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William M Putman

Publications and source records attributed to William M Putman.

4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Cascading toward a Kilometer‐scale GCM: Impacts of a Scale‐aware Convection Parameterization in the Goddard Earth Observing System GCM.

The NASA Goddard Earth Observing System global model is evaluated through a cascade of simulations with increasing horizontal resolution. This model employs a non-hydrostatic dynamical core and includes a scale-aware, deep convection parameterization (DPCP). 40-daysimulations at six resolutions (100km to 3km) with unvarying model formulation were produced. At the highest resolution, extreme experiments were carried out; one with no DPCP, and one with its scale-awareness eliminated. Simulated precipitation, radiative balance, and atmospheric thermodynamic and dynamical variables are well reproduced with respect to both observational and reanalysis data. As model resolution increases, the convective precipitation smoothly transitions from being mostly produced by the convection parameterization to the cloud microphysics parameterization. However, contrary to current thought, these extreme cases argue for maintaining, to some extent, the scale-aware deep convection parameterization even at 3kmscale, as the run relying solely on explicit grid-scale production of rainfall performs more poorly at this resolution.

Saulo Freitas↗
NASA NTRS2019

DYAMOND: The DYnamics of the Atmospheric General Circulation Modeled on Non-Hydrostatic Domains

A review of the experimental protocol and motivation for DYAMOND, the first intercomparison project of global storm-resolving models, is presented. Nine models submitted simulation output for a 40-day (1 August–10 September 2016) intercomparison period. Eight of these employed a tiling of the sphere that was uniformly less than 5 km. By resolving the transient dynamics of convective storms in the tropics, global storm-resolving models remove the need to parameterize tropical deep convection, providing a fundamentally more sound representation of the climate system and a more natural link to commensurately high-resolution data from satellite-borne sensors. The models and some basic characteristics of their output are described in more detail, as is the availability and planned use of this output for future scientific study. Tropically and zonally averaged energy budgets, precipitable water distributions, and precipitation from the model ensemble are evaluated, as is their representation of tropical cyclones and the predictability of column water vapor, the latter being important for tropical weather.

Bjorn Stevens↗
NASA NTRS2016

A Global Perspective of Atmospheric CO2 Concentrations

Carbon dioxide (CO2) is the most important greenhouse gas affected by human activity. About half of the CO2 emitted from fossil fuel combustion remains in the atmosphere, contributing to rising temperatures, while the other half is absorbed by natural land and ocean carbon reservoirs. Despite the importance of CO2, many questions remain regarding the processes that control these fluxes and how they may change in response to a changing climate. The Orbiting Carbon Observatory-2 (OCO-2), launched on July 2, 2014, is NASA's first satellite mission designed to provide the global view of atmospheric CO2 needed to better understand both human emissions and natural fluxes. This visualization shows how column CO2 mixing ratio, the quantity observed by OCO-2, varies throughout the year. By observing spatial and temporal gradients in CO2 like those shown, OCO-2 data will improve our understanding of carbon flux estimates. But, CO2 observations can't do that alone. This visualization also shows that column CO2 mixing ratios are strongly affected by large-scale weather systems. In order to fully understand carbon flux processes, OCO-2 observations and atmospheric models will work closely together to determine when and where observed CO2 came from. Together, the combination of high-resolution data and models will guide climate models towards more reliable predictions of future conditions.

CO2↗