Our current understanding of global mean near-surface (land and sea) air temperature (GMSAT) during the Cenozoic era relies on paleo-proxy estimates of deep-sea temperature combined with assumed relationships between global mean deep-sea temperature (GMDST), global mean sea-surface temperature (GMSST), and GMSAT. The validity of these assumptions is essential in our understanding of past climate states such as the Early Eocene Climate Optimum hothouse climate (EECO, 56–48 Ma). The EECO remains relevant today, because EECO-like CO2 levels are possible in the 22nd century under continued high CO2 emissions. We analyze the relationship between the three global temperature indicators for the EECO using 25 different millennia-long model simulations with varying CO2 levels from the Deep-Time Model Intercomparison Project (DeepMIP). The model simulations show limited spatial variability in deep-sea temperature, indicating that local temperature estimates can be regarded representative of GMDST. Linear regression analysis indicates that compared to GMSST, both GMDST and GMSAT respond more strongly to changes in atmospheric CO2 by factors of 1.18 and 1.17, respectively. Consequently, this model-based analysis validates the assumption that changes in GMDST can be used to estimate changes in GMSAT during the EECO. Paleo-proxies of GMDST, GMSST, and GMSAT during EECO show the best fit with model simulations having an atmospheric CO2 level of 1,680 ppm, which matches paleo-proxies of atmospheric CO2 during EECO. Similar analyses of other past climate states are needed to examine whether these results are robust throughout the Cenozoic, providing insight into the long-term future warming under various shared socioeconomic pathways.

A recent study by Agard and Emanuel (2017) proposed a simple equation for a quantity that scales with convective available potential energy (CAPE) that can be directly calculated from a limited number of environmental sounding parameters without lifting a hypothetical air parcel. This scaling CAPE was applied in a specific idealized framework, but the extent to which it can predict true CAPE in the real world has not been tested. This work uses reanalysis data over the U.S to demonstrate that this scaling CAPE does indeed scale very closely with CAPE, following a linear relationship with a scaling factor of 0.44. We then explain why they scale together via a step-by-step derivation of the theoretical assumptions linking scaling CAPE and real CAPE and their manifestation in the historical data. Overall, this work demonstrates that CAPE can be predicted from large-scale environmental parameters alone, which may be useful for a wide range of applications in weather and climate.

Seismologists have to deal with overlapping and noisy signals. Techniques such as source separation can be used to solve this problem. Over the past few decades, signal processing techniques used for source separation have advanced significantly for multi-station settings. But not so many options are available when it comes to single-station data. Using Machine Learning, we demonstrate the possibility of separating sources for single-station, one-component seismic recordings. The technique that we use for seismic signal separation is based on a dual-path recurrent neural network which is applied directly to the time domain data. Such source separation may find applications in most tasks of seismology, including earthquake analysis, aftershocks, nuclear verification, seismo-acoustics, and ambient-noise tomography. We train the network on seismic data from STanford EArthquake Dataset (STEAD) and demonstrate that our approach is a) capable of denoising seismic data and b) capable of separating two earthquake signals from one another. In this work, we show that Machine Learning is useful for earthquake-induced source separation. We provide a reproducible research repository with the algorithms here: https://github.com/IMGW-univie/source-separation.

Water-mediated linkages that connect landscape components are collectively referred to as hydrologic connectivity. In river-floodplain systems, quantifying hydrologic connectivity enables descriptions of hydrologic function that emerge from complex, heterogeneous interactions of underlying geomorphic, climatic and biologic controls. Here, we measure hydrologic connectivity using field indicators and develop a continuous connectivity metric that represents a vector strength between a source along the North St Vrain river to ten surface water target sites within the river-floodplain system. To measure this connectivity strength, we analyzed hydrometric, injected conservative tracers, and natural occurring geochemical and microbial indicators across streamflows in 2018. We developed empirical models of hydrologic connectivity as a function of river stage to predict daily connectivity strength across multiple floodplain sites for five years between May and September of 2016-2020. Three sites were either consistently connected or disconnected to the river, while seven varied across time in their hydrologic connectivity strength. Of the sites with variable connectivity, some disconnected very quickly and others had a prolonged disconnection phase. By scaling site dynamics to the system scale, we found across-system hydrologic connectivity always increased with streamflow while across-system variance in hydrologic connectivity peaked at intermediate streamflow. At sites with intermittent connections to the river, river stage disconnection thresholds were variable (308 to 650 mm) and their connectivity dynamics were sensitive to inter-annual variation in streamflows, suggesting that future connectivity behavior under climate change will depend on how flow durations change across a range of flow states.

