A sequence of three strong (M W 7.2, 6.4, 6.6) earthquakes struck the Pamir of Central Asia in 2015–2017. With a local seismic network, we recorded the succession of the fore-, main-, and aftershock sequences at local distances with good azimuthal coverage. We located 11,784 seismic events and determined 33 earthquake moment tensors. The seismicity delineates the tectonic structures of the Pamir in unprecedented detail, i.e., the thrusts that absorb shortening along the Pamir’s thrust front, and the strike-slip and normal faults that dissect the Pamir Plateau into a westward extruding block and a northward advancing block. Ruptures on the kinematically dissimilar faults were activated subsequently from the initial M W 7.2 Sarez event at times and distances that follow a diffusion equation. All mainshock areas but the initial one exhibited foreshock activity, which was not modulated by the occurrence of the earlier earthquakes. Modeling of the static Coulomb stress changes indicates that aftershock triggering occurred over distances of ≤90 km on favorably oriented faults. The third event in the sequence, the M W 6.6 Muji earthquake, ruptured despite its repeated stabilization through stress transfer in the order of -10 kPa. To explain the accumulation of M W > 6 earthquakes, we reason that the initial mainshock may have increased nearby fault permeability, and facilitated fluid migration into the mature fault zones, eventually triggering the later large earthquakes.

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.

Carbon and nitrogen dynamics in the Sea of Japan (SOJ) are rapidly changing. In this study, we investigated the carbon and nitrogen isotope ratios of particulate organic matter (δ13CPOM and δ15NPOM, respectively) at depths of ≤ 100 m in the southern part of the SOJ from 2016 to 2021. δ13CPOM and δ15NPOM exhibited multimodal distributions and were classified into four classes (I–IV) according to the Gaussian mixed model. A majority of the samples were classified as class II (n = 441), with mean ± standard deviation of δ13CPOM and δ15NPOM of –23.7 ± 1.2‰ and 3.1 ± 1.2‰, respectively. Compared to class II, class I had significant low δ15NPOM (-2.1 ± 0.8‰, n = 11), class III had low δ13CPOM (-27.1 ± 1.0‰, n = 21), and class IV had high δ13CPOM (-20.7 ± 0.8‰, n = 34). All the class I samples, whose δ15NPOM showed an outlier of total data sets, were collected in winter and had comparable temperature and salinity originating in Japanese local rivers. The generalized linear model demonstrated that the temperature and chlorophyll-a concentration had positive effects on δ13CPOM, supporting the active photosynthesis and phytoplankton growth increased δ13CPOM. However, the fluctuation in δ15NPOM was attributed to the temperature and salinity rather than nitrate concentration, which suggested that the δ15N of source nitrogen for primary production is different among the water masses.  These findings suggest that multiple nitrogen sources, including nitrates from the East China Sea, Kuroshio, and Japanese local rivers, contribute to the primary production in the SOJ.

A document by Euan G. Nisbet. Click on the document to view its contents.

From preventing scurvy to being part of religious rituals, citrus are intrinsically connected to human health and perception. From tiny mandarins to head-sized pummelos, citrus capability of hybridization provides a vastly diverse array of fruit sizes and shapes, which in turn corresponds to a diversity of flavors and aromas. These sensory qualities are tightly linked to oil glands in the citrus skin. The oil glands are also key to understanding fruit development, and the essential oils contained by them are fundamental in the food and perfume industries. We study the shape of citrus based on 3D X-ray CT scan reconstruction of 163 different citrus samples comprising 58 different species and cultivars, including samples of all fundamental citrus species. First, using the power of X-rays and image processing, we are able to compare and contrast size ratios between different tissues, such as the size of the skin compared to the rind or the flesh. Second, we model the fruit shape as an ellipsoidal surface, and later we study and infer possible oil gland distributions on this surface using principles of directional statistics. We finally compare and contrast these overall fruit shape models along their gland distributions across different citrus species. This morphological modeling will allow us later to link genotype with phenotype, furthering our insight on how the physical shape is genetically specified in DNA.

