A document by Nicola Tisato. Click on the document to view its contents.

Reconstructing atmospheric CO2 concentration in the Late Miocene is crucial for understanding the relationship between greenhouse gas concentrations and climate change in a warmer-than-modern world. Both δ11B-based and alkenone-ep-based CO2 estimates feature uncertainties due to poorly constrained past seawater chemistry, and algal physiological processes, respectively. Additionally, both proxies estimate CO2[aq], so they require reliable surface ocean temperatures to calculate solubility and atmospheric CO2. To evaluate proxy coherence, in this study we generate new records of alkenone ep and δ11B, from the western Tropical Atlantic ODP Site 926 during the Late Miocene. We provide surface ocean temperature estimates from coccolith clumped isotope thermometry, alkenone undersaturation ratios, and planktonic foraminiferal Mg/Ca ratios. The warm temperatures estimated from our new clumped isotope records, together with alkenone temperatures >29°C, confirm warm tropics, and provide constraints on the assumptions of seawater Mg/Ca and dissolution corrections for foraminiferal Mg/Ca SST estimates. The new alkenone ep CO2 estimates at 926 yield generally similar CO2 as the new and published δ11B-based CO2 records for the site, and are similar to published alkenone ep CO2 records from the South Atlantic ODP Site 1088. However, over the 7.3 to 7.8 Ma interval, the CO2 values from ep are lower than other records. We evaluate which proxy indicators can best predict variations in algal physiology which may bias the ep-based CO2 reconstructions in this interval at Site 926.

Managing and living with geohazards is especially challenging in mountain landscapes. Informed management relies on an understanding of catchment-scale sediment dynamics and system functioning. Sediment budgeting can be used as a framework and practical management tool to organize and analyze the necessary data. However, it can be challenging to constrain sediment budget components, partition sediment yield measurements by source and grain size, and resolve scale issues. In this study, we seek to better constrain the spatial and temporal patterns of bed material transfer by leveraging a suite of techniques to measure and quantify sediment transfers in a glacierized, mountain catchment. First, we quantify the historical bed material yield using field surveys and historical air photo analysis. Second, we utilize high-resolution, multi-temporal lidar data and detailed geomorphic mapping to construct a detailed sediment budget. A mixed-methods approach and careful uncertainty analysis was required to resolve the historical sediment yield and detailed sediment budgeting results. In the Fitzsimmons Creek Watershed, the annual sediment yield varied by up to a factor of 10 over the 76-year record. Sediment source partitioning suggests landslides, active channel, and floodplain sources each contributed 1/3 of the total sediment supply. Importantly, the landsliding occurs proximal to the outlet and transports glacial valley fill to the channel, significantly increasing sediment yield. Point-based sediment yield estimates are helpful for long-term evaluation of landscape denudation and system change, while detailed sediment budgets provide information necessary for understanding complex system functions and dynamics, and for management applications.

Predicting Earth systems weeks or months into the future is an important yet challenging problem due to the high dimensionality, chaotic behaviour, and coupled dynamics of the ocean, atmosphere, and other subsystems of the Earth. Numerical models invariably contain model error due to incomplete domain knowledge, limited capabilities of representation, and unresolved processes due to finite spatial resolution. Hybrid modeling, the pairing of a physics-driven model with a data-driven component, has shown promise in outperforming both purely physics-driven and data-driven approaches in predicting complex systems. Here we demonstrate two new hybrid methods that combine temporal or spatiotemporal models with a data-driven component that may be modally decomposed to give insight into model bias, or used to correct the bias of model projections. These techniques are demonstrated on a simulated chaotic system and two empirical Ocean variables: coastal sea surface elevation and sea surface temperature, which highlight that the inclusion of the data-driven components increases the skill of predicting their short-term evolution. Our work demonstrates that these hybrid approaches may prove valuable for: improving models during model development, creating novel methods for data assimilation, and enhancing the predictive accuracy of forecasts when available models have significant structural error.

