A document by Jesus Nunez. Click on the document to view its contents.

A document by MILUSKA ANTHUANNET ROSAS BARTUREN. Click on the document to view its contents.

The transport and delivery of low-abundance, bioactive trace elements to the surface ocean by aerosol mineral dust is a major planetary control over marine primary production and hence the global carbon cycle. Variations in the concentration of atmospheric dust have established links to global climate over geologic timescales and to regional biogeographic shifts over seasonal timescales. Constraining atmospheric dust variability is thus of high value to understanding oceanographic systems, especially vast, constitutively low-nutrient subtropical gyre ecosystems and high-nutrient/low-chlorophyll ecosystems where availability of the trace element iron is a dominant ecological control. Here we leverage the MERRA-2 reanalysis product to examine over four decades of surface-level atmospheric mineral dust concentrations in a domain of the subtropical North Pacific centered at ocean Station ALOHA. This study region has been sampled regularly since the mid-1980s and was the site of the Hawaii Aerosol Time-Series (HATS) project in 2022-2023. Two unequal semi-annual periods of elevated dust are evident in the long-term data are described and constrained. We look for evidence of shifts in total and seasonal atmospheric dust abundances and in the timing of the onset of the dominant spring/summer dusty period, finding year-to-year variations but little evidence for long-term trends. We observe significant but complex relationships between the Pacific Decadal Oscillation (PDO) index and both dust and precipitation. The data show that 2022 was among the dustiest years for the study domain in the preceding two decades and, by contrast, that 2023 exhibited a significant early-spring lull in dust.

The core of terrestrial planets, including Earth, Mars, and Mercury, etc., is majorly composed of iron alloyed with some lighter elements, such as silicon, sulfur, carbon, oxygen, and hydrogen. Core's thermal conductivity plays a key role in controlling its cooling rate, dynamics, thermochemical and dynamo evolution, as well as available energy budget to power dynamo for magnetic field. Recent successful advances in coupling an ultrafast optical pump-probe method with externally-and laser-heated diamond anvil cells have enabled us to precisely and systematically measure the thermal conductivity of a number of candidate core materials in terrestrial planets. With data modelling, we have created the first version of thermal conductivity profiles in the deep interiors of Earth and Mars, with potential critical impacts on their core's thermochemical and dynamo evolution. We acknowledge funding support from the Academia Sinica and the National Science and Technology Council of Taiwan, Republic of China, under Contract AS-IA-111-M02 and 113-2628-M-001-013-. The works presented here are also co-authored with Jung

Conventional approaches of training ecohydrologic models usually consider one hydrometric dataset, such as field measured matric potential or a soil moisture time series. In numerical modeling, these metrics can be used to optimize soil physical parameters using equations for water content and hydraulic conductivity. However, training a model on matric potential alone neglects valuable calibration potential available from transport processes, often described by the advection dispersion equation. Our group aims to quantify this potential by using stable isotopes of water as a tracer. In HYDRUS-1D, we couple daily stable isotope ratios and matric potential measurements to optimize soil hydraulic and transport parameters for two forested sites in the Upper Colorado River Basin. Using inputs of δ2H, five parameter optimization schemes were tested across four sampling intervals (daily, weekly, biweekly, and rainfall-based) to assess calibration performance. Results show that when KGEΨ values are highest (indicating strong model performance with regard to matric potential), unincorporated values of KGED are considerably low (indicating poor model performance with regard to stable isotopes). We hypothesize that the maximum average of both metrics, KGEx̄, yields a more robust parameter set, and consequently, a more physically representative model. Comparing KGEx̄ for daily and weekly sampling intervals shows a negligible difference, suggesting that weekly sampling is adequate for model calibration. Rainfall-based sampling (two days after precipitation ≥ 3 mm) shows promise, with a comparable model performance to daily and weekly sampling. Meanwhile, the model underperformed with biweekly sampling. We propose that weekly sampling is sufficient for producing an optimal parameter set, and that daily sampling is likely unnecessary. For good measure, less frequent sampling intervals are not recommended. This study aims to evaluate sampling frequency and model performance to benefit future efforts of ecohydrologic model calibration.

