Given the key role, wetlands play in climate regulation and shoreline stabilization, identifying their spatial distribution is essential for the management, restoration, and protection of these invaluable ecosystems. The increasing availability of high spatial and temporal resolution optical and synthetic aperture radar (SAR) remote sensing data coupled with advanced machine learning techniques have provided an unprecedented opportunity for mapping complex wetlands ecosystems. A recent partnership between the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO) resulted in the design of the NASA-ISRO SAR (NISAR) mission. In this study, the capability of L-band simulated NISAR data for wetland mapping in Yucatan Lake, Louisiana is investigated using two object-based machine learning approaches: Support Vector Machine -(SVM) and Random Forest (RF). L-band Unmanned Aerial Vehicle SAR (UAVSAR) data is exploited as a proxy for NISAR data. Specifically, we evaluated the synergistic use of different polarimetric features for efficient delineation of wetland types, extracting 84 polarimetric features from more than 10 polarimetric decompositions. High spatial resolution National Agriculture Imagery Program imagery is applied for image segmentation using the mean-shift algorithm. Overall accuracies of 74.33% and 81.93% obtained by SVM and RF, respectively, demonstrate the great possibility of L-band prototype NISAR data for wetland mapping and monitoring. In addition, variable importance analysis using the Gini index for RF classifier suggests that H/A/ALPHA, Freeman-Durden, and Aghababaee features have the highest contribution to the overall accuracy.

Headland sediment transport is dynamic and complex, but understanding the transport mechanisms is necessary for effective long-term management of downdrift beach compartments. In this study, we have develop a coastal process model using TUFLOWFV, that is used to calibrate an approximation tool for headland bypassing at the study site. The approximation tool is shown to reproduce sediment transport rates at the headland apexes accurately and efficiently. We have explored the headland sediment transport mechanism, the influence of wave height and direction, and the sensitivity in regional climate conditions. Headland sediment transport is shown to occur as ‘trickle’ bypassing under modal wave conditions or ‘sand slug’ migration under storm wave conditions that travel in either a headland-attached and a cross-embayment pathway. Bypassing during storm wave conditions produces 50% to 60% of total bypassing volume, despite only accounting for 6% of the recorded days. The results indicate that headland transport is sensitive to changes in wave direction and wave height, with the existing mean wave direction balancing sediment transport on the east and north faces of the headland. Seasonality is the most significant climatic control on headland transport, while ENSO phase is only significant for the headland apexes that are exposed to south-east wave conditions. The potential for anticlockwise rotation of the wave climate in future is explored, with greater erosion of the northern beaches of the headland likely due to a reduced supply of sediment around the eastern point of the headland and greater erosive wave power on the north side.

We present a test particle study of perturbations of a weakly relativistic electron distribution interacting with a rising-tone lower band chorus emission. Trajectories of interacting electrons are traced back in time from the equator to reconstruct the perturbed velocity distribution. The wave field is precalculated from a model based on the nonlinear growth theory and features a realistic subpacket structure. The perturbed distribution reveals a series of stripes of increased and decreased phase space density. This perturbation is associated with the electromagnetic hole structure which exists along the resonance velocity curve of each subpacket. Time-averaging of the final distribution shows that rising-tone lower band chorus emissions produce a sharp decrease in density at low parallel velocities, which might be detectable by spacecraft particle instruments.

