We describe a framework for the simultaneous estimation of model parameters in a partial differential equation using sparse observations. Monte Carlo Markov Chain (MCMC) sampling is used in a Bayesian framework to estimate posterior probability distributions for each parameter. We describe the necessary components of this approach and its broad potential for application in models of unsteady processes. The framework is applied to three case studies, of increasing complexity, from the field of cohesive sediment transport. We demonstrate that the framework can be used to recover posterior distributions for all parameters of interest and the results agree well with independent estimates (where available). We also demonstrate how the framework can be used to compare different model parameterizations and provide information on the covariance between model parameters.

New Zealand atmospheric river (AR) lifecycles are analyzed to examine the synoptic conditions that produce extreme precipitation and regular flooding. An AR lifecycle tracking algorithm, novel to the region, is utilized to identify the genesis location of New Zealand ARs: the location where moisture fluxes enhance and become distinct synoptic features capable of producing impactful weather conditions. Genesis locations of ARs that later impact New Zealand cover a broad region extending from the Southern Indian Ocean (90°E) into the South Pacific (170°W) with the highest genesis frequency being in the Tasman Sea. The most impactful ARs, associated with heavy precipitation, tend to originate from distinct regions based on landfall location. Impactful North Island ARs tend to originate from subtropical regions to the northwest of New Zealand, while impactful South Island ARs are associated with genesis over southeast Australia. The synoptic conditions of impactful AR genesis are identified with North Island ARs typically associated with a cyclone in the central Tasman Sea along with a distant, persistent low pressure off the coast of West Antarctica. South Island AR genesis typically occurs in conjunction with moist conditions over Australia associated with a zonal synoptic-scale wavetrain. The Madden–Julian oscillation (MJO) is examined as a potential source of variability that modulates New Zealand AR lifecycles. It appears that the MJO modulates AR characteristics, especially during Phase 5, typically bringing more frequent, slow moving ARs with greater moisture fluxes to the North Island of New Zealand.

The Marcell Experimental Forest (MEF) in northern Minnesota, USA may be the longest running research and monitoring program on the hydrology of peatland catchments. The MEF sits astride a continental divide where the headwaters of the Mississippi, St. Lawrence, and Hudson Bay adjoin. When established in 1961, the MEF, with little topographic relief and large fractions of watersheds in peatlands, was distinct from the steep, mountainous catchments that typified other research catchments of the USDA Forest Service. This terrain and the presence of peatlands are representative of vast areas of the Northern Hemisphere, and the research program fills an important role in environmental monitoring and research in hydrology, ecology, biogeochemistry, and environmental change. During the 1960s, six research catchments were established and hydrological, meteorological, and water chemistry monitoring were initiated. Since then, the research and collaborations have proliferated to include new monitoring and ecosystem manipulations, with several paired-watershed studies, that allow the assessment of land management and environmental change effects on forests, water availability, and biogeochemical cycles. Research at the MEF remains vibrant, especially now that the site hosts a large-scale climate manipulation study (the SPRUCE Experiment). Herein, we present information on the site, contacts, long-term monitoring, experiments, and key findings.

Loss & Damage (L&D) is the category of policies to address adverse climatic impacts beyond our ability at a given time to adapt to climate change. Policies addressing mitigation, adaptation, and L&D form three pillars of climate policy. While discussions of causal responsibility often group together these three pillars, I distinguish these pillars in a new, time-dependent way and argue that the causal responsibility for L&D is different from the other pillars. The primary reason is that time-dependent causal responsibility for adaptation and mitigation is shared but this is not the case with L&D; by the definition of L&D, there is no possibility of preventative intervening actions between the given time and the impacts. Furthermore, there may be may be cosmopolitan or other climate-independent distributive grounds to contribute to international adaptation meaning that causal responsibility for adaptation may plausibly be shared, unlike with L&D. In contrast, impacts requiring L&D are caused by historical climate emissions alone and would appear to be the responsibility of historical emitters alone. Finally, L&D differs from mitigation in that, for some impacts, historical emissions have led to those impacts becoming beyond adaptive capacity (that is, in the category of L&D) whereas, for all impacts, historical emissions have not led to mitigable impacts becoming non-mitigable. Among other implications of this account, since (a) the distinction between L&D and adaptation depends on the capacities at a given point and (b) the historical emitters on this account have incentive to avoid impacts being classed as L&D (since these are unshared responsibilities), historical emitters have incentive to show that impacts lie within our mitigative or adaptive capacities.

