Gijs de Boer, Ph.D. - US grants

The project will utilize results from several Arctic measurement sites, including long-term observations at Barrow, Alaska, and shorter-term observations from Summit, Greenland, and two measurement campaigns over the Arctic Ocean to improve upon previous estimates of Arctic aerosol-cloud interactions. To do this a critical first step will involve understanding the conditions under which surface aerosol conditions, as generally used in the compilation of statistical datasets addressing this issue, are comparable to those at cloud level. Using a variety of techniques, the distinction between surface-coupled and decoupled cloud states will focus evaluation of aerosol-cloud interactions on appropriate cases, reducing statistical contamination. From these cases, a statistical evaluation will be performed to reveal co-variation between key cloud (e.g. liquid water path, ice water path) and aerosol (e.g. concentration, size, hygroscopicity) properties. Combined with surface radiation measurements and idealized radiative transfer simulations, the results from this evaluation will provide an improved estimate of the net radiative impact of aerosols on Arctic clouds. Additionally, it will provide an overview of the seasonal evolution and surface-state dependence of these relationships. Outreach plans include several visits to Arctic regions, with a focus on the North Slope of Alaska and Greenland. The primary aim of these visits is to disseminate information relevant to the proposed research to seminar audiences consisting of local people (including underrepresented native people), primary school students, and local officials. A secondary aim of these visits is to give Arctic researchers (particularly early career researchers) who have never had a chance to visit the Arctic themselves an opportunity to see firsthand the region on which their efforts focus. This project will also support two early career scientists at the University of Colorado.

This study will use an emerging technology, unmanned aircraft systems, to collect measurements with the goal of improving weather and climate models of the Arctic system. It is part of the international MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) program, an extensive field effort to freeze an icebreaker into sea ice for an entire year to serve as a research platform for a comprehensive study of the atmosphere, ocean and ice system in the high Arctic. The unique and potentially transformative aspect of this project is that unmanned aircraft collect data at small spatial and temporal scales, providing new information about variability in temperature, humidity, and winds. In addition, direct measurements of these variables over breaks in the sea ice have been very limited to date. Therefore, this study will address a significant source of error in our current ability to forecast how energy is transferred between the atmosphere and underlying ice and sea surface. Together with information from collaborating scientists participating in the MOSAiC field effort, the investigators will evaluate a series of hypotheses related to the performance of model simulations of key processes over the central Arctic Ocean. The investigators will also give pubic lectures at schools and other venues, capitalizing on interest and excitement in use of new technology though use of videos and photos of the unmanned aircraft systems. They will support training for early career scientists by involving graduate students and postdoctoral scientists.

The investigators will deploy an unmanned aircraft system to measure atmospheric temperature, winds, and humidity, as well as surface albedo. Flights will take place from mid-winter (February) through late summer (August) to capture variable conditions in both the atmosphere and sea ice surface and will include routine profiling of the lower atmosphere, spatial mapping of thermodynamic quantities and surface albedo, and mapping of the lower atmospheric structure over leads. This data will be evaluated with measurements of the atmosphere, ocean and ice collected by other scientists as part of the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) project to address hypotheses related to the performance of modeling tools in simulating key processes over the central Arctic Ocean. These include questions about sub-grid scale variability of atmospheric and surface parameters and its influence on model-simulated surface energy budget; the influence of leads in the sea ice on energy transfer from the ocean to the atmosphere and how models represent this transfer; and the importance of vertical resolution in simulation of the Arctic atmosphere and its impact on the simulation of clouds and the surface energy budget. The investigators will compare observations from unmanned aerial systems to a variety of simulations, ranging from global products to fully-coupled regional simulations completed using the Regional Arctic System Model (RASM) to detailed single-column and 2D modeling at high resolution.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Understanding the temperature structure of the upper ocean in the Arctic is very important for properly simulating the formation and melt of sea ice in climate and weather models. The presence (or absence) is important for a variety of activities, including shipping, energy exploration and hunting by native populations. Therefore, forecasting the presence of ice at shorter timescales is critically needed. Sea ice additionally has a controlling influence on climate by acting as a bright surface capable of reflecting sunlight back to space, thereby highlighting a need to accurately forecast it on decadal time scales. A significant source of errors in forecasts at all scales is the ability to predict to what extent mixing of the upper ocean occurs and how this mixing helps to eliminate gradients in temperature and salinity that might change the rate of ice formation or melt. An important item to understand is to what extent atmospheric winds, which we generally forecast relatively well, contribute to this upper-oceanic mixing through the transfer of energy between the atmosphere and ocean. This project will support the collection of key measurements necessary to help inform the improvement of weather and climate models to support prediction of sea ice at a variety of time scales.

