Atmospheric & Oceanic Science Research Works

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http://hdl.handle.net/1903/1596

Formerly known as the Department of Meteorology.

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Now showing 1 - 5 of 14
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    Oceanic Electrical Conductivity Variability From Observations and Its Budget From an Ocean State Estimate
    (Wiley, 2022-09-12) Trossman, D. S.; Tyler, R. H.
    Because spatio-temporal variations in ocean heat content (OHC) are strongly predicted by ocean conductivity content (OCC) over most of the global ocean, we analyze the dynamical budget and behavior of the electrical conductivity of seawater. To perform these analyses, we use an ocean-model state estimate designed to accurately represent long-term variations in ocean properties in a dynamically and kinematically consistent way. We show that this model accurately reproduces the spatio-temporal variations in electrical conductivity seen in satellite-derived data and in a seasonal climatology product derived from in-situ data, justifying use of the model data to perform further analyses. An empirical orthogonal function analysis suggests that the vast majority of the variance in OHC and OCC can be explained by similar mechanisms. The electrical conductivity budget's most important term is the temperature forcing tendency term, suggesting that ocean heat uptake is the mechanism responsible for the strong relationship between OCC and OHC.
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    A Century of Observed Temperature Change in the Indian Ocean
    (Wiley, 2022-06-25) Wenegrat, J. O.; Bonanno, E.; Rack, U.; Gebbie, G.
    The Indian Ocean is warming rapidly, with widespread effects on regional weather and global climate. Sea-surface temperature records indicate this warming trend extends back to the beginning of the 20th century, however the lack of a similarly long instrumental record of interior ocean temperatures leaves uncertainty around the subsurface trends. Here we utilize unique temperature observations from three historical German oceanographic expeditions of the late 19th and early 20th centuries: SMS Gazelle (1874–1876), Valdivia (1898–1899), and SMS Planet (1906–1907). These observations reveal a mean 20th century ocean warming that extends over the upper 750 m, and a spatial pattern of subsurface warming and cooling consistent with a 1°–2° southward shift of the southern subtropical gyre. These interior changes occurred largely over the last half of the 20th century, providing observational evidence for the acceleration of a multidecadal trend in subsurface Indian Ocean temperature.
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    Regularization and tempering for a moment-matching localized particle filter
    (Wiley, 2022-05-31) Poterjoy, Jonathan
    Iterative ensemble filters and smoothers are now commonly used for geophysical models. Some of these methods rely on a factorization of the observation likelihood function to sample from a posterior density through a set of “tempered” transitions to ensemble members. For Gaussian-based data assimilation methods, tangent linear versions of nonlinear operators can be relinearized between iterations, thus leading to a solution that is less biased than a single-step approach. This study adopts similar iterative strategies for a localized particle filter (PF) that relies on the estimation of moments to adjust unobserved variables based on importance weights. This approach builds off a “regularization” of the local PF, which forces weights to be more uniform through heuristic means. The regularization then leads to an adaptive tempering, which can also be combined with filter updates from parametric methods, such as ensemble Kalman filters. The role of iterations is analyzed by deriving the localized posterior probability density assumed by current local PF formulations and then examining how single-step and tempered PFs sample from this density. From experiments performed with a low-dimensional nonlinear system, the iterative and hybrid strategies show the largest benefits in observation-sparse regimes, where only a few particles contain high likelihoods and prior errors are non-Gaussian. This regime mimics specific applications in numerical weather prediction, where small ensemble sizes, unresolved model error, and highly nonlinear dynamics lead to prior uncertainty that is larger than measurement uncertainty.
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    Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System
    (Wiley, 2022-03-14) Lahmers, Timothy M.; Kumar, Sujay V.; Rosen, Daniel; Dugger, Aubrey; Gochis, David J.; Santanello, Joseph A.; Gangodagamage, Chandana; Dunlap, Rocky
    The NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.
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    A CloudSat and CALIPSO-based evaluation of the effects of thermodynamic instability and aerosol loading on Amazon Basin deep convection and lightning
    (2023-08-14) Allen, Dale
    The Amazon Basin, which plays an important role in the carbon and water cycle, is under stress due to changes in climate, agricultural practices, and deforestation. The Basin includes a rainforest in the northwest and a mix of deforested areas, savannah-type vegetation, and agriculture in the southeast. The effects of instability and aerosol loading on thunderstorms in the Basin (75-45° W, 0-15° S) were examined during mid-August through mid-December, a period with large variations in aerosols, intense convective storms, and plentiful flashes. The analysis used measurements of radar reflectivity, ice water content (IWC), and aerosol type from instruments aboard the CloudSat and CALIPSO satellites, flash rates from the ground-based STARNET network, and aerosol optical depth (AOD) from a surface network and a meteorological re-analysis. After controlling for convective available potential energy (CAPE), a measure of instability, it was found that thunderstorms that developed under dirty (high-AOD) conditions were approximately 1.5 km deeper, had 50% more IWC, and more than two times as many flashes as storms that developed under clean (low-AOD) conditions. Flash rates were also found to be larger during periods when smoke rather than dust was common in the lower troposphere, likely because these periods were less stable.