Ocean waves interact with the environment in many ways. They transport energy and mass, and the resultant sea-surface roughness defines the drag coefficients that transmits wind energy to the ocean (Drennan et al., 2003). Through erosion and deposition, waves change the shape and landscape of coastal areas. Storm surge waves can cause flood damage in coastal areas. Recent studies revealed that wetlands are sensitive to the wave condition, which determines the retreat or growth of coastal ecosystems (Green and Coco, 2007; Mariotti and Fagherazzi 2010). Human activities rely on the condition of waves to conduct marine activities such as fishing, shipping, oil extraction, and offshore constructions. Thus, it is important to understand ocean waves to improve earth system modeling, protect the coastline, predict storm surge, preserve coastal ecosystems, and enhance the offshore business. This project will explore the application of synthetic aperture radar (SAR) imagery to predict significant wave height near the coast. High-frequency (HF) radar data of the ocean (aka CODAR) was used as ground truth data set to calibrate and validate the wave height estimator. Off-shore wind data was also included. The developed code will enhance the current capability to process the satellite data and create a new platform to monitor the coastal environment. The collected data will help further our understanding of the wave spectrum in a coastal environment and the data can support other research in the related topics, e.g. the interaction of waves and ice sheets, wetlands, shorelines, wind farm and aquaculture.

The ribosome is a universal molecular machine (comprised of RNA and proteins) which translates the message from the genome into proteins (polymers of amino acids) in biology. Similar to how Flight and Cockpit voice recorders record and preserve an aircraft’s flight history, the ribosome has recorded signatures of its evolution. Tapping this resource is important for understanding the origins of life. The electrostatic properties/net positive charge(s) of ribosomal proteins (RPs) stabilize interactions with the negatively charged ribosomal RNA (rRNA) and influence the assembly and folding of ribosomes. A high percentage of RPs from extremely halophilic archaea are known to be acidic/negatively charged. Recently the net charges (at pH 7.4) of the RPs from a highly conserved cluster of RPs were found to have an inverse relationship with the halophilicity/halotolerance (ability to survive under salt conditions) levels in bacteria and archaea. In non-halophilic bacteria, these RPs are generally basic, contrasting with the acidic proteomes of the extreme halophiles. We explore the use of a new mathematical modeling technique based on interaction graphs to provide a systematic understanding of the structural differences in the Large Subunit (LSU) of the bacterium Escherichia coli and that of the extremely halophilic archaeon Haloarcula marismortui.

Denitrification is a key process in the global nitrogen (N) cycle, causing both nitrous oxide (N2O) and dinitrogen (N2) emissions. However, estimates of seasonal denitrification losses (N2O+N2) are scarce, reflecting methodological difficulties in measuring soil-borne N2 emissions against the high atmospheric N2 background and challenges regarding their spatio-temporal upscaling. This study investigated N2O+N2 losses in response to N fertiliser rates (0, 100, 150, 200 and 250 kg N ha-1) on two intensively managed tropical sugarcane farms in Australia, by combining automated N2O monitoring, in-situ N2 and N2O measurements using the 15N gas flux method and fertiliser 15N recoveries at harvest. Dynamic changes in the N2O/(N2O+N2) ratio (< 0.01 to 0.768) were explained by fitting generalised additive mixed models (GAMMs) with soil factors to upscale high temporal-resolution N2O data to daily N2 emissions over the season. Cumulative N2O+N2 losses ranged from 12 to 87 kg N ha-1, increasing non-linearly with increasing N fertiliser rates. Emissions of N2O+N2 accounted for 31–78% of fertiliser 15N losses and were dominated by environmentally benign N2 emissions. The contribution of denitrification to N fertiliser loss decreased with increasing N rates, suggesting increasing significance of other N loss pathways including leaching and runoff at higher N rates. This study delivers a blueprint approach to extrapolate denitrification measurements at both temporal and spatial scales, which can be applied in fertilised agroecosystems. Robust estimates of denitrification losses determined using this method will help to improve cropping system modelling approaches, advancing our understanding of the N cycle across scales.