The Earth surface Mineral dust source InvesTigation (EMIT) is a remote visible to shortwave infrared (VSWIR) imaging spectrometer that will map the mineralogy of Earth's mineral dust forming regions. EMIT was launched to the International Space Station in July 2022, beginning a year-long mission. This article describes the EMIT spectroradiometric calibration and on-orbit validation experiments. EMIT achieves several notable firsts for an instrument of its class. It demonstrates successful on-orbit adjustments of Focal Plane Array (FPA) alignment with sub-micron precision. It offers spectral uniformity better than 98%. Optical artifacts in the measurement channels are at least three orders of magnitude below the primary signal. EMIT consists of just six optical elements, no shutter, and no onboard calibration systems. With this simple and efficient design, EMIT satisfies the stringent needs for the next generation of VSWIR imaging spectrometers.

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.

Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral species. As such genomic prediction in sugarcane presents an interesting case for machine learning methods, which are purportedly able to deal with high levels of complexity in prediction. Here we investigate deep learning networks (DL), including Multilayer networks (MLP) and convolution neural networks (CNN), and Random Forest (RF) for genomic prediction in sugarcane. The data set was 2912 sugarcane clones, scored for 26,086 genome wide SNP markers, with final assessment trial (FAT) data for total cane harvested (TCH), Commercial cane sugar (CCS) and Fibre content. The clones in the latest trial (2017) were used as a validation set. We compared performances of these methods to GBLUP extended to include dominance and epistatic effects. The prediction accuracies from GBLUPs were 0.37 for TCH, 0.37 for CCS and 0.48 for Fibre, while the DL models had accuracies of 0.33 for TCH prediction, 0.38 for CCS prediction and 0.43 for Fibre. Optimised RF achieved a prediction accuracy of 0.35 for TCH, 0.38 for CCS and 0.48 for Fibre. Both DL and RF predictions were more accurate additive GBLUP but generally lower than extended GBLUP. Finally, we identified a partially shared distribution of SNP selections between RF and GBLUP models. We conclude RF may have some utility for genomic prediction for crops with highly complex genomes, particularly if non-additive interactions can be captured with clonal propagation.

Enhanced weathering (EW) is a promising strategy to remove atmospheric CO2 by amending agricultural and forestry soils with ground silicate materials. However, the current model-based assessments of EW potential face uncertainties stemming from the intricate interplay among soil physical, chemical, and biotic processes, compounded by the absence of a detailed model-data comparison, mostly due to the limited availability of comprehensive data. Here, we address this critical gap by advancing and validating an ecohydrological and biogeochemical model for EW dynamics in soils. We conduct a hierarchical validation in which model results are critically compared to four experimental datasets of increasing complexity, from simple closed incubation systems to open mesocosm experiments. The comparison demonstrates the model ability to capture the dynamics of primary variables, including rock alkalinity release and CO2 sequestration. The comparison also reveals that weathering rates are consistently lower than traditionally assumed by up to two orders of magnitude. We finally discuss avenues for further theoretical and experimental explorations.

Optimizing the spatial configuration of diverse best management practices (BMPs) can provide valuable decision-making support for comprehensive watershed management. Most existing methods focus on selecting BMP types and locations but neglect their implementation time or order in management scenarios, which are often investment-restricted. This study proposes a new simulation-optimization framework for determining the implementation plan of BMPs by using the net present value to calculate the economic costs of BMP scenarios and the time-varying effectiveness of BMPs to evaluate the environmental effectiveness of BMP scenarios. The proposed framework was implemented based on a Spatially Explicit Integrated Modeling System and demonstrated in an agricultural watershed case study. This case study optimized the implementation time of four erosion control BMPs in a specific spatial configuration scenario under a 5-year stepwise investment process. The proposed method could effectively provide more feasible BMP scenarios with a lower overall investment burden with only a slight loss of environmental effectiveness. Time-varying BMP effectiveness data should be gathered and incorporated into watershed modeling and scenario optimization to better depict the environmental improvement effects of BMPs over time. The proposed framework was sufficiently flexible to be applied to other technical implementations and extensible to more actual application cases with sufficient BMP data. Overall, this study demonstrated the basic idea of extending the spatial optimization of BMPs to a spatiotemporal level by considering stepwise investment, emphasizing the value of integrating physical geographic processes and anthropogenic influences.

