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DOE OSTI2020

Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. Knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to improve understanding of atmospheric sulfur, aerosol/cloud dynamics, and albedo. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 82 996 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of latitude–longitude coordinates, time of day, time of year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, and silicate. Linear regressions of DMS against the environmental parameters show that on a global-scale mixed layer depth and solar radiation are the strongest predictors of DMS. These parameters capture ~9 % and ~7 % of the raw DMS data variance, respectively. Multilinear regression can capture more of the raw data variance (~39 %) but strongly underestimates DMS in high-concentration regions. In contrast, the artificial neural network captures ~66 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in surface ocean DMS with the highest concentrations and sea-to-air fluxes in the high-latitude summertime oceans. We estimate a lower global sea-to-air DMS flux (20.12±0.43 Tg S yr –1 ) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used. Our sensitivity test results show that DMS concentration does not change unidirectionally with each of the environmental parameters, which emphasizes the interactions among these parameters. The ANN model suggests that the flux of DMS from the ocean to the atmosphere will increase with global warming. Given that larger DMS fluxes induce greater cloud albedo, this corresponds to a negative climate feedback.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar

Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R^2 = 0.81, RMSE = 1.09 kg/sq. m) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R^2 = 0.74, RMSE = 1.26 kg/sq. m), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts./sq. m) to the G-LiHT SfM point cloud (500–2000 pts./sq. m) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.

Michael Alonzo↗

NASA NTRS2011

An Overview of the Total Lightning Jump Algorithm: Past, Present and Future Work

Rapid increases in total lightning prior to the onset of severe and hazardous weather have been observed for several decades. These rapid increases are known as lightning jumps and can precede the occurrence of severe weather by tens of minutes. Over the past decade, a significant effort has been made to quantify lightning jump behavior in relation to its utility as a predictor of severe and hazardous weather. Based on a study of 34 thunderstorms that occurred in the Tennessee Valley, early work conducted in our group at Huntsville determined that it was indeed possible to create a reasonable operational lightning jump algorithm (LJA) based on a statistical framework relying on the variance behavior of the lightning trending signal. We the expanded this framework and tested several variance-related LJA configurations on a much larger sample of 87 severe and non severe thunderstorms. This study determined that a configuration named the "2(sigma)" algorithm had the most promise in development of the operational LJA with a probability of detection (POD) of 87%, a false alarm rate (FAR) of 33%, a Heidke Skill Score (HSS) of 0.75. The 2(sigma) algorithm was then tested on an even larger sample of 711 thunderstorms of all types from four regions of the country where total lightning measurement capability existed. The result was very encouraging.Despite the larger number of storms and the inclusion of different regions of the country, the POD remained high (79%), the FAR was low (36%) and HSS was solid (0.71). Average lead time from jump to severe weather occurrence was 20.65 minutes, with a standard deviation of +/- 15 minutes. Also, trends in total lightning were compared to cloud to ground (CG) lightning trends, and it was determined that total lightning trends had a higher POD (79% vs 66%), lower FAR (36% vs 54 %) and a better HSS (0.71 vs 0.55). From the 711-storm case study it was determined that a majority of missed events were due to severe weather producing thunderstorms in low flashing environments. The latest efforts have been geared toward examining these low flashing storms in order to adjust the algorithm for such storms, thus enhancing the capability of the LJA. Future work will test the algorithm in real time using current satellite and radar based cell tracking methods, as well as, comparing total lightning jump occurrence to both satellite based and ground base observations of thunderstorms to create correlations between lightning jumps and the observed structures within thunderstorms. Finally this algorithm will need to be tested using Geostationary Lightning Mapper proxy data to transition the algorithm from VHF ground based lightning measurements to lower frequency space-based lightning measurements.

