Harvey was formed around 17 August with maximum winds 40 miles/hour. At the moment, Irma formation started on 20 August faster becoming a hurricane category five. Some days before these two events, the Uruguay and Brazil coast suffered a recede in the oceans near Punta del Este and Rio Grande do Sul it happened in the week of 11 August. The energy accumulated in the recede of waters was not released by a tsunami and the water slowly was back to the shore. This event repeated at August 25, however at higher latitudes as in Parana and Sao Paulo. And consequent high tides in Chile again. The absence of a recurrent tsunami at the Brazilian coast indicates the energy accumulated from the receding ocean was released as a tsunami in the oceans, it became another huge hurricane formation as Jose and Katia. All those events pointed out for an atmospheric pressure disturbance on the Atlantic East Coast. In South America happened a suddenly increase at the atmospheric pressure which made the ocean waves receded for many days. Another similar disturbance happened in Caribe area resulting in several huge hurricanes.

The biodiversity of reef fish in the Florida Keys National Marine Sanctuary was evaluated in terms of abundance, biomass, evenness, species richness, Shannon diversity, Simpson diversity, and functional diversity, using observations collected from 1999 – 2016 by the Reef Visual Census program. To compare the different diversity indices, species richness, Shannon diversity, Simpson diversity, and functional diversity were converted into effective number of species. We examined the seven indices by level of protection and type of no-take marine zones and by three habitat strata. The study detected abundance, biomass, and diversity were significantly greater (except evenness) inside no-take marine zones compared to areas open to fishing. Smaller reserves had higher abundance, biomass, and richness values than larger reserves and areas open to fishing, but had moderately higher diversity values. This may be attributed to a few species with many individuals that are dominant inside and outside no-take marine zones. Surprisingly, none of the indices were significantly different (except for functional diversity) between the larger Ecological Reserve and areas open for consumption. This may be due to spillover effects. Furthermore, the no-take marine zones only explained a small proportion of total percent deviance in the indices. Habitat type had a greater influence on patterns in composition and diversity where high relief reef habitats had the greatest abundance, biomass, and diversity indices. Based on our results managers should prioritize preserving high relief reefs through a network of small reserves to enhance reef fish composition and biodiversity.

Key Points: • We examine Indian summer monsoon rainfall interannual variability and telecon-nections 1901-2020 • All-India Rainfall Index (AIRI) closely tracks spatial and temporal extent of wet anomalies • ENSO teleconnection projects onto AIRI but Indian Ocean Dipole onto a known tripole pattern Abstract We revisit long-standing controversies regarding relationships among the all-India rainfall index (AIRI), sub-India summer rainfall variations, El Niño-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) using 120-year sea surface temperature and high-resolution rainfall datasets. AIRI closely tracks with the spatial extent of wet anomalies and with the average across gridpoints in rainy day count. The leading rainfall variability mode is a monopole associated primarily with rainy day count and ENSO. The second mode is a tripole with same-signed loadings in the high-rainfall Western Ghats and Central Monsoon Zone regions and opposite-signed loadings in Southeastern India between. The IOD projects onto this tripole and, as such, is weakly correlated with AIRI. However, when the linear influence of ENSO is removed, the IOD rainfall regressions become quasi-homogeneously more positive, making the ENSO-residual IOD and AIRI time-series significantly correlated. Plain Language Summary The Indian summer monsoon generates copious rainfall each June through Septem-ber, but more in some years than others, and more in some Indian sub-regions than others. We use observation-based datasets spanning 1901-2020 to reconcile past disagreements about these fluctuations. There has long been concern that the rainfall rate averaged over the whole summer and whole of India-the All-India Rainfall Index-doesn’t necessarily track with the spatial extent of wet or dry anomalies within India which is more relevant for many societal purposes, but we show that the two measures vary closely with one another. There has been disagreement about how the all-India average relates to rainfall in fixed sub-regions of India, and we show using high-resolution rainfall data that the issue stems in part from the use of coarse datasets in the past. Finally, we reconcile disagreements about whether or not the Indian Ocean Dipole (IOD) teleconnec-tion influences all-India rainfall by showing that, when the El Niño-Southern Oscillation signal that influences both the IOD and the monsoon rains is removed, the IOD significantly influences the all-India average and with a distinct sub-India spatial pattern.

