A constellation of NASA satellites orbiting Earth enables us to estimate air pollution in locations where there are no monitors. This presentation will discuss new satellite instruments and algorithm improvements that greatly enhance our ability to monitor air pollution from space.
Founded as part of the Manhattan Project, the Hanford Site has played a vital role in the Nation's nuclear weapons and energy production missions. Today, Hanford poses one of the most technically, financially, and socioeconomically difficult cleanup challenges in the world. This presentation will provide a brief history and overview of current and future Hanford remediation and waste treatment efforts, and highlight unique scientific and technical challenges therein.
Darwin (on the coast of northern Australia) sits in a mixed Monsoon/Equatorial region. This leads to clearly defined forcing regimes, perfect for testing links between large-scale forcing and atmospheric response. The C-band dual-polarization CPOL radar collected data from 1998 to 2017 providing a dataset where the underlying statistics of convection can be examined.
Math and soils may seem worlds apart. However, mathematical models and computer simulations are a critical tool to help understand our changing world. Soil carbon dynamics are a significant source of natural carbon dioxide (a major driver of climate change) and are expected to increase as temperatures warm. By blending math with soil science, we try to peer into the future to anticipate how human-emitted carbon dioxide will affect our crops and cities.
In this presentation, professor Berhe will discuss why and how soil erosion can constitute a C sink by: (a) defining the criterion necessary for erosion to constitute a C sink; (b) comparing the rates of soil organic matter decomposition at eroding and depositional settings; and (c) identifying the potential for soils to provide protective surfaces (physical and chemical stabilization mechanisms) for soil organic matter in dynamic landscapes.
The complexity of a multivariate self-organized system results from the nonlinear interactions and feedbacks among the components. Such complexity results in a variety of attributes such as stranger attractor and 1/f fractal behavior. How does the evolutionary dynamics involving multiple variables sustain self-organization?
This presentation will analyze the evidence on behavioral drivers of farmer decisions to produce perennial energy crops and its implications for the price of biomass and the spatial pattern of production of these energy crops in the rainfed region of the U.S. This seminar will also examine the efficacy of various market and policy incentives needed to induce production of these crops.
This seminar will present observational and modeling evidence of interactions between convective clouds, soil moisture, and vegetation, and their impacts on agricultural drought and heat-extremes. This presentation will also discuss single-column and global climate model experiments that explore multiple pathways by which soil moisture and vegetation can affect climate. The results point to sensitivities of simulated clouds and climate to model representation of surface water and energy fluxes that (in the presence of feedbacks) can yield large biases in climate prediction.
In this seminar, after reviewing regional climate projections and their problems related to surface‑atmosphere interactions, Professor Fochesatto will describe observational platforms and newly developed methodologies that would enable connecting surface turbulent fluxes with atmospheric boundary layer parameters. He will discuss multiscale turbulent fluxes experiments carried out in the most vulnerable ecosystems on earth. And, he will introduce new empirical approximations of multiscale turbulent fluxes. To conclude, Prof. Fochesatto will share his research strategy for the next generation of experiments integrating observations and modeling approaches.
Low stratiform clouds in the tropics and subtropics are a substantial cooling term in the global climate system but remain a stubborn challenge for Earth System Models (ESMs). Low clouds are represented in ESMs using parameterizations that are often based on output from high-resolution process models. But how reliable are the cloud properties and processes produced by these models? Process models are typically configured using forcing that may exhibit substantial uncertainty and be only loosely constrained by observations.