The geostationary Himawari-8 satellite offers a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational land surface temperature (LST) retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which usually shows directional artefacts and can be strongly impacted by viewing and illumination geometries and shadowing effects. To overcome this challenge, Solar Zenith Angle (SZA) serves as an ideal physical variable to quantify systematic differences between platforms. Here we presented an SZA-based Calibration (SZAC) method to operationally calibrate the daytime component of a split-window retrieved Himawari-8 LST (referred to here as the baseline). SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different products. The SZAC coefficient was spatiotemporally optimised against highest-quality assured (error < 1 K) pixels from the MODerate-resolution Imaging Spectroradiometer (MODIS) daytime LST between 01/Jan/2016 and 31/Dec/2020. We evaluated the calibrated LST data, referred to as the Australian National University LST with SZAC (ANUSZAC), against MODIS LST and the Visible Infrared Imaging Radiometer Suite (VIIRS) LST, as well as in-situ LST from the OzFlux network. Two peer Himawari-8 LST products from Chiba University and the Copernicus Global Land Service were also collected for comparisons. The median daytime bias of ANUSZAC LST against Terra-MODIS LST, Aqua-MODIS LST and VIIRS LST was 1.52 K, 0.98 K and -0.63 K, respectively, which demonstrated improved performance compared to baseline (5.37 K, 4.85 K and 3.02 K, respectively) and Chiba LST (3.71 K, 2.90 K and 1.07 K, respectively). All four Himawari-8 LST products showed comparable performance of unbiased root mean squared error (ubRMSE), ranging from 2.47 to 3.07 K, compared to LST from polar-orbiting platforms. In the evaluation against in-situ LST, the overall mean values of bias (ubRMSE) of baseline, Chiba, Copernicus and ANUSZAC LST during daytime were 4.23 K (3.74 K), 2.16 K (3.62 K), 1.73 K (3.31 K) and 1.41 K (3.24 K), respectively, based on 171,289 hourly samples from 20 OzFlux sites across Australia between 01/Jan/2016 and 31/Dec/2020. In summary, the SZAC method offers a promising approach to enhance the reliability of geostationary LST retrievals by incorporating the spatiotemporal characteristics observed by accurate polar-orbiting LST data. Furthermore, it is possible to extend SZAC for LST estimation by using data acquired by geostationary satellites in other regions, e.g., Europe, Africa and Americas, as this could improve our understanding of the error characteristics of overlapped geostationary imageries, allowing for targeted refinements and calibrations to further enhance applicability.

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.

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.

We present a new approach for the analysis of high-resolution digital camera photographs taken by photographers who have fortuitously been able to capture rare events such as the glowing sky phenomenon known as STEVE. This method is especially effective with a time lapse series of images of the night sky taken under constant camera settings with a steady pointing. Stars, planets and satellites seen in such images can be used to determine precise and accurate registration of camera pixels to coordinates of angular altitude and azimuth. The location of satellites in the image enables precise and accurate synchronization of the images. We apply these techniques to the series of photographs of STEVE taken on 25 July 2016. We confirm the altitude structure previously found for STEVE. We find it most likely that the green picket fence features often seen during STEVE events are produced by auroral electron precipitation. With the precipitation assumption, we are able to extract novel information about the energy spectrum of the particles responsible for the production of STEVE luminosity in this particular event. Similar analyses of archived digital photographs may constitute a treasure trove of important data for improved understanding of rare and transient events such as STEVE.

Greenhouse gas (GHG) emission metrics, that is, conversion factors to evaluate the emissions of non-CO2 climate forcers on a common scale with CO2, serve crucial functions upon the implementation of the Paris Agreement. While different metrics have been proposed, they have not been investigated under a range of pathways, including those significantly overshooting the temperature targets of the Paris Agreement. Here we show that cost-effective metrics that minimize the overall cost of climate mitigation are time-dependent, primarily determined by the period remaining before the eventual stabilization, and strongly influenced by temperature overshoot. Our study suggests that flexibility should be maintained to adapt the choice of metrics in time as the future unfolds, if cost-effectiveness is a key consideration for global climate policy, instead of hardwiring the 100-year Global Warming Potential (GWP100) as a permanent feature of the Paris Agreement implementation as is currently under negotiation.