Teleseismic back-projection has emerged as a widely-used tool for understanding the rupture histories of large earthquakes. However, its application often suffers from artifacts related to the receiver array geometry, notably the ‘swimming’ artifact. We present a teleseismic back-projection method with multiple arrays and combined P and pP waveforms. The method is suitable for defining arrays ad-hoc in order to achieve a good azimuthal distribution for most earthquakes. We present a catalog of short-period rupture histories (0.5-2.0 Hz) including all 54 earthquakes from 2010 to 2021 with M_w ≥ 7.5 and depth less than 200 km. The method provides semi-automatic estimates of rupture length, directivity, speed, and aspect ratio, which are related to the complexity of large ruptures. We determined short-period rupture length scaling relations that are in good agreement with previously published relations based on estimates of total slip. Rupture speeds were consistently in the sub-Rayleigh regime for thrust and normal earthquakes, whereas a tenth of strike-slip events propagated in the unstable supershear range. Many of the rupture histories exhibited complex behaviors such as rupture on conjugate faults, bilateral ruptures, and dynamic triggering by a P wave. For megathrust earthquakes, ruptures encircling asperities were frequently observed, with down-dip, up-dip, double encircling, and segmented patterns. Although there is a preference for short-period emissions to emanate from central and down-dip parts of the megathrust, emissions up-dip of the main asperities are more frequent than suggested by earlier results.

The space weather research community relies heavily on thermospheric density data to understand long-term thermospheric variability, construct assimilative, empirical, and semi-empirical global atmospheric models, and validate model performance. One of the challenges in resolving accurate thermospheric density datasets from satellite orbital drag measurements is modeling appropriate physical aerodynamic drag force coefficients. The drag coefficient may change throughout the thermospheric environment due to model dependencies on composition and altitude. As such, existing drag coefficient model errors may be altitude and solar cycle dependent, with greater errors at higher altitudes around 500 km near the oxygen-to-helium transition region. This can lead to errors in orbit-derived density datasets and models. In this paper, inter-satellite density comparisons at ~500 km are evaluated to constrain drag coefficient modeling assumptions. Density consistency results indicate that drag coefficient models with incomplete energy and momentum accommodation produce the most consistent densities, while the standard diffuse modeling approach may not be appropriate at these altitudes. Models with momentum accommodation between 0.5 - 0.9 and energy accommodation between 0.83 - 0.96 may be the most appropriate at upper thermospheric altitudes. Modeling drag coefficients with diffuse gas-surface interactions could lead to errors in derived density of ~25% and in-track satellite orbit prediction uncertainty during solar maximum conditions on the order of hundreds of meters.

Flash droughts are a recently recognized extreme climate phenomena that occur at the subseasonal timescale and develop with sudden onset and rapid intensity, which have significant socio-environmental impacts on agriculture, ecosystem, and water resources. However, the spatial and temporal variability of flash droughts in the continental United States (CONUS) are still not well understood. In this study, we characterize flash drought events using a novel evaporative demand flash drought (EDFD) index as well as a soil moisture flash drought (SMFD) index at the weekly timescale between September 1981 and December 2018. Hierarchical clustering divided CONUS into different clusters where SMFDs occur synchronously over space and time. The results show that flash droughts occur in all seasons and regions in CONUS with increasing trends in EDFDs (0.07 > tau > 0.28), but not equal trends in SMFDs (-0.12 > tau > 0.08). Periodic short-term behavior between EDFDs and SMPDs was identified in all clusters but more importantly long-term oscillatory behavior is identified between the two indexes in all clusters except the Northwest US. Our research into the spatiotemporal variability of flash droughts using both the EDFD index and SMPD index displays that CONUS is still at risk for increased flash drought events in the future to come.

