Fieldwork, including work done at sea, is a key component of many geoscientists' careers. Recent studies have highlighted the pervasive sexual harassment faced by women during fieldwork. However, transgender and gender diverse scientists face unique obstacles, which have not yet been studied. We partially fill this gap by sharing our experiences as transgender and gender diverse people. We have experienced sexual harassment, misconduct, privacy issues, and legal and medical struggles as we conduct seagoing work. We provide recommendations to make seagoing work safer to our communities. These recommendations are a starting point to make seagoing work more inclusive for all.
Rates of sea-level rise are increasing across the global ocean. Since $\sim 2008$, sea-level acceleration is particularly pronounced along the US Gulf of Mexico coastline. Here we use model solutions and observational data to identify the physical mechanisms responsible for enhanced rates of recent coastal sea-level rise in this region. Specifically, we quantify the effect of offshore subsurface ocean warming on coastal sea-level rise and its relationship to regional hypsometry. Using the Estimating the Circulation and Climate of the Ocean (ECCO) Version 5 ocean state estimate, we establish that coastal sea-level changes are largely the result of changes in regional ocean mass, reflected in ocean bottom pressure, on interannual to decadal timescales. These coastal ocean bottom pressure changes reflect both net mass flux into and out of the Gulf, as well as internal mass redistribution within the Gulf, which can be understood as an isostatic ocean response to subsurface offshore warming. We test the relationships among coastal sea-level, ocean bottom pressure, and subsurface ocean warming predicted by the model using data from satellite gravimetry, satellite altimetery, tide gauges, and Argo floats. Our estimates of mass redistribution explain a significant fraction of coastal sea-level trends observed by tide gauges. For instance, at St. Petersburg, Florida, this mass redistribution accounts for $>$ 50\% of the coastal sea-level trend observed over the 2008-2017 decade. This study elucidates a physical mechanism whereby coastal sea-level responds to open-ocean subsurface warming and motivates future studies of this linkage in other regions.
Numerical simulation of rupture dynamics provides critical insights for understanding earthquake physics, while the complex geometry of natural faults makes numerical method development challenging. The discontinuous Galerkin (DG) method is suitable for handling complex fault geometries. In the DG method, the fault boundary conditions can be conveniently imposed through the upwind flux by solving a Riemann problem based on a velocity-strain elastodynamic equation. However, the universal adoption of upwind flux can cause spatial oscillations in cases where elements on adjacent sides of the fault surface are not nearly symmetric. Here we propose a nodal DG method with an upwind/central mixed-flux scheme to solve the spatial oscillation problem, and thus to reduce the dependence on mesh quality. We verify the new method by comparing our results with those from other methods on a series of published benchmark problems with complex fault geometries, heterogeneous materials, off-fault plasticity, roughness, thermal pressurization, and various versions of fault friction laws. Finally, we demonstrate that our method can be applied to simulate the dynamic rupture process of the 2008 Mw 7.9 Wenchuan earthquake along/across multiple fault segments. Our method can achieve high scalability in parallel computing under different orders of accuracy, showing high potential for adaptation to earthquake rupture simulation on natural tectonic faults.
Large earthquakes, especially those occurring in a city or population centers, create devastation and havoc, and often times kindle several deaths and injuries, and significant infrastructure damage that lead to several billions of dollars in losses. Marine earthquakes are the leading cause of large tsunamis which cause deaths, destruction, displacement of population, and a possible nuclear meltdown. Thus, prediction of earthquake or its aftershocks or earthquake early warning system has a great potential to mitigate the loss of life as well as different kinds of damage. Earthquake prediction would mean forecasting the occurrence of an earthquake by providing both its magnitude estimate and accurate location. Earthquake prediction has been an important area of seismology research for quite a while, and it looks like it will continue to be an important area of research. Recently, with the implementation of deep learning in seismology, scientists have been able to detect, predict, and model seismic waves and earthquake aftershocks. Earthquake aftershocks are generally triggered by changes in stress formed by large earthquakes that happen within, or surrounding a given fault network system. The main goal of this study is to investigate the improvement of aftershock pattern predictions with the implementation of tuning and optimizing of deep learning parameters. To achieve these goals, we have developed an algorithm that can help first gather mainshock-aftershock sequence data. Some of the criteria used in identifying earthquakes that initiate an aftershock is to look at earthquakes that happen within a certain radius, the values we attempted are within about 0.5 degrees range, and within a certain period, from few seconds to several weeks of the occurrence of the main shock. For the sequence identification, we have been using seismic data from the United States Geological Survey (USGS)-National Earthquake Information Center (NEIC). We are also looking at different open-source data gathered by researchers for a similar study. The deep neural networks we are implementing make use of Keras python Toolkit, and Theano and Tensorflow libraries, with a plan to use PyTorch python library instead of Theano library in the future because of some maintenance issues. To this point our attempts have shown a good progress.