Several bills moving through Congress are likely to provide significant funding for expanding research and results in climate change solutions (CCS). This is also a priority of the Biden-Harris Administration. The National Science Foundation (NSF) will be expected to distribute and manage much of this funding through its grant processes. Effective solutions require both a continuation and expansion of research on climate change–to understand and thus plan for potential impacts locally to globally and to continually assess solutions against a changing climate–and rapid adoption and implementation of this science with society at all levels. NSF asked AGU to convene its community to help provide guidance and recommendations for enabling significant and impactful CCS outcomes by 1 June. AGU was asked in particular to address the following: 1. Identify the biggest, more important interdisciplinary/convergent challenges in climate change that can be addressed in the next 2 to 3 years 2. Create 2-year and 3-year roadmaps to address the identified challenges. Indicate partnerships required to deliver on the promise. 3. Provide ideas on the creation of an aggressive outreach/communications plan to inform the public and decision makers on the critical importance of geoscience. 4. Identify information, training, and other resources needed to embed a culture of innovation, entrepreneurialism, and translational research in the geosciences. Given the short time frame for this report, AGU reached out to key leaders, including Council members, members of several committees, journal editors, early career scientists, and also included additional stakeholders from sectors relevant to CCS, including community leaders, planners and architects, business leaders, NGO representatives, and others. Participants were provided a form to submit ideas, and also invited to two workshops. The first was aimed at ideation around broad efforts and activities needed for impactful CCS; the second was aimed at in depth development of several broad efforts at scale. Overall, about 125 people participated; 78 responded to the survey, 82 attended the first workshop, and 28 attended the more-focused second workshop (see contributor list). This report provides a high-level summary of these inputs and recommendations, focusing on guiding principles and several ideas that received broader support at the workshops and post-workshop review. These guiding principles and ideas cover a range of activities and were viewed as having high importance for realizing impactful CCS at the scale of funding anticipated. These cover the major areas of the charge, including research and solutions, education, communication, and training. The participants and full list of ideas and suggestions are provided as an appendix. Many contributed directly to this report; the listed authors are the steering committee.

The weakening of the Madden-Julian Oscillation (MJO) as it propagates over the Maritime Continent (MC) is often referred to as the MC barrier. Here, we use 3-hourly precipitable water vapor (PWV) data obtained from the Sumatran GPS Array and the ERA5 reanalysis to investigate the role played by the column moisture over the MC. Over Sumatra and the whole MC, we find a stronger dependence of precipitation on PWV over the ocean as compared to both inland and coastal regions. The MJO modulates the PWV over the ocean and over the MC by roughly the same amount, and the weaker precipitation variations over the MC between the MJO phases may be interpreted in terms of its weaker dependence on PWV over the MC. This different precipitation dependence on column moisture between the MC and the ocean may contribute to the MC barrier effect.

Two methods for the mapping of ocean surface currents from satellite measurements of sea level and future current vectors are presented and contrasted. Both methods rely on the linear and Gaussian analysis frameworkwith different levels of covariance definitions. The first method separately maps sea level and currents with single-scale covariance functions and leads to estimates of the geostrophic and ageostrophic circulations. The second maps both measurements simultaneously and projects the circulation onto 4 contributions: geostrophic, ageostrophic rotary, ageostrophic divergent and inertial. When compared to the first method, the second mapping moderately improves the resolution of geostrophic currents but significantly improves estimates of the ageostrophic circulation, in particular near-inertial oscillations. This method offers promising perspectives for reconstructions of the ocean surface circulation. Even the hourly dynamics can be reconstructed from measurements made locally every few days because nearby measurements are coherent enough to help fill the gaps. Based on numerical simulation of ocean surface currents, the proposed SKIM mission that combines a nadir altimeter and a Doppler scatterometer with a 300 km wide swath (with a mean revisit time of 3 days) would allow the reconstruction of 50% of the near-inertial variance around an 18 hour period of oscillation.

