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

McClenny, Elizabeth; Ullrich, P. A.; Grotjahn, Richard

doi:10.1029/2020jd033421

Cited by 25 publications

(22 citation statements)

References 70 publications

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“…This module enabled cyclonic storms to be examined using the thermal wind and thermal asymmetry phase space of Hart (2003). ExTraTrack was later applied to a suite of highresolution global simulations in Michaelis and Lackmann (2019) and Michaelis and Lackmann (2021). ETCs were also tracked in the Community Earth System Model Large Ensemble (CESM-LENS) in Zarzycki (2018) to understand the drivers responsible for snowstorms in the US northeast.…”

Section: Examples From the Existing Literaturementioning

confidence: 99%

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

et al. 2021

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Abstract. TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear or unstructured/native grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal (i.e., pointwise) and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, including algorithms for editing pointwise trajectory files, composition of fields around nodal features, generation of areal masks via thresholding and nodal features, and tracking of areal features in time. Several examples are provided of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes. These examples include analyzing the fraction of precipitation from tropical cyclones, compositing meteorological fields around extratropical cyclones, calculating fractional contribution to poleward vapor transport from atmospheric rivers, and building a climatology of atmospheric blocks.

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Section: Examples From the Existing Literaturementioning

confidence: 99%

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

et al. 2021

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“…To assess the quality of ARs in the SCREAM simulation, we track ARs over the simulation period using the TempestExtremes atmospheric river detection and tracking algorithm (McClenny et al, 2020;Ullrich & Zarzycki, 2017) as described in Appendix A3. In Figure 24 the properties of these tracked features are then compared to analogously tracked features from all January 20 through Mar 28 periods in ERA5 data , roughly following the approach discussed in Rutz et al (2019).…”

Section: Atmospheric Riversmentioning

confidence: 99%

“…Atmospheric river tracking is performed using the tracker employed in McClenny et al (2020). Grid points poleward of 15° N/S are flagged where the Laplacian of the integrated vapor transport (evaluated using eight points with radius 10° GCD) is less than 20,000 kg m −1 s −1 rad −2 .…”

Section: A3 Atmospheric Riversmentioning

confidence: 99%

Convection‐Permitting Simulations With the E3SM Global Atmosphere Model

Caldwell

Terai

Hillman

et al. 2021

J Adv Model Earth Syst

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This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40‐day prescribed‐sea‐surface‐temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long‐standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress.

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“…In this section we identify ARs in ERA5 reanalysis using the Tempest AR detection algorithm (Shields et al, 2018;Rhoades et al, 2020b, a;McClenny et al, 2020), and extract the poleward vapor transport in order to verify this claim.…”

Section: Atmospheric Riversmentioning

confidence: 99%

“…This choice was made to address with issues of stationarity generally present in trackers using an IVT threshold. TE's algorithm has since been used both for AR detection and tracking (with DetectBlobs and StitchBlobs) inRhoades et al (2020b),Rhoades et al (2020a),Patricola et al (2020), andMcClenny et al (2020).…”

mentioning

confidence: 99%

TempestExtremes v2.1: A Community Framework for Feature Detection, Tracking and Analysis in Large Datasets

Ullrich¹,

Zarzycki²,

McClenny³

et al. 2021

Preprint

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Abstract. TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth-system datasets on either structured and unstructured (native) grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, and gives several examples of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes.

show abstract

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

Cited by 25 publications

References 70 publications

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

Convection‐Permitting Simulations With the E3SM Global Atmosphere Model

TempestExtremes v2.1: A Community Framework for Feature Detection, Tracking and Analysis in Large Datasets

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