The oceanic storage of anthropogenic CO2 (Cant) that humans have emitted into the atmosphere has been pivotal for counteracting climate change. Yet multi-decadal trends in the ocean interior storage of Cant have not been assessed at global scale. Here, we determine storage changes of Cant by applying the eMLR(C*) regression method to ocean interior observations collected between 1989 and 2020. We find that the global ocean storage of Cant grew by 29 ± 3 Pg C dec-1 and 27 ± 3 Pg C dec-1 (±1σ) from 1994 to 2004 and 2004 to 2014, respectively. Although the two growth rates are not significantly different, they imply a reduction of the oceanic uptake fraction of the anthropogenic emissions from 36 ± 4 % to 27 ± 3 % from the first to the second decade. We attribute this reduction to a decrease of the ocean buffer capacity and changes in ocean circulation. In the Atlantic Ocean, the maximum storage rate shifted from the Northern to the Southern Hemisphere, plausibly caused by a weaker formation rate of North Atlantic Deep Waters and an intensified ventilation of mode and intermediate waters in the Southern Hemisphere. Between 1994 and 2004, the oceanic Cant accumulation exceeded the net air-sea flux by 8 ± 4 Pg C dec-1, suggesting a loss of natural carbon from the ocean during this decade. Our results reveal a substantial sensitivity of the ocean carbon sink to climate variability and change.
Seismology is a data-driven science with a huge amount of data gathered for over a century. Though seismic data recording started in 1900, the growth of seismic data has obviously been exponentially in the last three decades. This data growth can be easily noticed if one takes a close look at just one of the largest seismological data centers in the US, the Data Management Center (DMC) of the Integrated Research Institutions in Seismology (IRIS). Data at the DMC grew from less than 10 Tebibytes in 1992 to about 800 Tebibytes in 2022. With the availability of such a large amount of seismic data, it is paramount to develop new seismic data processing and management tools to help analyze and find new and better seismic models. Developing new big seismic data processing and management tools will be helpful to make the best use of such growing big seismological data sets. The main goal of this investigation is the development of efficient data manipulation and processing tools for retrieval, processing, merging, aggregation, and management of big seismic data from disparate data sources. In this study, such big seismic data processing tools are being developed using python programming language and open source python libraries, and the tools we are developing will be helpful to extract, split, and convert, merge and process big seismic data. In addition, python is very suitable for data science and has powerful libraries to process and manage data and applications. Significant contributions have been made in recent python based libraries for seismic data processing, though there are still some rooms for improvement when it comes to seismic applications to merge, convert, manage and process big seismic data from disparate data sources and converting different file formats. Seismic data from different networks surrounding the Rio Grande Valley have been collected from different data sources. Our attempt is to test the developed tools and evaluate their performance. This study has made important progress in this regard and the results are promising.
The true bottleneck of artificial intelligence (AI) is not access to the data, but rather labeling this data. We have tons of raw agriculture image data coming from various sources and manual labelling remains to be a crucial step to keep the data well organized which requires considerable amount of time, money, and labor. This process can be made more efficient if we can automatically label the raw data. We propose contrastive learning representations for agriculture images (AgCLR) model that uses self-supervised representation learning approach on unlabeled real-world agriculture field data, to learn the useful image feature representations from the images. Contrastive learning is a self-supervised approach that enables model to learn attributes by contrasting samples against each other without the use of labels. AgCLR leverages the state-of-the-art SimCLRv2 framework to learn representations by maximizing the agreement between differently augmented views of same sample. We have incorporated critical enablers like mixed precision, multi-GPU distributed parallel computing, and use of Google Cloud's Tensor Processing Units (TPU) for optimizing the training process. We achieved 80.2% accuracy while classifying the test data. We further applied AgCLR to unrelated task to determine the alleys and rows in corn field videos for corn phenotyping and we observed two cluster formations for alleys and rows when plotted embeddings in a 3-dimensional space. We also developed a content-based image retrieval tool (pixel affinity) to identify similar images in our database and results were visually very promising.
The Hunga Tonga-Hunga Ha’apai (HTHH) volcanic eruption in January 2022 injected extreme amounts of water vapor (H2O) and a moderate amount of the aerosol precursor (SO2) into the Southern Hemisphere (SH) stratosphere. The H2O and aerosol perturbations have persisted and resulted in large-scale SH stratospheric cooling, equatorward shift of the Antarctic polar vortex, and slowing of the Brewer-Dobson circulation associated with a substantial ozone reduction in the SH winter midlatitudes. Chemistry-climate model simulations forced by realistic HTHH inputs of H2O and SO2 reproduce the observed stratospheric cooling and circulation effects, demonstrating the observed behavior is due to the volcanic influences. Furthermore, the combination of aerosol transport to polar latitudes and a cold polar vortex enhances springtime Antarctic ozone loss, consistent with observed polar ozone behavior in 2022.
