The Pacific Northwest (PNW) saw an unprecedented heatwave between June 25 to July 3 of 2021, with temperatures reaching up to 15℃ above the climatological mean. Previous research has examined the impact of this event on plants in Western Washington and Oregon through observational studies, and has focused on the economic implications for poor crop turnout. We used remote sensing data to take a top-down approach and examined how all plants throughout the PNW fared during and after this historical heatwave. Solar induced fluorescence (SIF) and Near-Infrared Reflectance of vegetation (NIRv) are two remotely sensed products that have been used to estimate plant health and gross primary productivity (GPP). SIF is more closely connected to plant processes like photosynthesis but has a short record (2018-2021) compared to VIIRS NIRv (2012-2021). We compared the responses of SIF to NIRv and found that both vegetation indices increased in trees and woody savannas, but decreased in grasslands and crops. However, SIF showed more intense and geographically larger increases in areas covered by trees. We then compared these vegetation indices to in-situ flux tower measurements of carbon fluxes, which did not always agree with SIF during the heatwave in woody areas. This study shows how remote sensing can further our understanding of how extreme events impact plant health, which is increasingly important as heatwaves become more intense and frequent in the future.

How much global warming will the Earth experience? This depends mostly on how quickly fossil fuels and other heat trapping gasses are phased out. We used reduced-complexity climate models to calculate whether a given emissions scenario meets temperature targets and other global effects. Our research starts with writing code that pulls and compiles the most recent data on various global environmental factors. This is used alongside existing data that break down emissions by industrial sectors, such as agriculture, electricity and transportation as well as by fuel, such as coal, oil, gas and land use. Using the updated historical data, we created various scenarios that ramp down emissions to zero over a specified number of years into the future. These scenarios were run through the Finite-amplitude Impulse-Response (FaIR) model to create plots demonstrating the resulting effect on global temperature. Additionally, we are considering the current decarbonization trends in our analysis. We noted current rates of decarbonization and continued these trends into the future to determine how much warming the earth will experience as a result. This data can be compared to the critical two degrees of global average temperature increase. By running these models, we can use current trends to estimate if we will exceed two degrees of global warming. Additionally, by modifying the rate of emission reduction, we can see what economic changes need to be made to stay under two degrees of global warming.

Amid Earth’s two periods of glaciation known as the Snowball Earth, evidence has been found for the survival of photosynthetic eukaryotic algae. Therefore, there must have been warm areas — with liquid water, or very thin ice permitting photosynthesis underneath — where microbial activity survived extreme cold. Previous research found that inland seas in the subtropics, like the Red Sea, were likely areas for refugia because friction from the seafloor and sidewalls, and relatively high sublimation thins the ice. However, this is only one criteria for a refugium. Deep inside the inland sea, it must also be warm enough to prevent freezing and allow water and nutrients to circulate and replenish regularly. Using the Community Earth System Model version 2, I analyze data from simulations of the Snowball Earth Climate with a simplified supercontinent geometry to understand the conditions for this warmth, as well as the robustness and frequency of warmth. We expect the warming to mainly appear along the north and south ends of the supercontinent due to the effect of Hadley convection cells at the equator. However, in our most recent runs, the model has exhibited unexpected instabilities in the occurrence of ice. After we adjust the topography of the continent, atmospheric carbon dioxide concentration and land solar radiation absorption to identify stable areas of warmth, I will continue with running the model to compare temperature data with other climate variables like wind speed, snow depth, and solar flux. Understanding the conditions of the Snowball Earth will give insight on how the climate has evolved in the past, and how it may evolve in the future: which is becoming increasingly important as the Earth faces dramatic change today.

Gas-phase emissions from sea-spray generate aerosols which are an important source of atmospheric halogens. Halogens (chlorine, bromine, and iodine-containing species) are important in the atmosphere because they affect the abundance of greenhouse gasses such as ozone and methane. The Bermuda boundary Layer Experiment on the Atmospheric Chemistry of Halogens (BLEACH) is a campaign that studies the abundance and cycling of atmospheric halogens. Filter samples from field campaigns are often frozen to preserve them for future analysis. However, after freezing a mixture of anion standards that replicate atmospheric composition for measurement on an Inductively Coupled Plasma Mass Spectrometer (ICP-MS), total aerosol iodine showed a tenfold increase in concentration in two separate trials compared to room temperature. Understanding the impact of freezing filter samples on aerosol iodine is crucial in interpreting BLEACH observations and could change the understanding of aerosol iodine speciation in the scientific community. I investigated this total iodine enrichment after a series of experiments on frozen and room temperature laboratory standards using Ion Chromatography (IC), which measures iodate and iodine separately. The tenfold iodine enrichment observed after freezing measured on ICP-MS was not replicated in IC trials. The total iodine ratio of frozen to room temperature was 1.1 on the IC and 9.8 on ICP-MS. Our results also show that the ratios iodide/iodate are the same for frozen (1.3) and room-temp (1.3) samples, suggesting that the conversion between iodide and iodate is not responsible for the enrichment in ICP-MS. Our observations of total aerosol iodine concentrations in Bermuda’s atmosphere are consistent with previous studies in the same region. This either suggests that the iodine enrichment after freezing is unique to the “simulated atmosphere” standard prepared in this study, or all field observations using ICP-MS may be overestimated by an order of magnitude.

