This work integrates machine learning into an atmospheric parameterization to target uncertain mixing processes while maintaining interpretable, predictive, and well-established physical equations. We adopt an eddy-diffusivity mass-flux (EDMF) parameterization for the unified modeling of various convective and turbulent regimes. To avoid drift and instability that plague offline-trained machine learning parameterizations that are subsequently coupled with climate models, we frame learning as an inverse problem: Data-driven models are embedded within the EDMF parameterization and trained online using output from large-eddy simulations (LES) forced with GCM-simulated large-scale conditions in the Pacific. Rather than optimizing subgrid-scale tendencies, our framework directly targets climate variables of interest, such as the vertical profiles of entropy and liquid water path. Specifically, we use ensemble Kalman inversion to simultaneously calibrate both the EDMF parameters and the parameters governing data-driven lateral mixing rates. The calibrated parameterization outperforms existing EDMF schemes, particularly in tropical and subtropical locations of the present climate, and maintains high fidelity in simulating shallow cumulus and stratocumulus regimes under increased sea surface temperatures from AMIP4K experiments. The results showcase the advantage of physically-constraining data-driven models and directly targeting relevant variables through online learning to build robust and stable machine learning parameterizations.

Reducing atmospheric CO 2 is essential to mitigating the effects of climate change. Marine macroalgae has garnered interest as a potential carbon sink to sequester atmospheric CO 2. Efforts to improve macroalgae for use as a carbon sink are just beginning, and understanding the phenotypic diversity of macroalgae is a critical part of identifying favorable characteristics for a marine carbon sink. In this study, we surveyed the natural variation of the marine macroalgae, Saccharina latissima (sugar kelp), from eight sites along the western coast of Iceland: three in Breiđafjörđur, four in Faxaflói, and one in the Atlantic Ocean south of Reykjanes. All collected sugar kelp samples were in the non-reproductive stage, and environmental data was collected at each site. All collected sugar kelp samples were phenotyped manually and through image analysis. Chlorophyll content, carbon and nitrogen content, and relative carbohydrate contents were also quantified. Strong correlations were observed between many morphological phenotypes and with environmental variables such as air temperature. While phenotypic differences between sampling locations were observed in some individual traits like blade length and chlorophyll content, principal component analysis did not reveal significant grouping of individuals by sampling location based on phenotype. These results suggest that phenotypic variation in sugar kelp populations across the Western Icelandic range is not strongly influenced by changes in environmental variation. These results are consistent with natural variation studies conducted in sugar kelp samples from New England, United States and indicate that sugar kelp can be a favorable choice for carbon sequestration across multiple environments.

Accurate particle size distribution (PSD) measurements of suspended particulate matter composed of flocs and aggregates are important to improve understanding of ecological and geomorphological processes, and for environmental engineering applications. PSD can be measured in situ (in the field) using a submersible sensor, or ex situ (in the laboratory) using samples. The methodological choice is often guided by logistical factors, and the differences in PSDs acquired by in situ and ex situ measurements are not acknowledged. In this study, a laser-diffraction instrument (LISST-200X) was used to compare in situ and ex situ PSD measurements. Samples measured ex situ were stored for three consecutive weeks and measured each week in a laboratory using different stirrer speeds. We observed that ex situ measurements display a higher D50 (median particle size) than in situ measurements of the same sample (up to 613% larger, 112% on average). Our experiments show that the difference between in situ and ex situ measurements can be explained by flocculation of the riverine sediments during the first week of storage. During the subsequent ex situ measurements, the stirring results in a significantly lower D50. Ex situ measurements are therefore unsuitable for flocculated suspended particulate matter. This study provides recommendations for optimizing PSD measurements by calculating the measurement times required to obtain robust PSD measurements (exceeding three minutes per sample), which are larger for field samples with coarser particles and wider PSDs.