The Eocene (55-34 Mya) was marked by a transition from a greenhouse to an icehouse climate, and also experienced several hyperthermals, which are rapid, extreme global warming events. These hyperthermal events were caused by rapid increases in atmospheric CO2 concentration, so they provide the opportunity to study how Earth systems respond to rapid and high magnitude changes in atmospheric CO2. Understanding the drivers of major climate change events, like these ones, is an important step in understanding modern climate change. One such event, the Middle Eocene Climatic Optimum, was a major climatic shift lasting approximately 500 kyr. Orbital forcers, most notably variations in the amount of solar irradiance reaching Earth, are believed to explain many of the observed climatic variations in both this epoch and others. Changes in orbital patterns are widely believed to explain cyclicities on the 20,000 to 100,000 year scale, while changes in solar irradiance have effects on the sub-1,000 year scale. However, little work has been done to understand the influence of orbital cycles of periods between these, in the millennial range. This study aims to bridge this knowledge gap by performing spectral analyses of Middle Eocene Climatic Optimum temperature proxy records to determine if millennial-range orbital cycles have an observable influence on climate dynamics. Using temperature proxy data from four Ocean Drilling Program sites, spectral analysis was performed and prominent signals in the spectra were identified. This analysis showed that all studied sites had a statistically significant signal in the 2500 ± 250-year range, which was identified as the Hallstatt cycle. A suggested mechanism for the Hallstatt cycle, a spin-orbit coupling of the Jovian planets, is astronomical in origin. This adds further evidence that dynamical solar system chaos during this period contributed to the abnormal climate patterns at play and suggests that external factors have a larger climate forcing ability than previously thought. Delving into the intricacies of solar system chaos and its impact on past climates enhances our understanding of present and future climates, thus empowering more accurate and informed predictions going forward.

Urban irrigation is an essential process in land-atmosphere interaction. It is one of the uncertain parameters of the urban hydrology because of the presence of various microclimates. This study investigates the microclimate effects and irrigation water requirements of three landscape types in an arid region of Phoenix, AZ. The microclimate effect encompassed surface temperature, air temperature, and wind speed. The three landscapes include mesic, oasis, and xeric. The simulation was conducted using ENVI-met software for the hottest day of the year (23rd June 2011). The simulated model was validated using ground data. Results show that the mesic landscape induced cooling effects, both in the day-time and nighttime, by reducing the surface temperature and air temperature. However, the mesic landscape showed high-water consumption because of high leaf area density. The oasis landscape showed more day-time cooling than the mesic landscape, but the nighttime warming was similar to the xeric landscape. However, the potential irrigation water requirement was lower than the mesic landscape. Moreover, microclimate conditions varied spatially in each neighborhood. The xeric landscape showed lower wind speeds and air temperatures between the buildings. Overall, the oasis landscape proved to be the most efficient of the three landscapes for water consumption and day-time cooling.

In this study, we find significant sensitivity to the choice of time step for the Energy Exascale Earth System Model's atmospheric component, leading to large decreases in the magnitude of cloud forcing when the time step is reduced to 10 seconds. Reducing the time step size for the microphysics increases precipitation, leading to a drying of the atmosphere and an increase in surface evaporation. This effect is amplified when the microphysics is substepped together with other cloud physics processes. Coupling the model's dynamics and physics more frequently reduces cloud fraction at lower altitudes, while producing more cloud liquid at higher altitudes. Reducing the deep convection time step also reduces low cloud mass and cloud fraction. Together, these results suggest that cloud physics in a global circulation model can depend strongly on time step, and in particular on the frequency with which cloud-related processes are coupled with each other and with the model dynamics.

Some spherical harmonic expressions (SHEs) of gravitational and geomagnetic field elements will become infinite when computation point approaches polar regions, as the sine function of the geocentric co-latitude contained in the denominator tends to be zero. Currently, this singularity problem has been solved for gravitational field case, however, it remains unsolved for geomagnetic vectors (GVs) and geomagnetic gradient tensors (GGTs). The reason is that the latter use Schmidt semi-normalized associated Legendre function (SNALF), which is different from fully-normalized associated Legendre function (FNALF) used in the former. To overcome this singularity problem, we derive new non-singular expressions of the first- and second-order derivatives of Schmidt SNALF, and the corresponding two kinds of spherical harmonic polynomials. When the novel expressions are applied to the traditional formulae of GVs and GGTs, more practical expressions of GVs and GGTs with non-singularity are formulated by refining the cases that the order m equals 0, 1, 2 and other values. Furthermore, to provide flexible calculation strategies for Schmidt SNALF, we derive four kinds of recursive formulae, including the standard forward row recursion (SFRR), the standard forward column recursion (SFCR), the cross degree and order recursion (CDOR), and the Belikov recursion (BR). Besides, we demonstrate the effectiveness of the new derived non-singular expressions of GVs and GGTs and analyze the computation speed and stability of the four recursive formulae of Schmidt SNALF by extensive numerical experiments. Results achieve significant improvements in solving the singularity problem of the SHEs of GVs and GGTs.

