Using a large NOAA Storm Prediction Center database covering United States and Canadian tornadoes, our group analyses the traits and tendencies of tornadic activity across North America. With recent improvements in tornadic data collection, there is a surplus of topics to thoroughly explore. Some of our work includes investigating relationships between two parameters such as CAPE (convective available potential energy), and CIN (convective inhibition) or precipitable water and temperature. Furthermore, we created histograms with Gaussian curves and probability plots for the most critical parameters for tornadic development to better understand the distribution of data and how much it deviates from a centered mean. Currently, we are in the process of creating filled contours to visualize the geographic distribution of parameters across the United States. Our goal is to create a more nuanced tornado climatology. In doing so, we hope to further understand tornadoes and their tendencies with hopes of making our interactions with these events much safer.

Lightning is an intensely energetic atmospheric phenomenon that causes significant loss of life and property, ignites wildfires, and sustains the natural cleansing power of the atmosphere. Lightning is also an indicator of storm intensity and of the microphysical properties of cloud precipitate. Thornton et al. (2017) showed that lightning frequency over two of the world’s busiest oceanic shipping lanes is on average double that over neighboring ocean areas. Fuel combustion by ships emits sub-micrometer particles which alter cloud microphysics and possibly storm intensity, providing a potential explanation for the increased lightning density over the shipping lanes. Starting January 2020, the International Maritime Organization imposed a seven-fold reduction in the amount of sulfur in shipping fuel. Fuel sulfur is a significant contributor to particulate matter in ship exhaust. In addition, there was a significant temporary reduction in maritime ship traffic related to economic impacts of COVID-19. We use lightning frequency observations from the World-Wide Lightning Location Network (WWLLN) to analyze how these changes in shipping activity and emissions affect lightning enhancements over major shipping lanes in 2020 compared to the past decade. We find that the lightning enhancements over shipping lanes in 2020 were significantly lower compared to the past decade, with more than 100% reduction in the lightning enhancement, temporarily, as well as sustained, but smaller reductions in over half of the seasonally active months. Together, these changes suggest both the temporary COVID-19 reductions in ship traffic and prolonged reduction in ship exhaust emissions may have significantly altered lightning activity in major oceanic shipping lanes.

Extreme precipitation events have the potential to threaten physical infrastructure, property, and human lives, and are predicted to become heavier due to climate change. Understanding past, present, and future precipitation is important in analyzing how precipitation risks change spatially and temporally. The observational record, from which point precipitation frequency estimates such as NOAA Atlas-14 are derived, is limited by its lack of spatial coverage and it represents just one realization of past climate. When using a high-resolution large ensemble global climate model, we have multiple realizations of climate, consistent spatial coverage, and the added benefit of being able to incorporate climate change scenarios into precipitation risk analysis. Here, we use the GFDL (Geophysical Fluid Dynamics Laboratory) 50-km horizontal atmospheric resolution global SPEAR (Seamless System for Prediction and EArth System Research) 30-member ensemble to analyze how U.S. 24-hour precipitation extremes at various return periods change over the 1921-2100 time period. We quantify extreme precipitation risks across the U.S. and locally under different climate change scenarios (SSP2-4.5, SSP5-8.5, and natural forcings alone). With the large ensemble, we are also able to explore methodology and uncertainties in characterizing extreme precipitation risks.

It is commonly known that climate has shaped the photosynthesis type, height, and leaves of plants. However, it’s less commonly considered how plants affect climate. Major forest loss events have occurred due to deforestation and tree die off over the past decade. Prior research has shown that which types of plants grow where, and how those plants function, can impact climate both nearby and across larger spatial scales. Because this prior research has focused on theoretical simulations, it remains an open question if the impact of changes in plants can be seen in the real and very noisy climate system. We have compiled maps of actual forest loss as observed by satellites, to create initial conditions for simulation experiments in order to test if the impact of plants can be identified in observations of the atmosphere during the satellite era. We are assessing differences between simulations with and without forest loss to identify how that forest loss impacted the atmosphere. Using these identified impacts we will analyze if these same patterns are found in observed environmental conditions. This project aims to advance our understanding of the effect of forest loss on global climate, atmospheric circulation, and energy balances. Better understanding of this will help us to coordinate efforts to mitigate climate change by planting forests, while minimizing unwanted impacts. Additionally, this allows us to further predict and understand the impact of forest loss.

A simple, but important, measure of the ability of models to simulate cloud properties is whether the vertical structure of cloud occurrence in the model is consistent with that from data. Observed cloud occurrence profiles in the tropical western Pacific typically exhibit three peaks, one near the top of the boundary layer, one near the freezing level, and a broad peak in the upper troposphere. There is considerable variation in the probability of occurrence and the strength of these peaks. Here, we investigate the ability of a new generation of high-resolution models to simulate these profiles. Our study uses Global Storm Resolving Models (GSRMs) from the DYAMOND project. Nine models were run globally for 40 days starting from initial conditions on August 1, 2016. We use two data sources: ground-based data from the Atmospheric Radiation Measurement (ARM) program site in Manus Island, Papua New Guinea and Nauru, as well as data from National Aeronautics and Space Administration (NASA) satellite products (CCCM). Our study consists of a determination of local variability in cloud occurrence profiles. We make use of the ARM data to construct profiles for each August in the data series. The ARM data are available at high frequency at a single location but the monthly average profiles are influenced by local weather variation. The CCCM data are sampled over a broader spatial region but at lower spatial and temporal resolution. These two data sets provide us with an accurate assessment of cloud occurrence and a measure of internal variability. We then compare the profiles from the models. Our results suggest that the models simulate the rough structure of cloud occurrence but that there are large differences in the relative strengths of the peaks among the models and the overall probability of occurrence.

Every winter, snowstorms impact millions of people throughout the Northeast United States (U.S.). The origin of most east coast snowfalls, mid-latitude cyclones, vary significantly in strength, size, and temperature, leading to a broad range in snowfall amounts. With a better understanding of the microphysical processes of East Coast snowstorms, remote measurement and weather model accuracy will significantly improve. That is precisely the goal of the NASA-funded project: Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS). The observational strategy for IMPACTS includes one airplane equipped with multi-frequency radars flying above the cyclone and remotely observing clouds and precipitation while a second airplane flies within these clouds and collects direct information about the shapes, sizes, and concentrations of particles (e.g. microphysics). This particular study investigates a mid-latitude cyclone that occurred during IMPACTS and affected the Northeast U.S. on January 25, 2020. This storm featured periods of convection embedded in broad regions of storm clouds and small-scale convection originating near the top of these clouds in what is termed ‘generating cells.’ This study will relate radar observations from the aircraft above the clouds to the observations of particles from the aircraft that flew within the clouds. The ultimate goal is to document the microphysical processes both within and outside the convective regions and the generating cells and how these processes contribute to enhanced snowfall at the surface. We hypothesize that convection leads to a large variance in snowfall totals. Understanding the microphysical processes within convection will help improve snowfall forecasts at regional and local scales, and our observations and analysis will help us understand these processes in further detail.