Compound extremes exhibit greater adverse impacts than their univariate counterparts. Studies have reported changes in frequency and the spatial extent of extremes in India; however, investigation of compound extremes is in the infancy state. This study investigates the historical variation of compound dry and hot extremes (CDHE) and compound wet and cold extremes (CWCE) during the Indian summer monsoon period from 1951-2019 using monthly data. Results are analyzed for 10 identified homogeneous regions for India. Our results unraveled that CDHE (CWCE) frequency has increased (decreased) by 1-3 events per decade for the recent period (1977-2019) relative to the base period (1951-1976). Overall, there is increasing (decreasing) pattern of CDHE (CWCE) is high across North-central India, Western India, North-eastern India and South-eastern coastlines. Our findings help in identification of the parts of the country were affected by frequent and widespread CDHE during the recent period, which is alarming. More detailed assessments are required to disentangle the complex physical process of compound extremes to improve risk management options.

Warm and dry fohn winds on the Antarctic Peninsula (AP) cause surface melt that can destabilize vulnerable ice shelves. Topographic funneling of these downslope winds through mountain passes and canyons can produce localized wind-induced melt that is difficult to quantify without direct measurements. Our Fohn Detection Algorithm (FonDA) identifies the surface fohn signature that causes melt using data from twelve Automatic Weather Stations on the AP, used to train a machine learning model to detect fohn in 5km Regional Atmospheric Climate Model 2 (RACMO2.3p2) simulations and in the ERA5 reanalysis model. We estimate the fraction of AP surface melt attributed to fohn and possibly katabatic winds and identify the drivers of melt, temporal variability, and long-term trends and evolution from 1979-2018. We find fohn wind-induced melt accounts for 3.1% of the total melt on the AP but can be as high at 18% close to the mountains where the winds are funneled through mountain canyons. Fohn-induced surface melt does not significantly increase from 1979-2018, despite a warmer atmosphere and more positive Southern Annular Mode. However, a significant increase (+0.1Gt y-1) and subsequent decrease/stabilization occurred in 1979-1998 and 1999-2018, consistent with the AP warming and cooling trends during the same time periods. Fohn occurrence more than fohn strength drives the annual variability in fohn-induced melt. Long-term fohn-induced melt trends and evolution are attributable to seasonal changes in fohn occurrence, with increased occurrence in summer, and decreased occurrence in fall, winter, and early spring over the past 20 years.

Groundwater is the largest source of Earth’s liquid freshwater and plays a critical role in global food security. With the rising global demand for drinking water and increased agricultural production, overuse of groundwater resources is a major concern. Because groundwater withdrawals are not monitored in most regions with the highest use, methods are needed to monitor withdrawals at a scale suitable for implementing sustainable management practices. In this study, we combine publicly available datasets into a machine learning framework for estimating groundwater withdrawals over the state of Arizona. This extends a previous study in which we estimated groundwater withdrawals in Kansas, where the climatic conditions and aquifer characteristics are significantly different. Datasets used in our model include energy-balance (SSEBop) and crop coefficient evapotranspiration estimates, precipitation(PRISM), and land-use (USDA-NASS Cropland Data Layer), and a watershed stress metric. Random forests, a widely popular machine learning algorithm, are employed for predicting groundwater withdrawals from 2002-2018 at 5 km spatial resolution. We used in-situ groundwater withdrawals available over the Arizona Active Management Area (AMA) and Irrigation Non-Expansion Area (INA) from 2002-2010 for training and 2011-2018 for validating the model respectively. The results show high training (R2 ≈ 0.98) and good testing (R2 ≈ 0.82) scores with low normalized mean absolute error ≈ 0.28 and root mean square error ≈ 1.28 for the AMA/INA region. Using this method, we are able to spatially extend estimates of groundwater withdrawals to the whole state of Arizona. We also observed that land subsidence in Arizona is predominantly occurring in areas having high yearly groundwater withdrawals of at least 100 mm per unit area. Our model shows promising results in sub-humid and semi-arid (Kansas) and arid regions (Arizona), which proves the robustness and extensibility of our integrated approach combining remote sensing and machine learning into a holistic, automated, and fully-reproducible workflow. The success of this method indicates that it could be extended to areas with more limited groundwater withdrawal data under different climatic conditions and aquifer properties.

