The Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) and the Global Precipitation Measuring (GPM) Microwave Imager (GMI) have been used as the radiometric transfer standard one after another for the GPM constellation radiometers, during the past nearly two decades. Given that GMI and TMI share only a 13-month common operational period, for the time there is no overlap in between, WindSat can serve as the calibration bridge to provide additional intercalibration for the realization of a consistent multi-decadal oceanic brightness temperature (Tb) product. Thus, we conducted the intercalibration of TMI/GMI for 13-month period, TMI/WindSat for >9 years’ overlap period, and WindSat/GMI XCAL for one year, to assess the Tb bias of one to another. A multi-decadal oceanic Tb dataset was thereafter achieved to ensure a consistent long-term precipitation record that covers TRMM and GPM eras. Moreover, a generic uncertainty quantification model (UQM) was developed by taking various sources of uncertainties into account rigorously and orderly. This UQM model was then applied to quantify the uncertainty estimates associated with these Tb biases. This allows the unified high-sampling-frequency and globally-covered Tb product with associated boundary uncertainties to be much improved for scientific utilization as compared to existing Tb products that are with ad-hoc uncertainties estimates. Moreover, based upon the results of uncertainty quantification process, it is recognized that there is room for improvement in the intercalibration for the water vapor sensitive channels. Further analysis indicates that the issue may be associated with the atmospheric water vapor profile input to the radiative transfer model. Suggestions are subsequently made to use water vapor profile retrieved from millimeter radiometer sounders’ measurements (rather than numerical weather predictions) to determine the impact on the Tb biases of these problematic channels.

As a result of uneven density of data collection, level-2 satellite gravimetry data suffer from global north-south striping. By applying various filtering methods, several studies have addressed the mitigation of the data. However, the studies mainly addressed the issue on a global scale, and the local effects were not considered. On the other hand, water research, especially inland hydrology, usually deals with small-scale fitures such as lakes and watersheds. Therefore, the local data de-striping methods need special attention. This research presents a new analytical method to de-stripe gravimetry data based on the spatial contrast of signals. The approach strikes a balance between de-striping and signal preservation. Using a-priori information obtained from the gravimetry data, the de-striping method first estimates the spatial gradient of the signal and optimizes a Poisson filter based on this information to de-stripe the data. Unlike the other approaches, the optimized filter is dynamic and accounts for temporal variations in the signal contrast, such as seasonality. The proposed approach is applied to ten globally distributed study areas to derive a general scheme. Detailed processes and evaluations are applied to two study areas: the Caspian Sea and the Congo River Basin. Results are visually assessed for spatial fit and for temporal consistency by comparison with results from other filters. The use of a dynamic filter set specified for each region and time point allows us to preserve local hydrologic signals that are susceptible to globally optimized filters. It also allows filter-related errors to be effectively constrained.

Thawing of ice-rich permafrost and subsequent ground subsidence can form characteristic landforms, and the resulting topography they create are collectively called “thermokarst”. The impact of wildfire on thermokarst development remains uncertain. Here we report on the post-wildfire ground deformation associated with the 2014 wildfire near Batagay, Eastern Siberia. We used Interferometric Synthetic Aperture Radar (InSAR) to generate both long-term (1-4 years) and short-term (sub-seasonal to seasonal) deformation maps. Based on two independent satellite-based microwave sensors, we could validate the dominance of vertical displacements and their heterogeneous distributions without relying on in-situ data. The inferred time-series based on L-band ALOS2 InSAR data indicated that the cumulative subsidence at the area of greatest magnitude was greater than 30 cm from October 2015 to June 2019, and that the rate of subsidence slowed in 2018. The burn severity was rather homogeneous, but the cumulative subsidence magnitude was larger on the east-facing slopes where the gullies were also predominantly developed. The correlation suggests that the active layer on the east-facing slopes might have been thinner before the fire. Meanwhile, C-band Sentinel-1 InSAR data with higher temporal resolution showed that the temporal evolution included episodic changes in terms of deformation rate. Moreover, we could unambiguously detect frost heave signals that were enhanced within the burned area during the early freezing season but were absent in the mid-winter. We could reasonably interpret the frost heave signals within a framework of premelting theory instead of assuming a simple freezing and subsequent volume expansion of pre-existing pore water.

