Stream temperature is critical for maintaining ecological health and managing water resources. Stream temperature is influenced by a complex interplay of factors, including air temperature, solar radiation, discharge, and land use, with additional complexities introduced by urbanization and climate change. This study explores relationships between land use, riparian vegetation, and stream temperature within the Chesapeake Bay Watershed (CBW), utilizing Long Short-Term Memory (LSTM) model-a type of recurrent neural network model designed for time-series prediction. Here, we use both 30-m National Land Cover Database data as well as new, highresolution (1-m) land cover data developed for the CBW to explore the effects of both reach-scale and upstream watershed-scale riparian land cover on stream temperature. Findings from this work can help us identify stream reaches or watershed areas where land-use change or lack of riparian vegetation have the most significant impact on stream temperature. Such understanding allows us to better prioritize restoration and conservation efforts to mitigate stream temperature increases, thus supporting the development of more targeted environmental management strategies within the Chesapeake Bay Watershed.

A compilation of many data sets indicates that the exponents for the cumulative length distribution of small faults of L < T, the brittle thickness, and for large faults of L > T, are both approximately 2. The same result was found by Zou and Fialko (2024), but that was fortuitous: their finding being contingent on two cancelling errors. If is 2 for both distributions, it indicates that the exponent B of the cumulative moment distribution is 2/3 for small faults and 1 for large faults, the same values as found for small and large earthquakes. The moment release rates obtained from both earthquake and fault data have also been found to correspond. This demonstrates that in continental regions the long-assumed correspondence between earthquakes and faults is exclusive. All tectonic earthquakes must occur on faults and faults must slip predominately in earthquakes. The relative contribution of small earthquakes/faults to strain is minor for a single large fault over the recurrence time of the fault-rupturing earthquake because that case is governed by the characteristic earthquake model. In contrast, over a larger region containing many moderate sized faults, for which the Gutenberg-Richter distribution holds, the relative contribution of small earthquakes/faults to strain may be significant. These different behaviors characterize mature smooth vs. immature segmented fault systems.

Siderophores, small ferric iron (Fe)-binding metabolites, are biomarkers of microbial Fe-stress. Here we report the results from two experiments profiling the distribution of 57Fe-siderophore uptake performed in late summer of 2021 and 2022 near Station ALOHA, the long-term ecological study site of the Hawaii Ocean Time-series program located in the North Pacific Subtropical Gyre. The experiments were designed to investigate how changes in microbial Fe-stress in upper mesopelagic waters (200-400 m) respond to temporal changes in dissolved Fe. Nearly complete uptake (92-99%) of added 57Fe-siderophores was observed in the upper mesopelagic; however, the depths at which siderophore uptake was most active differed between experiments. In 2021 nearly complete uptake of added siderophores occurred at depths between 200 m and 350 m, while in 2022 the region of complete uptake was restricted to waters between 300 m and 350 m. The differences in siderophore uptake between the 200 m and 250 m experiments likely reflects the elevated concentrations of dissolved Fe in the upper mesopelagic waters of 2022, which may have alleviated Fe-stress. Our results confirm the presence of microbial Fe-stress in the upper mesopelagic and suggest that Fe-stress in this region of the water column is responsive to temporal changes in the supply of Fe.

Antarctic sea ice has changed significantly over the past four decades; yet limited understanding of fundamental processes, including drivers of the seasonal cycle, hinders our ability to interpret these changes. Here, we examine the processes determining the moment when sea ice disappears each summer, defined as the retreat date, over 1994-2020. We find that climatological retreat date is driven by sea ice melt in most of the seasonal ice zone and strongly constrained by the seasonal maximum ice thickness. Ice drift is the dominant driver of sea ice retreat only in coastal polynyas. At interannual timescales, retreat date anomalies are also preconditioned by prior maximum ice thickness, which affects melt-driven spring ice loss through the ice-albedo feedback, though this effect appears limited to specific regions. Winds emerge as a primary driver of interannual variability in the retreat date, influencing both drift- and melt-related spring ice removal processes.

