Global climate models (GCMs) predict warming in response to increasing greenhouse gases, partly due to decreased tropical low-level cloud cover and reflectance. We use satellite observations that discriminate stratocumulus (Sc) from shallow cumulus (Cu) clouds to separately evaluate their sensitivity to warming and constrain the tropical contribution to low-cloud feedback. We find an observationally inferred low-level feedback two times smaller than a previous estimate.
Much of climate science is viewed as a signal-to-noise problem and the field has many statistical methods for extracting the signal of interest. Here, we argue that artificial neural networks (ANNs) are an additional useful tool for the “climate toolbox”.
This presentation will cover ongoing research on marine cold air outbreaks (CAOs). This research is part of the multi-year NASA EVS-3 field campaign ACTIVATE that is devoting more than half its resources to a process-study focus on CAOs during wintertime and shoulder seasons off the mid-Atlantic coast. CAOs drastically affect the local energy budget by forming a (nearly) overcast deck consisting of roll-like boundary layer (BL) clouds that typically transition into a broken, open-cellular cloud field downwind. State-of-the-art earth system and weather forecast models struggle to faithfully represent CAOs and their radiative effects.
Kathleen Schiro is an Assistant Professor in the Department of Environmental Sciences at the University of Virginia. Prof. Schiro earned her B.A. in Earth and Planetary Science at Johns Hopkins University in 2011 and her PhD in Atmospheric and Oceanic Sciences at the University of California, Los Angeles in 2017. Prior to joining the faculty at UVA, Prof. Schiro was a postdoctoral scholar at the Jet Propulsion Laboratory, California Institute of Technology. As an atmospheric scientist, she specializes in studying clouds, convection, and precipitation across scales in the tropics using field campaign data, satellite observations, and climate models.
The 2017 Decadal Survey recommended the planetary boundary layer (PBL) as a high priority incubation measurement. To support agency efforts in PBL mission planning, the Global Modeling and Assimilation Office (GMAO) is increasing its focus on data assimilation and physical parameterization of the boundary layer in the GEOS modeling system.
The title for my talk is taken from the 2009 National Academy Committee report “Observing the Weather and Climate from the Ground Up: A Nationwide Network of Networks” that made the case for a thermodynamic profiling of the immediate atmospheric layer (Planetary Boundary layer or PBL) we influence.
At least seven major wildfires were burning across California as early as ~10:30 AM local time on 20 August 2020.
This talk will begin with an overview of recent development in the NASA GISS General Circulation Model (GISS-E3 GCM, currently about to be submitted to CMIP6).
An accurate assessment of both how Earth’s climate is changing and which physical processes and feedbacks are driving those changes requires highly accurate and stable measurements, stable retrieval algorithms, and measurements with sufficient information content for climate change detection and attribution.
This presentation will cover multiple aspects of my research on Earth's energy balance and planetary heat uptake. We will discuss different approaches to estimate Earth’s energy imbalance (EEI) including the assessment of the contemporary sea level budget using Argo, altimetry and GRACE/GRACE-FO observations.
Aerosols continue to be responsible for the largest uncertainty in determining the anthropogenic radiative forcing of the climate. To both reconcile the large range in satellite-based estimates of the aerosol direct radiative effect (DRE, the direct interaction with solar radiation by all aerosols) and to optimize the design of future observing systems, we build a framework for assessing uncertainty in aerosol DRE and the aerosol direct radiative forcing (DRF, the radiative effect of just anthropogenic aerosols, RF_ari).
Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging, it is extensively used for diagnostics via X-ray computed tomography (CT). Optical diffusion tomography is an alternative to X-ray CT that uses multiple scattered light to deliver coarse density maps for soft tissues.