It is important that we prepare tomorrow’s scientists, decision makers, and communities to address the societal impacts of a changing climate. In order to respond to, manage, and adapt to those changes, citizens of all ages need accurate, up-to-date information, knowledge of the sciences, and analytical skills to make responsible decisions and long-term resiliency plans regarding these challenging topics. The Climate Literacy and Energy Awareness Network (CLEAN, http://cleanet.org) is 1) providing teaching resources for educators through the CLEAN Collection and pedagogical support for teaching climate and energy science; and 2) facilitating a professionally diverse community of climate and energy literacy stakeholders, called the CLEAN Network, to share and leverage efforts to extend the reach and effectiveness of climate and energy education. This presentation will provide an overview of the CLEAN web portal and techniques we have used to market it. We will showcase the CLEAN Collection, which is comprised of 700+ resources (curricula, activities, videos, visualizations, and demonstrations/experiments) that have been reviewed for scientific accuracy, pedagogical effectiveness, and technical quality. Recent activities of the CLEAN Network will be highlighted. We will present findings from our web analytics work, which monitors visitor use of the CLEAN web portal. Through analytics data, we will show evidence of successful CLEAN marketing efforts. The results of our recent pop-up survey, which has been completed by CLEAN visitors from six continents, will also be discussed. Survey results will provide detailed information about how our audiences use the web portal. We anticipate that our insights from the CLEAN network can aid other climate and energy education programs in effectively increasing the visibility of their vital work.

Fronts are ubiquitous in the climate system. In the Southern Ocean, fronts delineate water masses, which correspond to upwelling and downwelling branches of the overturning circulation. A robust understanding of Southern Ocean fronts is key to projecting future changes in overturning and the associated air-sea partitioning of heat and carbon. Classically, oceanographers define Southern Ocean fronts as a small number of continuous linear features that encircle Antarctica. However, modern observational and theoretical developments are challenging this traditional framework to accommodate more localized views of fronts [Chapman et al. 2020]. In this work, we present two related methods for calculating fronts from oceanographic data. The first method uses unsupervised classification (specifically, Gaussian Mixture Modeling or GMM) and an interclass metric to define fronts. This approach produces a discontinuous, probabilistic view of front location, emphasising the fact that the boundaries between water masses are not uniformly sharp across the entire Southern Ocean. The second method uses Sobel edge detection to highlight rapid changes [Hjelmervik & Hjelmervik, 2019]. This approach produces a more local view of fronts, with the advantage that it can highlight the movement of individual eddy-like features (such as the Agulhas rings). The fronts detected using the Sobel method are moderately correlated with the magnitude of the velocity field, which is consistent with the theoretically expected spatial coincidence of fronts and jets. We will present our python GitHub repository, which will allow researchers to easily apply these methods to their own datasets. Figure caption Two methods for interpretable front detection. Solid lines represent classical fronts. (a) The “inter-class” metric, which indicates the probability that a grid cell is a boundary between two classes. The classes are defined by GMM of principal component values (PCs) derived from both temperature and salinity. The different colors indicate different class boundaries. (b) Sobel edge detection: approximately the magnitude of the spatial gradient of the PCs divided by each field’s standard deviation, which highlights locations of rapid change.

Combining auxiliary variables and ground data of forest parameters using the model-based approach has become a frequently used methodology to produce synthetic estimates for small areas. These small areas arise as it may not be financially feasible to take ground measurements or some areas may be inaccessible to ground-based personnel. Until recently, these estimates have been calculated without providing a measure of the variance or in large homogeneous forested areas where the variability would be minimal. This paper uses a Random Forest algorithm to produce estimates of QMDBH (cm) and volume (m3 ha-1) and a k-nn technique to produce variance estimates in a previously unproven heterogeneous forest environment. The area of interest (AOI) was the commercial forest in the Slieve Bloom Mountains in the Republic of Ireland, where the majority species are Sitka spruce (Picea sitchensis (Bong.) Carr.) and Lodgepole pine (Pinus contorta Dougl.). Field plots were measured during the Summer in 2018 during which a LiDAR campaign was flown and Sentinel 2 satellite imagery captured, which were both used as auxiliary variables. Root mean squared errors (rmse) for the modeled estimates of QMDBH and volume were 4.4 cm and 111 m3 ha-1 and the r2 values were 0.65 and 0.76 respectively. An independent dataset of pre-harvest forest stands was used to validate the modeled variance estimates. The results showed that 73% of QMDBH measurements and 60% of volume measurements were within the confidence intervals of the estimated values. The mean percentage standard deviation for QMDBH and volume were 17% and 21% respectively. This application of variance estimation in such a heterogeneous forest landscape adds further weight to the applicability of the methodology in a range of forest landscapes. This finding is extremely important as it highlights the benefits of using earth observation data to produce estimates and confidence intervals for small areas in heterogeneous forest landscape.

A primary source of uncertainty in terrestrial biosphere model (TBM) projection of carbon uptake and water cycling from ecosystems is the relationship between CO2 assimilation (A) and water loss via stomatal conductance (gs). A common mathematical framework for modeling this relationship is the “Unified Stomatal model”, which relates A to gs over environmental conditions and is governed by two terms, the stomatal slope (g1) and intercept (g0). Given their importance in determining the relationship between forest productivity and climate, an accurate and mechanistic understanding of the g1 and g0 parameters is crucial, particularly in wet tropical broadleaf forests where changes in water cycling could impact global weather patterns. These stomatal parameters are estimated using leaf-level gas exchange by two alternative methods: (1) a response curve where the environmental conditions are modified for a single leaf, or (2) a survey approach, where repeated measurements are made on multiple leaves over a diurnal range of environmental conditions. We compare the curve and survey approaches by conducting a comprehensive measurement campaign in which we paired diurnal gas exchange surveys with leaf level response curves for the estimation of g1 and g0 on six tropical species across a full range of leaf phenological stages. We examine how these different estimates impact model projection of gs, and how the consideration of a diurnal effect on g1 and g0 can improve predictions relative to a model using parameter estimates which are fixed over the photoperiod. Our results showed that age is an important factor to consider in estimates of g0, however there was no effect of leaf age on estimates of g1. The survey approach identified a diurnal trend associated with g1 and g0, which when accounted for improved model projections of diurnal trends in gs. We found that while both approaches yield equally statistically valid estimates of g1 and g0 at a fixed point in time, they are not directly comparable across diurnal timescales, where shifting water supply and carbon demand lead to dynamic canopy scale water use efficiency (WUE). These results suggest that to improve the accuracy of modelled gs in tropical forests, TBMs should recognize and implement diurnal variation in stomatal parameters which are associated with diurnal shifts in WUE.