Internal tides and NLIW produce the strongest currents on the Aquitaine shelf. • Coastal wind-driven upwelling alters the cross-shelf evolution of the NLIW. • First field observations of co-existing mode 1 NLIW of opposite polarity under doublepycnocline conditions.

Accurate weather forecasts are crucial to planning disaster management, protecting existing infrastructure, and sustaining federal mission areas. Geospatial foundation models (GFMs) have recently been developed as impactful tools for various stakeholders as vision transformers increasingly outperform numerical weather prediction (NWP) models. The state-of-the-art for machine learning-based weather foundation models has been recently fueled by the vision transformer architecture and may be finetuned for regional or local weather predictions. The vision transformer architecture uses patches representing regions of space and time over the input data. However, weather reanalysis datasets, such as ERA5, provide weather data as projections of the Earth’s spheroidal topology onto a rectangle. This introduces both position-dependent warping and an arbitrary seam at the 180° line of longitude. This may be eliminated by considering the topology of the ERA5 dataset as a cylinder, padding certain longitudes to effectively achieve a wrap-around patch embedding that is not affected by a seam, analogous to previous work developing cylindrical convolutional layers. We demonstrate promising results on smaller vision transformers and propose that this method improves performance on regional finetuning tasks in the vicinity of the 180° line of longitude. To mitigate the warping effect of projection and reduce overreliance on positional embedding, we employ an inverse projection of the source data back to spherical coordinates. We then rotate the sphere so that data are projected back onto the source projection, where the patches are centered and minimally warped. This ensures consistent patch representation across latitudes. By selecting patches in spherical coordinates, the model weights geospatial weather data equally across the globe. We propose these techniques to improve the model’s generalization and are likely to scale to larger models. Further work is required to fully evaluate the impact on state-of-the-art GFMs. We implement these techniques on Microsoft’s ClimaX foundation model, finetuning to make forecasts local to government installations in the Pacific, working towards improved mitigation of weather uncertainty for operational assets near the 180° line of longitude.

A document by Erle Ellis. Click on the document to view its contents.

A document by Chenyang Wei. Click on the document to view its contents.

▪ Forests play a crucial role in global carbon cycling, yet their carbon sink capability and resilience are increasingly threatened by drought under climate change. The influence of plant traits on ecosystem functions and their responses to drought remains poorly understood. ▪ In this study, we analyzed the control of plant traits and climate on forest ecosystem functions and their sensitivity to water stress across eight NEON forest sites spanning diverse temperature and moisture gradients. ▪ Each site, equipped with eddy covariance flux towers, provided data on intrinsic water-use efficiency (iWUE), water-use efficiency (WUE), and light-use efficiency (LUE). We measured key photosynthetic and hydraulic traits (V c,max , K max , P 50) for the most abundant canopy species at each site, and assessed their sensitivity to soil water content (SWC) by dividing SWC into site-specific quantiles, representing dry to wet conditions. ▪ To further investigate trait-climate interactions, we integrated the measured traits into the GFDL LM4.1 Land Model and calibrated it using flux tower-derived ecosystem functions. Our findings provide critical insights into how plant traits and climate regulate ecosystem functions and their resilience under water stress, advancing our ability to predict forest responses to future climate scenarios. This project is funded by NSF #2017949. 𝑎 1 ,𝒃 𝟏-intercept and slope (i.e. control) that controls how plant trait 𝑥 𝑖 influences 𝛽 1𝑖 at site I 𝜀 𝑖𝑗 , 𝜂 0𝑖 , 𝜂 1𝑖-random errors