The Himalayan orogen exposes a range of metamorphosed assemblages, from low-grade Indian shelf sediments of the Tethyan Formation to eclogite and ultra-high pressure rocks documented near the suture zone between the Indian craton and Asian subcontinent. Barrovian-grade pelites and mafic protoliths are exposed in the Himalayan core and include the Greater Himalayan Crystallines and Lesser Himalayan Formations. These units are separated by the Main Central Thrust (MCT). This fault system accommodated a significant amount of India-Asia convergence and is the focus of several models that explore ideas about the development of the range and collisional belts in general. These units provide critical information regarding the mechanisms of heat transfer within collisional belts. Garnets collected across the MCT record their growth history through changes in chemistry. These chemical changes can be extracted and modeled using a variety of thermodynamic approaches. This paper reviews the geological framework of the Himalayas with a focus on the protolith of its metamorphosed assemblages. It describes and applies particular thermobarometric techniques to decipher the metamorphic history of several garnet-bearing rocks collected across the MCT in central Nepal. Comparisons are made between the results of previously-reported conventional rim P-T conditions and P-T paths extracted using the Gibb’s method to isopleth thermobarometry and high-resolution P-T path modeling using the same data and assemblages. Predictions of the paths on garnet zoning are also presented for the high-resolution P-T path modeling and Gibb’s method using the program TheriaG. Although the approaches yield different absolute conditions and P-T path shapes, all are consistent with the development of the MCT shear zone due to imbrication of distinct rock packages. Greater Himalayan Crystalline garnets experienced higher-grade conditions that make extracting its P-T conditions and paths a challenge. Lesser Himalayan garnets appear to behave as closed systems and are ideally suited for thermodynamic approaches.

Yeongtae Kim, Nyunhee Kim, Eunsook An, Younguk kim, JaeYoung Kim, Chaewon Lee, JeongHo Baek, HwangWeon Jeong, Hyenso Ji, Song Lim Kim

It is well-established that positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land-atmosphere feedback mechanisms, disrupt the global carbon cycle, and accelerate climate change. Permafrost dynamics are relevant to the global community because the distribution of this frozen ground substrate characterizes nearly 23 million square kilometers of the northern latitudes. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impact. Current earth system models do not account for permafrost carbon feedback mechanisms; we are exploring, simulating, and quantifying this limitation with field-scale surveys and numerical modeling, image processing, and machine learning at scale across the tundra and taiga ecosystems (TTE). This research seeks to identify, interpret, and explain the causal links and feedback sensitivities attributed to permafrost degradation and terrestrial carbon cycling asymmetry with in situ observations, remote sensing imagery, modeling and reanalysis products, and a hybridized multimodal deep learning ensemble of recurrent, convolutionally-layered, memory-based networks (GeoCryoAI). Preliminary metrics obtained from mirroring freeze-thaw dynamics and soil carbon flux across four subdomain in Alaska yield a root mean square error of 6.3637 and 4.7973, respectively. More specifically, this data-driven modeling ensemble is composed of a convolutional neural network-filtered (CNN) long short-term memory-encoded (LSTM) recurrent neural network that integrates teacher learning from in situ observations while embedding satellite-based measurements and time series datasets into a network of activation functions and processing layers. These outputs are then trained within a variational autoencoder framework (VAE) that encodes and imputes proper decoding protocol necessary for generative adversarial training, benchmarking, and reconstructing synthetic time series data for gap-filling and feature learning. Ongoing work demonstrates the fidelity of monitoring active layer thickness (ALT) variability as a sensitive, silent-but-pronounced harbinger of change; a unique signal for characterizing and forecasting permafrost degradation, soil carbon flux, and other biogeochemical drivers facilitating land cover change and earth system feedbacks. These multimodal approaches to knowledge discovery will not only improve sensitivity analyses and disentangle the spatial processes and causal links behind drivers of change, but also reconcile disparate estimations and below-ground uncertainty across the Arctic system.