This study identifies the most predictable patterns of winter temperature over the contiguous United States for subseasonal timescales, specifically weeks 1-2 and weeks 3-4. The influence of ENSO was removed to focus on genuinely subseasonal predictability. Using Canonical Correlation Analysis (CCA), coupled with recent advancements in significance testing and feature selection, correlated temperature variations were identified. For weeks 1-2, the most predictable modes were found to be shifted versions of the Pacific North American (PNA) pattern, while for weeks 3-4, the most predictable mode closely resembled the PNA itself. The second most predictable mode for weeks 3-4 is independent of standard indices of subseasonal predictability, suggesting a potential new source of predictability. Soil moisture was identified as a causal factor for this second mode. The Climate Forecast System version 2 (CFSv2) captures the leading mode for weeks 3-4, but not for weeks 1-2.

Methane (CH4) is the second most significant greenhouse gas following carbon dioxide (CO2), and the reduction in methane emissions would swiftly lower global methane concentrations, thus aiding in mitigating climate warming within the decadal timeframe to align with the 1.5 to 2°C reduction target outlined in the Paris Agreement. Methane emissions come from both natural sources and human activities, with anthropogenic emissions contributing to approximately 60% of the total methane atmospheric concentration increase. Major anthropogenic sources include livestock, oil and gas systems, waste, coal mining, rice cultivation and fossil fuel combustion. Urban areas are a critical focus for greenhouse gas emissions mitigation due to their local control and policy independence from federal policies. In the United States, fossil fuel combustion accounts for 6% total methane emissions. While not a larger emitter, it is a primary source of controllable methane emissions in urban areas. Therefore, we would like to understand if fossil fuel combustion is considered a major methane source in urban areas, excluding some emissions typically occurring outside urban settings, such as those from livestock.We consider methane emissions from fossil fuel combustion in urban areas to be primarily divided into two categories: mobile combustion by onroad vehicles and stationary combustion from power generation, as well as industrial, commercial and residential sectors. The Vulcan v4 data product provides comprehensive and newly updated FFCO2emissions estimates from fossil fuel combustion across the 50 United States and District of Columbia for the calend

Predicting water and gas movement through the vadose zone is of key importance for applications such as flow of liquids and gases in the subsurface in response to underground explosions. Understanding a rock’s fluid flow properties, such as porosity, permeability, and the capillary pressure curve are crucial to predict these water and gas movements in soil and rock. We use centrifugal drainage to track water loss in volcanic tuff core samples through time. For two tuff samples from Aqueduct Mesa, permeability, porosity, and van Genuchten model parameters were estimated by fitting observations of mass of water lost to PFLOTRAN predictions. The method works well for relatively high permeable samples; testing less permeable samples is still possible but took longer to drain and required higher centrifugal speeds. Drainage testing provides the most information about the portion of the capillary pressure curve near full liquid saturation, while imbibition tests constrain the behavior of samples near residual liquid saturation. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. This is SAND2024-09706A.

Marine Carbon Dioxide Removal (mCDR) implementations require robust monitoring. The monitoring approaches in the literature are catered for specific use cases, hence, often dispersed, lacking consistency for each mCDR method. We surveyed the mCDR scientific literature to identify measurable indicators used across different ecosystems and methods for monitoring the carbon removed and environmental side-effects, and explore the main common challenges. Our results indicate that it is often difficult to establish direct linkage from the rates of chemical and biological changes to the amount of carbon removed and the magnitude of associated side-effects. The heterogeneity of marine biogeochemical and ecological processes together with the absence of regional boundaries represent the most common challenges. The lack of standardized indicators and monitoring procedures inhibits the verification, and hence, creates risk for further investment, prevents the entering of mCDRs into existing carbon certificate trading systems, and hinders the long term growth of this sector.