Turfgrass growth resulting from soil organic matter (SOM) mineralization in putting green root zones has been explored sparingly. Glasshouse and laboratory studies were conducted in Knoxville, TN to discern if SOM content would result in differential mineralization affecting (CBG; Agrostis stolonifera L.) growth. Cores (10.8-cm width by 10-cm depth) were collected from CBG putting greens varying in SOM content: low (0.0064 g kg -1 to 0.0026 g kg -1), medium (0.0027 g kg -1 to 0.00194 g kg -1), and high (0.00195 g kg -1 to 0.00256 g kg -1). One putting green from each SOM group was selected for further investigation. After eight weeks, SOM decreased 0.005 g kg-1 within cores originating from putting greens in the high and medium SOM groups, compared to only 0.001 g kg-1 for those in the low SOM group. Inorganic N within cores from putting greens in the high and medium SOM groups increased more than those from the low SOM group, suggesting a greater percentage of inorganic N was allocated for CBG growth. When incubated at 25 C under dark conditions, inorganic N increased 36 mg kg-1 in cores from the high SOM group, indicating the potential for as much as 50 kg ha-1 of N to become available within the root zone. If unaccounted for when making synthetic fertilizer applications, available N on CBG putting greens could lead to excessive growth under field conditions requiring additional maintenance practices. Future research to confirm this response under field conditions is needed.

The ability to distinguish observed climate change from naturally arising climate variability is one of the most fundamental findings in climate science. Climate change emergence is a key statistic used to quantify the impact of human activities on climate and inform climate change mitigation strategies. Previous studies determining when climate change has emerged, however, do not account for uncertainty in historical observations or climate models. Here, we show that the emergence of regional warming is early, widespread, and robust to observational and model uncertainty. Warming has emerged over more than 90% of the global land surface as of 2020, and had emerged over more than 50% of the earth's land surface by 2000. Emergence occurs despite observational and model uncertainty creating delays of more than 10 years over 75% of the land surface. These conclusions demonstrate that a complete uncertainty accounting is necessary for robust detection of climate change emergence.

Yulu Peng1, Shasha Zou1, Zihan Wang2, Xiantong Wang1, Aaron Ridley1, Grace Kwon1 and Mary Smirnova11Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA2Department of Physics, University of Texas at Arlington, Arlington, TX, USACorresponding author: Yulu Peng (yulupeng@umich.edu)

There is a growing call to increase collaboration, inclusion, and engagement with end-users in applications such as remote sensing, Earth observation, artificial intelligence, and fire sciences. In response, Canada is making significant investments in cultivating collaboration and user engagement in fire and fire monitoring. Initiatives include the Canadian Forest Service’s Wildfire Resilient Futures Initiative and the upcoming WildFireSat mission. The fire monitoring community relies heavily on geospatial technologies, Earth observation, and remote sensing, benefiting greatly from researchers with diverse expertise and experiences. To advance inclusion and collaboration in fire monitoring, it is important to understand who is working in the field, their networks, and perceived barriers to research and collaboration. This presentation will overview the multi-year “Underserved Actors in Canadian Fire Monitoring” program and its early findings. The program is divided into three phases: 1) a bibliometric and initial network analysis to identify fire monitoring actors in Canada, 2) a survey of fire monitoring actors and enhanced social network analysis using identity-based information, 3) interviews with key actors to identify opportunities for improving collaboration and inclusion in fire monitoring. We will present outcomes from Phase 1, including an overview of the Canadian fire monitoring actors database and the results from a spatial social network analysis. Data on Canadian fire monitoring actors was collected using a systematic search of publication databases in combination with purposive sampling of research consortiums and fire organizations. Our results characterize the types and levels of connectivity within the Canadian fire monitoring community, which will guide subsequent survey and interview questions for the second phase of the study. This research has contributed to broader international efforts to characterize global fire monitoring users as part of the Committee on Earth Observation Satellite Working Group on Disasters Wildfire Pilot program. Additionally, outcomes from this research can be used to tailor resources from future collaboration and capacity-building efforts in fire monitoring.

