Current Visiting Fellows
Assessing extratropical-tropical interactions in the climate system
The deep tropics (10 ̊S-10 ̊N) account for only 17% of the Earth’s surface; however, 32% of globally integrated annual rainfall falls here due to a narrow band of heavy precipitation called the Intertropical Convergence Zone (ITCZ). In addition, atmospheric teleconnections forced by tropical climate modes like the El Niño-Southern Oscillation (ENSO) can have far reaching impacts on the hydrology of extratropical latitudes. The presence of the ITCZ in conjunction with thermally driven atmospheric teleconnections suggests the deep tropics play an outsized role in modulating much of the planet’s regional rainfall. As a result, many studies have examined tropical climate variability and tropical teleconnections to higher latitudes. However, the climate community has recently identified important pathways by which extratropical variability can directly influence the ITCZ and tropical climate modes such as ENSO. My project will utilize Community Earth System Model (CESM) to develop novel modeling techniques to isolate the physical mechanisms that govern these extratropical-tropical connections in an effort to improve their predictability on seasonal to decadal timescales.
State-Specific Emissions and Control of Short-Lived Climate Pollutants
Short-lived climate pollutants (SLCP) contribute as much as 40% to anthropogenic radiative forcing. Bart Croes will collaborate with Steve Montzka to apply California’s SLCP emission inventory methods from 2005 to 2030 for the other 49 States, and identify control paths to meet California’s targets – 40% below 2013 levels by 2030 for hydrofluorocarbons and methane emissions, and 50% below for anthropogenic black carbon. Since the 1970 Clean Air Act, the U.S. improved air quality while the economy grew and vehicle miles traveled increased. Nevertheless, unhealthy PM2.5 and ozone exposures are estimated to cause about 90,000 premature deaths each year, and recent data show a general leveling off of progress since 2013-2015. While several publications have identified climate, global background, and emission control factors as likely contributors, a collaboration with Joost de Gouw will synthesize more recent emission models with satellite data to explore additional hypotheses, and apply environmental justice screening techniques demonstrated in California to national air quality trends.
Using observations of a “natural” experiment to constrain the sensitivity of clouds to pollution and global warming in climate models
Summary: Clouds confound our understanding of how human activities are changing our climate system. The cooling effect of interactions between small airborne pollution particles (aerosol) and clouds has “masked” some of the warming from increasing greenhouse gas concentrations. Unknowns about how much such masking has already occurred and about how clouds will respond to warming make it difficult to estimate future climate changes. A major factor that complicates the study of aerosol-cloud interactions is the challenge of disentangling aerosol effects on cloud properties from other weather-related changes. "Natural experiments," in which there is a known aerosol change that is independent of the weather, offer a promising framework for improving our understanding of causation in aerosol-cloud relationships. Michael Diamond will work with Graham Feingold and Jen Kay to study one such natural experiment: a highly-trafficked shipping corridor in the southeast Atlantic in which cloud brightening from pollution effects has been observed. I will use a hierarchy of models to determine whether biases in simulated cloud changes are related to aerosol effects on how much clouds precipitate or how much dry air is mixed into the cloudy layer by turbulent motions. These processes are key to understanding how clouds respond to both pollution and global warming.
Turbulent fluxes in the atmospheric boundary layer over Arctic sea-ice during MOSAiC
Ulrike will work with John Cassano and Matt Shupe on analyzing turbulent energy fluxes in the Arctic cloudy atmospheric boundary layer (ABL). Turbulent fluxes constitute a major part of the atmospheric energy budget and influence the surface heat balance by distributing energy vertically in the atmosphere. However, only few in-situ measurements exist of the vertical profile of turbulent fluxes in the Arctic ABL, especially under cloudy conditions. The project is based on measurements with an uncrewed aerial vehicle (UAV) during the field campaign MOSAiC in the central Arctic ocean during the sea-ice melt season 2020. First, a suitable method will be elaborated to derive turbulent fluxes from the UAV turbulence measurements. Second, the resulting vertical profiles of turbulent fluxes will be analyzed concerning different ABL and sea-ice conditions, including the influence of atmospheric stability, stratification, clouds, leads, and melt ponds. This study will help to advance our understanding of how the turbulent fluxes influence the surface energy budget and interact with the melting sea ice.
Automatically quantifying the development of retrogressive thaw slumps in northern Alaska
Lingcao Huang will work with Kevin Schaefer, Kristy Tiampo, and Michael Willis on a project using machine learning to automatically quantify the development of retrogressive thaw slumps (RTS) whose formation is due to the thawing the ice-rich permafrost. Retrogressive thaw slumps are the most dynamic landforms in cold regions and in which thaw of ice-rich permafrost on slopes causes mass-wasting of soil and vegetation. As reported by many local studies, their number and affected areas have increased dramatically in recent decades. However, their spatial distribution as well as development are poorly quantified and understood because they are widespread but localized features. By applying machine learning technology, especially, deep learning, Lingcao will develop a method to delineate and quantify RTS occurrence and dynamics in northern Alaska. The objectives are to provide a tool to monitor RTS dynamics in larger areas and also advance the understanding of permafrost degradation in northern Alaska related to external controlling factors such as climatic variables.