Stable isotope geochemistry of terrestrial carbonates provides important opportunities to answer questions about climates, environments, and ecosystems both in the present day and the geologic past. Here we present a case study from the Cretaceous Newark Canyon Formation (NCF) type section (~98–113 Ma), where we explore how climate and depositional settings influence the stable isotope record in highly variable lacustrine and palustrine carbonates. The NCF was deposited within the hinterland of the Sevier orogenic belt and allows us to examine how North American terrestrial climate changed during the mid-Cretaceous, a time of potentially significant regional surface uplift and increasing global temperatures related to the Cretaceous Thermal Maximum (Di Fiori et al., 2020; Huber et al., 2018). In this study, we find substantial inter- and intra-facies heterogeneity, despite having formed in the same overall climate setting, highlighting the differences between lacustrine and palustrine environments. Stable carbon, oxygen, and clumped isotopes (δ13C, δ18Ocarbonate, and Δ47) paired with optical and cathodoluminescence petrography from along-strike lateral and vertical stratigraphic sections show significant isotopic variability between and within seven carbonate facies (Fetrow et al., 2020). Palustrine deposition is interpreted to have occurred along a spectrum of shallow water depths preserved in two key palustrine sub-facies endmembers – shallower mottled micrite and deeper pebbly, peloid-rich micrite. These record mean Δ47 temperatures of 51ºC and 44°C, respectively. The mottled micrite has heavier calculated δ18O of formation water (δ18Owater) values indicating increased evaporative enrichment, which suggests more intense desiccation during deposition. Lacustrine sediments preserved in laminated biomicrite to massive micrite have mean Δ47 temperatures of 50ºC and 37°C, respectively. Elevated temperatures and δ13C, δ18Ocarb, and δ18Owater values more similar to values from NCF secondary spar veins indicate that the biomicrite sub-facies underwent diagenetic alteration. We will discuss the implications of these results for the NCF and the Cretaceous western USA paleoclimate record, as well as general lessons learned for interpreting mixed terrestrial carbonate facies records.

Wildfire is common across the pan-Arctic tundra. Tundra fires exert significant impacts on terrestrial carbon balance and ecosystem functioning. Interactions between fire and climate change can enhance their impacts on the Arctic. However, the driving mechanisms of tundra fire occurrences remain poorly understood. This study focuses on identifying key environmental factors controlling fire occurrence in Arctic tundra of Alaska. Our random forest models, considering ignition source, fuel, fire weather, and topography, have shown a strong predictive capability with an overall accuracy above 91%. We found cloud-to-ground (CG) lightning probability by far the dominant driver controlling tundra fire occurrence. Warmer and drier near-surface weather was required to support burning, while fuel composition and topography have modest impacts on fire occurrence. Our results highlight the critical role of CG lightning in driving tundra fires and that incorporating lightning modeling is essential for fire monitoring, forecasting, and management in the Arctic.

This paper introduces a comprehensive C++ software package, HatchFrac, for stochastic modelling of fracture networks in two and three dimensions. Two main methods, the inverse CDF method and acceptance-rejection method, are applied to generate random variables from the stochastic distributions commonly used in discrete fracture network (DFN) modelling. The multilayer per-ceptron (MLP) machine learning approach, combined with the inverse CDF method, is implemented to generate random variables following any sampling distribution. To make the code faster, we extend the Newman-Ziff to determine clusters in the fracture networks. When combined with the block method, the Ziff algorithm improves the coding efficiency significantly. The software generates the T-type fracture intersections in the network, which can be used in applications involving fracture growth or incorporating geomechanics. We introduce three applications of HatchFrac that demonstrate the versatility of our software: percolation analysis, fracture intensity analysis, and flow and connectivity analysis.