Unmanned aerial vehicles (UAV) have been increasingly used for field data collection and remote sensing purposes. Their ease of use, ability to carry sensors and cameras, low cost, and precise maneuverability and navigation makes them a versatile tool for a field researcher. Procedures and instrumentation to use UAVs in the field are largely undefined, especially for atmospheric and hydrologic applications. A field study was conducted to test the UAV’s ability to collect atmospheric data in order to locate and analyze the canopy boundary layer (CBL) above a Costa Rican tropical montane rainforest. This study aims to give further insight on the changes of the CBL throughout the day and for atmospheric comparison to land development. Through the study, it was found that there was little previously defined procedures and preferred instrumentation that directly applied to the UAV field study. Because of this, the methodology of using an UAV for atmospheric and CBL remote sensing and data collection was developed and refined by testing and comparing precision and performance of sensors and executing systematic flight patterns throughout the day. The UAV allowed for quick and specific access to sampling locations and for all of the variables to be measured by sensors over vertical profiles. Flights were scheduled at different locations throughout the day over the Texas A&M Soltis Center and surrounding forest in San Isidro, Costa Rica. Vertical profiles were also measured over the Soltis Center grounds to determine how development has affected the presence of the CBL. The UAV was successful in gathering data above the forest canopy and the Soltis Center at varying elevations during clear and cloudy conditions. The procedure produced reliably consistent vertical profiles over small domains in space and time, validating the general approach. The technique also identified unique profiles at spatially and temporally distinct sample sites including response to meteorological events. These findings suggest a healthy ability to diagnose CBL characteristics of interest. It was also found that there is a distinct increase in temperature and dew point over land development when compared to over forest. Future studies include flights at other locations and determining preferred instrumentation for UAV atmospheric data collection.

Volcanic meteo-tsunamis, though rare, can pose significant threats to people, as exemplified by the 2022 Hunga Tonga – Hunga Ha’apai (HT-HH) eruption in the SW Pacific. While various studies have delved into the complexities of such phenomena, none have explored analogous scenarios in regions with potential occurrence of large eruptions near or under the sea. We focus on coastal areas along the South China Sea (SCS), among the most densely populated on Earth and historically prone to volcanic activity, including the catastrophic 1883 Krakatau eruption. Here we strategically chose one intra-basin volcano, KW-23612 in the northern SCS, and three extra-basin volcanoes, Banua Wuhu in the Celebes Sea, and Kikai and Fukutoku-Oka-no-Ba in the northern Philippines Sea (southern Japan), from which we simulated volcanic meteo-tsunamis with scaled intensities of the HT-HH event, to assess which countries around the SCS could be more at risk from the occurrence of such phenomena. Our results show that the worst-case scenarios are produced by eruption/tsunamis from the northern SCS, producing offshore waves up to 10 cm offshore Macau and Hong Kong, and up to 20 cm offshore Manila. In contrast, countries bordering the shallow Sunda Shelf (Malaysia, Thailand, Cambodia, and southern Vietnam) seem less at risk from volcanic meteo-tsunamis, though we observed some amplification effects along the deeper Singapore Strait. This study is the first of its kind in the region and sets the basis to investigate amplification effects, and shallow coastal dynamics at key locations, after integrating higher resolution bathymetry data.

Two rock magnetic methods can be used to determine the emplacement temperature of pyroclastic deposits. The first is by looking at the unblocking temperature spectra of the thermoremanent magnetization (TRM) and the second is through the repeatability of thermomagnetic behavior. Chachimbiro volcanic complex is an andesitic-dacitic stratovolcano located at the northern zone of the Ecuadorian volcanic arc. The lateral blast eruption that occurred at 3640-3510 BC originated from a ∼650 m wide and ∼225 m high rhyodacite dome. This satellite lava dome, located ∼6 km to the east of the main vent, erupted, resulting in a large pyroclastic density current (PDC). PDCs are hot mixtures of lithic fragments, gas and pumice, varying in size from fine ash up to metric blocks that descend the flanks of a volcano at great speeds, being the primary cause of death during explosive eruptions. The resulting PDC from this violent laterally directed explosion covered an area of 62 km^2, with the thickest parts of the deposit displaying as much as 15 m. We collected ~63 oriented block samples from 6 locations; their distances varying between 1.8 km to 6.7 km away from the source. Here we present the emplacement temperatures of the Chachimbiro pyroclastic deposits and the potential factors controlling them. Our rock-magnetic results indicate low titanium Ti-magnetite as the main magnetization carrier; maghemite being present in trace amounts. We have recognized that, based on the unblocking analysis of the TRM, the overall temperatures vary from 250 °C to 450 °C depending on the clast size and type. In general, our results suggest a minimum temperature of ~250 °C, with a large portion of the juvenile clasts having temperatures up to about ~450 °C. Furthermore, the analysis and the comparison of the Curie temperature executed in ~30 samples, against the emplacement temperatures obtained through the typical paleomagnetic studies will be presented. This work highlights the usefulness of paleomagnetism and rock magnetism to evaluate the emplacement temperatures of PDCs, thereby allowing to better assess the associated risk.