The Lagrangian and Eulerian surface current signatures of a low-mode internal tide propagating through a turbulent balanced flow are compared in idealized numerical simulations. Lagrangian and Eulerian total (i.e. coherent plus incoherent) tidal amplitudes are found to be similar. Compared to Eulerian diagnostics, the Lagrangian tidal signal is more incoherent with comparable or smaller incoherence timescales and larger incoherent amplitudes. The larger level of incoherence in Lagrangian data is proposed to result from the deformation of Eulerian internal tide signal induced by drifter displacements. Based on the latter hypothesis, a theoretical model successfully predicts Lagrangian autocovariances by relating Lagrangian and Eulerian autocovariances and the properties of the internal tides and jet. These results have implications for the separation of balanced flow and internal tides signals in the sea level data collected by the future Surface Water and Ocean Topography (SWOT) satellite mission.

In Mongolia, overuse and degradation of groundwater is a serious issue, mainly in the urban and economic hub, Ulaanbaatar, and the Southern Gobi mining hub. In order to explicitly quantify spatio-temporal variations in water availability, a process-based eco-hydrology model, NICE (National Integrated Catchment-based Eco-hydrology) (Nakayama and Watanabe, 2004), was applied to two contrasting river basins including these hubs. The authors built a high-resolution grid data representing water use for livestock, urban populations, and mining by combining a global dataset, statistical data, GIS data, observation data, and field surveys. The model simulated the effects of climatic change and human-induced disturbances on water resources during 1980-2018 (Nakayama et al., 2021). Although drinking by herders’ livestock had some impact on the hydrologic change, the groundwater level in the Tuul River was shown to have been extremely degraded by water use in Ulaanbaatar over the last few decades whereas that in the Galba River has declined markedly as a result of Oyu Tolgoi mining since 2010. Analysis of the relative contribution of environmental factors also helped us to separate the effects of climatic change and human activities on spatio-temporal change in the groundwater level. Further, they extended NICE to couple with inverse method for sensitivity analysis and parameter estimation of anthropogenic water uses (NICE-INVERSE). This new model quantified the spatio-temporal variations of livestock water use in these river basins (Nakayama, et al., in press). The livestock water use was generally small for each soum (district), and could also be heavily returned back to the ecosystems. The result also showed a temporal decreasing trend of unit water use in some typical livestock (cattle, sheep, and goats), suggesting a substantial increase in water stress due to local-regional eco-hydrological degradation by urbanization and mining. Sensitivity analysis and inverse estimation of model parameters helped to improve the accuracy of hydrologic budgets in basins. This methodology is powerful for evaluating spatio-temporal variations of water availability and supporting water management in regions with fewer inventory data.

Tracking the onset of inelastic (permanent) deformation is critical to quantifying the stress experienced by an aquifer system so that the effects of current groundwater extraction practices are put in the context of the sedimentary and geological histories of a region. However, the pre-consolidation stress is rarely known due to the lack of multi-decadal ground-based data. In this paper, we propose a new approach to track the onset and spatial evolution of inelastic deformation based on a 2003-2020 multi-sensor Interferometric Synthetic Aperture Radar time series analysis. Our study reveals that in central Iran, many locations that used to experience elastic (recoverable) deformation just a few years ago, are now deforming inelastically, leading to irreversible lowering of the ground surface and irreversible loss of aquifer storage. Lithologic data reveals that the total thickness of the drained clay layers controls the extent and timing of the observed inelastic deformation, while groundwater data confirms that the multi-decadal lowering of groundwater levels is driving the long-term compaction. These results highlight that we are now at or near a tipping point in time between sustainability and permanent damage to our underground water resources, emphasizing the fact that current decisions have the potential to change the natural resources landscape permanently.

Transient deformations associated with foreshocks activity has been observed before large earthquakes, suggesting the occurrence of a detectable pre-seismic slow slip during the initiation phase. In this respect, a critical issue consists in discriminating the relative contributions from seismic and aseismic fault slip during the preparation phase of large earthquakes. We focus on the April-May 2017 Valparaíso earthquake sequence, which involved a Mw=6.9 earthquake preceded by an intense foreshock activity. To assess the relative contribution of seismic and aseismic slip, we compare surface displacements predicted from foreshock source models to the transient motion measured prior to the mainshock. The comparison between observed and predicted displacements shows that only half of the total displacement can be explained by the contribution of foreshocks. This result suggests the presence of aseismic preslip during an initiation phase preceding the mainshock.