The debate over the historical and future evolution of the Atlantic Meridional Overturning Circulation (AMOC) has united scientists around a single topic, but this community has yet to unite around a single definition of the AMOC. In an effort to focus the debate around dynamics rather than semantics, we recommend that the community universally adopt a definition of the AMOC in density coordinates. We present evidence that the traditional depth space definition is insufficient at capturing elements of this circulation, especially at high latitudes where the northward and southward limbs of the AMOC are separated horizontally rather than vertically. Instead, the AMOC in density coordinates more realistically captures the water mass transformation process at high latitudes, shifts the maximum AMOC from the subtropical to the subpolar North Atlantic where the majority of the deep waters are formed, and depicts the peak in meridional heat transport associated with the subtropical gyre.

Accurately predicting terrestrial ecosystem responses to climate change is crucial for addressing global challenges. This relies on mechanistic modelling of ecosystem processes through Land Surface Models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially in carbon cycle predictions. This paper reviews the progress made in using data assimilation (DA) for LSM parameter optimisation, focusing on carbon-water-vegetation interactions, as well as discussing the technical challenges faced by the community. These challenges include identifying sensitive model parameters and their prior distributions, characterising errors due to observation biases and model-data inconsistencies, developing observation operators to interface between the model and the observations, tackling spatial and temporal heterogeneity as well as dealing with large and multiple datasets, and including the spin-up and historical period in the assimilation window. We then outline how machine learning (ML) can help address these issues, proposing different avenues for future work that integrate ML and DA to reduce uncertainties in LSMs. We conclude by highlighting future priorities, including the need for international collaborations, to fully leverage the wealth of available Earth observation datasets, harness machine learning advances, and enhance the predictive capabilities of LSMs.

Scientific programming has become increasingly essential for manipulating, visualizing, and interpreting the large volumes of data acquired in earth science research. Yet few discipline-specific instructional approaches have been documented and assessed for their effectiveness in equipping geoscience undergraduate students with coding skills. Here we report on an evidence-based redesign of an introductory Python programming course, taught fully remotely in 2020 in the School of Oceanography at the University of Washington. Key components included a flipped structure, activities infused with active learning, an individualized final research project, and a focus on creating an accessible learning environment. Cloud-based notebooks were used to teach fundamental Python syntax as well as functions from packages widely used in climate-related disciplines. By analyzing quantitative and qualitative data from surveys, online learning platforms, student work, assessments, and a focus group, we conclude that the instructional design facilitated learning and supported self-guided scientific inquiry. Students with less or no prior exposure to coding achieved similar success as peers with more experience, an outcome likely mediated by higher engagement with course resources. We believe that the constructivist approach to teaching introductory programming and data literacy that we present could be broadly applicable across the earth sciences and in other scientific domains.

In the coming decades, the frequency of coastal flooding will increase due to sea-level rise and changes in climate extremes. We force the Global Tide and Surge Model (GTSM) with a climate model ensemble from the CMIP6 High Resolution Model Intercomparison Project (HighResMIP) to produce global projections of extreme sea levels (defined as tides and storm surge) from 1950 to 2050. This is the first time that an ensemble of global ~25km resolution climate models is used for this purpose, which increases the credibility of projected storm surges. Here we validate the historical simulations (1985-2014) against the ERA5 climate reanalysis. The overall performance of the HighResMIP ensemble is good with mean bias smaller than 0.1 m. However, there is a strong large-scale spatial bias. Future projections for the high emission SSP5-8.5 scenario indicate changes up to 0.1 m or 20% in 10-year return period surge level from 1951-1980 to 2021-2050. Increases are seen in parts of the coastline of the Caribbean, Madagascar and Mozambique, Alaska, and northern Australia, whereas the Mediterranean region may see a decrease. The full dataset underlying this analysis, including timeseries and statistics, is openly available on the Climate Data Store and can be used to inform broad-scale assessment of coastal impacts under future climate change.

Current eddy-permitting and eddy-resolving ocean models require dissipation to prevent a spurious accumulation of enstrophy at the grid scale. We introduce a new numerical scheme for momentum advection in large-scale ocean models that involves upwinding through a weighted essentially non-oscillatory (WENO) reconstruction. The new scheme provides implicit dissipation and thereby avoids the need for an additional explicit dissipation that may require calibration of unknown parameters. This approach uses the rotational, "vector invariant" formulation of the momentum advection operator that is widely employed by global general circulation models. A novel formulation of the WENO "smoothness indicators" is key for avoiding excessive numerical dissipation of kinetic energy and enstrophy at grid-resolved scales. We test the new advection scheme against a standard approach that combines explicit dissipation with a dispersive discretization of the rotational advection operator in two scenarios: (i) two-dimensional turbulence and (ii) three-dimensional baroclinic equilibration. In both cases, the solutions are stable, free from dispersive artifacts, and achieve increased "effective" resolution compared to other approaches commonly used in ocean models.