In this paper, the effect of the rolling and damping values on the macro friction coefficient have been investigated. The introduction of the viscous rolling relaxation induces a friction weakening. Hence, it has been highlighted that this element can act against the angular spring and decreases the sample stiffness. This influence must be considered to not overestimate the toughness of a fault.

Parameterizations of gas transfer velocities are needed for climate predictions. Single parameter models typically only include wind dependence and may readily be used in climate studies. Their application is however gas specific and limited to select environments. Mechanistic parameterizations incorporating multiple forcing factors allow modelling the transfer of gases with differing solubilities for a wide range of conditions. A novel framework is put forward to model gas transfer in the open ocean in the presence of breaking waves. It incorporates both the turbulence- and bubble-mediated transfers based on statistics determined from the breaking crest length distribution ($\Lambda(c)$). Testing the mechanistic model with measurements from the HiWinGS field campaign shows promising results for both CO\textsubscript{2} and DMS. Uncertainties remain in the quantification of bubble clouds which are at the core of the formulation of the bubble-mediated transfer.

A key step in model development is selection of an appropriate representational system, including both the representation of what is observed (the data), and the formal mathematical structure used to construct the input-state-output mapping. These choices are critical, because they completely determine the questions we can ask, the nature of the analyses and inferences we can perform, and the answers that we can obtain. Accordingly, a representation that is suitable for one kind of investigation might be limited in its ability to support some other kind. Arguably, how different representational approaches affect what we can learn from data is poorly understood. This paper explores three complementary representational strategies as vehicles for understanding how catchment-scale hydrological processes vary across hydro-geo-climatologically diverse Chile. Specifically, we test a lumped water-balance model (GR4J), a data-based dynamical systems model (LSTM), and a data-based regression-tree model (Random Forest). Insights were obtained regarding system memory encoded in data, spatial transferability by use of surrogate attributes, and informational deficiencies of the dataset that limit our ability to learn an adequate input-output relationship. As expected, each approach exhibits specific strengths, with LSTM providing the best characterization of dynamics, GR4J being the most robust under informationally deficient conditions, and RF being most supportive of interpretation. Overall, the complementary nature of the three approaches suggests the value of adopting a multi-representational framework in order to more fully extract information from the data. Our results show that a multi-representational approach better supports the goals of prediction, understanding, and scientific discovery in Hydrology.

The astrogeodetic or Helmert deflection of the vertical (DoV) data, as opposed to data obtained through other geodetic instruments (e.g., GNSS (geodetic coordinates), levelling (normal or orthometric heights) and gravimeters (gravity data)), cannot be readily obtained by astrogeodetic instruments. In order to obtain these data, geodetic and astronomical latitude and longitude information is required. Currently, geodetic latitude and longitude (φ, λ) are measured with GNSS receivers, while astronomical latitude and longitude (Φ, Λ) are observed with Digital Zenith Camera Systems (DZCSs) or the QDaedalus system. The result of these observations are the astrogeodetic North-South (ξ=Φ-φ) and East-West (η=(Λ-λ)cosφ) DoV components. This study investigates the precision and accuracy of the newly installed tachymeter-based (multi station (MS) 60) QDaedalus astrogeodetic measurement system’s DoV data with the aid of promising DoV data from the Compact Digital Astronomical Camera (CODIAC) DZCS. For this study, we used the CODIAC (accuracy of ∼0.05″) and QDaedalus (accuracy of ~0.20″) systems to conduct parallel measurements over 2 nights (measurement duration of more than 20 hours) at the Zimmerwald Observatory in Switzerland. Both the CODIAC and QDaedalus systems were developed at ETH Zurich. CODIAC has the standard DZCS components seen in other DZCSs in Germany, Latvia, Turkey and China: telescope, charge-coupled device (CCD) camera, tiltmeters, focuser, single frequency GNSS receiver and antenna, laptop, and substructure. While each DZCS has a unique design, CODIAC has upgraded hardware components (such as the use of four tiltmeters-two Wyler and two Lipmann High-Resolution Tiltmeters) as well as advanced software and processing technology. The key components of the QDaedalus which differ from the DZCS are tachymeter, interface box, and mountable meniscus lens. Also, the measurement principle and data evaluation processes of these two systems differ; therefore, we also describe the data evaluation methods for both the CODIAC and QDaedalus systems. Finally, we discuss the results of the parallel measurements and their implications for further astrogeodetic work.

