Publications

Roesler, Erika L.

Abstract not provided.

Bosler, Peter A.; Bova, S.W.; Demeshko, Irina P.; Fike, Jeffrey A.; Guba, Oksana G.; Overfelt, James R.; Roesler, Erika L.; Salinger, Andrew G.; Smith, Thomas M.; Kalashnikova, Irina; Watkins, Jerry E.

The Next Generation Global Atmosphere Model LDRD project developed a suite of atmosphere models: a shallow water model, an x - z hydrostatic model, and a 3D hydrostatic model, by using Albany, a finite element code. Albany provides access to a large suite of leading-edge Sandia high- performance computing technologies enabled by Trilinos, Dakota, and Sierra. The next-generation capabilities most relevant to a global atmosphere model are performance portability and embedded uncertainty quantification (UQ). Performance portability is the capability for a single code base to run efficiently on diverse set of advanced computing architectures, such as multi-core threading or GPUs. Embedded UQ refers to simulation algorithms that have been modified to aid in the quantifying of uncertainties. In our case, this means running multiple samples for an ensemble concurrently, and reaping certain performance benefits. We demonstrate the effectiveness of these approaches here as a prelude to introducing them into ACME.

Bambha, Ray B.; LaFranchi, Brian L.; Schrader, Paul E.; Roesler, Erika L.; Taylor, Mark A.; Lucero, Daniel A.; Ivey, Mark D.; Michelsen, Hope A.

Concern over Arctic methane (CH 4 ) emissions has increased following recent discoveries of poorly understood sources and predictions that methane emissions from known sources will grow as Arctic temperatures increase. New efforts are required to detect increases and explain sources without being confounded by the multiple sources. Methods for distinguishing different sources are critical. We conducted measurements of atmospheric methane and source tracers and performed baseline global atmospheric modeling to begin assessing the climate impact of changes in atmospheric methane. The goal of this project was to address uncertainties in Arctic methane sources and their potential impact on climate by (1) deploying newly developed trace-gas analyzers for measurements of methane, methane isotopologues, ethane, and other tracers of methane sources in the Barrow, AK, (2) characterizing methane sources using high-resolution atmospheric chemical transport models and tracer measurements, and (3) modeling Arctic climate using the state-of-the-art high- resolution Spectral Element Community Atmosphere Model (CAM-SE).

Roesler, Erika L.

Abstract not provided.

Geoscientific Model Development

Bosler, Peter A.; Roesler, Erika L.; Taylor, Mark A.; Mundt, Miranda R.

This article discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared: the commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. The Stride Search algorithm is defined independently of the spatial discretization associated with a particular data set. Results from the two algorithms are compared for the application of tropical cyclone detection, and shown to produce similar results for the same set of storm identification criteria. Differences between the two algorithms arise for some storms due to their different definition of search regions in physical space. The physical space associated with each Stride Search region is constant, regardless of data resolution or latitude, and Stride Search is therefore capable of searching all regions of the globe in the same manner. Stride Search's ability to search high latitudes is demonstrated for the case of polar low detection. Wall clock time required for Stride Search is shown to be smaller than a grid point search of the same data, and the relative speed up associated with Stride Search increases as resolution increases.

Dexheimer, Darielle D.; Bendure, Albert O.; Hardesty, Jasper O.; Ivey, Mark D.; Helsel, Frederick M.; Roesler, Erika L.

Abstract not provided.

Dexheimer, Darielle D.; Roesler, Erika L.; Bendure, Albert O.; Hardesty, Jasper O.; Helsel, Frederick M.; Ivey, Mark D.

Abstract not provided.

Helsel, Frederick M.; Dexheimer, Darielle D.; Hardesty, Jasper O.; Ivey, Mark D.; Lucero, Daniel A.; Roesler, Erika L.

Abstract not provided.

Roesler, Erika L.; Bosler, Peter A.; Taylor, Mark A.

The Department of Energy’s (DOE) Biological and Environmental Research project, “Water Cycle and Climate Extremes Modeling” is improving our understanding and modeling of regional details of the Earth’s water cycle. Sandia is using high resolution model behavior to investigate storms in the Arctic.

Helsel, Frederick M.; Zirzow, Jeffrey A.; Lucero, Daniel A.; Cook, Robert N.; Yellowhorse, Larry Y.; Desilets, Darin M.; Sparks, Valerie S.; Roesler, Erika L.

Abstract not provided.

Roesler, Erika L.; Taylor, Mark A.; Boslough, Mark B.; Bosler, Peter A.

Abstract not provided.

Michelsen, Hope A.; Bambha, Ray B.; Liu, Zhen L.; Schrader, Paul E.; Ivey, Mark D.; Roesler, Erika L.; Taylor, Mark A.; LaFranchi, Brian L.; Helsel, Frederick M.; Najm, H.N.; Sargsyan, Khachik S.; Safta, Cosmin S.

Abstract not provided.

Michelsen, Hope A.; Schrader, Paul E.; Roesler, Erika L.; Taylor, Mark A.; Ivey, Mark D.

Abstract not provided.