Carbon balances of croplands are often assessed using models that depend critically on accurate estimates of soil carbon inputs such as crop residues, dead roots, and root exudates. Here, we develop a method to estimate soil carbon inputs by combining satellite-based gross primary productivity (GPP) estimates with harvest yields extracted from agricultural statistics. We model the daily GPP as a statistical regression on photosynthetically active radiation and the red edge chlorophyll index measured by the Sentinel-2 satellites and train the model using data from five eddy covariance flux measurement sites. When tested with leave-one-site-out cross validation, the model predicted the yearly GPP with a root mean squared error equal to about 10 % of the mean. We furthermore show that the predicted cumulative GPP explains 60-70 % of the observed variability within a set of 135 aboveground biomass measurements collected on 40 agricultural fields. Finally, we apply the method to three Finnish regions and estimate the soil carbon inputs as the difference between the net primary productivity (NPP), assumed 50 % of the GPP, and the carbon removed in harvest. Compared to the allometric method used in the current national greenhouse gas inventory, our annual carbon inputs are within 5-30 % for wheat but 50-100 % higher for barley, 40-50 % higher for green fallows, and 100-160 % higher for forage grasses. These results highlight a discrepancy between the current national greenhouse gas inventory and carbon budgets derived from flux measurements at eddy covariance sites.

Palaeoceanographic proxies are essential in reconstructing past climate change and accurate estimates of past conditions require reliable calibrations. The chemical signature of benthic foraminiferal calcite reflects bottom water conditions, but the effects of their microhabitat, biomineralization, metabolism, ontogeny can challenge a straightforward interpretation of past seawater conditions. In this study we compared stable isotopes of oxygen (δ18OBF) and carbon (δ13CBF) values of age-constrained benthic foraminifera from surface sediments of the Southeast Pacific (SEP) with contemporary in-situ water column values for δ18O equilibrium calcite (δ18Oeqcal) and δ13C of dissolved inorganic carbon (δ13CDIC). The ages of benthic foraminifera, based on radiocarbon and oxygen isotope stratigraphy, show a cluster of older surface sediments sites at intermediate water depths off north Chile, likely associated with sediment erosion. Epifaunal benthic foraminifera δ18OBF values reveal a 1:1 relationship with δ18Oeqcal of ambient bottom waters across the region, while infaunal foraminifera show positive δ18OBF-δ18Oeqcal offsets. Epifaunal benthic foraminifera δ13CBF values closely match ambient bottom waters δ13CDIC in most cases. In contrast, infaunal benthic foraminifera show negative δ13CBF-δ13CDIC offset. At sites shallower than 1000 m, both epifaunal and infaunal benthic foraminifera display significant positive δ13CBF-δ13CDIC offsets. This offset is likely attributed to a combination of microbiological processes occurring within the bottom and pore waters under low to intermediate oxygen conditions, influencing the carbonate pool and the inorganic carbon speciation in the SEP. This is not likely a regionally restricted phenomenon and may have broader implications for improving the reliability of δ13C- and δ18O-based proxies in palaeoceanographic studies.

The atmosphere’s flow becomes unpredictable beyond a certain time due to the inherent growth of small initial-state errors. While much research has focused on tropospheric predictability, predictability of the middle atmosphere remains less studied. This work contrasts the intrinsic predictability of different layers, with a focus on the mesosphere/lower thermosphere (MLT, ~50-120 km altitude). Ensemble simulations with the UA-ICON model for an austral winter/spring season are conducted with a gravity-wave permitting horizontal resolution of 20 km. Initially small perturbations grow fastest in the MLT, reaching 10% of saturation after 5-6 days, compared to 10 days in the troposphere and two weeks in the stratosphere. However, perturbation energy in the MLT reaches 50% saturation only after about two weeks, similar to the troposphere. Those saturation times are overestimated by up to a factor of two when using a coarser resolution (grid size 160 km), highlighting the need for gravity wave-resolving models. Predictability in the MLT depends on horizontal scales. Motions on scales of hundreds of kilometers are predictable for less than five days, while larger scales (thousands of kilometers) remain predictable for up to 20 days. This scale-dependent progression of predictability cannot be explained by simple scaling for upscale error growth. Vertical wave propagation plays a significant role, with gravity waves transmitting perturbations upward at early lead times and planetary waves enhancing long-term predictability. In summary, the study shows that MLT predictability is scale-dependent and highlights the necessity of high-resolution models to capture fast-growing perturbations and assess intrinsic predictability limits accurately.

A document by Jagos Radovic. Click on the document to view its contents.

