Assessing the large-scale drivers of precipitation in the northeastern United States via linear orthogonal decomposition

Sukhdeo, Raymond; Ullrich, P. A.; Grotjahn, Richard

doi:10.1007/s00382-022-06289-y

Cited by 4 publications

(8 citation statements)

References 51 publications

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“…11i) than is observed in cluster 1. Given the importance of southerly IVT along the Atlantic seaboard for providing moisture to this region (Sukhdeo et al 2022), these results provide additional context for the Q850 anomalies discussed below. As the midlevel streamfunction LSMPs develop, they act to divert the normal IVT away from the Northeast, leading to negative Q850 anomalies there during the onset period.…”

Section: Thermodynamical Differences Driving Two Types Of Dry Spellsmentioning

confidence: 74%

“…Extratropical cyclones (ETCs) and their tracks play a key role in the seasonal cycle of precipitation in the Northeast (Kunkel et al 2012;Pfahl and Wernli 2012;Agel et al 2015;Sukhdeo et al 2022). There are generally two broad regions with the highest storm track densities: one over the Great Lakes region and another along the Atlantic seaboard (Hoskins and Hodges 2002;Kocin and Uccellini 2004;Pfahl and Wernli 2012;Agel et al 2015).…”

Section: Changes In Etc Activity and Storm-track Density During Dry S...mentioning

confidence: 99%

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Two Large-Scale Meteorological Patterns are Associated with Short-Duration Dry Spells in the Northeastern United States

Sukhdeo,

Grotjahn,

Ullrich

2023

Monthly Weather Review

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Large-scale meteorological pattern (LSMP)–based analysis is used in a novel way to understand meteorological conditions before and during short-duration dry spells over the northeastern United States. These LSMPs are useful to assess models and select better-performing models for future projections. Dry-spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting 12 days or longer, which correspond to ∼10% of dry-spell events, are examined. The 500-hPa streamfunction anomaly fields for the first 12 days of each event are time averaged, and k-means clustering is applied to isolate the dry-spell-related LSMPs. The first cluster has a strong low pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong high pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold-air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm tracks are generally shifted southward of the region during the dry spells. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but different remote, properties. Although dry spells occur with greater frequency during drought months, most dry spells occur during nondrought months. Significance Statement This study examines the large-scale weather patterns and meteorological conditions associated with dry-spell events lasting at least 2 weeks while affecting the northeastern United States. A statistical approach groups events together on the basis of similar atmospheric features. We find two distinct sets of patterns that we call large-scale meteorological patterns. These patterns reduce moisture, foster localized sinking, and shift the storm track southward along the Atlantic seaboard, all of which reduce precipitation. Besides greater understanding, knowing the meteorological patterns during short-term dryness in the region provides an important tool to assess how well atmospheric models reproduce these specific patterns. More dry spells occur in nondrought months than in drought months, which means that dry spells can occur without preexisting drought conditions.

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Section: Thermodynamical Differences Driving Two Types Of Dry Spellsmentioning

confidence: 74%

Section: Changes In Etc Activity and Storm-track Density During Dry S...mentioning

confidence: 99%

Two Large-Scale Meteorological Patterns are Associated with Short-Duration Dry Spells in the Northeastern United States

Sukhdeo,

Grotjahn,

Ullrich

2023

Monthly Weather Review

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“…Additionally, given the modest PV200 anomalies for the unclassified EPE with a lower precipitation rate, we see chances to incorporate quantitative analysis between atmospheric drivers and precipitation rates. Some quantitative analysis, like Sukhdeo et al (2022), could be used to quantify the predictability. Overall, the work presented here aims to better quantify the relative importance of meteorological drivers to EPEs in different monsoonal subregions.…”

Section: Discussionmentioning

confidence: 99%

Meteorological Drivers of North American Monsoon Extreme Precipitation Events

Duan

Ullrich

Boos

2022

Preprint

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In this paper the meteorological drivers of North American Monsoon (NAM) extreme precipitation events (EPEs) are identified and analyzed. First, the NAM area and its subregions are distinguished using self-organizing maps (SOM) applied to the Climate Prediction Center (CPC) global precipitation dataset. This delineation emphasizes the distinct extreme precipitation character and drivers in each subregion, and we subsequently argue these subregions are more suitable for regional analysis given the inhomogeneous geographical features in the NAM area. For each EPE, defined as daily precipitation exceeding the 95th precipitation percentile, five synoptic features and one mesoscale feature are investigated and assigned as potential drivers. Essentially all EPEs can be associated with at least one selected driver, with only one event remaining as unclassified. The attribution result demonstrates the dominant role of Gulf of California moisture surges, followed by mesoscale convective systems. Finally, a frequency and probability analysis is conducted to contrast precipitation distributions conditioned on the associated meteorological drivers. Interactions and influences among candidate features are revealed by the precipitation probability density functions.

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“…Similarly, Sukhdeo et al. (2022) applied linear orthogonal decomposition to identify the large‐scale atmospheric drivers of precipitation over the NEUS. While they did tie ETC activity into their analysis, the focus is primarily on the dynamics behind monthly precipitation variability.…”

Section: Introductionmentioning

confidence: 99%

“…They found that extreme precipitation days were typically accompanied by a collocated deep upper-level trough and pronounced ridge downstream. Similarly, Sukhdeo et al (2022) applied linear orthogonal decomposition to identify the large-scale atmospheric drivers of precipitation over the NEUS. While they did tie ETC activity into their analysis, the focus is primarily on the dynamics behind monthly precipitation variability.…”

mentioning

confidence: 99%

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Gore,

Zarzycki,

Gervais

2023

Earth's Future

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Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

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Assessing the large-scale drivers of precipitation in the northeastern United States via linear orthogonal decomposition

Cited by 4 publications

References 51 publications

Two Large-Scale Meteorological Patterns are Associated with Short-Duration Dry Spells in the Northeastern United States

Two Large-Scale Meteorological Patterns are Associated with Short-Duration Dry Spells in the Northeastern United States

Meteorological Drivers of North American Monsoon Extreme Precipitation Events

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

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