Schultz, Christopher J.↗

NASA NTRS2012

Using Statistical Multivariable Models to Understand the Relationship Between Interplanetary Coronal Mass Ejecta and Magnetic Flux Ropes

In-situ measurements of interplanetary coronal mass ejections (ICMEs) display a wide range of properties. A distinct subset, "magnetic clouds" (MCs), are readily identifiable by a smooth rotation in an enhanced magnetic field, together with an unusually low solar wind proton temperature. In this study, we analyze Ulysses spacecraft measurements to systematically investigate five possible explanations for why some ICMEs are observed to be MCs and others are not: i) An observational selection effect; that is, all ICMEs do in fact contain MCs, but the trajectory of the spacecraft through the ICME determines whether the MC is actually encountered; ii) interactions of an erupting flux rope (PR) with itself or between neighboring FRs, which produce complex structures in which the coherent magnetic structure has been destroyed; iii) an evolutionary process, such as relaxation to a low plasma-beta state that leads to the formation of an MC; iv) the existence of two (or more) intrinsic initiation mechanisms, some of which produce MCs and some that do not; or v) MCs are just an easily identifiable limit in an otherwise corntinuous spectrum of structures. We apply quantitative statistical models to assess these ideas. In particular, we use the Akaike information criterion (AIC) to rank the candidate models and a Gaussian mixture model (GMM) to uncover any intrinsic clustering of the data. Using a logistic regression, we find that plasma-beta, CME width, and the ratio O(sup 7) / O(sup 6) are the most significant predictor variables for the presence of an MC. Moreover, the propensity for an event to be identified as an MC decreases with heliocentric distance. These results tend to refute ideas ii) and iii). GMM clustering analysis further identifies three distinct groups of ICMEs; two of which match (at the 86% level) with events independently identified as MCs, and a third that matches with non-MCs (68 % overlap), Thus, idea v) is not supported. Choosing between ideas i) and iv) is more challenging, since they may effectively be indistinguishable from one another by a single in-situ spacecraft. We offer some suggestions on how future studies may address this.

Riley, P.↗

DOE OSTI2023

Production of ice-nucleating particles (INPs) by fast-growing phytoplankton

Sea spray aerosol contains ice-nucleating particles (INPs), which affect the formation and proper ties of clouds. Here, we show that aerosols emitted from fast-growing marine phytoplankton produce effective immersion INPs, which nucleate at temperatures significantly warmer than the atmospheric homogeneous freezing (-38.0 °C) of pure water. Aerosol sampled over phytoplankton cultures grown in a Marine Aerosol Ref erence Tank (MART) induced nucleation and freezing at temperatures as high as -15.0 °C during exponential phytoplankton growth. This was observed in monospecific cultures representative of two major groups of phy toplankton, namely a cyanobacterium (Synechococcus elongatus) and a diatom (Thalassiosira weissflogii). Ice nucleation occurred at colder temperatures (-28.5 °C and below), which were not different from the freezing temperatures of procedural blanks, when the cultures were in the stationary or death phases of growth. Ice nucleation at warmer temperatures was associated with relatively high values of the maximum quantum yield of photosystem II ( Φ PSII ), an indicator of the physiological status of phytoplankton. High values of Φ PSII indicate the presence of cells with efficient photochemistry and greater potential for photosynthesis. For comparison, field measurements in the North Atlantic Ocean showed that high net growth rates of natural phytoplankton assemblages were associated with marine aerosol that acted as effective immersion INPs at relatively warm temperatures. Data were collected over 4 d at a sampling station maintained in the same water mass as the water column stabilized after deep mixing by a storm. Phytoplankton biomass and net phytoplankton growth rate (0.56 d -1 ) were greatest over the 24 h preceding the warmest mean ice nucleation temperature (-25.5 °C). Collectively, our laboratory and field observations indicate that phytoplankton physiological status is a useful predictor of effective INPs and more reliable than biomass or taxonomic affiliation. Ocean regions associated with fast phytoplankton growth, such as the North Atlantic during the annual spring bloom, may be significant sources of atmospheric INPs.

54 ENVIRONMENTAL SCIENCES↗