Majority of currently applied environmental models relay on linear relations between environmental endmembers. In this research, a detailed and comprehensive comparison between linear (L) and non-linear (NL) models are presented. The L and NL models are realized in a framework of Artificial Neural Networks (ANN). The evolution process of the ANN-L and ANN-NL models is based on estimation of fractional snow cover through data-fusion between high resolution (IKONOS) and medium resolution (Landsat TM/ETM+) remotely sensed images. The statistical measure values of R2, RMSE, MAE, Accuracy, Precision, Recall, and Specificity indicate better performance of the ANN-NL model in comparison to the ANN-L in estimation of ANN Landsat-FSC. The presented results, strongly indicate that to fully capture, untangle, and characterize internal environmental relations high-sensitivity non-linear models are required. Non-linear relations are particularly visible the complex in alpine-forested environments.

Predicting geomagnetic conditions based on in-situ solar wind observations allows us to evade disasters caused by large electromagnetic disturbances originating from the Sun to save lives and protect economic activity. In this study, we aimed to examine the relationship between the Kp index, representing global magnetospheric activity level, and solar wind conditions using an interpretable neural network known as potential learning (PL). Data analyses based on neural networks are difficult to interpret; however, PL learns by focusing on the “potentiality of input neurons” and can identify which inputs are significantly utilized by the network. Using the full advantage of PL, we extracted the influential solar wind parameters that disturb the magnetosphere under southward Interplanetary magnetic field (IMF) conditions. The input parameters of PL were the three components of the IMF (Bx, By, -Bz(Bs)), solar wind flow speed (Vx), and proton number density (Np) in geocentric solar ecliptic (GSE) coordinates obtained from the OMNI solar wind database between 1998 and 2019. Furthermore, we classified these input parameters into two groups (targets), depending on the Kp level: Kp = 6- to 9 (positive target) and Kp = 0 to 1+ (negative target). Negative target samples were randomly selected to ensure that numbers of positive and negative targets were equal. The PL results revealed that solar wind flow speed is an influential parameter for increasing Kp under southward IMF conditions, which was in good agreement with previous reports on the statistical relationship between the Kp index and solar wind velocity, and the Kp formulation based on the IMF and solar wind plasma parameters. Based on this new neural network, we aim to construct a more correct and parameter-dependent space weather forecasting model.

The next-generation, broadband geodetic very-long-baseline interferometry system, named VGOS, is being developed globally with an aim to achieve 1~mm accuracy for station positions. Currently, the systematic errors in VGOS observations are still about one order-of-magnitude larger than this aim. In this study, we demonstrate that it is feasible to make images directly from VGOS observations without the need of complicated calibrations and determine the source structure effects in VGOS broadband delays through the process of model fitting to the structure phases from our imaging results. Source structure effects are investigated in detail, and it is shown that the systematic errors in VGOS observations are well explained by these effects. For instance, the root-mean-square (RMS) closure delays of the observations of sources 0016$+$731 and 1030$+$415 are 24.9~ps and 50.2~ps in session VO0034, respectively; by correcting source structure effects based on the images, the RMS values of the residual closure delays are 5.5~ps and 10.1~ps. The jumps in delay observables with magnitudes of several hundreds of picoseconds are found to be caused by 2$\pi$ phase shifts among the four bands due to strong source structure effects. The impact of the alignment of the images at the four frequency bands in VGOS is discussed. Our study provides a methodology of deriving images of radio sources at the four bands of VGOS observations and discusses the alignment of the four-band images, which is fundamental to mitigate systematic effects.