“Climate tipping elements” often refer to large-scale earth systems with the potential to respond nonlinearly to anthropogenic climate change by transitioning towards substantially different long-term states upon passing key thresholds, frequently referred to as “tipping points.” In some but not all cases, such changes could produce additional greenhouse gas emissions or radiative forcing that could compound global warming. Improving understanding of tipping elements is important for predicting future climate risks. Here we review mechanisms, predictions, impacts, and knowledge gaps associated with ten notable earth systems proposed to be climate tipping elements. We evaluate which tipping elements are more imminent and whether shifts will likely manifest rapidly or over longer timescales. Some tipping elements are significant to future global climate and will likely affect major ecosystems, climate patterns, and/or carbon cycling within the 21st century. However, assessments under different emissions scenarios indicate a strong potential to reduce or avoid impacts associated with many tipping elements through climate change mitigation. Most tipping elements do not possess the potential for abrupt future change within years, and some proposed tipping elements may not exhibit tipping behavior, rather responding more predictably and directly to the magnitude of forcing. Nevertheless, significant uncertainties remain associated with many tipping elements, highlighting an acute need for further research and modeling to better constrain risks.
The Finnish Meteorological Institute (FMI) provides a relative humidity measurement sensor (HS) for NASA’s Mars 2020 rover. The sensor is a part of the Mars Environmental Dynamic Analyzer (MEDA), a suite of environmental sensors provided by Spain’s Centro de Astrobiología. The main scientific goal of the humidity sensor is to measure the relative humidity of the Martian atmosphere near the surface and to complement previous Mars mission atmospheric measurements for a better understanding of Martian atmospheric conditions and the hydrological cycle. Relative humidity has been measured from the surface of Mars previously by Phoenix and Curiosity. Compared to the relative humidity sensor on board Curiosity, the MEDA HS is based on a new version of the polymeric capacitive humidity sensor heads developed by Vaisala. Calibration of humidity devices for Mars conditions is challenging and new methods have been developed for MEDA HS. Calibration and test campaigns have been performed at the FMI, at University of Michigan and the German Aerospace Center (DLR) in Berlin to achieve the best possible calibration. The accuracy of HS and uncertainty of the calibration has been also analysed in detail with VTT Technical Research Centre of Finland. Assessment of sensor performance after landing on Mars confirms that the calibration has been successful, and the HS is delivering high quality data for the science community.
Small stress changes such as those from tidal loading can be enough to trigger earthquakes. If small and large earthquakes initiate similarly, high resolution catalogs with low detection thresholds are best suited to illuminate such processes. Below the Sea of Marmara section of the North Anatolian Fault, a segment of 150 km is late in its seismic cycle. We generated high-resolution seismicity catalogs for a hydrothermal region in the eastern Sea of Marmara employing both AI-based and template matching techniques to investigate a complex long-lasting sequence including seismicity up to MW 4.5. We document a strong effect of the Sea of Marmara level changes on the local seismicity. Both high resolution catalogs show that local seismicity rates are significantly larger during time periods shortly after local minima on sea level. Local strainmeters indicate that the associated strain changes, on the order of 30-300 nstrain, are sufficient to promote seismicity.
Automated monitoring and evaluation systems for plant phenotyping are one of the keys to advance and strengthen crop breeding programs. In this study, the improvements of the camera-based sensor system and a weather station from a previous study-assembled mainly from Raspberry Pi products-board with dual cameras (RGB and NoIR) providing high spatial and temporal resolution data-is outlined. Hardware for the internet connection and the power supply system of the sensor were upgraded. Previously, the sensor could automatically capture plant images following user-defined time points; thus, an image processing algorithm (edge computing) was developed and installed to extract digital phenotypic traits from the images after capturing process. With the development, the new sensor system could be integrated with the internet, and a cloud server was configured to store data online (digital traits and raw images). A real-time monitoring system was created to visualize the time series data of a trait development and plant images throughout the season. With such a system, plant breeders will be able to monitor multiple trials for timely crop management and decision-making process, which is also resources efficiency.
The risk of floods has increased in South Asia due to high vulnerability and exposure. The August 2022 Pakistan flood shows a glimpse of the enormity and devastation that can further rise under the warming climate. The deluge caused by the Pakistan floods in 2022, which badly hit the country’s southern provinces, is incomparable to any recent events in terms of the vast spatial and temporal scale. The 2022 Pakistan flood ranked third in human mortality, while this was the top event that displaced about 32 million people. Using observations and climate projections, we examine the causes and implications of the August 2022 flood in Pakistan. Multiday (~ 15 days) extreme precipitation on wet antecedent soil moisture conditions was the primary driver of the flood in August 2022. The extreme precipitation in August was caused by two atmospheric rivers that passed over southern Pakistan. Streamflow simulations from the multiple hydrological models show that extreme precipitation was the primary driver of floods as several stations in the flood-affected regions experienced anomalously higher flow than the stations located upstream. The frequency of similar multi-day extreme precipitation events is projected to rise four-fold under the high emission scenario. The 2022 Pakistan flood highlights the adaptation challenges that South Asia is facing along with the substantial need for climate mitigation to reduce the risk of such events in the future.