The report of the 2024 decadal survey for the solar and space physics community was released on 2024 December 5 and has space weather science and applications as a central goal to explore and safeguard humanity's home in space. I discuss some of the main recommendations associated with space weather that will drive our field in the next decade.

This study addresses the challenges of managing transboundary water resources characterized by data scarcity, focusing on the Indus Basin as a case study. Groundwater depletion in such regions poses a critical threat to food and water security, necessitating effective management strategies. We leverage Gravity Recovery and Climate Experiment (GRACE) satellite data, a valuable alternative in data-scarce environments to monitor terrestrial (TWS) and groundwater storage (GWS) changes. GRACE/GRACE-FO products have widely being used by researchers for studying terrestrial water cycle. However, the existing resolutions of GRACE/GRACE-FO products has limited the ability to identify local-scale changes in TWS and GWS across smaller catchments. The research downscaled GRACE data to 1km2 resolution using data-driven machine learning models and spatial downscaling techniques to explore local vulnerabilities of TWS and GWS dynamics associated with both anthropogenic and climatic changes across 20 sub-regions in the Indus Basin. Comparison with in-situ GWS from 2200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in-situ data, evidenced by higher correlation coefficients (0.60-0.95). The integration of GRACE into machine learning models enables the identification of local vulnerabilities. Hotspots with the highest TWS and GWS decline rate between 2002-2023 were in downstream sub-regions Dehli Doab (-437, -457 mm/year), BIST Doab (-348, -410 mm/year), Rajasthan (-266, -286 mm/year), and Ravi (-190, -278 mm/year), primarily linked to anthropogenic stressors. The findings underscore the potential of GRACE not only as a data source but as a powerful tool for actionable decision-making in the sustainable management of shared water resources in transboundary basins.

Serpentine carbonation could provide a safe, and environmentally friendly, opportunity to store the amount of anthropogenic CO2 emission necessary to meet the Paris Agreement target and limit global warming to a safe level for humanity. Mineral carbonation relies on the reaction of CO2 with cations abundant in ultramafic rocks (e.g. Mg2+) to form carbonate minerals (\citealp{Kelemen_2019}). Serpentinites are an ideal starting material because they are abundant on or near the Earth’s surface and are rich in Mg2+, which is required to form carbonates, primarily magnesite (MgCO3) that traps CO2 (\citealp{Lackner_1995}). This process stores CO2 in a solid state with no risk of release and has large storage capacity. Even if this technique is promising, it is at a lower stage of readiness and is yet to be applied at the industrial scale.The Ligurian ophiolites (Italy) host several magnesite deposits that formed when deep CO2 was injected into serpentinite lenses through regional faults at 100-200°C and depths of 1-2 km (\citealp{Rielli_2022}, \citealp{Boschi_2009}). These deposits effectively resemble the in-situ storage of CO2 by “natural pilot plants” active millions of years ago, where seemingly the conditions where fine-tuned to best sequester CO2. Thus, they provide a fast lane for understanding the best conditions for serpentinite carbonation.The PRIN 2022 PNRR STORECO2 project (granted by the European Union—NextGeneration EU) aims to understand these conditions through a geochemical, petrological and mineralogical study of the Castiglioncello magnesite deposit (Tuscany, Italy). Developing a deeper understanding on carbonation processes through the study of natural analogues, discussed in this work, we will able also to predict what to expect when CO2 is injected into serpentinites.

Surface water and groundwater are changing rapidly because of significant climate warming in the Arctic region \cite{Fichot_2013,Morison_2012}. Arctic amplification has intensified the melting of snow cover and glaciers, as well as widespread permafrost degradation, leading to a prominently increase of the annual discharge of some Arctic rivers \cite{McClelland_2006,Wang_2021}. Investigations on the Arctic Freshwater have increased \cite{Bring_2016} but the knowledge of processes that govern water flow dynamics in High Arctic basins is still quite limited \cite{Pedersen_2022}.