Vivo 1606 model is used for the study of standalone use of smartphone as a positioning device with FM supported location (Assisted- GNSS) for GPS, GLONASS, BeiDou. The observations were collected at every 5-minute interval for the analysis. The sessions were divided into three shifts morning, afternoon, and evening to check the accuracy in real time static mode. Mobile topographer app was used for capturing WGS-84 geographic coordinates having horizontal and vertical position with time in an urban environment to analyze which time of a day is good and till what time one can take GPS reading to get the minimum errors using smartphone. It is found that the positional dilution of precision (PDOP) stabilizes in about 35 minutes and minimizes at a value 0.2. The observations beyond have minor changes, so the position at 35 minutes has been used as reference for evaluation of statistical parameters. The real time observations of horizontal, vertical, and positional accuracy seem to increase with time as the PDOP, HDOP, VDOP values decrease i.e. improves with time. It has been observed that, RMSE of PDOP is 0.0485 and HDOP is 0.029 during afternoon is higher as compared to morning and evening values. The study suggest that the preference of survey shall be in the order: morning, evening and afternoon, which may further depend on the season at time. The survey during noon in summers can give much higher values of PDOP and thus the survey can be carried out during morning and evening times preferably.

The outer areas of Jupiter and Saturn have multiple zonal winds, reaching the high latitudes, that penetrate deep into the planets' interiors, as suggested by gravity measurements. These characteristics are finally replicable in numerical simulations by including both a shallow stably stratified layer, below a convecting envelope, and increasing electrical conductivity. A dipolar magnetic field, assumed to be generated by a dynamo below our model, is imposed. We find that the winds' depth into the stratified layer depends on the local product of the squared magnetic field strength and electrical conductivity. The key for the drop-off of the zonal winds is a meridional circulation which perturbs the density structure in the stable layer. In the stable region its dynamics is governed by a balance between Coriolis and electromagnetic forces. Our models suggest that a stable layer extending into weakly conducting regions could account for the observed deep zonal wind structures.

Declining sea ice in the Arctic Ocean is exposing its coasts to more frequent and intense forms of wave energy and storm surge. As a result, erosion rates along some stretches of coastline in the Alaskan Arctic have doubled since the middle of the 20th century and now rank among the highest in the world. People concentrated near the coast are being heavily impacted by erosion, with some facing relocation. Coastal erosion is projected to increase the cost of maintaining infrastructure by billions of dollars in the coming decades. The financial impact of enhanced erosion will likely be further exacerbated by emerging geopolitical pressures, including the discovery of natural resources, opening of new shipping routes, and construction of support facilities in the Arctic. Scientific knowledge and engineering tools for predicting coastal erosion and guiding land-use decision are not well-suited for the ice-bonded bluffs of the Alaskan Arctic. Investigation of the oceanographic, thermal, and mechanical processes that are relevant to permafrost bluff failure along Arctic coastlines is thus needed. We introduce a geomechanical simulation framework, informed by field observation and laboratory testing, that focuses on the impact of bluff geometry and material variability on permafrost bluff stress states associated with a 9-km stretch of Alaskan Arctic coastline fronting the Beaufort Sea that is prone to toppling-mode block failure. Our approach is advantageous in that it is based on measurable physical properties (e.g., the bluff geometry, permafrost bulk density, Young’s Modulus, and Poisson’s Ratio) and does not require the potential failure to be defined a priori, but rather, the failure area can be interpreted from the multidimensional patterns of stress produced by the model. Our findings highlight how (1) block failure characteristics could be tied to variations in the intensity and duration of the storm energy that intersects the coastline and (2) how deformation processes that create non-uniform patterns of displacement may play a role in localizing block failure. We propose that this kind of physics-based simulation approach can facilitate hypothesis testing regarding the prediction of decadal-scale erosion rates for increasingly dynamic coastal permafrost systems.