This study explores the impact of deep ( >5 m) critical zone (CZ) architecture on vegetation distribution in a semi-arid snow-dominated climate. Utilizing seismic refraction surveys, we identified a significant correlation between saprolite thickness and LiDAR-derived canopy heights (R²=0.47). We argue that CZ structure, specifically shallow fractured bedrock under valley bottoms, redirects groundwater to locations where trees are established—suggesting they are located in specific locations with access to nutrients and water. This work provides a unique spatially exhaustive perspective and adds to growing evidence that in addition to other factors such as slope, aspect, and climate, deep CZ structure plays a vital role in ecosystem development and resilience.

We present seismic reflection images of the plate interface up to depth of ~65 km across a ~130,000 km 2 area offshore the Alaska Peninsula. These images capture systematic along-strike and downdip variations in the thickness of the plate interface reflection package, which we interpret in terms of megathrust properties and slip nature. A relatively thin reflection package (<2 km) characterizes the megathrust at depths <24-34 km, marking an area of localized brittle deformation that is likeliest to host the largest seismic asperities. The plate boundary reflection band thickness transitions from 2-5 km over depths of ~24-41 km, suggesting a thickening zone of cumulative deformation and greater heterogeneity corresponding to conditionally stable megathrust behavior. At depths >35-41 km, the reflection package maintains a thickness of 5-6 km and marks the predominantly aseismically slipping megathrust areas of which only the shallower part can host earthquake slip. Historic and recent megathrust earthquakes in the SW Kodiak Asperity and Semidi Segment generally occur within the thin and/or transitional reflection bands. In contrast, the 2020 Simeonof and earlier M7.x earthquakes in the Shumagin Gap are estimated to also rupture the shallowest section of megathrust characterized by the thick reflection band (~35-41 km depth). Although the shallow part of the plate boundary ( in the Semidi and Shumagin segments did not rupture in recent 2020 M7.8 Simeonof and 2021 M8.2 Chignik earthquakes, the seismic properties of the megathrust suggest it may be capable of earthquake slip in the future, as may have occurred in 1788.

Lakes are sources of nitrous oxide (N2O). However, labor-intensive gas analysis has confined evaluations of temporal variability in lakes to the seasonal time scale. We used a state-of-the-art laser-based N2O analyzer combined with a floating chamber to examine sub-daily variations in N2O emissions from a shallow eutrophic lake in Japan. We conducted intense daytime field campaigns during different seasons to reveal differences in sub-daily variations in N2O emissions from the water surface in conjunction with the dynamics of dissolved N2O concentrations in the lake water. The study revealed that N2O fluxes varied within a day: emissions increased in response to increased wind speeds. Variations in surface dissolved N2O concentrations caused by water mixing were also important during the summer, when accumulation of N2O initially occurred in the lake bottom layer during stably stratified conditions. During winter, the lake water was well mixed and dissolved N2O concentrations were uniform throughout the water column; thus, wind speed was the only factor controlling the diurnal variations in N2O emissions. These findings demonstrate the need to consider diurnal variability when estimating cumulative N2O emissions accurately over long periods.

A document by Tyler Eddy. Click on the document to view its contents.

High-resolution water budget estimates benefit from modeling of human water management and satellite data assimilation (DA) in river basins with a large human footprint. Utilizing the Noah-MP land surface model, in combination with an irrigation module, Sentinel-1 backscatter and snow depth observations, we produce a set of 0.7-km$^2$ digital water budget replicas of the Po river basin (Italy) for 2015-2023. The results demonstrate that irrigation modeling consistently improves the seasonal soil moisture variation and summer streamflow at all gauges in the valley after withdrawal of irrigation water from the streamflow (12\% error reduction relative to observed low summer streamflow), even if the basin-wide irrigation amount is underestimated. Sentinel-1 backscatter DA for soil moisture updating strongly interacts with irrigation modeling: when both are activated, the soil moisture updates are limited, and the simulated irrigation amounts are reduced. Backscatter DA systematically reduces soil moisture in the spring, which improves downstream spring streamflow. Assimilating Sentinel-1 snow depth retrievals over the surrounding Alps and Apennines further improves spring streamflow in a complementary way (2\% error reduction relative to observed high spring streamflow). Despite the seasonal improvements, irrigation modeling and Sentinel-1 backscatter DA cannot significantly improve short-term or interannual variations in soil moisture, irrigation results in a systematically prolonged high vegetation productivity, and snow depth DA only impacts the deep snowpacks. These findings help to advance the design and production of digital water budget replicas for river basins.