International frameworks for climate mitigation that build from national actions have been developed under the United National Framework Convention on Climate Change and advanced most recently through the Paris Climate Agreement. In parallel, sub-national actors have set greenhouse gas (GHG) reduction goals and developed corresponding climate mitigation plans. Within the U.S., multi-state coalitions have formed to facilitate coordination of related science and policy. Here, utilizing the forum of the NASA Carbon Monitoring System’s Multi-State Working Group (MSWG), we collected and reviewed climate mitigation plans for 11 states in the Regional Greenhouse Gas Initiative (RGGI) region of the Eastern U.S. For each state we reviewed the 1) policy framework for climate mitigation, 2) GHG reduction goals, 3) inclusion of forest carbon in the state’s climate action plan, 4) existing science used to estimate forest carbon, and 5) stated needs for carbon monitoring science. Across the region, we found important differences across all categories. While all states have GHG reduction goals and framework documents, nearly three-quarters of all states do not account for forest carbon when planning GHG reductions; those that do account for forest carbon use a variety of scientific methods with various levels of planning detail and guidance. We suggest that a common, efficient, standardized forest carbon monitoring system would provide important benefits to states and the geographic region as a whole. In addition, such a system would allow for more effective transparency and progress tracking to support state, national, and international efforts to increase ambition and implementation of climate goals.

Solid-vapor phase equilibria describe the volatile ices on Pluto’s surface (Tan & Kargel 2018, MNRAS 474, 4254). A simple model of the atmosphere with three components N2/CH4/CO may have solved the long-standing puzzle of the existence of CH4-rich ice in addition to the expected N2-rich ice. An isobaric treatment using CRYOCHEM equation of state naturally results in one solid phase of either ice, which is in equilibrium with the atmosphere, depending on the local temperature variations of Pluto’s surface. CH4-rich ice forms at higher temperatures, while N2-rich ice forms at lower temperatures. A temperature also exists on Pluto where three phases coexist, including vapor in equilibrium with two ices, and where the ices can switch from one type to the other upon cooling or warming. Our model relies on fundamental physics-based thermodynamics, and it explains New Horizons observations of the distributions of these ices, as presented by Bertrand et al. (Nat. Commun. 2020, 11, 1), without invoking a vertically distributed atmospheric CH4 that has not been verified with observation. As observed by New Horizons, Pluto’s surface has valley networks and channels, perhaps resulting from either fluvial (Moore et al. 2016, Science 351, 1284) or glacial (Howard et al. 2017, Icarus 287, 287; Umurhan et al. 2017, Icarus 287, 301) mechanisms, or both, at the present or in the past. Considering the present freezing condition on the surface, if the mechanisms are still in action, they must occur under the surface. Therefore, it is of great interest to know the phase equilibria involving the ices and liquid at conditions that may exist underground. Similar to the treatment of the surface ices, this work also applies CRYOCHEM to describe the phase equilibria that progress through depth as the temperature and pressure increase. The fate of the ices can be determined by examining the resulting phase diagrams at conditions at different depths, specifically the appearance of a liquid phase.