Capability of TEC ’s CoRrelation Analysis (CRA) (Iwata and Umeno, JGR-Space Physics, 2016) for detecting preseismic anomalies is explained from the view point of the increase in signal-to-noise ratio to amplify preseismic TEC’s small anomaly signals with multiple sensor data synchronization and correlation. Furthermore, deceleration at propagation velocities of MSTID before the 2016 Kumamoto earthquake firstly observed by CRA (Iwata and Umeno, JGR-Space Physics, 2017) as velocity reduction of MSTID propagation in the F Layer of the ionosphere is then elucidated as a candidate of preseismic anomalies. A new physical model (Umeno, Nakabayashi, Iwata, Kao, 2021,DOI: 10.4236/ojer.2021.104008 ) which is recently constructed from the first principle to explain such ionospheric anomaly (ΔV=αΔE, Linear response theory of deceleration ΔV in propagation velocities of MSTID before large earthquakes with electric field change ΔE) is also presented to characterize preseismic ionospheric TEC anomalies by associating deceleration, acceleration, moving to the inverse direction of macroscopic ionic velocities before various large earthquakes such as our existing findings on ionospheric TEC anomaly before 2016 Tainan earthquake (Goto, Uchida, Igarashi, Chen, Kao, Umeno, JGR-Space Physics, 2019). In particular, this physical model predicts that the 35 m/s change in MSTID propagation velocities estimated by TEC’s CRA for 2016 Kumamoto earthquake requires 0.58 mV/m electric field change in the F Layer ionosphere, which is almost consistent with the estimation (Kelley et. al. JGR-Space Physics, 2017) in that the E✖︎B/B^2 drift of 12 m/s for dislocations of electrons requires 0.5 mV/m electric field in the E Layer to explain Heki’s finding of TEC anomaly behavior before the Tohoku-Oki earthquake. The 10000 times amplification effect of weak signals such as 0.5-0.58 mV/m in electrical field to affect MSTID propagation velocity change as is firstly observed by Iwata and Umeno, 2017 by CRA which means a significant signal amplification capability in this multi-sensor TEC correlation analysis (CRA). To summarize, various interrelation between physical models to exhibit TEC anomalies and observed TEC anomalies as above will be presented to understand preseismic ionospheric anomaly before large earthquakes.

In the Subantarctic sector of the Southern Ocean, vertical entrainment of dissolved iron (DFe) triggers the seasonal productivity cycle. However, diminishing physical supply of new Fe during the spring to summer transition rapidly drives epipelagic microbial communities to rely upon recycled DFe for growth. Hence, subpolar waters evolve seasonally from a high fe ratio system (i.e., [uptake of new Fe]/[uptake of new+recycled Fe]) to a low fe ratio system. Here, we tested how resident microbes within a cyclonic eddy respond to different Fe/ligand inputs which mimic entrained new DFe (Fe-NEW), diffusively-supplied regenerated DFe (Fe-REG), and a control with no addition of DFe (Fe-NO). After 6 days, 3.5 (Fe-NO, Fe-NEW) to 5-fold (Fe-REG) increases in Chl a were observed despite ~2.5-fold range between treatments of initial DFe. Marked differences were also evident in the proportion of in vitro DFe derived from recycling to sustain phytoplankton growth (Fe-REG, 30% recycled c.f. 70% Fe-NEW, 50% Fe-NO). This trend supports the concept that DFe/ligands released from subsurface particles are more bioavailable than new DFe collected at the same depth. This additional recycling may be mediated by bacteria. Indeed, by day 6 bacterial production (BP) was comparable between Fe-NO and Fe-NEW but~2 fold higher in Fe-REG. Interestingly, a preferential response of phytoplankton (haptophyte-dominated) relative to bacteria was also found in Fe-REG. In contrast, in Fe-NEW and Fe-NO the proportion of diatoms increased. Hence, different modes of Fe/ligand supply modify BP and Fe bioavailability to phytoplankton that may drive distinctive floristic shifts and biogeochemical signatures.