The flow and deformation of glacier ice in response to stress is often described using Glen’s Flow Law, a power-law relation that compactly represents ice rheology with a prefactor, A, and stress exponent, n. For natural ice, these parameters (and the parameters subsumed within them) come with large uncertainties that have not been robustly quantified with observations. Modern remote sensing technologies that collect data with finer resolutions and broader coverage provide us with an opportunity to robustly calibrate these rheological parameters for certain environments. Here, we utilize publicly available observations of ice sheet surface velocity and elevation acquired with remote sensing platforms to calibrate the flow law parameters over select Antarctic ice shelves. We build upon recent work that used remote sensing observations to quantify the relationship between ice stress and strain rate in extensional flow to infer an exponent of n = 4.1 +/- 0.4 for Antarctica. Here, we model two-dimensional flow and perform parameter calibration by constructing and training physics-informed neural networks (PINNs) to learn spatially-varying A and uniform n for each ice shelf. We cast the parameter estimation problem as a neural network optimization problem through minimization of a cost function that includes both data reconstruction errors and momentum balance residuals derived from the 2D shallow-shelf approximation. Additionally, we formulate the networks to predict spatially-varying uncertainties for A by using variational inference techniques, which approximate Bayesian inference (traditionally a computationally-intensive procedure) as an additional optimization objective. Finally, we demonstrate the use of time-dependent surface velocities, which are becoming increasingly more available over the ice sheets, to independently constrain the stress exponent n, confirming the appropriateness of n = 4 derived from previous work. Overall, calibration of these parameters with robust uncertainties are critical for placing observational constraints on prognostic ice flow model parameters and to improve our understanding of flow and fracture processes on ice shelves in Antarctica.

Nowadays, the time evolution of ITRF station positions is described by piece-wise linear models extended with exponential and logarithmic functions to account for post-seismic displacements. The ITRF2020 will also account for seasonal deformation by means of annual and semi-annual sine waves. However, part of the Earth’s surface deformation is not captured by those deterministic functions, such as inter-annual hydrological loading deformation, or high-frequency atmospheric loading deformation. To account for such aperiodic displacements, a reference frame in the form of a time series could be considered. This would require aperiodic motions of the different space geodetic stations to be tied in a common frame by means of co-motion constraints. The relevance of such constraints is however debatable. Indeed, common aperiodic movements between co-located space geodetic stations have thus far not been evidenced. This presentation describes the comparison of station position time series from the different space geodetic techniques in order to highlight whether or not common aperiodic movements can be detected at co-location sites. Those time series are extracted from the solutions provided by the techniques international services for the ITRF2014. They are first carefully aligned to a common reference frame in order to minimize differential network effect. Then, they are cleaned from linear, post-seismic and periodic signals (including seasonal deformation and technique systematic errors). Residual time series from co-located stations are finally confronted with each other.