Each winter, the northeastern U.S. experiences powerful storms that cover cities in snow and ice, which result in millions of dollars in damage, halt travel, and disrupt essential services. Yet, the type, intensity, and distribution of precipitation is unique to each winter storm. This research project aims to provide a greater understanding of the precipitation properties and distribution in snowstorms, through focusing on a major winter storm that occurred over the Midwest on 17 February 2022 and was the target of a research flight conducted during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Radar data collected during this research flight provides a unique perspective of the vertical cloud and precipitation structure, and numerical model fields provide the environmental context of the structures observed in the radar measurements. This storm had a frontal boundary, or a strong thermal contrast, that provided lift needed for the production of precipitation and had sub-freezing temperatures so that the precipitation fell as snow.This frontal boundary consisted of warm air originating from southern latitudes riding over colder air originating from northern latitudes. Analysis of the vertical cloud and precipitation structure from radar data and the in situ cloud particle measurements collected during the flight revealed that regions of higher reflectivity had larger particles and greater ice water content, compared to regions with lower reflectivity. The analysis also includes examining how the cloud particle properties are different depending on the origin of the air masses (from the north or south) that form the storm. By relating the temporal and spatial information regarding the air masses to the high-resolution radar and microphysics data collected by the IMPACTS airborne instruments, the results of this analysis will ultimately support increasing the accuracy of snow prediction.

Western U.S. wildfires are a growing threat to human lives, societal infrastructure, and global climate. While it is well known that meteorological factors impact wildfire intensity and growth rate, quantitative relationships between meteorology and wildfire are scale-dependent. For example, a recent study evaluating all recently observed California wildfires found that explosive fire growth was strongly related to short periods of strong winds and dryness. However, that study used data from a global atmospheric reanalysis (which cannot resolve local winds). As such, even the strong relationships found between meteorology and wildfire growth may have been underestimated. Given the potential consequences involved in predicting and mitigating future wildfires, it is important to understand the real-world accuracy of previously determined fire-environment relationships. To do so, this project compares how local meteorological observations from Remote Automated Weather Stations (RAWS) differ from reanalysis observations during known wildfires. The seasonal and spatial variation in the different relationships is also evaluated. Analysis has shown that the RAWS network is dense enough to adequately represent conditions at each fire being examined. Early results indicate that RAWS and reanalyses have similarly timed wind events during the max growth period. These results are promising, as they indicate that global atmospheric reanalyses can be used as a proxy for ground observations in remote terrain when analyzing periods of extreme wildfire growth.

Sulfate aerosols cause pollution and affect climate by influencing cloud properties and incoming solar radiation. Emissions and abundances of sulfur-containing aerosols are one of the largest sources of uncertainties in global climate modeling. The largest biogenic and most uncertain emission source of sulfur aerosols is from phytoplankton in the form of dimethyl sulfide (DMS). In the atmosphere, DMS is oxidized to methanesulfonic acid (MSA), sulfur dioxide, and hydroperoxymethyl thioformate (HPMTF), all of which can form sulfate. Historical emissions of DMS are studied by measuring MSA concentrations in ice cores as a proxy for DMS oxidation. Declining levels of MSA have been found in ice core records, implying that production of DMS has also been decreasing; however, anthropogenically driven changes in atmospheric chemistry have altered the ratio of MSA to sulfate produced from DMS over time. To better understand DMS oxidation mechanisms and its relationship to the production of MSA and sulfate aerosols, we need more recent ice core records of MSA and sulfur isotopes of sulfate (δ³⁴S(SO₄^2–)) at higher temporal resolution. To measure δ³⁴S(SO₄^2–) at monthly resolution in an ice core, the measurement size is smaller than previously measured by an order of magnitude, at about 1 µg S per sample. We will develop a method to isolate 1 µg of sulfur from an ice core sample by concentrating the sulfur using an anion-retaining resin, precipitating with barium chloride, and drying in an oven. We will quantify the efficacy of our method using a stable isotope mass spectrometer compared to laboratory-prepared standards. We expect that we will reduce our sample size by an order of magnitude (to 0.1 μg sulfur) and improve the accuracy by 50%. Quantifying sulfur isotopes at this resolution will provide information about the seasonality and change in phytoplankton sulfate production.