We present a kernel auto-regressive (KA) method which can be used to represent the daily to multi-day auto-correlation structure of precipitation time series, using information both in the occurrence and intensity of measured rainfall events. The method is able to capture a larger fraction of the memory in multiple time series than commonly-used occurrence-based Markov chain models, even when the intensity distribution is allowed to be conditioned on the Markov state. The KA method is less sensitive to the spatial scale at which the data is reported, as it is not strictly reliant on patterns of wet and dry days for providing correlation. Output from the KA model can be used as weather generator model simulations, as empirical representations of process structure, as representation of weather/climate variability partitioning, or as climatological null models against which observations can be compared for statistical significance. The KA method demonstrates improvements in each of these over classic occurrence Markov chain models and daily independent climatology, in both representations of interannual precipitation variability and in downstream water balance variables. We provide climate null confidence intervals for precipitation trends (driven largely by autumn increases), and decompose variability into trend, interannual, and weather components (in increasing order of magnitude) for the Contiguous United States.

This paper will report on the effects of an extreme space weather event. On January 20th, 2005, a balloon-borne experiment intended to measure relativistic electron precipitation and its effects was aloft over Antarctica (~32 km; near 70º S, 345º W geographic) throughout the duration of the solar energetic particle (SEP) event. The balloon carried an x-ray scintillation counter, dc electric field, and scalar electrical conductivity sensors. Intense energetic proton precipitation and large increases in the energetic proton population of the outer radiation belts were observed by a global array of observatories and spacecraft. The stratospheric conductivity increased by nearly a factor of 20 above ambient at the time of the SEP event onset and returned to within a factor of two above normal levels within 17 hours. The electric field decreased to near zero following the increase in particle flux at SEP onset. Combined with an atmospheric electric field mapping model, these data are consistent with a shorting out of the global electric circuit and point toward substantial ionospheric convection modifications. It is shown that the conductivity profile predicted by the Sodankylä Ion and Neutral Chemistry (SIC) model does not shield the balloon payload at 32 km from the ionospheric horizontal field. Thus, the data really do indicate a very low level of ionospheric convection over the balloon during the 6 hours following the SEP event. We have used global magnetometer and satellite data to interpret the changes in the vertical field as indicators of large scale convection changes.

The subtropical northeastern Pacific (SNEP) acts as a bridge conveying information between the North and tropical Pacific. While most studies have investigated the SNEP sea surface temperature (SST) interannual variability, less attention has been paid to the pronounced decadal variability in this region. Here, by analyzing observational data and an ensemble pacemaker simulation, we investigate SNEP SST decadal variability in terms of atmospheric forcing processes. We find that both tropical Pacific-forced and internal (independent of tropical Pacific forcing) atmospheric variability play important forcing roles. In particular, tropical Pacific-excited Aleutian low variability, whose center of action shifts southeastward compared to the climatology, contributes to forcing SNEP SST decadal variability. This contribtution could offer an important source for the predictability of SNEP SST decadal variability.

Tropical cyclones (TCs) and their economic cost risk under climate change are significant concerns globally. Previous studies on TC damage functions and risk assessment are mostly performed based on modeling TC-level damage and thus obtaining the annual average loss for a country or region. The scalability of these damage functions at finer scales has been less systematically explored. In this study, we examine how the model structure, estimated parameters, and model performance of TC damage functions vary with spatial scale. The comparisons are illustrated by fitting two types of damage functions based on reported damage data at the county, province, and TC scales. We find that the newly proposed precipitation-calibrated sigmoidal damage function significantly outperforms the wind-calibrated sigmoidal damage function at three scales of county, province and TC event. Another type of power-law damage function that integrates hazard, exposure, and vulnerability complements the typical sigmoidal damage function because it yields a better fit when estimating direct economic loss above the province scale. Our work provides an empirical assessment of the role of spatial scale and damage function in TC economic impact evaluation and demonstrates the importance of spatially scale-specific policy-making in TC risk management and climate adaptation strategies.