Sustainability of China’s numerous cities are threatened by both quantity- and quality-induced water scarcity, which can be measured by the water footprint from a consumption (WFcons) or production (WFprod) perspective. Although WFcons was widely assessed, the changes in WFprod of China’s cities were still unclear. Taking 31 major cities as examples, this study revealed the dynamics of urban WFprod in China from 2011 to 2016. First, the spatiotemporal patterns of WFprod and water deficit were evaluated and then the main reasons for the WFprod dynamics and its implications for urban sustainability were explored. A large-scale decrease in urban WFprod in China was found, with the average WFprod decreasing from 13.8 billion m³ to 10.3 billion m³ and the per capita WFprod decreasing from 1614.8 m³/person to 1184.0 m³/person (i.e., falling by more than a quarter in just six years). Such shrinkage was particularly evident in drylands, eliminating the water deficit in Xi’an and Xining. The reduction in grey WFprod caused by implementing water pollution prevention policies and other relevant measures played the most important role in the savings. In the future, the implementation of updated pollution discharge standards is projected to allow more cities to escape water deficits; however, the rapid growth of the domestic and ecological blue WFprod caused by urbanization and urban greening would destabilize this prospect. Thus, attention should be given to both water pollution prevention and domestic and ecological blue WFprod restriction to further alleviate urban water scarcity in China.

Accurate soil moisture and streamflow data are an aspirational need of many hydrologically-relevant fields. Model simulated soil moisture and streamflow hold promise but numerical models require calibration prior to application to ensure sufficient model performance. Manual or automated calibration methods require iterative model runs and hence are computationally expensive. In this study, we leverage the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to help constrain soil parameter uncertainties in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. After calibration, WRF-Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in-situ soil moisture observing stations. Compared to four well-established soil moisture datasets including Soil Moisture Active Passive Level 4 data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-calibrated WRF-Hydro produces the highest mean KGE (0.67) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases especially in the case where the model domain is set up with an SSURGO-informed total soil thickness. Both the magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean Nash-Sutcliffe Efficiency across seven of the nine gages increases from 0.19 in default WRF-Hydro to 0.63 after calibration. Our soil data-informed calibration approach, which is transferable to other spatially-distributed hydrological models, uses open-access data and non-iterative steps to improve model performance and is thus operationally and computationally attractive.

Stable carbon (δ¹³C) and oxygen (δ¹⁸O) isotope measurements in lacustrine ostracodes are widely used to infer past climatic conditions. Previous work has used individual ostracode valves to resolve seasonal and subdecadal climate signals, yet environmental controls on geochemical variability within co-occurring specimens from modern samples are poorly constrained. Here we focus on individual ostracode valves in modern-aged Lake Turkana sediments, an alkaline desert lake in tropical East Africa. We present individual ostracode valve analyses (IOVA) of δ¹³C and δ¹⁸O measurements (n = 329) of extant species Sclerocypris clavularis from 17 sites spanning the entire lake (n-avg ~19 specimens per site). We demonstrate that the pooled statistics of individual valve measurements at each site overcome inter-specimen isotopic variance and are driven by hydrological variability in the lake. Mean IOVA-δ¹³C and -δ¹⁸O across the sites exhibit strong spatial trends with higher values at more southerly latitudes, modulated by distance from the inflow of the Omo River. Whereas the latitudinal δ¹³C gradient reflects low riverine δ¹³C and decreasing lacustrine productivity towards the southern part of the lake, the δ¹⁸O gradient is controlled by evaporation superimposed on the waning influence of low-δ¹⁸O Omo River waters, sourced from the Ethiopian highlands. We show that ostracode δ¹⁸Oproximal to Omo River inflow is deposited under near-equilibrium conditions and that inter-specimen δ¹⁸O variability across the basin is consistent with observed temperature and lake water δ¹⁸O variability. IOVA can provide skillful constraints on high-frequency paleoenvironmental signals and, in Omo-Turkana sediments, yield quantitative insights into East African paleohydrology.

