Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is sea surface salinity (SSS), which is an important proxy for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the effects of SSS on TC RI, we use a previously built statistical model consisting of a variety of machine learning (ML) methods. A calibrator was trained on top of the ML models to correct probability forecasts. The ML model performance is improved with the addition of SSS in the Eastern North Pacific and the Caribbean subregion of the North Atlantic. Limited improvement is found in the Western North Pacific. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.

Simulations of human behavior in water resources systems are challenged by uncertainty in model structure and parameters. The increasing availability of observations describing these systems provides the opportunity to infer a set of plausible model structures using data-driven approaches. This study develops a three-phase approach to the inference of model structures and parameterizations from data: problem definition, model generation, and model evaluation, illustrated on a case study of land use decisions in the Tulare Basin, California. We encode the generalized decision problem as an arbitrary mapping from a high-dimensional data space to the action of interest and use multi-objective genetic programming to search over a family of functions that perform this mapping for both regression and classification tasks. To facilitate the discovery of models that are both realistic and interpretable, the algorithm selects model structures based on multi-objective optimization of (1) their performance on a training set and (2) complexity, measured by the number of variables, constants, and operations composing the model. After training, optimal model structures are further evaluated according to their ability to generalize to held-out test data and clustered based on their performance, complexity, and generalization properties. Finally, we diagnose the causes of good and bad generalization by performing sensitivity analysis across model inputs and within model clusters. This study serves as a template to inform and automate the problem-dependent task of constructing robust data-driven model structures to describe human behavior in water resources systems.

The growth of biofilms changes the hydrodynamics of porous media, which will influence the transport of bacteria and contaminants in natural and engineered systems. Traditionally, biofilms have been modeled as an impermeable domain in porous media, such that no water can enter biofilms and contaminants can only enter biofilms via molecular diffusion. Such a modeling approach is based on the assumption that the permeability of biofilms is the same as that of Extracellular Polymeric Substances (EPS), which are considered to have very low permeability. In this study, we investigate the impacts of biofilm properties on water flow using microfluidic device experiments and pore-scale modeling. E. coli biofilm was established inside a microfluidic channel packed with unisized glass beads in a single layer. A 5*5 mm2 area was live-stained and imaged via confocal microscopy at three different growth stages to represent three biofilm levels in the system. After image analysis using FIJI and AutoCAD software, the flow in the bio-clogged porous media was simulated using COMSOL Multiphysics. In these simulations, biofilm was modeled as a separate permeable domain in porous media instead of an impermeable domain. A Forchheimer-corrected version of the Brinkman equations was applied to simulate the flow in the porous biofilm regions. Two properties of biofilms, namely biofilm porosity (BPO) and biofilm permeability (BPE), were altered to examine their effects on the permeability of the system. It was found that different values of BPO and BPE clearly affect the flow paths, velocity patterns, and permeability of the system. Considering biofilms as impermeable results in significant underestimations of the flow properties. In addition, two simplified modeling scenarios, namely uniform coating and symmetric contact filling, were investigated for a possible abridging in the arduous modeling procedure of the real biofilm geometry.

As a result of uneven density of data collection, level-2 satellite gravimetry data suffer from global north-south striping. By applying various filtering methods, several studies have addressed the mitigation of the data. However, the studies mainly addressed the issue on a global scale, and the local effects were not considered. On the other hand, water research, especially inland hydrology, usually deals with small-scale fitures such as lakes and watersheds. Therefore, the local data de-striping methods need special attention. This research presents a new analytical method to de-stripe gravimetry data based on the spatial contrast of signals. The approach strikes a balance between de-striping and signal preservation. Using a-priori information obtained from the gravimetry data, the de-striping method first estimates the spatial gradient of the signal and optimizes a Poisson filter based on this information to de-stripe the data. Unlike the other approaches, the optimized filter is dynamic and accounts for temporal variations in the signal contrast, such as seasonality. The proposed approach is applied to ten globally distributed study areas to derive a general scheme. Detailed processes and evaluations are applied to two study areas: the Caspian Sea and the Congo River Basin. Results are visually assessed for spatial fit and for temporal consistency by comparison with results from other filters. The use of a dynamic filter set specified for each region and time point allows us to preserve local hydrologic signals that are susceptible to globally optimized filters. It also allows filter-related errors to be effectively constrained.