Providing accurate forecasts of human modified streamflow is critical for applications ranging from natural resource management to hydropower generation. In this study we evaluate the performance of Long Short Term Memory (LSTM) based neural networks, which maintain an internal set of states and are therefore well-suited to modeling dynamical processes. This research builds on previous work demonstrating that an LSTM model can predict streamflow in out of sample basins with similar or greater accuracy than traditional forecast models specifically calibrated on those basins [1]. Using meteorological data from NOAA’s Global Forecasting System (GFS) and North American Land Data Assimilation System (NLDAS), remote sensing data including snow cover, vegetation and surface temperature from NASA’s MODIS sensors and streamflow data from USGS, we first train an LSTM model on 100 unmodified river basins, and evaluate its predictions on previously unseen human-altered basins. We then train models on a combination of natural and human modified basins and experiment with the effects of new data sources and additional model architecture in predicting human altered streamflow. By training on multiple basins with dynamic climate, land surface and human inputs, we can test the model’s understanding of general hydrologic relationships and human use patterns. We evaluate our models on “out of sample” rivers (previously unseen by the model) that have been altered by dam operations and agricultural withdrawals in northern California. We find that the models trained on natural and modified basins capture human modified flows better than our baseline model trained on natural basins. [1] Kratzert, F., Klotz D., Shalev, G., Klambauer, G., Hochreiter, S. & Nearing, G. Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling. Preprint at https://arxiv.org/abs/1907.08456 (2019)

The North American monsoon (NAM) is an important source of precipitation across the southwestern United States (US). The approximate northern boundary of this feature crosses the Navajo Nation, in the Four Corners region, where NAM rains have long been important to the livelihoods of Native Americans. Relatively little is known about the characteristics and hydrological significance of the NAM in this region. Here we report a new 4-year record of stable H and O isotope ratios in monsoon-season rainfall and water resources across the Navajo Nation. Monthly precipitation samples collected at 39 sites document a characteristic pattern of 2H- and 18O-enrichment associated with monsoonal precipitation. These changes are weakly correlated with local precipitation intensity, however, and the correlation that does exist is dominated by sub-cloud evaporation effects. In contrast to precipitation amount, monsoon-season isotopic values exhibited limited spatial variability across the region, and after correction for sub-cloud evaporation Navajo Nation values were similar to those from a site in southern Arizona. Airmass back-trajectory analysis suggests that the uniformly high NAM isotope values across the region may reflect 1) a region-wide shift from mid-latitude to low-latitude moisture sources at the onset of the peak monsoon, and 2) substantial land-surface recycling of NAM moisture in upwind regions. Comparison of precipitation isotope data with surface and groundwater values implies that, despite its hydroclimatic significance, monsoon rainfall contributes little to rand subsurface water resources. This highlights the monsoon’s importance for warm-season land-surface ecology and hydrology critical to residents of the Four Corners region.

The Glacier Energy and Mass Balance (GEMB) module of NASA’s Ice-sheet and Sea-level System Model (ISSM) can simulate the evolution of firn density profiles in the ice sheet snowpack. It is a column model that can give detailed subsurface parameters such as layer depth, snow grain growth, depth dependent albedo, layer density etc. These parameters are model simulation outputs, and lack of observations makes them difficult to evaluate on a broader scale. In this work, we take advantage of remotely-sensed snow conditions in the top ~15m of the firn; more specifically, the layer depth vs density profiles, measured through the Snow Radar sensor from NASA’s Operation IceBridge mission. We use these observations to calibrate the GEMB module over the Greenland Ice Sheet. The module optimized with the help of actual observations will become more reliable for predicting glacial mass loss and associated global sea level rise over time.