We present an integrated image of the lithosphere of Alaska and its western Chukchi and Bering seas shelves based on joint modeling of potential field data constrained by thermal analysis and seismic data. We also perform 3D forward modelling and inversion of Bouguer anomalies to analyze crustal density heterogeneities. The obtained crustal model shows NW regional thickening (32 to 36 km), with localized trends of thicker crust in the Brooks Range (40 km) and in the Alaska and St. Elias ranges (50 km). Offshore, 28–30 km thick crust is obtained near the Bearing slope break and 36–38 km in the northern Chukchi Shelf. In interior Alaska, the crustal thickness changes abruptly across the Denali fault, from 34-36 to the N to above 30 km to the S, that agrees with the presence of a crustal tectonic buttress guiding block motion W and S to the subduction zone. The average crustal density is 2810 kg∙m-3. Denser crust (2910 kg∙m-3) is found S of the Denali Fault related to the oceanic nature of the Wrangellia Composite Terrane rocks. Offshore, less dense crust (< 2800 kg∙m-3) is found along the basins of the Chukchi and Beaufort shelves. At LAB levels, there is a regional SE–NW trend that coincides with the Pacific plate motion, with a lithospheric root beneath the Brooks Range, Northern Slope, and Chuckchi Sea, that may be a relic of the Chukotka-Artic Alaska microplate. The lithospheric root (> 180 km) agrees with the presence of a boundary of cold, strong lithosphere that deflects the strain to the south. South of the Denali Fault the LAB topography is quite complex. East of 150 °W, below Wrangellia and the eastern side of Chugach terranes, the LAB is much shallower than it is west of this meridian. The NW trending limit separating thinner lithosphere in the East and thicker in the West agrees with the two–tiered slab shape of the subducting Pacific Plate. This research has been funded by the We–Me project (PIE–CSIC–201330E111), AGUR 2017–SGR–847, Alpimed (PIE-CSIC-201530E082), Subtetis (PIE-CSIC-201830E039) and funds from the University of Houston. This is a contribution within the PolarCSIC platform. Ref: M. Torne, I. Jiménez–Munt, J. Vergés, M. Fernàndez, A. Carballo, M. Jadamec. Geophysical Journal International, Volume 220, Issue 1, January 2020, Pages 522–540, https://doi.org/10.1093/gji/ggz424.

Deep-sea δ18O records show a pronounced difference in Milankovitch periodicity between the Early and Late Pleistocene. δ18O is interpreted as a proxy for ice sheet volume and temperature, which led to the conclusion that glacial-interglacial cycles considerably changed their rhythm during the mid-Pleistocene. This transition is referred to as the mid-Pleistocene Transition (MPT). Specifically, the precessional component of the Milankovitch cycles is absent in Early Pleistocene δ18O records, despite its continuous presence in solar insolation forcing to the ice sheets. Climate feedbacks involving (sea) ice, geological processes and carbon and nutrient cycling have been proposed as causes of this marked change. We however show that the absence of an Early Pleistocene precession signal in deep-sea δ18O records could be the result of destructive interference of the precessional cycle in the interior ocean. Such cancellation is caused by the anti-phasing of the precessional cycle between the North Atlantic and Southern Ocean deep-water sources (see Figure). We explore the potential for cancellation in the transient setup of the Total Matrix Intercomparison model for a wide range of source signal strengths. Our results show that cancellation can cause the absence of the precessional signal due to cancellation in the interior South-Atlantic, Indian and Pacific basins. Cancellation is especially widespread for a relative end-member contribution typical for the Early Pleistocene. We therefore conclude that the precessional component is likely incompletely archived in Early Pleistocene δ18O records, and appears as an actual change in Milankovitch periodicity across the MPT. Proxies not susceptible to cancellation of precession (such as those currently retrieved across the MPT from Antarctica) would be able to verify to what extent deep-sea δ18O correctly represents Pleistocene climate.