Local earthquake tomography has been carried out in the Tjornes Fracture Zone. This transform region connects the Mid-Atlantic Ridge with the Northern Volcanic Zone in Iceland in a mostly offshore area. The challenge to record seismic information in this area was the motivation for the North ICeland Experiment (NICE). Fourteen ocean-bottom seismometers and eleven on-land stations were installed in the project and operated simultaneously with the permanent Icelandic seismic network (SIL) during summer 2004. Data from the experiment were used to estimate P- and S-wave crustal velocities. Also, the Bouguer gravity anomaly was derived for comparison with the tomographic results. Upper-crustal velocities are found to be relatively low in the offshore region. In particular, low velocities are mapped along the Husavik-Flatey Fault, where a more confined negative gravity anomaly and a sedimentary basin are found. Low velocities are also mapped along the Grimsey Oblique Rift and in a zone connecting these two main lineaments north of Skjalfandi Bay. The northern half of the aseismic Grimsey Shoal appears as a fast anomaly. Furthermore, localized high-velocity anomalies are found beneath northern Trolaskagi and Flateyjarskagi Peninsulas, where bedrock dates from Upper and Middle Miocene (10-15 Ma). Regions of low Vp/Vs ratio are mapped at depth along the main lineaments. Low velocities along the lineaments are interpreted as due to fracturing extending into the middle crust, while fast upper-crustal velocities beneath Tertiary formations are associated with relic volcanoes. Low Vp/Vs ratios along the lineaments are interpreted as due to the presence of supercritical fluids.

The future uncertainty and complexity of alternative socioeconomic and climatic scenarios challenge the model-based analysis of sustainable development. Obtaining robust insights requires a systematic processing of uncertainty and complexity not only in input assumptions, but also in the diversity of model structures that simulates the multisectoral dynamics of human and Earth system interactions. Here, we implement the global change scenarios, i.e., the Shared Socioeconomic Pathways and the Representative Concentration Pathways, in a feedback-rich, integrated assessment model of human-Earth system dynamics, called FeliX, to serve two aims: (1) to provide modellers with well-defined steps for the adoption of established scenarios in new integrated assessment models; (2) to explore the impacts of model uncertainty and its structural complexity on the projection of these scenarios for sustainable development. Our modelling shows internally consistent scenario storylines across sectors, yet with quantitatively different realisations of these scenarios compared to other integrated assessment models due to the new model’s structural complexity. The results highlight the importance of enumerating global change scenarios and their uncertainty exploration with a diversity of models of different input assumptions and structures to capture a wider variety of future possibilities and sustainability indicators.

The ferrimagnetic (FM) and antiferromagnetic (AFM) particles of iron oxides are considered pedogenic and climatic indicators due to their enrichment with comparable increasing in rainfall and temperature. However, the opposite changes in rainfall and temperature result in rapid change of relative humidity (RH), which could lead to their competition and transformation. We examined two soil sequences undergone contrary climate development on the eastern edge of the Tibetan Plateau. The dry and warm climate with low RH favors the coordinative enrichment of AFM hematite and FM particles, while the wet and cool climate with high RH mainly produces goethite but leads to competition between low content AFM hematite and FM particles. The outcome well interprets the changing relationship between color and magnetism in soils and sediments, and suggests that temperature is as important as precipitation in paleoclimate reconstruction based on iron oxides, especially during strong dry-wet cycles and climate pattern shifts.

Science Centers and Museums are indeed becoming communication hubs for many research areas, including, of course, Earth and Environmental Sciences. Over the last decades numerous new channels have opened for two-way communication and museums have embraced them enthusiastically, promoting dialogue and participation. The incorporation of citizen science, for example, into exhibitions and programming is one of the most recent trends in this direction. Often the question arises, however, of what such activities have to do with the objects and exhibits in the museum, and this perceived disconnect is used as an objection against such activities, which end up being considered as simple contingent add-ons that could just as well be done elsewhere, instead of necessary elements of museum communication. I will present a vision of museum communication that integrates such activities as part of its narrative, as long as they are incorporated using the unique and specific power of the language of exhibitions, a.k.a. the museographic language. To do so I ask the question: what is the museographic language good at communicating? In other words – what do museums communicate? If we center the answer around the concept of “phenomena” or “processes” we will be able to see how museum objects as well as interactive exhibits and a whole range of participatory activities can be successfully combined into a unique mode of communication through exhibitions that complements other channels in the ecosystem of science communication. While there are many scientific disciplines that can be communicated well using primarily collections of objects, other research areas, like Earth and Environmental Sciences need to extend their communication in Science Centers and Museums to include phenomena or processes (as well as objects) in order to actively engage audiences and harness their participation to shape the future of research and of science in society. I will share practical examples and recommendations for these disciplines.