Minirhizotron imagery can be used to assess plant root health, and the amount of data for analysis motivates automation of root detection through use of neural networks. Building upon previous work, we show that we can use transfer learning from our PRMI dataset to assess root health across twelve classes of a new dataset to answer questions regarding how root health is affected by access of a tree by large herbivores, site infestation by Pheidole megacephala, and location of the tree. This dataset was collected from three paired sites at the Ol Pejeta Conservancy in Laikipia, Kenya, and consists of 20,000 images collected between September 2021 and May 2022. Each paired site represents four locations based on all four possible combinations of site infestation by Pheidole megacephala for at least 20 years and existence of herbivore-exclusion fence to keep large herbivores out. 1,332 images across all twelve classes of site and treatment combination were labeled with respective ground truths for model training. Our work uses the UNet architecture using pretrained weights on the network encoder and decoder which were obtained in 2019 in work which achieved over 99% accuracy on a dataset of peanut and switchgrass imagery. In our work, we found that training the model with our new dataset resulted in consistent performance across all classes of our new dataset, with over 99% accuracy for each class.

It is well-known that equatorial plasma bubbles (EPBs) are highly correlated to the post-sunset rise of the ionosphere on a climatological basis. However, when proceeding to the daily EPB development, what controls the day-to-day/longitudinal variability of EPBs remains a puzzle. In this study, we investigate the underlying physics responsible for the day-to-day/longitudinal variability of EPBs using the Sami3 is A Model of the Ionosphere (SAMI3) and the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCM-X). Simulation results on October 20, 22, and 24, 2020 were presented. SAMI3/WACCM-X self-consistently generated midnight EPBs on October 20 and 24, displaying irregular and regular spatial distributions, respectively. However, EPBs are absent on October 22. We investigate the role of gravity waves on upwelling growth and EPB development and discuss how gravity waves contribute to the distributions of EPBs. Of particular significance is that we found the westward wind associated with solar terminator waves and gravity waves causes midnight vertical drift enhancement and collisional shear instability, which provides conditions favorable for upwelling growth and EPB development. The converging and diverging winds associated with solar terminator waves and midnight temperature maximum also affect the longitudinal distribution of EPBs. The absence of EPBs on October 22 is related to the weak upward drift induced by weak westward wind associated with solar terminator waves.

Water and carbon exchanges between the land and atmosphere reflect key ecohydrologic processes, from global climate change to local watershed dynamics. Environmental stable isotope ratios of H2O and CO2 fluxes have been used to study these processes, yet measurement constraints have limited macroscale surface-atmosphere isotope flux evaluations. Across North American biomes within the US National Ecological Observation Network (NEON), we have worked as a team to translate raw measurements of carbon and water stable isotopes into calibrated daily surface-atmosphere flux isotope ratios for precipitation, evapotranspiration, and net ecosystem carbon exchange. Using information theory metrics, we demonstrate that these isotope observations contain meaningful information about the bulk water and carbon fluxes, with isotope measurements carrying about the same amount of information as wind speed measurements. Decomposition of this multivariate mutual information further shows that: (1) this information is unique, i.e. not carried by other traditional ecosystem measurements; and (2) the information added by isotopes is larger in more arid and cool ecosystems. Combining these isotope fluxes with bulk hydrologic fluxes drawn from a suite of land surface models in a first-order mass balance framework also allows for evaluation of hydrologic model structure and estimated uncertainties in partitioning of fluxes into transpiration, evaporation, overland, and subsurface water fluxes. An inter-model comparison suggests distinct patterns in isotope flux composition associated with disparities in the relative contributions of partitioned fluxes. Our results show that conservative isotope tracers provide novel validation metrics for evaluation of land surface model performance across ecosystems at a continental scale. Broadly, this compilation of datasets - combined with both empirical and process-based isotope modeling - suggests NEON stable isotope observations can improve general understanding of land-surface processes influencing the water and carbon cycles from regional to global scales.

PlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python that has been actively developed since 2014. A new version of PlantCV was recently released. Major goals of the version 4 release were to 1) simplify the process of developing workflows by reducing the amount of coding needed; 2) broadening the set of supported data types; and 3) introducing interactive annotation tools that can be used directly in PlantCV workflow notebooks. Here we highlight the use of point annotations that can be used to quickly collect sets of points for parameterization of functions such as regions of interest or the identification of landmark points. Another application of point annotations this for image annotation, which is a major bottleneck in plant phenomics. For example, we have used point annotations to analyze microscopy images aimed at measurement of quinoa salt bladders, the number and size of stomata, and scoring of pollen germination. These tasks have traditionally been low throughput and have required manual scoring, but our point annotation tools can be used along with traditional segmentation methods to semi-automatically detect and annotate images. The PlantCV point annotation tools also allow users to correct semi-automated detection results before classification (e.g., germinated vs non-germinated pollen) and extraction of size & color traits per object. Once images are annotated, results can be analyzed directly or potentially can be used as labeled data in supervised learning methods.