To stabilize global warming within the 1.5-2.0°C Paris Agreement goal, greenhouse gas (GHG) emissions need to reach net-zero. Offsetting is required for net-zero emissions (emissions minus offsets), as achieving zero emissions is unrealistic. Offset schemes differ widely in their capacity to mitigate anthropogenic global warming (AGW). Rayer et al. (2021) provide offsetting guidelines to strengthen their long-term climate benefit, this chapter further develops this topic. We propose a framework for rating different offset types based on their climate risk, moving beyond the usual criteria of verification, audit, and additionality. We apply our offset climate ratings to some offsetting types and case studies including the sorts of schemes proposed by major oil companies, and the PAS 2060 carbon-neutrality standard. Some offsets provide little more than economic nudges towards emissions reduction. Climate offsetting requirements are more exacting. The proposed framework grades offsets from most to least benefit. Grades help classify offsets’ climate risks based on: helping terminate it, slowing warming, or only offering economic incentives with little climate benefit. Ultimately, to stabilize global warming, it will be necessary for policy to encourage the highest rated offsets under the proposed framework. Reference Rayer, Q. G., Jenkins, S., & Walton, P. (2021). Defining net-zero and climate recommendations for carbon offsetting. In Walker, T., Wendt, S., Goubran, S., & Schwartz, T. (Eds.), Beyond the 2ºC: Business and Policy Trajectories to Climate Change Adaptation (chapter 2). Palgrave Macmillan.

Abrupt fluid emissions from shallow marine sediments pose a threat to seafloor installations like wind farms and offshore cables. Quantifying such fluid emissions and linking pockmarks, the seafloor manifestations of fluid escape, to flow in the sub-seafloor remains notoriously difficult due to an incomplete understanding of the underlying physical processes. Here, using a compositional multi-phase flow model, we test plausible gas sources for pockmarks in the south-eastern North Sea, which recent observations suggest have formed in response to major storms. We find that the presence of free gas in the subsurface effectively damps storm wave-induced pressure changes due to its high compressibility, so that the mobilization of pre-existing gas pockets is unlikely. Rather, our results point to spontaneous appearance of a free gas phase via storm-induced gas exsolution from pore fluids. This mechanism is primarily driven by the pressure-sensitivity of gas solubility. We show that in highly permeable sediments containing gas-rich pore fluids, wave-induced pressure changes result in the appearance of a persistent gas phase. This suggests that seafloor fluid escape structures are not always proxies for overpressured shallow gas and that periodic seafloor pressure changes can induce persistent free gas phase to spontaneously appear.

A new soil moisture index for monitoring drought occurrence and intensity is presented. The index is based on the integration of different remote sensing products and in situ observations. Due to a shortage of precipitation, droughts reduce vegetation productivity, and thus, aggravate the impact of moisture stress on pastureland. The spatial distribution of soil moisture index with high-resolution images in Mongolia is still being one of the essential goals in the remote sensing and rangeland community. Specifically, we examined a new composite Gobi soil moisture index (GI) based on the combination of Ocean Salinity (SMOS) Soil Moisture, several products from the MODIS satellite, and in situ Soil Moisture (SM) observations. A multiple linear regression method was used for the estimation of GI soil moisture index. The former includes the surface soil moisture from the Soil Moisture and Ocean Salinity (SMOS) mission, the Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST), normalized difference vegetation index (NDVI), potential evapotranspiration (PET). The latter includes a standardized precipitation index (SPI) from in-situ data. The validation of the approach is based on the relationship between SPI and in-situ soil moisture (SM) observations, and their comparison to remote sensing (RS) – derived indices. The results show that the correlation was statistically significant between GI and in-situ SM observations from the meteorological stations at 10 – 15 cm depths (p < 0.0001). The correlation between GI and SPI, as represented by the correlation coefficient (r) was 0.64. The GI empirical equations that utilize at least three key atmospheric variables are (a) NDVI, (b) land surface temperature, and (c) potential evapotranspiration. The established new GI soil moisture index was retrieved at the 1 km spatial resolution for Southwest Mongolia from 2000 to 2018, and their two summer months (July, August) were used for monitoring drought and vegetation response to the varying soil/climatic conditions. Now, based on the assessment of drought severity, the new soil moisture index allowed us to assess a large-scale spatial coherence of droughts across the Southwestern part of Mongolia.