In this study, an unmanned aircraft system will be deployed to provide measurements of atmospheric temperature, winds and humidity. This information will be used together with information from surface buoys and ice imagery to understand atmosphere-ocean energy transfer during the fall freeze-up period. Specifically, this work will help to address questions related to the role of the presence of sea ice in energy transfer and how that role is simulated in numerical models, the extent to which mechanisms supporting transfer vary at small spatial scales and how those are handled in today?s state of the art modeling tools, and the importance of vertical resolution of models in accurately capturing this energy transfer. Measurements will be compared to high-resolution models that couple the atmosphere, ice and ocean together into single simulations. Flights will take place from northern Alaska in September and October of 2018 and will interface with a broader effort (the Stratified Ocean Dynamics of the Arctic, or SODA, project) to understand the upper ocean in this part of the world. Additionally, this work will interface with the ongoing Year of Polar Prediction, providing extra connections to the modeling communities who can benefit from these measurements.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The Planning Grants for Engineering Research Centers competition was run as a pilot solicitation within the ERC program. Planning grants are not required as part of the full ERC competition, but intended to build capacity among teams to plan for convergent, center-scale engineering research.

As many people know from experience, weather forecasting is an imprecise science that relies on averaging results from many different predictions. Thus, forecasts are often inaccurate in the degree of prediction and miss the mark in important details of timing, location, or severity. Not uncommonly, the forecast is just plain wrong with sometimes disastrous consequences. While weather forecasts at the large scale have improved over the last few decades, accurate weather prediction at local scales is within our reach, but not possible without paradigm changes that will transform the weather enterprise and unlock solutions to many of our food, energy, transportation, and climate-triggered weather challenges. If successful, this initiative will have a broad range of benefits to the general public. Consider the following scenarios: Imagine if airports had reliable predictions of when icing will end, if a grower was assured that the wind-born fungus at a nearby farm would not drift into their fields, and if first responders knew of localized wind shifts governing wildfire spread or heavy rain in locations likely to be impacted by flooding. Consider annual gains from accurate local prediction for optimized wind farm production and solar energy output. Picture an aviation warning network for invisible wind shear and clear air turbulence at low altitudes. Imagine highly reliable autonomous air taxi and delivery networks enabled by microscale weather prediction in urban and rural settings. Consider the relief of advance warning for migraine sufferers and others with weather-induced health issues. Think of the possibility to alleviate disparities in severe weather warnings and associated emergency response using a mobile, flexible atmospheric observing system. These and other opportunities yet to be imagined will be possible with Precision Meteorology. Therefore, this team proposes a planning grant to evaluate a proposed Engineering Research Center for Precision Meteorology.

Highly accurate, timely, local-scale weather prediction - Precision Meteorology - will enable solutions to issues that impact a broad array of problems impacting society, including food, energy, and climate related challenges. The key to improving accuracy of microscale meteorological models, and thus local prediction, is to couple increased in situ observations within the lower atmosphere, including the atmospheric boundary layer (ABL), with high-resolution numerical weather prediction (NWP) optimized to utilize these observations. A key enabling technology for increasing ABL observations includes Unmanned Aircraft Systems (UAS); emerging with proven value for sensing in the ABL and beyond, initiating a significant shift in the scientific foundations of meteorological observations. Optimizing the coupling between these measurements and numerical weather prediction to maximize impact requires convergent research among engineering, atmospheric science, data science, and social science. If successful, this center will achieve economic and societal impacts through data-enabled localized weather prediction offering significant lead-time, accuracy, and resolution improvements over current meteorological predictions. Through a comprehensive approach, we believe a Center for Precision Meteorology (CPM) will achieve this vision.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.