The formation of crystal clusters may influence the mechanical behaviour of magmas. However whether clusters form largely from physical contact in a mobile state during sedimentation and stirring, or require residence in a crystal mush is not well understood. We use discrete-element fluid dynamics numerical experiments to illuminate the potential for clustering from both sedimentation and open-system mixing in a model olivine basalt reservoir for three different initial solid volume fractions. Crystal clustering is quantified using both bulk measures of clustering such as the R index and Ripley’s L(r) and g(r) functions and with a variable scale technique called Voronoi tessellations, which also provide orientation data. Probability density functions for the likelihood of crystal clustering under freely circulating conditions indicates that there is nearly an equal likelihood for clustering and non-clustered textures in natural examples. A crystal cargo in igneous rock suites exhibiting a dominance of crystal clusters may be largely sampling magmatic materials formed in a crystal mush.

The climatology of upwelling in the tropical tropopause layer (TTL) in current climate simulations and in future climate projections is examined using models participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Large intermodel differences in upwelling in the TTL appear in the current climate simulations. Model composite analysis and upwelling diagnosis based on the zonal momentum budget indicate that the intermodel differences in upwelling are controlled by meridional eddy momentum fluxes associated with tropical planetary waves and midlatitude synoptic waves. Future climate simulations indicate that upwelling changes in the TTL are significantly correlated with the upwelling in current climate simulations. Models with strong (weak) TTL upwelling in the current climate simulations tend to project strong (weak) upwelling enhancement in the future climate. The intermodel differences in the upwelling change arise from the same dynamical factors as the current climate cases. The contribution of sea surface temperature (SST) to the intermodel upwelling differences is examined by SST-prescribed simulations in CMIP5. The contribution of intermodel SST differences to the upwelling is smaller than that of intrinsic atmospheric intermodel differences. The significant correlation of the tropical upwelling between the current climate simulations and the future changes appears to be independent of the target latitude range.

Due to its inherent ability to estimate the background error covariances, an ensemble Kalman filter (EnKF) is thought to be a practical approach to the strongly coupled data assimilation problems, where an entire coupled model state is estimated as if it was a single integrated system. However, increased complexity and the multiple time scale of the coupled system aggravate the rank-deficiency and spurious correlation problems caused by limited ensemble size available for the analysis. To alleviate these problems, a distance-independent localization method to systematically select the observations to be assimilated into each model variable has been developed and successfully tested with a nine-variable coupled model with slow and fast modes. This method, called correlation-cutoff method, utilizes the mean squared ensemble error correlation between each observable and model variable to identify where the cross-update should be used, and we cut off the assimilation of observations when the squared error correlation becomes small. To implement the method on a more realistic model, we thoroughly investigate inter-fluid background covariances in an atmosphere-ocean coupled general circulation model where the spatiotemporal scales of coupled dynamics significantly vary by latitudes and driving processes.