In the semi-arid extratropical Andes Cordillera, the seasonal snowpack acts as a natural water reservoir, releasing spring snowmelt runoff that accounts for more than 60 % of the total annual streamflow and sustains multiple productive uses, population needs, and unique ecosystems. Official seasonal streamflow forecasts (Sept-Mar runoff volumes) are currently generated by the General Water Directorate of Chile (DGA), based solely on regression-based methods that incorporate in-situ meteorological variables observed during winter as predictors. This work aims to assess the potential of the ensemble streamflow prediction (ESP) methodology for improved seasonal forecasts in high mountain basins in Central Chile, incorporating simple post-processing methods. To this end, we apply the GR4J rainfall-runoff model, as implemented in the airGR package, combined with the snow accumulation and ablation model CemaNeige in a set of case study basins. Preliminary results show that ESP forecast errors are smaller than those produced by the General Water Directorate of Chile (DGA). Ongoing efforts are aimed to identify potential differences and shortcomings in these techniques – using different verification measures – in terms of their capability to harness climatic and hydrologic sources of predictability.

We assessed the effect of temperature and tropospheric concentration of Nitrogen dioxide (NO2) and Carbon monoxide (CO) before and during the COVID 19 curfew/lockdown period (22 March-19 May 2020) on mortality due to COVID-19 in Indian Punjab. Time series daily data of TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor was used to study the spatio-temporal changes in NO2 and CO from 15 March to 19 May 2020. Visible Infrared Imaging Radiometer Suite onboard Suomi NPP satellite was used to detect the active fires (from 15 April to 19 May 2020) due to crop residue burning. The COVID-19 mortality was calculated from number of deaths relative to number of cases preceding two weeks of deaths due to COVID-19 virus. The weekly (15-21 March 2020) averaged tropospheric concentration of NO2 and CO was higher before the first day of curfew (22 March 2020). The concentration of NO2 decreased during the curfew period followed by increasing concentration from 11 April to 19 May 2020 and the concentration of CO increased from 19 April to 19 May 2020. There was a continuous increase in daily air temperature from 15 March to 19 May 2020. Mortality due to COVID-19 virus was significantly negatively correlated with NO2, CO and temperature. These results show that increasing temperature and concentration of NO2 and CO decrease COVID-19 mortality, but further studies should be conducted in different continents of the world to verify the impact of NO2, CO and temperature on COVID-19 mortality.

Accurate estimates of the soil water balance components are critical for optimizing irrigation water use in agricultural fields. Estimates are normally obtained using simple water balance models and for representative areas, not taking into consideration the within variability of soil properties. In this study, we used the MOHID-Land distributed process-based model to compute the variability of the soil water balance components in a 23ha almond field located in southern Portugal, at a resolution of 5m. The main objective was the possible assessment of management zones for improving water productivity in that water-scarce region. An electromagnetic induction survey was carried out first to obtain electromagnetic conductivity images which provided the spatial distribution of the real soil electrical conductivity (ff) with depth. The spatial distribution of ff was then correlated to soil particle size distribution using an in-situ calibration. Afterward, pedotransfer functions were applied to define the soil hydraulic parameters necessary to run the distributed model and map the within soil variability at the field scale. Irrigation data was monitored on-site, at two locations, while weather data was extracted from a local meteorological station. The distributed modeling approach included the definition of potential evapotranspiration fluxes computed from the product of the reference evapotranspiration obtained according to the FAO56 Penman-Monteith equation and a crop coefficient for each stage of almond’s growing season, the variable-saturated flow using the Richards equation, and root zone water stress following a macroscopic approach. Modeling results were used to present the maps of the variability of the seasonal actual crop transpiration and soil evaporation, the mean soil moisture, seasonal runoff, and seasonal percolation. Then, management zones for improving irrigation water use in the studied almond field were proposed.