We synthesize knowledge from numerical weather prediction, inverse theory and statistics to address the problem of estimating a high-dimensional covariance matrix from a small number of samples. This problem is fundamental in statistics, machine learning/artificial intelligence, and in modern Earth science. We create several new adaptive methods for high-dimensional covariance estimation, but one method, which we call NICE (Noise-Informed Covariance Estimation), stands out because it has three important properties: (i) NICE is conceptually simple and computationally efficient; (ii) NICE guarantees symmetric positive semi-definite covariance estimates; and (iii) NICE is largely tuning-free. We illustrate the use of NICE on a large set of Earth-science-inspired numerical examples, including cycling data assimilation, geophysical inversion of field data, and training of feed-forward neural networks with time-averaged data from a chaotic dynamical system. Our theory, heuristics and numerical tests suggest that NICE may indeed be a viable option for high-dimensional covariance estimation in many Earth science problems.

Coal fly ash has long been considered a potential resource for recovery of valuable elements, such as rare earth elements (REE), which are retained and concentrated upon combustion of coal feedstocks. Understanding REE occurrence within fly ash is a key to developing possible recovery methods. Recent results using modern analytical approaches shed light on the distribution REE in fly ash and the approaches required for their recovery. Some of the highest REE contents occur in fly ash derived from U.S. Appalachian Basin coals, and among these, coals influenced by input volcanic ash (Fire Clay coal, Kentucky) are especially enriched. Leaching studies of bulk fly ash show that, as a proportion of the total REE present, samples from eastern U.S. coals are generally less readily extractible than fly ash derived from western U.S. coals having lower REE contents. Direct determinations by ion microprobe show that REE in a range of fly ash samples are partitioned into aluminosilicate glasses formed during melting at boiler temperatures. These glasses comprise the largest mass fraction of coal fly ash. REE-enriched domains are present locally in fly ash at the nanometer scale (as shown by TEM), and these REE coexist with the glass phase. To enable systematic study of these REE, Ce has been proposed as a proxy for the trivalent lanthanides, as supported by speciation determinations demonstrating that Ce occurs in the trivalent form in fly ash. Despite a decreasing proportion of coal use for electric power generation in the U.S. and elsewhere, annual fly ash production, combined with coal ash already in storage, make up a large resource for potential recovery of rare earths and associated critical elements. Further developments in extraction technologies are needed to overcome difficulties in REE concentration and purification to produce REE materials of saleable purity derived from coal ash.

Soil moisture influences near-surface air temperature by partitioning downwelling radiation into latent and sensible heat fluxes, through which dry soils generally lead to higher temperatures. The strength of this coupled soil moisture-temperature (SM-T) relationship is not spatially uniform, and numerous methods have been developed to assess SM-T coupling strength across the globe. These methods tend to involve either idealized climate-model experiments or linear statistical methods which cannot fully capture nonlinear SM-T coupling. In this study, we propose a nonlinear machine learning-based approach for analyzing SM-T coupling and apply this method to various mid-latitude regions using historical reanalysis datasets. We first train convolutional neural networks (CNNs) to predict daily maximum near-surface air temperature (TMAX) given daily SM and geopotential height fields. We then use partial dependence analysis to isolate the average sensitivity of each CNN’s TMAX prediction to the SM input under daily atmospheric conditions. The resulting SM-T relationships broadly agree with previous assessments of SM-T coupling strength. Over many regions, we find nonlinear relationships between the CNN’s TMAX prediction and the SM input map. These nonlinearities suggest that the coupled interactions governing SM-T relationships vary under different SM conditions, but these variations are regionally dependent. We also apply this method to test the influence of SM memory on SM-T coupling and find that our results are consistent with previous studies. Although our study focuses specifically on local SM-T coupling, our machine learning-based method can be extended to investigate other coupled interactions within the climate system using observed or model-derived datasets.