People spend most of their time indoors, and therefore exposure to aerosols and precursor-gasses in the indoor environment is of extreme concern with respect to people’s health, well-being, working efficiency, and overall life quality. Such sources include both those emitted inside as well as those which may intrude from the outside. To better quantify and understand these sources and their impacts we employ multiple air quality monitors and simple models, both within and outside of various residential environments located in different cities of different development levels in China. To enhance the livability of the indoor environment, we further work to quantify two newly relevant factors: portable indoor aerosol filters and the idea of “increased ventilation”. The combined goal of this work is to understand the combination of factors leading to an improvement in Indoor Air Quality (IAQ), including but not limited to: mass transfer to/from the indoor environment, removal and/or enhancement due to the filters and ventilation, removal from the room itself, emissions, and other possible non-linearities not accounted for on this list. Concentrations of aerosols across different sizes from 0.3um to 10um are measured and analyzed at 2-minute intervals over a minimum of 20 days, allowing for an analysis that encompasses all of the possibilities of natural and anthropogenic variability typically encountered in a real environment. This includes analysis both with and without filters, under extreme outdoor loading conditions, with intense indoor emissions sources, pseudo-equilibrium conditions, and measurements made during different meteorological events, both with and without high rates of indoor to outdoor air exchange. Results show that the impacts are relatively large under both equilibrium and high event conditions, include both the magnitude of the drop as well as the time taken to achieve the reduction. A further conclusion is that increased ventilation may lead to a worsening of IAQ. A final conclusion is that larger particles more likely associated with PM2.5 are removed at a different rate from much smaller particles more associated with PM0.5 or PM1.

1. Noble gas tracers can infer the rate of sea ice production in polynyas. 2. Frazil ice in polynyas appears to block air-sea gas exchange mechanisms. 3. The solubility pump is influenced by glacial ice melt and sea ice formation.

The Weddell Gyre is a dominant feature of the Southern Ocean and an important component of the climate system; it regulates air-sea exchanges, controls the formation of deep and bottom water, and hosts upwelling of relatively warm subsurface waters. It is characterized by extremely low sea surface temperatures, active sea ice formation, and widespread salt stratification that stabilizes the water column. Studying the Weddell Gyre is difficult, as it is extremely remote and largely covered with sea ice; at present, it is one of the most poorly-sampled regions of the global ocean, highlighting the need to extract as much value as possible from existing observations. Thanks to recent efforts of the EU SO-CHIC project, much of the existing Weddell Gyre data, including ship-based CTD, seal tag, and Argo float profiles, has been assembled into a coherent framework, enabling new comprehensive studies. Here, we apply unsupervised classification techniques (e.g. Gaussian Mixture Modeling) to the new comprehensive Weddell Gyre dataset to look for coherent regimes in temperature and salinity. We find that, despite not being given any latitude or longitude information, unsupervised classification algorithms identify spatially coherent thermohaline domains. The highlighted features include the Antarctic Circumpolar Current, the central Weddell Gyre, and the Antarctic Slope current; we also find potential signatures of the inflow of Weddell Deep Water and export pathways of Antarctic Bottom Water. We show how varying the statistical, machine learning derived representations of the data can reveal different physical structures and circulation pathways that are relevant to the delivery of relatively warm waters to the higher-latitude seas and their associated ice shelves.

We use Wasserstein Generative Adversarial Networks to learn and integrate multi-scale features in segmented three-dimensional images of porous materials, enabling the dependable generation of large-scale representations of complex porous media. A Laplacian pyramid generator is introduced which creates pore-space features with a hierarchy of spatial scales. Feature statistics mixing regularization enhances the ability of the model generation to reliably maintain multi-scale pore-space features of images by increasing diversity. The method is tested on a variety of X-ray images of porous rocks. The generated images can be of any size -- cm-scale ten-billion-cell images are generated to demonstrate the power of the approach -- which have two-point correlation functions, porosity, permeability, Euler characteristic, curvature, and specific surface area close to the training datasets. The images demonstrate a considerable improvement over previously- published studies using generative adversarial networks.

Forests are under major pressures due to contemporary land-use, which creates mosaics of stand-stage development that follow different successional paths, that imply ecosystem complexity. The interplay of carbon and water dynamics across succession involves physical and biological interactions that shape net ecosystem production (NEP) and water use efficiency. Here we present 13 years-site of eddy covariance data (2016-2020) from a seasonally dry tropical forest in Northwestern Mexico to elucidate the environmental controls on ecosystem fluxes and explore the interactions with changes in resource availability. Across a successional gradient, an early (9 years since abandonment) and a mid-successional (about 45 years with natural recruitment and regrowth) sites were net carbon sinks (in the order of 100 to 500 g C m-2 y-1) while an old- growth forest was a chronic net source over the 5 years studied (losing between 100 and 300 g C m-2 y-1). In contrast evapotranspiration was alike at sites and close to the precipitation input. Ecosystem water use efficiency tended to be higher at the old-growth forest site (ca. 3.0 g C m-2 /mm H2O vs. ca. 2.0 g C m-2 / mm H2O at the secondary sites). Water availability and radiation where clearly dominant environmental controls across sites, but notably vapor pressure deficit was not a controlling factor for gas exchange at the old-growth forest. Surface characteristics, canopy structure and species composition may explain differences in NEP across succession in TDF at its northernmost extent.