This paper addresses the critical aspect of water conservation in public buildings within the context of sustainable urban water resources management. While conventional approaches rely on pricing controls and water-saving appliances, this research emphasizes the key consideration of psychological factors influencing users' willingness to conserve water. Through a survey involving 1,206 volunteers, an expanded theory of planned behavior model is constructed and analyzed to evaluate the impact of water-saving attitudes, subjective norms, self-efficacy, and perceived behavioral control on individuals' water-saving behavior. Intervention experiments conducted in three types of public buildings equipped with smart water meters unveil the nuanced dynamics of real-time water-saving behavior and its responsiveness to external interventions. Findings underscore the collective influence of subjective norms, water-saving attitudes, self-efficacy, and perceived behavioral control on individuals' intentions and behaviors. Noteworthy is the observed time lag and diminishing impact of external interventions, where economic, feedback, and subjective norms interventions prove more effective. This study not only contributes a theoretical framework but also provides practical insights, emphasizing the need for consistent and targeted external interventions. Practitioners, decision-makers, and stakeholders are urged to recognize the profound impact of users' psychological factors on public water-saving behavior and strategically employ interventions for sustained positive outcomes.

Tropical South America’s hydroclimate is influenced by ocean-atmospheric oscillations. The physical mechanisms that teleconnect the Atlantic modes of variability with the evaporation, soil moisture and streamflow of the region remain unclear. This study uses composites of reanalysis, satellite and gauge data to identify the processes linking land-surface anomalies and ocean modes. It shows that the Atlantic Meridional Mode (AMM) generates cross-equatorial wind anomalies that affect moisture convergence, in turn modifying cloud cover, precipitation, radiation availability and hence evaporation. Evaporation anomalies are water- or energy-driven depending on the phase of the mode; streamflow anomalies follow those of rainfall. The spatial pattern of the impact of the AMM depends on the season analysed. The Atlantic El Niño (Atl3) affects the Guianas and eastern Orinoco because of pressure and trade wind strength variability. Therefore, these ocean-atmospheric modes impact the water and energy cycles and might influence regional climate extremes (e.g. droughts and floods).

The occurrence of cold spells over different regions of North America has been linked to windy extremes over western Europe. These events – termed pan-Atlantic extremes – are mediated by an anomalous state of the North Atlantic storm track. While it is known that the occurrence of European windstorms is modulated by the state of the storm track, the relative contribution of the North American cold spells to European wind extremes is not easy to quantify. In this study, cold spells over two regions of North America are clustered with respect to the evolution of the large-scale circulation over the North Atlantic. The contribution of cold spells to the European wind extremes is then ascertained using circulation analogs, so that different states of the North Atlantic storm track can be compared for days with and without cold spells. Consistent with previous work, two main pathways emerge from the analysis, called “zonal” and “wavy” for simplicity. For a wavy pathway, North American cold spell occurrence is associated with more frequent European wind extremes than expected from the state of the North Atlantic storm track. For the other pathways, on the other hand, the anomalous state of the storm track was able to account alone for the more frequent wind extremes than climatology observed across Europe, with no or little ascertainable contribution from the cold spells. This analysis clarifies the causality of wintertime pan-Atlantic extremes and how these link to different atmospheric dynamical pathways.

Vertical motions associated with mesoscale ocean eddies modulate the light and nutrient environment, stimulating phytoplankton biomass and chlorophyll anomalies. Populations within eddies may be trapped by the horizontal circulation or laterally diluted by mixing with surrounding waters. Conventionally, eddy boundaries are determined using Eulerian methods from daily satellite sea level anomaly fields. However, Eulerian methods do not delineate the bounds of trapped water masses, which is an important consideration when interpreting eddy-induced changes in chlorophyll concentration. Integrated Lagrangian particle tracking methods, on the other hand, more accurately identify coherent structure boundaries that trap fluid masses for time scales relevant to phytoplankton bloom evolution. From two decades of remote sensing observations in the North Pacific Subtropical Gyre, we compared coincident Eulerian and Lagrangian eddy atlases to assess the impact of eddy trapping on chlorophyll concentration. We found more positive chlorophyll anomalies within Lagrangian coherent vortices than in Eulerian eddy boundaries and outside-eddy waters. Yet, there are striking differences seasonally and regionally within the gyre. Chlorophyll is most enriched within coherent boundaries of the Hawaiian Lee eddies and in the region southeast of the Islands in fall and winter. Our results suggest that typical Eulerian analyses underestimate the role of mesoscale eddies in enhancing chlorophyll by not accounting for lateral mixing. Consequently, quantifying the contribution of mesoscale eddies toward open ocean primary production is more complex than previously assumed.