In light of the magnitude and pace of the environmental changes in the northern permafrost zone (NPZ) and their feedbacks to climate, contemporary, accurate and quantitative ecological forecasting has never been so paramount to the development of climate change adaptation and mitigation strategies. Yet, uncertainties associated with carbon (C) projections in the NPZ remain the largest to projections of global C budget and climate. While there are persisting lacks of data documenting important and emerging soil and vegetation dynamics in the NPZ, the volume, variety and accessibility of observational data in the NPZ has grown exponentially over the past decades and significantly improved our understanding of terrestrial C dynamic. Yet, a lag persists between large availability of historical, new and iterative data collections and the capacity of terrestrial biosphere models to fully incorporate this information, limiting advances in reducing the uncertainty of ecological forecasting in the NPZ. In this new project, we are developing the Arctic Carbon Monitoring and Prediction System (ACMPS), a data assimilation system that will use the information from field observations from ecological networks, remote sensing data and ecological modeling to reduce the uncertainty of the terrestrial carbon balance in the NPZ. The ACMPS will be coupling model development and testing, data-assimilation techniques and near-term forecasting capacity to improve the accuracy of historical and future simulations of ecosystem permafrost and C dynamics across the NPZ. We will present the structure and workflow of the ACMPS, as well as preliminary assessment of model sensitivity and uncertainty analysis of soil and vegetation carbon fluxes, using a terrestrial biosphere model specifically developed to represent permafrost, vegetation and carbon dynamics in arctic and boreal ecosystems. Plain-language Summary We are presenting the Arctic Carbon Monitoring and Prediction System, a data assimilation system that uses field observation, remote sensing data and ecological modeling to reduce the uncertainty of the terrestrial carbon balance in the northern permafrost zone, and to better inform development of climate change adaptation and mitigation strategies.

Recent worldwide heatwaves have shattered temperature records in many regions. In this study, we applied a dynamical downscaling method on the high-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-1-2-HR) to obtain projections of the summer thermal environments and heatwaves in the Pearl River Delta (PRD) considering three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in the middle and late 21st century. Results indicated that relative to the temperatures in the 2010s, the mean increases in the summer daytime and nighttime temperatures in the 2040s will be 0.7–0.8 °C and 0.9–1.1 °C, respectively. In the 2090s, they will be 0.5–3.1 °C and 0.7–3.4 °C, respectively. SSP1-2.6 is the only scenario in which the temperatures in the 2090s are expected to be lower than those in the 2040s. Compared with those in the 2010s, hot extremes are expected to be more frequent, more intense, more extensive, and longer-lasting in the future in the SSP2-4.5 and SSP5-8.5 scenarios. In the 2010s, a heatwave occurred in the PRD lasted for 6 days on average, with a mean daily maximum temperature of 34.4 °C. In the 2040s, the heatwave duration and intensity are expected to increase by 2–3 days and 0.2–0.4 °C in all three scenarios. In the 2090s, the increase in these values will be 23 days and 36.0 °C in SSP5-8.5. Moreover, a 10-year extreme high temperature in the 2010s is expected to occur at a monthly frequency from June to September.

West African precipitation is characterized by a strong variability from intra-seasonal to climatic scales. In this region, it typically rains from March to October with differences in the annual cycle of precipitation (e.g. Sow et al., Atmosphere 2020). Studies based on the Coupled Model Inter-comparison Project (CMIP) phase 3 and 5 show that the representation of the West African Monsoon (WAM) mean state and its response to global warming, are both highly model-dependent (Roehrig et al., J. Atmos. Sci. 2013). CMIP5 simulations display large inter-model spread in both the mean precipitation climatology and higher-frequency indices by overestimating the frequency of wet days (Sow et al, 2020). Likewise, strong uncertainties are found in the simulation of the consecutive wet and dry days (CWD and CDD respectively) over west Africa. In this study we analyse the added-value of the new generation models available from the CMIP6 archive in simulating precipitation and its extreme indices over West Africa. The same group of CMIP5 and CMIP6 models is evaluated with CRU and REGEN datasets for a long time period (more than 30 years) as well as more than four other observational datasets in a shorter time period (15 years). Results show that the inter-model spread as well as the overestimation of these indices slightly decreases in most CMIP6 historical simulations compared to their CMIP5 counterpart. When considering experiments where sea surface temperature (SST) is prescribed over a long period (1870-2014), performed in the Global Monsoons Model Intercomparison Project (GMMIP), we find more improvement in the annual structure of precipitation over the Guinean region with less inter-model spread compared to observations. The inter-annual variability of precipitation is then relatively well captured by all CMIP6 models with significant correlations. Almazroui et al. (2020) found that precipitation in CMIP6 models will increase in the central part and decrease in the western part of west Africa during the twenty-first century under Shared Socio-economic Pathways (SSP). In this study we further analyse how extreme precipitation indices are represented in SSP5-8.5 over West Africa.