Access to reliable and sustainable electricity is essential for meeting the needs of society, such as communications, clean water, healthcare, and heating and cooling, both today and into the future. Shifts in extreme weather and a changing climate are challenging traditional and renewable power grids, as evidenced by widespread outages from events like hurricanes and heat waves, which are increasing in intensity and frequency and have the propensity to harm infrastructure and diminish generation capacity. Understanding the changing climate allows utilities to be more resilient in proactively producing or distributing energy. Earth observations (EOs) provide actionable data for monitoring such change, but better collaboration between scientists and end users is needed to ensure data is accessible and relevant to decision-making. Utilizing a capacity building approach, this NASA-funded initiative aims to promote broader utilization of NASA EOs within the energy sector by transferring knowledge and bridging the gap between scientists and end users. To the untrained user, satellite data can be onerous to find and challenging to apply. To address these concerns, we engaged the U.S. Department of Energy and stakeholders across the sector to solicit input on the greatest challenges and opportunities utilities face relevant to resiliency and the usage of EOs. In response, and through an iterative process with end users, we compiled relevant NASA EOs into a user-friendly Esri StoryMap® and developed the first energy-focused NASA ARSET training, both publicly available, followed by broad outreach. The StoryMap® aims to reduce the burden of accessing and using EOs by including only the most applicable data with a focus on terrestrial variables, such as soil moisture and land surface temperature, along with tutorials and use cases. The ARSET training provides an in-depth look at using NASA products to support a more climate resilient energy sector and presents real-world, illustrative examples of the ways in which EOs can be used to better understand the impact of extreme events. This talk will report on the successes and challenges of this capacity building initiative, highlight components of the StoryMap® and ARSET training, and share lessons learned in facilitating increased uptake and use of EOs by the energy sector.

Rivers and streams are an important component of the global carbon budget, emitting CO2 to the atmosphere. However, our ability to accurately predict carbon fluxes from stream systems remains uncertain due to small scales of pCO2 variability within streams (100-102 m), which make monitoring intractable. Here we incorporate CO2 input and output fluxes into a stream network advection-reaction model, representing the first process-based representation of stream CO2 dynamics at watershed scales. This model includes groundwater (GW) CO2 inputs, water column and benthic hyporheic zone (BZ) respiration, downstream advection, and atmospheric exchange. We evaluate this model against existing statistical methods including upscaling techniques and multiple linear regression models through comparisons to high-resolution stream pCO2 data collected across the East River Watershed in the Colorado Rocky Mountains. The stream network model accurately captures topography-driven pCO2 variability and significantly outperforms multiple linear regressions for predicting pCO2. Further, the model provides estimates of CO2 contributions from internal versus external sources and suggests that streams transition from GW- to BZ-dominated sources between 3rd and 4th Strahler orders, with GW and BZ accounting for 53 and 47% of CO2 fluxes from the watershed, respectively. Lastly, stream network model CO2 fluxes are 5-13x times smaller than upscaling technique predictions, largely due to inverse correlations between stream pCO2 and atmosphere exchange velocities. Taken together, the stream network model presented improves our ability to predict and monitor stream CO2 dynamics, and future applications to regional and global scales may result in a significant downward revision of global flux estimates.

The Atlantic meridional overturning circulation (AMOC) is a key component of the global climate system. Many models predict a weakening or even a collapse of the AMOC under future climate change. Recent studies suggested a 20th century weakening of the AMOC of unprecedented amplitude ( 15%) over the last millennium. Here, we present δ18O of benthic foraminifera in a sediment core from the Laurentian Channel and demonstrate that the δ18O trend is linked to the strength of the AMOC. In this 100-year record, the AMOC signal decrease steadily to reach its minimum value in the late 1970’s. The weakest AMOC signal is constant until 2000. We present a longer δ18O record of 1,500 years and highlight the uniqueness of these high δ18O values over that period. Moreover, the long record is also characterized by statistically heavier δ18O during the Little Ice age suggesting a relatively weak AMOC.

The One Health concept provides a framework to examine the linkages between human, animal, plant, environmental, and ecosystem health. This framework can be represented as a multi-dimensional matrix tool that can be used to examine and address complex GeoHealth challenges. This matrix tool facilitates comprehensive, systems-based thinking and can include up to four dimensions depending upon users' needs. This presentation will briefly review the One Health matrix tool and apply it, as an example, to examine the global impact of human and animal fecal wastes. Applying the tool reveals linkages between food-borne illnesses, food insecurity, antimicrobial resistance, and climate change. Understanding these linkages is necessary for developing effective and equitable public policies that are needed to achieve many of the United Nations' Sustainable Development Goals.