Freshwater from rivers influences Indian summer monsoon rainfall and tropical cyclones by stratifying the upper layer and warming the subsurface ocean in the Bay of Bengal. Here, we use {\it in situ} and satellite data with reanalysis to showcase how river water experiences a significant increase in salinity on sub-seasonal timescales. This involves the trapping and homogenization of freshwater by a cyclonic eddy in the Bay. Using a specific example from 2015, river water is shown to enter an eddy along its attracting manifolds within a period of two weeks. This leads to the formation of a highly stratified subsurface layer within the eddy. When freshest, the eddy has the largest sea-level anomaly, spins fastest, and supports strong lateral gradients in salinity. Subsequently, observations reveal a progressive increase in salinity inside the eddy within a month. In particular, salty water spirals in, and freshwater is pulled out across the eddy boundary. Lagrangian experiments elucidate this process, whereby horizontal chaotic mixing provides a mechanism for the rapid increase in surface salinity. A salinity budget also suggests that horizontal advection explains much of the change in mixed layer salinity. Further, the adjustment of this freshwater eddy triggers submesoscale dynamics which appear to be an integral part of the process of salinity homogenization. This pathway is distinct from vertical diffusive mixing and is likely to be important for the evolution of salinity in the Bay of Bengal.

In 2015, at the United Nations Conference of the Parties in Paris, France, countries agreed to limit the global mean surface temperature (GMST) increase to 2°C above preindustrial levels, and to pursue efforts to limit it to 1.5°C. However, risks from sea level rise are not well encapsulated by temperature targets. Near term emissions will dictate long term sea level rise responses, but the tendency for policy and negotiations to concentrate on the year 2100 can limit our understanding of intergenerational justice concerns arising from this commitment. Here we present an analysis of the long term spatial variability of sea level rise, and an interdisciplinary review of associated justice considerations from across a wide range of literatures. We center the positioning of the Alliance of Small Island States (AOSIS) to show that AOSIS nations are disproportionately impacted by sea level rise, and that ice sheet instabilities, which could dominate the long term trend in sea level, are associated with feedbacks which can potentially exacerbate climate justice implications.

To understand the relationships between eclogite-facies mineral assemblages, deformation microstructures, and the seismic properties of subducting oceanic crust, eclogites from the Yuka terrane, North Qaidam ultrahigh-pressure metamorphic belt, NW China were studied. Observations of mineral textures, deformation microstructures, and petrofabrics in the eclogites indicate that garnet, omphacite, and phengite were deformed by intra-crystalline deformation (i.e., dislocation creep) during prograde metamorphism. In contrast, amphibole, which was formed by the topotactic replacement of omphacite at fluid-present conditions, is considered to have been deformed by diffusional flow (dissolution–precipitation creep) during amphibolite-facies retrogression associated with exhumation. Based on the petrofabrics in the samples, the seismic properties of the eclogites were calculated depending on eclogite-facies mineral assemblages such as garnet + omphacite (GO), garnet + omphacite + phengite (GOP), garnet + omphacite + phengite + lawsonite (GOPL), garnet + omphacite + phengite + amphibole (GOPA), and garnet + omphacite + amphibole (GOA). We found that the seismic signatures of each of the eclogite-facies mineral assemblages were different. In particular, phengite-bearing eclogites (the GOP/GOPA assemblages), depending on phengite content, produced the strongest seismic anisotropy (AVp and AVs), with a strong polarization anisotropy, that was at least three times higher than bimineralic (phengite-absent) eclogite (GO assemblage). Our results show that phengite, as a stable phase at high pressure and temperature conditions, can play an important role in the creation of trench-parallel seismic anisotropy in the eclogite-facies mineral assemblages found in subduction zones.

We report the results of our investigations on ionospheric effects potentially caused by the 15 February 2013 Chelyabinsk meteor explosion. We used the observation data from a number of digisonde stations located in Europe and Russia to detect the traveling ionospheric disturbances (TIDs) likely to have been caused by the meteor explosion. We found that certain characteristic signatures of the TIDs can be identified in individual ionogram records, mostly in the form of Y‐forking/splitting of the ionogram traces. Based on the arrival times of the disturbances, we have inferred the overall propagation speed of the TIDs from Chelyabinsk to be 171 ± 14 m/s. In addition to the natural fulfillment of scientific endeavors, this work also highlights the importance of maintaining the mastery of ionosondes as ionospheric diagnostic instruments (in terms of operation, data analysis, representation, and interpretation) for many generations of space researchers to come.