Here we analyze the rupture process of the December 29th, 2020 MW6.4 Petrinja earthquake (Croatia), the largest event instrumentally recorded in this area characterized by a moderate strain-rate intraplate setting. We use foreshocks and aftershocks, recorded at more than 80 broadband stations located 70 km to 420 km from the earthquake, as empirical Green's functions (EGFs) to separate source effects from propagation and local site effects. First, we deconvolve the mainshock P-wave time windows from the EGFs in the frequency domain to obtain the corner frequency (fc). Spectral analysis based on the Brune's source model reveals a large stress drop of 24 MPa. Next, by deconvolving the Love waves in the time domain, we calculate the Apparent Source Time Functions (ASTFs). We find that the average duration of the source is ~5 s, with no significant directivity effects, indicating a bilateral rupture. To extract physical rupture parameters such as rupture velocity, slip distribution and rise time, we deploy two techniques: (1) Bayesian inversion and (2) backprojection onto isochrones of ASTFs. Both techniques show a low rupture velocity (40-50% of the shear wave velocity) and a rupture length of less than 10 km, i.e. much less than would typically be expected for a magnitude 6.4 earthquake. This apparent anticorrelation between stress drop and rupture velocity may be attributed to the complex and segmented fault system characteristic of immature intraplate settings.

Based on over 8 years of moored observation data near the Xisha Islands in the South China Sea (SCS), this study investigated the seasonal variability and vertical distribution of near-inertial waves (NIWs) and internal tides (ITs). It also investigated the impact of mesoscale cyclonic eddies on the downward propagation of NIWs, as well as the mechanism of nonlinear interactions between NIWs and ITs following typhoons. Both NIWs and ITs exhibited significant seasonal variations. Near-inertial kinetic energy (NIKE) was stronger in autumn and winter, while internal tide energy peaked in both summer and winter. Over the entire observation period, ITs were primarily dominated by low modes. In contrast to internal tides, which are dominated by low modes, NIWs exhibit high-mode structures under the influence of negative vorticity. During periods of positive vorticity, the modal structures of NIWs show significant variability. In addition, the study found that cold eddies can also facilitate the downward transfer of near-inertial energy. Taking the autumn of 2011 as an example, under the influence of positive vorticity, NIKE was transmitted downward to the maximum observed depth of 543m, contributing to deep-ocean mixing. The study also revealed that typhoon-induced NIWs, characterized by strong vertical shear, played a dominant role in the nonlinear interactions between NIWs and ITs, leading to the enhancement of nonlinear coupled waves.

This study aimed to examine the variations in vorticity in the northern South China Sea (NSCS) resulting from the cutoff of the Kuroshio main path east of Taiwan Island. Absolute dynamic topography and satellite altimeter eddy tracking data from 1993 to 2021 were employed to analyze the momentum ratio of the Kuroshio and mesoscale eddies concerning Kuroshio cutoff events through their interaction. The results revealed 12 events where mesoscale cyclonic eddies east of Taiwan Island cut off the Kuroshio main path, resulting in intensified loop currents in the Luzon Strait and increased negative vorticity in the NSCS. Six of these events occurred between 1998 and 2013, coinciding with the negative Pacific Decadal Oscillation (PDO) regime. During this period, negative vorticity triggered by Kuroshio cutoff events rose by 79% in the NSCS, compared to 34% during the positive PDO regime. The Kuroshio interacted with mesoscale cyclonic eddies east of Taiwan for 28 days during the negative PDO regime and 41 days during the positive PDO regime. The Kuroshio velocity strengthened east of Luzon Island but weakened east of Taiwan Island during the negative PDO regime, elucidating the observed disparity. The average duration between the occurrence of mesoscale cyclonic eddy impinging on the Kuroshio east of Taiwan Island and the peak negative vorticity observed in the NSCS was approximately 28 days. This was comparable to data obtained using field measurements.