Studying bedrock rivers during their transient states helps understand the response of a fluvial system to changed boundary conditions. Although studies show how river form adjusts to changes in incision or rock uplift rates, field constraints on the timescale of this adjustment are limited. We present a method that uses knickpoint travel time to estimate the adjustment times of channel width and angle of valley-side slopes to accelerated incision. The travel time of knickpoints between their current positions and the points where changes in width or hillslope angle have just finished represents the time required for morphological adjustment after knickpoint passage. We documented channel slopes, channel widths, and hillslope angles along six rivers that cross an active normal fault in Iwaki, Japan, and identified river sections in a transient state. Channel slopes and basin-averaged erosion rates determined from 10Be concentrations are distinct between rivers near and distant from the fault, suggesting that past increases in fault throw rates triggered the knickpoint formation and the observed transient response. Adjustment time depends on the slope exponent in the detachment-limited model and is 2–5 times greater for channel width than hillslope angle, indicating that catchment adjustment times can be much longer than times predicted only by knickpoint travel time. The fact that channel slope, channel width, and hillslope angle have distinct adjustment times underlines the importance of correctly identifying river sections that are fully adjusted to the new boundary conditions when inferring erosion or relative uplift rates for bedrock rivers.

Regionalization approaches wherein utilities in close geographic proximity cooperate to manage drought risks and co-invest in new infrastructure are increasingly necessary strategies for leveraging economies of scale to meet growing demands and navigate deeply uncertain risks. Successful regional cooperative investment and management pathways, however, must equitably balance the interests of multiple partners while navigating power relationships between regional actors. In long-term infrastructure planning contexts, this challenge is heightened by the evolving system-state dynamics, which may be fundamentally reshaped by infrastructure investment. This work introduces Equitable, Robust, Adaptive, and Stable Deeply Uncertain Pathways (DU PathwaysERAS), an exploratory modeling framework for developing regional water supply management and infrastructure investment pathways. Our framework explores equity and power relationships within cooperative pathways using multiple rival framings of robustness, each representing a competing hypothesis about how performance objectives should be prioritized. To capture the time-evolving dynamics of infrastructure pathways, DU PathwaysERAS features new tools to measure the adaptive capacity of pathway policies and evaluate time-evolving vulnerability. We demonstrate our framework on a six-utility water supply partnership seeking to develop cooperative infrastructure investment pathways in the Research Triangle, North Carolina. Our results indicate that commonly employed framings of robustness can have large and unintended adverse consequences for regional equity. Results further illustrate that regional and individual vulnerabilities are highly interdependent, emphasizing the need to craft agreements that limit counterparty risks from the actions of cooperating partners. Beyond the Research Triangle, these results are broadly applicable to cooperative water supply infrastructure investment and management globally.

This manuscript discusses the challenges in detecting and attributing recently observed trends in the Atlantic hurricanes and the epistemic uncertainty we face in assessing future hurricane risk. Data used here include synthetic storms downscaled from five CMIP5 models by the Columbia HAZard model (CHAZ), and directly simulated storms from high-resolution climate models. We examine three aspects of recent hurricane activity: the upward trend and multi-decadal oscillation of the annual frequency, the increase in storm wind intensity, and the downward trend in the forward speed. Some datasets suggest that these trends and oscillation are forced while others suggest that they can be explained by natural variability. Future projections under warming climate scenarios also show a wide range of possibilities, especially for the annual frequencies, which increase or decrease depending on the choice of moisture variable used in the CHAZ model and on the choice of climate model. The uncertainties in the annual frequency lead to epistemic uncertainties in the future hurricane risk assessment. Here, we investigate the reduction of epistemic uncertainties on annual frequency through a statistical practice – likelihood analysis. We find that historical observations are more consistent with the simulations with increasing frequency but we are not able to rule out other possibilities. We argue that the most rational way to treat epistemic uncertainty is to consider all outcomes contained in the results. In the context of hurricane risk assessment, since the results contain possible outcomes in which hurricane risk is increasing, this view implies that the risk is increasing.