Over the last two decades, the Himalayan region is affected by deformation events such as landslides and land subsidence, spread over different regions located within the Himalayas. The spatial distribution of these events may well depend on the tectonics occurring beneath, which requires understanding of long-term deformation over large areas. Studies related to the identification of deformation zones has earlier been carried out using conventional methods to identify deformation zones. However, we need efficient methods to update these earlier generated maps, which may require adding later events, which may also belong to different class of events left unencountered earlier. In this study, we make use of Sentinel-1 interferometric wide swath (IW) datasets from the European Space Agency to localize deformation prone sites in the Himalayan region. With over seven years of acquisition (2014-2021) and large coverage (~270 km) of the dataset, we attempt to monitor and understand the deformation activities occurring in various parts of the Himalayas. This involves processing gigantic stacks of interferograms generated from a large time series of IW images, which may require devising better processing strategies. and further analysis to generate a map showing sites affected by different types of deformation events. This information can be used to identify sites which require critical monitoring (using field surveys) and rescue. The upcoming NISAR mission from NASA and ISRO is also focused towards understanding deformation activities in the Himalayas, and the proposed study may also benefit some of the studies planned for the NISAR mission.

The earthquake cycle of stress accumulation and release is associated with the elastic rebound hypothesis proposed by H.F. Reid following the M7.9 San Francisco earthquake of 1906. However, observing details of the actual values of time- and space-dependent tectonic stress is not possible at the present time. In previous research, we have proposed two methods to image the earthquake cycle in California by means of proxy variables. These variables are based on correlations in patterns of small earthquakes that occur nearly continuously in time. One of these is based on the construction of a time series by the unsupervised detection of small earthquake clusters. The other is based on expanding earthquake seismicity in PCA-derived patterns, to construct a weighted correlation time series. The purpose of the present research is to compare these two methods by evaluating their information content using decision thresholds and Receiver Operating Characteristic methods together with Shannon information entropy. Using seismic data from 1940 to present in California, we find that both methods provide nearly equivalent information on the rise and fall of earthquake correlations associated with major earthquakes in the region. We conclude that the resulting time series can be viewed as proxies for the cycle of stress accumulation and release associated with major tectonic activity. The figure shows the PCA patterns of small earthquakes associated with 5 major M>7 earthquakes in California since 1950.

Teleseismic shear-wave splitting analyses are typically performed by reversing the splitting process through the application of frequency- or time-domain operations minimizing transverse-component waveforms. These operations yield two splitting parameters, φ (fast-axis orientation) and δt (delay time). In this study, we investigate the applicability of a recurrent neural network, SWSNet, for determining the splitting parameters from pre-selected waveform windows. Due to the scarcity of sufficiently labelled real waveform data, we generate our own synthetic training dataset. The model is capable of determining φ and δt with a root mean squared error (RMSE) of 9.58◦ and 0.143 s for noisy synthetic test data. The application to real data involves a deconvolution step to homogenize the waveforms. When applied to data from the USArray dataset, the results exhibit similar patterns to those found in previous studies with mean absolute differences of 11.12◦ and 0.25 s in the calculation of φ and δt, respectively.