Droughts severely affect water resources, exacerbating food insecurity and economic instability. Drought impacts are influenced by environmental and socio-economic factors but their link is not always clearly understood. This study examines the relation between drought hazard and impacts under different socio-economic vulnerability factors in 21 counties in Kenya. Impacts considered are household distance to water (HWD), livestock distance to water (LDW), milk production (MPR), and child malnutrition. Using Random Forest models, we linked these drought impacts for the period 2013-2022 and drought indices by pooling counties with similar characteristics. We then used these models to hindcast drought impacts for 1984-2014 and determine average annual loss (AAL) for each impact. The results revealed spatial variability in AAL between counties: northwestern counties experience high HDW risks, while eastern and southeastern counties face elevated LDW, MPR, and malnutrition risks. Both environmental (particularly aridity) and socio-economic factors (particularly access to improved sanitation) were found to be important vulnerability factors affecting all four risks in the counties. PML curves showed some more underlying differences, with some counties having a resilient situation (e.g. Makueni w.r.t HDW), a constant but worrying level of drought impacts (e.g. Mandera w.r.t HDW), or a gradual increase of impact with return periods (e.g. malnutrition impacts in Tana River double between once in 2 and 10 year droughts). These findings emphasize the importance of socio-economic and environmental factors in shaping drought risk, and can be used as entry points for interventions. Particularly addressing aridity and sanitation infrastructure seem to be important.

Solar radiation management with stratospheric aerosol injection has been proposed as a potential mechanism to mitigate global warming and prevent it from reaching the tipping point. This study investigates the response of surface cloud radiative effects (CREs) to stratospheric aerosol injection (SAI) relative to Shared Socio-Economic Pathways (SSP2-4.5) across three regions: southern West Africa (SWA), Central Africa (CA) and the Sahara (SA). We utilize simulations from the Community Earth System Model version 2 (CESM2) with the Whole Atmosphere Community Climate Model version 6 (WACCM6) under SAI deployment, comparing the outputs to those from the SSP2.4-5 scenario, which aligns with current climate policy scenarios. The findings indicate that SAI has the potential to mitigate the decreasing trend of shortwave cloud cooling by -0.7W/m², -0.81W/m², -0.17W/m², while enhancing the longwave warming by +1.11W/m², +0.65W/m² and +0.31W/m², over CA, SWA, and SA, respectively. However, it is important to note that these observed changes may be attributable to natural variability rather than the direct effects of SAI, and should be taken with caution. An exception is the longwave cloud warming, which exhibits robust changes over CA. The results also reveal that changes in shortwave cloud cooling effect demonstrate high sensitivity to changes in liquid water path whereas changes in longwave cloud warming tend to exhibit greater sensitivity to variations in cloud fraction. It is noteworthy that current simulations involving SAI lack sufficient variables to facilitate comprehensive comparisons among different outputs.

Satellite-based quantitative precipitation estimates (QPE), such as NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG), provide easily accessible continental-to-global precipitation forcings for flood prediction and other hydrologic applications. Nevertheless, when used in hydrologic prediction, uncertainty in satellite-based QPE often leads to significant bias. This forcing uncertainty is further blended with other error sources, including process representation, parameter values, and their interactions. The identification and decoupling of these uncertainties can enhance our understanding of their respective impacts, thereby improving hydrologic prediction. Addressing this issue worldwide is challenging, however, largely due to the scarcity of precipitation ground truth and complex uncertainty interactions. Therefore, we propose an efficient uncertainty quantification framework for ensemble streamflow prediction, which keeps different uncertainty sources separable through hierarchical Bayesian inference. Satellite-based QPE uncertainty is characterized by a novel near-realtime quasi-global satellite-only ensemble precipitation dataset (STREAM-Sat), which is completely independent of ground-based precipitation measurements. Model parameter uncertainty in a distributed physics-based hydrologic model is inferred by an Iterative Ensemble Smoother (IES). To illustrate the impact and limitations of precipitation uncertainty, we compared ensemble streamflow predictions driven by both model parameter and satellite precipitation uncertainties and ensemble streamflow predictions driven by model parameter uncertainty and deterministic QPE. We demonstrate that the quantification of satellite-based QPE uncertainty notably improves the accuracy and reliability of streamflow predictions. This study also lays a foundation for satellite-based streamflow prediction in ungauged regions.