Drivers of Pacific Decadal Variability and its changing impacts in a warming world
Nicola Maher will work with Jen Kay and Antonietta Capotondi to investigate what drives Pacific Decadal Variability and how this variability and its impacts are projected to change under varying levels of global warming. The far reaching impacts of Pacific Decadal Variability mean that it is highly relevant under a changing climate to investigate these questions. Previously it was difficult to investigate the decadal variability under a changing climate due to the low sampling of decadal modes in both climate models and observations. By using a new set of large-ensembles, where there is enough data to investigate the decadal variability and by combining new deep learning techniques with other more traditional analyses this research project aims to further the understanding of the mechanisms that drive IPO phase transitions, and how extreme events are modulated by the IPO around the globe under different levels of global warming.
Using Distributed Acoustic Sensing to Monitor Induced Seismicity and Evaluate Seismic Hazard in Paradox Valley, Colorado
Deep injection of wastewater fluid associated with oil and gas production is known to be responsible for the triggering of earthquakes near dense population centers. This practice increases the seismic hazard for regions that are typically devoid of earthquakes and poses a risk to buildings and critical structures where appropriate safety measures are deficient. To address this, I will work with Anne Sheehan and Ge Jin (CSM) in utilizing a novel seismological technique known as distributed acoustic sensing (DAS), in Paradox Valley, Colorado, to improve our understanding of induced seismicity where conventional seismic networks may be insufficient or absent. DAS operates by using an opto-electronic system that emits pulses of light down a fiber optic cable and records naturally backscattered energy returning from varying segments. In essence, this instrument acts as a dense and mechanically flexible seismic array that consists of multitudinous broadband sensors sensitive enough to detect tiny subsurface strain events in high fidelity. We aim to exploit the capabilities of DAS to identify key features missed by the surrounding (yet sparse) network of seismometers in Paradox Valley; features that can offer more detailed and accurate information on the evolution of faults and stress transfer mechanisms that drive the induced seismicity phenomenon.
Interactions between tectonics, erosion, climate during Late Cenozoic global cooling and Northern Hemisphere glaciation
Yani Najman will work with Peter Molnar to understand interactions between tectonics, erosion and climate during the onset of Late Cenozoic global cooling and Northern Hemisphere glaciation. The degree of interaction between these entities is debated, in part because the records are incomplete and difficult to disentangle. For example, is the Late Cenozoic proposed increase in global erosion rates the result of a change to a cooler, less equable, perhaps stormier climate, or the result of mountain ranges rising, or simply the result of an incomplete record of sediment accumulation? Moreover, the feedbacks between these variables are multifaceted: erosion affects climate both by increasing silicate weathering which causes drawdown of atmospheric CO2, and because higher sedimentation rates result in faster burial of organic carbon; growth of mountain belts impacts climate by interfering with atmospheric circulation patterns; and the influence of isostasy moderates these variables. Understanding the complex interlinkages and feedbacks requires a knowledge of the precise timing of events. This project will undertake a new approach to interrogating these records, utilising a compilation of low temperature thermochronology data to seek to determine the level of temporal correlation between such events.
Habitat assessment for snow-dependent species using climate change analogues and snow reanalyses in the western United States
Snow volume and spatial distribution are vital components of ecosystem health in mountainous terrain. However, our representations of snowpack at fine spatial scales, and its connections with snow-dependent species like wolverine and bull trout, are currently lacking. This is particularly true for expected changes in future climates, making our assessments of climate-driven threats to many wildlife species difficult to quantify. In this project, Justin Pflug, and CIRES sponsors Ben Livneh and Jennifer Balch, are investigating climate-driven impacts on mountainous snowpack and wildlife habitat. Using multidecadal snow reanalyses, which estimate snow volume and spatial distribution using historic satellite-based and ground-based observations, they seek to understand the connections between meteorological conditions and the resulting snow connectivity and streamflow. This information is crucial for our understanding of how historic periods with abnormal meteorological conditions could be applied to simulate snow in future years with analogous conditions. The results in this project will be used to inform how future species status assessments should be performed in many snow-covered mountainous landscapes.
Leveraging multi-mission satellites to assess the impacts of recent storage changes in global lakes and reservoirs on sea level change and land surface modeling
Fangfang Yao is working with Ben Livneh and Balaji Rajagopalan to improve the understanding of recent global changes in surface water storage. Surface water provides easily accessible water resources for human beings. Spatial and temporal variations of surface water storage not only affect local water supply, but also have implications for the hydrological cycle. Despite its importance, changes in open surface water (especially lakes and reservoirs) are not simulated by existing land surface models due to the challenges of modeling human impacts including damming and water withdrawal. Partially owing to this, land surface models have large uncertainties in estimating both seasonality and long-term trends of terrestrial water storage (TWS; the summation of surface water, soil moisture, groundwater, snow and ice). In this context, Fangfang proposes to investigate global surface water storage dynamics and the impacts on the hydrological cycle using a combination of satellite observations and land surface models. He aims to answer two science questions: i) how global open-surface water has contributed to the recent sea level change, and ii) how an improved observation of open-surface water changes can further benefit the modeling of other terrestrial water components (such as soil moisture and groundwater). These questions are essential to the understanding of terrestrial water cycle and the closure of global sea level budget.