by the 95th-percentile labels displayed the most significant changes and were most influenced by the teleconnection patterns. The twelve Arctic AR labels and the detailed graphics in the atlas can help navigate the uncertainty of detecting and quantifying Arctic ARs and their associated effects in current and future studies.

show abstract

In Search of the Optimal Atmospheric River Index for US Precipitation: A Multifactorial Analysis

Cited by 3 publications

References 61 publications

Climatology and Decadal Changes of Arctic Atmospheric Rivers Based on ERA5 and MERRA-2

Climatology and Decadal Changes of Arctic Atmospheric Rivers Based on ERA5 and MERRA-2

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

Climatology and decadal changes of Arctic atmospheric rivers based on ERA5 and MERRA-2

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