A blanket of sedimentary and regolith material covers approximately three-quarters of the Australian continent, obscuring the crustal geology below and potential mineral resources within. Sedimentary basins also trap seismic energy increasing seismic hazard and generating noisy seismograms that make determining deeper crustal and lithospheric structure more challenging. The most fundamental question that can first be asked in addressing these challenges is how thick are the sediments? Borehole drilling and active seismic experiments using a controlled seismic source (e.g. vibroseis) provide excellent constraints, but they are limited in geographical coverage due to their expense, especially when operating in remote areas. On the other hand, passive-seismic experiments that involve the deployment of seismic receivers only (i.e. seismometers) are relatively low-cost and portable, providing a practical alternative for initial surveys. Here we utilize receiver functions obtained for both temporary and permanent seismic stations in South Australia, covering regions with a diverse sediment distribution. We present a straightforward method to determine the basement depth based on the arrival time of the P-converted-to-S phase generated at the boundary between the crustal basement and sedimentary strata above. Utilizing the available borehole data, we establish a simple predictive relationship between Ps arrival time and the basement depth, which could then be applied to other sedimentary basins with some consideration. The method is found to work best for Phanerozoic sediments and offers a way to determine the sediment-basement interface in unexplored areas requiring only temporary seismic stations deployed for < 6 months.

The formation of strong sporadic E-layers (Es) is frequently observed during the recovery phase of the magnetic storms over Boa Vista (BV, 2.8°N, 60.7°W), a low latitude region over the Brazilian sector. To provide some explanation for this behavior, we investigated in details the ionospheric response to the disturbed electric fields in these atypical Es layers appearance during the magnetic storm of 21-22 January 2016. The analysis was based on F region and Es layers ionospheric parameters obtained from digisonde, as well as on the Total Electron Content (TEC) obtained from Global Navigation SatelliteSystem (GNSS). Furthermore, a theoretical model for the E region named MIRE is used to simulate the Es layers development. Such simulation takes into account the E region winds and electric fields. The results show that the storm time electric field is enough to drive the strong Es layers development. Moreover, it is seen that the intensification of the Es layers is related to the inhibition of the F-region pre-reversal enhancement of the vertical drift due to a westward electric field during the disturbance dynamo effect. Finally, the combined results from the model and observational data seemed to contribute significantly to advance our understanding of the role of the electric fields in the Es layer formation.

Understanding climate change impacts on global marine ecosystems and fisheries requires complex marine ecosystem models, forced by global climate projections, that can robustly detect and project changes. The Fisheries and Marine Ecosystems Model Intercomparison Project (FishMIP) uses an ensemble modelling approach to fill this crucial gap. Yet FishMIP does not have a standardised skill assessment framework to quantify the ability of member models to reproduce past observations and to guide model improvement. In this study, we apply a comprehensive model skill assessment framework to a subset of global FishMIP models that produce historical fisheries catches. We consider a suite of metrics and assess their utility in illustrating the models’ ability to reproduce observed fisheries catches. Our findings reveal improvement in model performance at both global and regional (Large Marine Ecosystem) scales from the Coupled Model Intercomparison Project Phase 5 and 6 simulation rounds. Our analysis underscores the importance of employing easily interpretable, relative skill metrics to estimate the capability of models to capture temporal variations, alongside absolute error measures to characterise shifts in the magnitude of these variations between models and across simulation rounds. The skill assessment framework developed and tested here provides a first objective assessment and a baseline of the FishMIP ensemble’s skill in reproducing historical catch at the global and regional scale. This assessment can be further improved and systematically applied to test the reliability of FishMIP models across the whole model ensemble from future simulation rounds and include more variables like fish biomass or production.