Joanne S. Boden1*, Sanjoy M. Som2,5, William J. Brazelton2,4, Rika E. Anderson3, Eva E. Stüeken11 School of Earth and Environmental Sciences, University of St. Andrews, Bute Building, Queen’s terrace, St. Andrews, Fife, KY16 9TS, United Kingdom2 Blue Marble Space Institute of Science, Seattle, WA, USA3 Department of Biology, Carleton College, Northfield, MN, USA4 School of Biological Sciences, University of Utah, Salt Lake City, Utah, USA5 Space Science and Astrobiology Division, Exobiology Branch, NASA Ames Research Center, Moffett Field, Mountain View, CA, USA* Corresponding author ([email protected])Key Words: Phosphite, serpentinization, hydrothermal vent, metagenomics, Archean, phosphorus, geothermal

Valentí Rull1,2

Analysis of forty years of tide gauge data and reanalysis wind stresses from the Middle Atlantic Bight indicate that along-shelf wind stresses are a dominant driver of coastal dynamic sea level (sea level plus atmospheric pressure) variability at daily to yearly time scales. The sea-level response to along-shelf wind stress varies substantially along the coast and is accurately reproduced by a steady, barotropic, depth-averaged model (Csanady 1978, Arrested Topographic Wave). The model indicates that the sea-level response in the MAB depends primarily on the along-shelf distribution of the along-shelf wind stress, the Coriolis frequency, the bottom drag coefficient, and the cross-shelf bottom slope. The along-shelf wind stress varies along the MAB shelf due primarily to changes in the shelf orientation. The sea-level response depends on both the local and upstream (in the sense of Kelvin wave propagation) along-shelf wind stresses. Consequently, sea-level variability at daily, monthly and yearly time scales along much of the central MAB coast is more strongly driven by upstream winds along the southern New England shelf than by local winds along the central MAB shelf. The residual coastal sea-level variability, after removing the wind-driven response and the trend, is roughly uniform along the MAB coast. The along-coast average of the residual sea level at monthly and yearly time scales is caused by variations in shelf water densities primarily associated with the large annual cycle in water temperature and interannual variations in salinity.

Mesoscale Convective Systems (MCSs) play a critical role in tropical rainfall patterns and circulations. To reduce persistent biases and improve understanding of the climate system, international groups have called for unprecedented investment in global convection-permitting (CP) climate models. It is essential such models accurately represent MCSs, and in particular environmental interactions such as dynamical control by wind shear. We show that in representative current generation CP simulations, MCS updraft entrainment decreases with shear, leading to a realistic increase of extreme rainfall. We find the control of environmental shear extends to mean storm rainfall and anvil heights. The simulation of these effects depends strongly on model physics in both CP and parameterised models. We show that in West Africa, MCS shear response influences the zonal distribution of storm diabatic heating, modifying upscale impacts of convection. Our results demonstrate key tests for focused process-based assessment of CP model fidelity.

As the accessibility of satellite imagery grows, the necessity for precise classification models intensifies. This research endeavors to construct robust classification models capable of accurately discerning ten distinct land-use categories from satellite images. To attain this objective, the study delves into various methodologies, such as transfer learning, convolutional neural networks (CNNs), and the application of a Random Forest classifier in conjunction with Principal Component Analysis (PCA). Each approach presents distinct strengths and obstacles in tackling the intricacies inherent in satellite image classification. The dataset utilized for this investigation originates from the EuroSAT dataset, providing a standardized foundation for analysis and comparison. Through rigorous evaluation and comparison of these methodologies, this study aims to contribute to the advancement of land-use classification techniques, facilitating more accurate and efficient utilization of satellite imagery in diverse applications.