The properties of the auroral electrojets are studied by modeling the relationships between the solar-wind parameters and the AU and AL indices with a trained echo state network (ESN), a kind of recurrent neural network. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The synthetic data show that the AU and AL indices are significantly affected by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF). A contributions from IMF By is are also suggested. In addition, the synthetic data indicate nonlinear effects from the solar-wind density, which is strong when the solar-wind speed is high and when IMF Bz is near zero.

Fault zones are usually composed of a granular gouge, coming from the wear material of previous slips, which contributes to friction stability. Once considering a mature enough fault zone that has already been sheared, different types of infill materials can be observed, from mineral cementation to matrix particles that can fill remaining pore spaces between clasts and change the rheological and frictional behaviors of the gouge. We aim to understand and reproduce the influence of grain-scale characteristics on slip mechanisms and gouge rheology (Riedel bands) by employing the Discrete Element Method. A 2D-direct shear model is considered with a dense assembly of small polygonal cells of matrix particles. A variation of gouge characteristics such as interparticle friction, gouge shear modulus or the number of particles within the gouge thickness leads to different Riedel shear bands formation and orientation that has been identified as an indicator of a change in slip stability (Byerlee et al., 1978). Interpreting results with slip weakening theory, our simulated gouge materials with high interparticle friction or a high bulk shear modulus, increase the possible occurrence of dynamic slip instabilities (small nucleation length and high breakdown energy). They may give rise to faster earthquake ruptures.

A series of survey protocol for deep sea polymetallic sulfides using not electromagnetic waves but acoustic, electromagnetic, gravity and magnetic, and geochemical methods has been proposedby Japanese national project, the Next-Generation Technology for Ocean Resources Exploration (Zipangu in the Ocean Program) launched in 2014, which enables effective and efficient explorations. The first version of the survey protocol has been made to determine the potential polymetallic sulfides areas using detailed scientific researches on their genesis. Deep sea polymetallic sulfides are produced beneath the sea floor because of the interaction between sea water and sub-bottom hydrothermal solutions driven from volcanic magma, indicating that they must be discovered at mid-ocean ridges, back-arc and arc regions, and hot spots. Recent studies further show that back-arc basins such as Okinawa Trough probably produce deep sea polymetallic sulfides bodies much larger than other regions (Tsuji et al., 2012). Some interesting topics on the survey protocol which further constrains the candidate areas, include acoustic, natural electric potential and seismic survey methods, that are 1) the shipboard Multi Beam Echo Sounding (MBES) survey that can acoustically detect hydrothermal plume including abundant CO2 for regional survey (100km2 <), 2) continuous self-potential measurements with tremendously low noise in the sea water that can identify individual sulfide deposit for semi-detailed survey (10km2- 100km2), and 3) state of the art vertical cable seismic (VCS) system which enables to delineate detailed structure of hydrothermal ore deposit body for detailed survey (< 10km2). These three methods, in combination with other geophysical and geochemical surveys, have been turned out to be effective and efficient for surveying deep sea polymetallic sulfides. Multiple types of data would be integrated into a single visualization to gain a clear view of the structure of the sulfides. Integrating core lithologies obtained via drilling with the seismic reflection structure data has been found to show the seismic velocity structure along with the extent of the polymetallic sulfides bodies.

To explore the repeating mud volcanoes influences on the seismicity behaviors, we measured the spatial distribution z and b value changes with the time in the mud volcanoes developing area. We find a weak correlation between the b-z values and the mud volcanoes. Generally, the z-values anomalies of the mud volcanoes area show unchanged negative values and indicate the seismic quiescence before a strong earthquake, while the b-values show the frequently periodicity fluctuations around the value of 0.5. It may indicate the conjunct triggering relationships between the mud volcanoes and earthquakes stuck. We infered that the mud volcanoes eruptions help to partition and release part of the regional stress accumulation from the earthquake seismogenic structures, thus balanced the local stress in the strata. Keywords: Mud volcano; SW Taiwan; coulomb stress change; b-z-value; conjunct triggering relationship