Research on modification to snowpacks as a result of forest disturbance has typically focused on spatiotemporal patterns of snow depth and snow water equivalent, snowpack energy fluxes, and melt/ablation characteristics. However, little work has been conducted on relationships between tree trunks and snowpack dynamics. Insight into drivers of internal snowpack thermodynamics around trees and their response to forest disturbance is crucial to understanding hydrological processes in forested regions of the cryosphere, especially as forest disturbance through climate change continues. This work investigates relationships between energy fluxes and thermodynamic patterns surrounding tree trunks and within the greater snowpacks of forest stands in the Snowy Mountains of the Australian Alps. Measurements of vertical and horizontal snowpack temperature profiles and sub-canopy energy fluxes were collected during the 2018 winter season in non-disturbed and fire-disturbed Eucalyptus pauciflora (Snow Gum) stands. Primary heat sources were identified for each measurement location in the snowpack through employing the Random Forest machine learning regression method. Preliminary results indicate that soil heat flux is the dominant control on snowpack temperature at all locations in the un-disturbed forest stand. However, outgoing longwave radiation is shown to be the prevalent driver at numerous locations within the fire-disturbed stand that are close to the snowpack surface and tree well. This work aims to develop the physical basis for a 3-dimensional thermodynamic model of snowpacks contained in forests that could be used in conjunction with existing 1-dimensional snowpack models to determine melt and variability.

The Great Lakes create complex meteorological conditions that influence air quality throughout the region. Lake-breeze circulation, lake-induced low level jets, shoreline boundary layer processes, and photochemistry at the land-water interface affect the magnitude and timing of regional ground-level ozone episodes during the summer months. We simulated nine different Weather Research Forecasting Model (WRF) sensitivity runs for a two-week period in June 2016 during which high surface ozone concentrations were observed in the Lake Michigan region. The WRF simulations tested various combinations of physical options, forcing data, sea surface temperature integration, and nudging options for three nested modeling domains. Given the multiple model simulations, we needed a way to select the best WRF configuration for simulating the key atmospheric physical processes that drive ground level ozone formation in the region. We developed a new diagnostic approach for identifying the best WRF model configuration for our application. The approach uses statistical significance testing for comparing multiple model simulations for different periods in the diurnal cycle. We will present how this diagnostic approach is used for understanding the differences between WRF simulations and for building our confidence in selecting a configuration to support regulatory air quality modeling applications in the Midwest.

The vicinity of the electron diffusion region (EDR) at the core of magnetic reconnection is frequently characterized by agyrotropic electron velocity distributions such as perpendicular velocity-space crescents [J. L. Burch et al., Science, DOI:10.1126/science.aaf2939 (2016)]. Although the evolving EDR is not itself an equilibrium state, its evolution may be slow on electron-cyclotron time scales. When this is the case, homogeneous equilibrium models are limited in their ability to model dynamical processes, such as instabilities and wave generation, in the presence of agyrotropic populations. In order to better study these processes, we initiate implicit PIC simulations with inhomogeneous kinetic equilibria built upon agyrotropic electron velocity distributions measured by the FPI spectrometers on MMS. The methodology involves the following elements: 1. Modeling the observed agyrotropic (e.g., crescent) and background plasma distributions 2. Numerically evaluating self-consistent inhomogeneous equilibria – including anisotropy 3. Initializing 1D, 2D, and 3D PIC simulations with the equilibria 4. Evaluating the simulation output for instabilities and the persistence of the crescents Particle tracing and other visualization tools will be employed to illustrate the underlying dynamics of particles and fields – and their interactions.

As climate change adaptation is becoming a recognized policy issue, the needs are growing about quantitative economic evaluation of adaptation-related public investments, particularly in the context of climate finance. Irrigation, which enhances and stabilizes water supply for farming, is a potential means of climate change adaptation, but attempts of economic evaluation of its effectiveness as an adaptation measure are few in part because such assessment requires an integration of various types of simulation analyses. Against this background, we conduct a case study of a Kenyan irrigation development project (Mwea Irrigation Development Project), which is undertaken by the Kenyan government with a loan from the Japan International Cooperation Agency (JICA), to evaluate the effectiveness of the project for climate change adaptation by using a combination of simulation models. Specifically, we conduct a simple downscaling of CMIP5 climate simulation data, whose outputs are fed into a hydrological model (SHER model) and a yield forecasting model (DSSAT model). With these simulation data, together with data of socioeconomic parameters drawn from existing and original surveys, we compute economic variables such as farmers’ average income. Climate and other uncertainties are incorporated into analysis without probabilistic weights (conforming to the Robust Decision Making approach, e.g., Lempert et al., 2013) to highlight vulnerabilities for local farming. The results show that despite uncertainties of precipitation trends, increased temperatures due to climate change have a general tendency to reduce rice yields, and that irrigation development will mitigate that income impacts from the yield loss, i.e., it will likely be effective as a means for climate change adaptation.