Fungi in marine ecosystems play crucial roles as saprotrophs, parasites, and pathogens. The definition of marine fungi has evolved over the past century. Currently, “marine fungi” are defined as any fungi recovered repeatedly from marine habitats that are able to grow and/or sporulate in marine environments, form symbiotic relationships with other marine organisms, adapt and evolve at the genetic level, or are active metabolically in marine environments. While there are a number of recent reviews synthesizing our knowledge derived from over a century of research on marine fungi, this review article focuses on the state of knowledge on planktonic marine fungi from the coastal and open ocean, defined as fungi that are in suspension or attached to particles, substrates or in association with hosts in the pelagic zone of the ocean, and their roles in remineralization of organic matter and major biogeochemical cycles. This review differs from previous ones by focusing on biogeochemical impacts of planktonic marine fungi and methodological considerations for investigating their diversity and ecological functions. Importantly, we point out gaps in our knowledge and the potential methodological biases that might have contributed to these gaps. Finally, we highlight recommendations that will facilitate future studies of marine fungi. This article first provides a brief overview of the diversity of planktonic marine fungi, followed by a discussion of the biogeochemical impacts of planktonic marine fungi, and a wide range of methods that can be used to study marine fungi.

The concentration of carbon dioxide (CO2) in Earth’s atmosphere is increasing due to human activities and the resulting effects on the global climate system have initiated sev- eral policy-driven approaches to reduce emissions of this greenhouse gas. Quantifying the effectiveness of such policies requires both bottom-up and top-down approaches to estimate CO2 emissions. This work investigates, for the first time, the potential of using SAM observations from NASA’s OCO-3 instrument to disaggregate sector-specific emissions from instrument observations. Optimized sector-specific timeseries were produced using Bayesian inversion techniques and compared to proxy activity data from the transportation, commercial maritime, and industrial sectors. Results demonstrate that dense space-based observations of atmospheric CO2 are capable of disentangling sector-specific CO2 fluxes, paving the way for accurate monitoring of the effects of carbon-reduction policies and operational carbon monitoring systems.

The Government of India announced its commitment to reach net-zero greenhouse gas emissions by 2070 at the recent COP 26 summit. Modeling projections suggest that meeting this target would likely require substantial amounts of CO2 capture and storage (CCS) from large-point sources (LPS). Our analysis first reveals the key co-benefits for India in the adoption of CCS, viz. energy security, lower aggregate costs of carbon mitigation, higher resilience and lower stranded assets. For instance, we estimate that stranding of >100 GW and >70 GW of coal- and gas-fired power capacity could be avoided with the presence of CCS in the power sector mix.This analysis is further supplemented by our recent estimates on CO2 storage potential estimates in Indian geologic formations. Our results indicate that the storage capacity via enhanced oil recovery (EOR) is 1.2 GtCO2 after incorporating engineering and geologic constraints. Similarly, the storage capacity in unminable coal fields is estimated to be 3.5-6.3 GtCO2. Even though the combined storage potential in these formations is constrained, they should be actively considered within policy-making as they predominantly lie within areas of dense areas of LPS, thus creating possibilities of CCS hubs and clusters. In addition, 291 GtCO2 could be sequestered in saline aquifers and 97-316 GtCO2 in basalts; though, these values are subject to higher uncertainties. A number of saline aquifers may be characterized as having storage potential equivalent to several years of LPS emissions (>10 GtCO2) along with high storage feasibility.Our ongoing analysis attempts a more evolved approach towards source-sink mapping in India by combining the storage potential estimates with geospatial layers of LPS. Large power plants, which emit >20 MtCO2 annually, and high-purity CO2 sources such as refineries, are of particular interest. Preliminary source-sink mapping results show substantial clustering opportunities in eastern India, which has active coalbed methane extraction undertaken by five companies, and western India, with large industrial sources interspersed with EOR sites. The results of this analysis will also inform decision-makers on future LPS siting opportunities if a policy thrust on CCS is undertaken for meeting net-zero targets over the next two decades.