The long-existing double-ITCZ problem in Global Climate Models (GCMs) hampers accurate climate simulation. Using a regional climate model (RCM) over the tropical and sub-tropical Atlantic with a horizontal resolution of 12 km and explicit convection, we develop a bias-correction downscaling methodology to remove GCM biases. The methodology is adapted from the pseudo-global warming (PGW) approach, typically used to exert the climate-change signal to a reanalysis-driven RCM simulation. We show that the double ITCZ problem persists with conventional dynamical downscaling, but with our bias-corrected downscaling, the double ITCZ problem is removed. Detailed analysis attributes the main cause of the double ITCZ problem of the selected GCM to the sea surface temperature (SST) bias. Compared to the GCM’s AMIP simulations, RCMs with higher resolution allow explicit deep convection and enable a better simulation of tropical convection and clouds. The developed methodology is promising for constraining climate sensitivity by removing double-ITCZ biases.

Flash droughts are a recently recognized extreme climate phenomena that occur at the subseasonal timescale and develop with sudden onset and rapid intensity, which have significant socio-environmental impacts on agriculture, ecosystem, and water resources. However, the spatial and temporal variability of flash droughts in the continental United States (CONUS) are still not well understood. In this study, we characterize flash drought events using a novel evaporative demand flash drought (EDFD) index as well as a soil moisture flash drought (SMFD) index at the weekly timescale between September 1981 and December 2018. Hierarchical clustering divided CONUS into different clusters where SMFDs occur synchronously over space and time. The results show that flash droughts occur in all seasons and regions in CONUS with increasing trends in EDFDs (0.07 > tau > 0.28), but not equal trends in SMFDs (-0.12 > tau > 0.08). Periodic short-term behavior between EDFDs and SMPDs was identified in all clusters but more importantly long-term oscillatory behavior is identified between the two indexes in all clusters except the Northwest US. Our research into the spatiotemporal variability of flash droughts using both the EDFD index and SMPD index displays that CONUS is still at risk for increased flash drought events in the future to come.

A reduction of dust emission over the major dust source regions in East Asia in the twenty-first century is diagnosed in the climate change simulations of the Sixth Climate Model Intercomparison Project (CMIP6). Such change is attributable to the reduction of surface wind speeds in the dust source regions. To evaluate how the magnitude of warming affects dust emission, we examined two model scenarios, one high-forcing pathway and one medium-forcing pathway. We find dust optical depth over dust source regions would decrease by 5.6% by the end of the twenty-first century under the high-forcing pathway. Under the medium-forcing pathway, dust optical depth would decrease by less than 2%. These results provide a quantitative understanding of how global warming affects dust emission in the major dust source regions in East Asia.

We report observations of a relatively long period of 3He-rich solar energetic particles (SEPs) measured by Solar Orbiter. The period consists of several well-resolved ion injections. The high-resolution STEREO-A imaging observations reveal that the injections coincide with extreme ultraviolet jets and brightenings near the east limb, not far from the nominal magnetic connection of Solar Orbiter. The jets originated in two adjacent, large, and complex active regions, as observed by the Solar Dynamics Observatory when the regions rotated into the Earth’s view. It appears that the sustained ion injections were related to the complex configuration of the sunspot group and the long period of 3He-rich SEPs to the longitudinal extent covered by the group during the analyzed time period.

In Greek mythology, Cassandra was cursed with a prophetic knowledge of the future that no one would believe. The premise of this mythological prophet is also an apt description of the challenge facing scientists in communicating climate change. Central to addressing this challenge is the ability to examine, interpret and understand the individual scaffolding that underlies people's perception of climate change. Human-centered design incorporates a deep assessment of human needs in the design of systems and services, providing an alternative lens to engage in action towards climate change. Over the course of three separate design classes, attracting a diverse array of students from undergraduate to PhD to professional degrees, from engineers to humanists, we have explored the intersection of design education and climate change communication. In Design for a Habitable Planet, students conducted broad unstructured interviews to populate a matrix of beliefs and experiences around climate change, allowing them to bridge a perceived empathy gap. Their subjects included local fisherman, who are experiencing climate change but are ideologically opposed to the concept, and Alaska Native students who are experiencing climate change as part of a larger portfolio of challenges facing their communities. Leveraging theories from behavioral psychology, students then developed and prototyped an array of climate action campaigns, using an array of mediums from Snapchat filters to crowd-sourced climate haiku on beer labels. A unique outcome of the design process was a set of strategies intended to engage voices from all corners of the climate change debate and in turn inspire action. In Design of Data, students were challenged to design a data journalism piece highlighting positive action to address climate change. Students struggled with their pre-conceived negative bias around climate change, but after an examination of their own communities, what emerged were eight compelling stories daylighting positive and collective action to address global change. Ultimately, through our classroom experiments integrating design, behavioral psychology and climate change communication, students have not only learned to engage across differences, but we have developed a first glimpse of the power of design to inspire novel strategies to incite positive action to address climate change.