The mid-Piacenzian (Pliocene) climate represents the most geologically recent interval of long-term average warmth, relative to the last million years, sharing similarities with the climate projected for the end of the 21st century. Therefore, this period has been studied by both geoscientists and climate modelers for many years. A better understanding of regional late Pliocene conditions can provide insight into potential climate change impacts, enabling more informed policy decisions for mitigation and adaptation. Previous work comparing climate model results with geologic data highlighted key regional and dynamic situations where there was discord between mean annual SST estimates generated by climate model simulations and paleoenvironmental reconstructions. One key area identified was the mid- to high-latitude North Atlantic. Here, we present a comparison between alkenone-based North Atlantic PRISM4 (Pliocene Research, Interpretation and Synoptic Mapping Project, Phase 4) mean annual SST estimates and an ensemble of ten climate model simulations produced as part of PlioMIP2 (Pliocene Model Intercomparison Project, Phase 2). Our latest research demonstrates that improved experimental design incorporating temporal refinement of the paleoenvironmental reconstruction, and inclusion of new PRISM4 boundary condition data sets, significantly reduces discord between data and models.

The warm rain process is an important part of shallow cumulus convection and can influence both cloud microphysics and cloud radiative effects. Model studies suggest that as shallow cumulus grow in size, the likelihood of warm rain increases due to a decreasing impact of entrainment on cloud updrafts. This implies a reduction in evaporative effects near cloud center that may result in more efficient conversion from cloud water to precipitation as cloud size increases. While these findings have been illustrated with cloud resolving models, the likelihood of precipitation and the sensitivity of precipitation efficiency to cloud size has not yet been tested by global observations. A-Train satellite observations, with sensors sensitive to both cloud and precipitation water, can be used to examine shallow cumulus behavior with cloud size. We combine CloudSat and MODIS observations to create a warm cloud climatology by identifying warm oceanic contiguous cloud objects with top heights below the freezing level from August 2006 - December 2010. The characteristics of each cloud object, including cloud top height, along-track extent (size), vertical reflectivity gradients, integrated cloud and precipitation water, and column water vapor (CWV) environment, are calculated. As a proxy for warm rain efficiency, the ratio of precipitation to cloud water is also analyzed for varying cloud object sizes. For a fixed top height, our results show rain likelihood increases with cloud size. Our initial results support the hypothesis that as shallow cumulus size increases and/or environmental moisture increases, shallow cumulus updrafts are able to support larger droplets that are more likely to fall out as rain. Planned analysis will determine how our proxy for warm rain efficiency changes with cloud size.

The Clouds and the Earth’s Radiant Energy System (CERES) project monitors Earth’s long-term energy balance and produces data products which have improved our understanding of the role clouds and aerosols play in that balance. Cloud property retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) are a major component of many of these data products. It is important for cloud property retrievals to be consistent over the course of the record so that artificial discontinuities are not introduced into the Earth radiation budget record. In practice, the MODIS and VIIRS instruments have different characteristics and different sets of spectral bands so deriving completely consistent cloud properties from the two instruments is a complex task. This paper investigates differences in the cloud properties retrieved from MODIS and VIIRS using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument as an independent validation source. Particular consideration is given to CALIOP’s sensitivity to optically thin clouds and the effect these clouds have on the retrieved cloud properties, especially cloud thermodynamic phase. Differences in cloud phase and cloud optical depth from MODIS and VIIRS are characterized by different cloud types including multi-layer scenarios. Characterizing these retrieval differences will help understand and mitigate artifacts in the long-term record.

Climate change and disruptions to disturbance regimes contribute to uncertainty about the best ways to manage forests and promote individual tree health. Indicators of tree health include annual growth, formation of defensive structures (resin ducts), and ability to produce defensive compounds (resin). Higher growth and resin duct area are associated with a lower chance of mortality from disturbances (Ferrenberg et al. 2014). Past research suggests that prescribed burning and thinning treatments can have positive impacts on growth and resin duct metrics (Kolb et al. 1998; Hood et al. 2016), indicating that management activities have the potential to help increase and restore tree health. However, there is limited research comparing different disturbances and management activities in terms of impacts on tree growth, resin ducts, and resin flow. In this research we explore the questions: 1) What factors most influence resin flow in ponderosa pine (Pinus ponderosa)? 2) What is the impact of fire or timber harvest on tree growth, resin flow, and resin ducts? 3) Are there changes in the relationships between tree growth and resin duct metrics that occur after disturbance, which may indicate shifts in tree resource allocation to growth vs. defenses?