The instrument package SEIS (Seismic Experiment for Internal Structure) with the three very broadband and three short-period seismic sensors is installed on the surface on Mars as part of NASA’s InSight Discovery mission. When compared to terrestrial installations, SEIS is deployed in a very harsh wind and temperature environment that leads to inevitable degradation of the quality of the recorded data. One ubiquitous artifact in the raw data is an abundance of transient one-sided pulses often accompanied by high-frequency spikes. These pulses, which we term “glitches”, can be modeled as the response of the instrument to a step in acceleration, while the spikes can be modeled as the response to a simultaneous step in displacement. We attribute the glitches primarily to SEIS-internal stress relaxations caused by the large temperature variations to which the instrument is exposed during a Martian day. Only a small fraction of glitches correspond to a motion of the SEIS package as a whole caused by minuscule tilts of either the instrument or the ground. In this study, we focus on the analysis of the glitch+spike phenomenon and present how these signals can be automatically detected and removed from SEIS’ raw data. As glitches affect many standard seismological analysis methods such as receiver functions, spectral decomposition and source inversions, we anticipate that studies of the Martian seismicity as well as studies of Mars’ internal structure should benefit from deglitched seismic data.

Current lightning predictions are uncertain because they either rely on empirical diagnostic relationships based on the present climate or use coarse-scale climate scenario simulations in which deep convection is parameterized. Previous studies demonstrated that simulations with convection-permitting resolutions (km-scale) improve lightning predictions compared to coarser-grid simulations using convection parameterization for different geographical locations but not over the boreal zone. In this study, lightning simulations with the NASA Unified-Weather Research and Forecasting (NU-WRF) model are evaluated over a 955x540 km2 domain including the Great Slave Lake in Canada for six lightning seasons. The simulations are performed at convection-parameterized (9 km) and convection-permitting (3 km) resolution using the Goddard 4ICE and the Thompson microphysics (MP) schemes. Four lightning indices are evaluated against observations from the Canadian Lightning Detection Network (CLDN), in terms of spatiotemporal frequency distribution, spatial pattern, daily climatology, and an event-based overall skill assessment. Concerning the model configuration, regardless of the spatial resolution, the Thompson scheme is superior to the Goddard 4ICE scheme in predicting the daily climatology but worse in predicting the spatial patterns of lightning occurrence. Several evaluation metrics indicate the benefit of working at a convection-permitting resolution. The relative performance of the different lightning indices depends on the evaluation criteria. Finally, this study demonstrates issues of the models to reproduce the observed spatial pattern of lightning well, which might be related to an insufficient representation of land surface heterogeneity in the study area.

The Apollo 16 sample 66095, named „Rusty Rock”, is enriched in volatile and moderately volatile elements. The impact melt breccia is characterized by the abundant occurrence of Fe-rich sulfide and chloride alteration phases, including FeS, ZnS and FeCl2. These phases have previously been interpreted to be the result of fumarolic alteration of the breccia. Here we present the results of two different experimental approaches, which aim to constrain the temperature conditions and the process under which the „Rusty Rock” alteration formed. The first experimental set-up assumes that the metals Zn, Cu and Fe were introduced into the rock by a C-O-S-Cl gas phase, and that the Fe-rich sulfides and chlorides were deposited from this gas phase. This “gas deposition” experiment suggests that the alteration assemblage formed over the temperature range of 538-638±5 °C. The second experimental set-up simulates a scenario, where Fe metal particles in the lunar rock react with a Zn-C-O-S-Cl gas phase at six different temperatures between 396±5 °C and 1005±5 °C. This latter “metal reaction” experiment resulted in the formation of sulfide and chloride coatings on the Fe metal chips. The „Rusty Rock” alteration phases FeCl2 and (Zn,Fe)S were abundantly present in the coating of the Fe metal chip reacted at 580±5 °C. Both experiments lead to results which are in agreement, providing a temperature of 580 ± 50 °C for the fumarolic alteration on the Moon, as observed in the Apollo 16 „Rusty Rock”.