The Sandur greenstone belt (SGB) is a distinctive greenstone belt as it is perched within the Closepet granitoid rocks (CG). The emplacement of the CG is attributed to intrusion in a crustal scale transcurrent shear zone towards the end of Archaean (Moyen et al., 2003). The Chitradurga shear zone that forms the eastern margin of the Chitradurga greenstone belt, located west of the Closepet granite, is considered as the boundary between WDC and EDC. As per the division of the Dharwar craton, the domination of volcano-sedimentary sequences in SGB with abundant BIF and considerable greywacke-argillite lithologies, attest their similarity to the greenstone belts of WDC. Closer to the SGB, the rocks of the CG are known to host excellent mafic microgranular enclaves (MME) which might indicate interaction between the granitic magma with the older greenstone belt lithologies during intrusion. It is interesting to note that there is a progressive increase in crustal thickness from north towards south in the Dharwar craton and the SGB is found associated with the CG in the shallow zones of the north (Moyen et al., 2003). The mafic volcanic rocks are predominantly basaltic in composition and are composed of amphibole, pyroxene, plagioclase and quartz with titanite and magnetite as accessory minerals. The rocks are classified as tholeiitic basalts that were metamorphosed to amphibolite grade. Preliminary geochemical studies on these rocks show significant differences in their trace element distribution. The chondrite-normalized REE patterns show moderate to high contents of REE and have unfractionated pattern. The basalts show a flat to slightly LREE enriched pattern. Some samples show slight negative Eu anomaly and some do not show any significant anomaly. Some associated rocks also have complementary enrichment-depletion of certain elements. All of these point to multiple petrogenetic processes involved in the generation of these magmatic precursor rocks.

The Wang-Sheeley-Arge (WSA) model estimates the solar wind speed and interplanetary magnetic field polarity at any point in the inner heliosphere using global photospheric magnetic field maps as input. WSA employs the Potential Field Source Surface (PFSS) and Schatten Current Sheet (SCS) models to determine the Sun’s global coronal magnetic field configuration. The PFSS and SCS models are connected through two radial parameters, the source surface and interface radii, which specify the overlap region between the inner SCS and outer PFSS model. Though both radii are adjustable within the WSA model, they have typically been fixed to 2.5 R sol. Our work highlights how the solar wind predictions improve when the radii are allowed to vary over time. Data assimilation using particle filtering (sequential Monte Carlo) is used to infer the optimal values over a fixed time window. The Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model generates an ensemble of photospheric maps that are used to drive WSA. When the solar wind model predictions and satellite observations are used in a newly-developed quality-of- agreement metric, sets of metric values are generated. These metric values are assumed to roughly correspond to the probability of the two key model radii. The highest metric value implies the optimal radii. Data assimilation entails additional choices relating to input realization and timeframe, with implications for variation in the solar wind over time. We present this work in its theoretical context and with practical applications for prediction accuracy.

Terrestrial biosphere models offer a comprehensive view of the global carbon cycle by integrating ecological processes across scales, yet they introduce significant uncertainties in climate and biogeochemical projections due to diverse process representations and parameter variations. For instance, different soil water limitation functions lead to wide productivity ranges across models. To address this, we propose the Differentiable Land Model (DifferLand), a novel hybrid machine learning approach replacing unknown water limitation functions in models with neural networks (NNs) to learn from data. Using automatic differentiation, we calibrated the embedded NN and the physical model parameters against daily observations of evapotranspiration, gross primary productivity, ecosystem respiration, and leaf area index across 16 FLUXNET sites. We evaluated six model configurations where NNs simulate increasingly complex soil water and photosynthesis interactions against test datasets to find the optimal structure-performance tradeoff. Our findings show that a simple hybrid model with a univariate NN effectively captures site-level water and carbon fluxes on a monthly timescale. Across a global aridity gradient, the magnitude of water stress limitation varies, but its functional form consistently converges to a piecewise linear relationship with saturation at high water levels. While models incorporating more interactions between soil water and meteorological drivers better fit observations at finer time scales, they risk overfitting and equifinality issues. Our study demonstrates that hybrid models have great potential in learning unknown parameterizations and testing ecological hypotheses. Nevertheless, careful structure-performance tradeoffs are warranted in light of observational constraints to translate the retrieved relationships into robust process understanding.