Explosions and earthquakes are effectively discriminated by P/S amplitude ratios for moderate magnitude events (M≥4) observed at regional to teleseismic distances (≥200 km). It is less clear if P/S ratios are effective explosion discriminants for lower magnitudes observed at shorter distances. We report new tests of P/S discrimination using a dense seismic array in a continental volcanic arc setting near Mount St. Helens, with 23 single-fired borehole explosions (ML 0.9-2.3) and 406 earthquakes (ML 1-3.3). The array provides up to 95 three-component broadband seismographs and most source-receiver distances are <120 km. Additional insight is provided by ~3,000 vertical component geophone recordings of each explosion. Potential controls on local distance P/S ratios are investigated, including: frequency range, distance, magnitude, source depth, number of seismographs, and site effects. A frequency band of about 10-18 Hz performs better than lower or narrower bands because explosion-induced S-wave amplitudes diminish relative to P for higher frequencies. Source depth and magnitude exhibited weak influences on P/S ratios. Site responses for earthquakes and explosions are correlated with each other and with shallow crustal Vp and Vs from travel-time tomography. Overall, the results indicate high potential for local distance P/S explosion discrimination in a continental volcanic arc setting, with ≥98% true positives and ≤6.3% false positives when using the array median from ≥16 stations. Performance is reduced for smaller arrays, especially those with ≤4 stations, thereby emphasizing the importance of array data for discrimination of low magnitude explosions.

Twenty-two sites, subjected to an IZZI-modified Thellier-Thellier experiment and strict selection criteria, recover a paleomagnetic axial dipole moment (PADM) of 62.24$\pm$ 30.6 ZAm$^2$ in Northern Israel over the Pleistocene (0.012 - 2.58 Ma). Pleistocene data from comparable studies from Antarctica, Iceland, and Hawaii, re-analyzed using the same criteria and age range, show that the Northern Israeli data are on average slightly higher than those from Iceland (PADM = 53.8 $\pm$ 23 ZAm$^2$, n = 51 sites) and even higher than the Antarctica average %\cite{asefaw21} (PADM = 40.3 $\pm$ 17.3 ZAm$^2$, n = 42 sites). Also, the data from the Hawaiian drill core, HSDP2, spanning the last half million years (PADM = 76.7 $\pm$ 21.3 ZAm$^2$, n = 59 sites) are higher than those from Northern Israel. These results, when compared to Pleistocene results filtered from the PINT database (www.pintdb.org) suggest that data from the Northern hemisphere mid-latitudes are on average higher than those from the southern hemisphere and than those from latitudes higher than 60$^{\circ}$N. The weaker intensities found at high (northern and southern) latitudes therefore, cannot be attributed to inadequate spatio-temporal sampling of a time-varying dipole moment or low quality data. The high fields in mid-latitude Northern hemisphere could result from long-lived non-axial dipole terms in the geomagnetic field with episodes of high field intensities occurring at different times in different longitudes. This hypothesis is supported by an asymmetry predicted from the Holocene, 100 kyr, and five million year time-averaged geomagnetic field models.

Accurate flood inundation modelling using a complex high-resolution hydrodynamic (high-fidelity) model can be very computationally demanding. To address this issue, efficient approximation methods (surrogate models) have been developed. Despite recent developments, there remain significant challenges in using surrogate methods for modelling the dynamical behaviour of flood inundation in an efficient manner. Most methods focus on estimating the maximum flood extent due to the high spatial-temporal dimensionality of the data. This study presents a hybrid surrogate model, consisting of a low-resolution hydrodynamic (low-fidelity) and a Sparse Gaussian Process (Sparse GP) model, to capture the dynamic evolution of the flood extent. The low-fidelity model is computationally efficient but has reduced accuracy compared to a high-fidelity model. To account for the reduced accuracy, a Sparse GP model is used to correct the low-fidelity modelling results. To address the challenges posed by the high dimensionality of the data from the low- and high-fidelity models, Empirical Orthogonal Functions (EOF) analysis is applied to reduce the spatial-temporal data into a few key features. This enables training of the Sparse GP model to predict high-fidelity flood data from low-fidelity flood data, so that the hybrid surrogate model can accurately simulate the dynamic flood extent without using a high-fidelity model. The hybrid surrogate model is validated on the flat and complex Chowilla floodplain in Australia. The hybrid model was found to improve the results significantly compared to just using the low-fidelity model and incurred only 39% of the computational cost of a high-fidelity model.

Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power-grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long- and short-period characteristics?” is addressed. The study focuses on the long-term variability of measured GIC, and establishes the extent to which the variability relates to quiet-time geomagnetic activity. GIC quiet-day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of Thermosphere-Ionosphere- Electrodynamics General Circulation Model (TIE-GCM)-simulated neutral wind and height-integrated current density. The results show strong evidence that the middle-latitude nodes routinely respond to the tidal-driven Sq variation, with a local time and seasonal dependence on the the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC-based significance score computed in this study provides power utilities with a node-specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.

Chemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth’s land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth’s mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo-photogrammetry at ~30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo-photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root-mean-square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine-scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth’s mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth’s surface.

Earth System Models’ complex land components simulate a patchwork of increases and decreases in surface water availability when driven by projected future climate changes. Yet, commonly-used simple theories for surface water availability, such as the Aridity Index (P/E0) and Palmer Drought Severity Index (PDSI), obtain severe, globally dominant drying when driven by those same climate changes, leading to disagreement among published studies. In this work, we use a common modeling framework to show that ESM simulated runoff-ratio and soil-moisture responses become much more consistent with the P/E0 and PDSI responses when several previously known factors that the latter do not account for are cut out of the simulations. This reconciles the disagreement and makes the full ESM responses more understandable. For ESM runoff ratio, the most important factor causing the more positive global response compared to P/E0 is the concentration of precipitation in time with greenhouse warming. For ESM soil moisture, the most important factor causing the more positive global response compared to PDSI is the effect of increasing carbon dioxide on plant physiology, which also drives most of the spatial variation in the runoff ratio enhancement. The effect of increasing vapor-pressure deficit on plant physiology is a key secondary factor for both. Future work will assess the utility of both the ESMs and the simple indices for understanding observed, historical trends.

Our understanding of geomagnetic field intensity prior to the era of direct instrumental measurements relies on paleointensity analysis of rocks and archaeological materials that serve as magnetic recorders. Only in rare cases absolute paleointensity datasets are continuous over millennial timescales, in sub-centennial resolution, and directly dated using radiocarbon. As a result, fundamental properties of the geomagnetic field, such as its maximal intensity and change rate have remained a subject of lively discussion. Here, we place firm constraints on these two quantities using Bayesian modeling of well-dated archaeomagnetic intensity data from the Levant and Upper Mesopotamia. We report new data from 23 groups of pottery collected from 18 consecutive radiocarbon-dated archaeological strata from Tel Megiddo, Israel. In the Near East, the period of 1700–550 BCE is represented by 84 groups of archaeological artifacts, 55 of which were dated using radiocarbon or a direct link to clear historically-dated events, providing unprecedented sub-century resolution. Moreover, stratigraphic relationships between samples collected from multi-layered sites enable further refinement of the data ages. The Bayesian curve shows four geomagnetic spikes between 1050 and 600 BCE, with virtual axial dipole moment (VADM) reaching values of 155–162 ZAm2 – much higher than any prediction from geomagnetic field models. Rates of change associated with the four spikes are ~0.35–0.55 μT/year (~0.7–1.1 ZAm2/year), at least twice the maximum rate inferred from direct observations spanning the past 190 years. The increase from 1750 BCE to 1030 BCE (73 to 161 ZAm2) depicts the Holocene’s largest change in field intensity.

Reaction with iodide (I-) at the sea surface is an important sink for atmospheric ozone, and causes sea-air emission of reactive iodine which in turn drives further ozone destruction. To incorporate this process into chemical transport models, improved understanding of the factors controlling marine iodine speciation, and especially sea-surface iodide concentrations, is needed. The oxidation of I- to iodate (IO3-) is the main sink for oceanic I-, but the mechanism for this remains unknown. We demonstrate for the first time that marine nitrifying bacteria mediate I- oxidation to IO3-. A significant increase in IO3- concentrations compared to media-only controls was observed in cultures of the ammonia-oxidising bacteria Nitrosomonas sp. (Nm51) and Nitrosoccocus oceani (Nc10) supplied with 9-10 mM I-, indicating I- oxidation to IO3-. Cell-normalised production rates were 15.69 (±4.71) fmol IO3- cell-1 d-1 for Nitrosomonas sp., and 11.96 (±6.96) fmol IO3- cell-1 d-1 for Nitrosococcus oceani, and molar ratios of iodate-to-nitrite production were 9.2±4.1 and 1.88±0.91 respectively. Preliminary experiments on nitrite-oxidising bacteria showed no evidence of I- to IO3- oxidation. If the link between ammonia and I- oxidation observed here is representative, our ocean iodine cycling model predicts that future changes in marine nitrification could alter global sea surface I- fields with potential implications for atmospheric chemistry and air quality.