A convolutional neural network was used to detect occurrences of pockets of open cells (POCs). Trained on a small hand-logged dataset and applied to 13 years of satellite imagery the neural network is able to classify 8,491 POCs. This extensive database allows the first robust analysis of the spatial and temporal prevalence of these phenomena, as well as a detailed analysis of their micro-physical properties. We find a large (30%) increase in cloud effective radius inside POCs as compared to their surroundings and similarly large (20%) decrease in cloud fraction. This also allows their global radiative effect to be determined. Using simple radiative approximations we find that the instantaneous global mean top-of-atmosphere perturbation by all POCs is only 0.01Wm-2.

We give a preliminary report on results of detecting low-frequency earthquakes (LFs) occurring at Mt. Fuji, Japan, using the matched filter method (MF method: e.g., Peng & Zhao, 2009). LFs have been observed in the depth 10-25km beneath Mt. Fuji (Hamada, 1981; Ukawa et al, 2005). These LFs seem to occur at an almost constant rate at all times, but it may become remarkably active as in the fall of 2000 (Yoshida et al., 2006). Because it is considered that the activity of LFs is associated with behavior of magmatic fluid at depth (e.g., Nakamichi et al., 2003), an investigation into the relationship between LFs and volcanic activity (e.g., Harada et al., 2010) is important. Understanding of the details of LFs activity is the first step for this investigation. Here, a system using the MF method for detecting LFs at Hakone volcano, Japan (Yukutake, 2017; Yukutake et al., 2019), was modified to be applicable to the detection of LFs at Mt. Fuji. Then, this was applied to continuous seismic record at seismic stations around Mt. Fuji during the period of 2012-2020. Next, the template waveforms of LFs were prepared on the basis of the earthquake catalog maintained by the Japan Meteorological Agency (JMA). Then, the cross-correlation analysis was conducted between the template waveforms and the seismic records. Finally, a catalog of LFs, obtained by using the MF method, was created. Using this catalog, we confirmed that LFs in 2012-2020 occurred at an almost constant rate, and that this is also true for LFs included in the JMA catalog. However, our case shows that LFs occurred at a rate of about 1,250 per year, which is about 10 times higher than that shown for the JMA case (a rate of about 125 per year). It was also confirmed that the larger LFs tend to have fewer numbers and smaller LFs tend to have more numbers, again a feature found by using the JMA catalog. Our research is underway, and tackling challenges such as selection of appropriate template waveforms of LFs, correction of magnitude estimate, and extension of analysis period will improve our results, which will be reported in the presentation.

A large-scale seafloor observation network, known as S-net, has been operated by National Research Institute for Earth Science and Disaster Resilience (NIED) in the Japan Trench area since 2016. The main objective of the observation is to provide reliable earthquake and tsunami early warnings. The network consists of 150 in-line-type observatories with sensors housed in cylindrical pressure vessels. The stations and cables were buried about one meter beneath the seafloor in the region of water depth less than 1500 m while they were sited on the seafloor in the deeper area. Previous studies have shown that the S-net sensors rotate after some threshold peak ground accelerations due to poor coupling between the sensor house and sediment, resulting in large rotational noises. Another serious concern at the S-net sites is the amplification effect of sediments. To minimize the various effects, researchers have devised equations using the displacement amplitude of vertical component of motions to estimate the magnitude for Japan Meteorological Agency earthquake early warning (Hayashimoto et al. 2019). The vertical motions are affected as well by the presence of water layer in the oceanic area. Thus, the analysis of seafloor records is challenging due to the multiply effects of sediments and water layer. In this meeting, we report the results of analyses of strong-motion records at the ground in the Kanto Basin and adjoining areas and the S-net sites from nearby moderate to strong offshore earthquakes. Strong motions at ground and borehole sites were taken from K-NET/KiK-net and MeSO-net, respectively. We have found that the horizontal PGAs and PGVs at the S-net sites were, on average, similar to or larger than those at the ground and borehole sites at equal distances. In contrast, the vertical PGAs and PGVs at the S-net sites tended to be smaller than those at the ground sites for the S wave. Notably, the PGAs and PGVs for the P-wave parts on the vertical records of S-net were smaller than those at the ground sites. A portion of the results was published in Dhakal and Kunugi (2021). We are now analyzing a larger dataset and report the detailed results elsewhere. This study was supported by the Advanced Earthquake and Tsunami Forecasting Technologies Project of NIED and JSPS KAKENHI Grant Number JP20K05055.

Anthropogenic global warming caused by increased atmospheric carbon forcing is expected to cause a decrease in peak snow water equivalent (SWE), shift the timing of snowmelt to earlier in the year, and lead to slower melt rates in the mountains of the Western United States. High-elevation forests in mountainous terrain represent a critical carbon sink. Understanding the ecohydrology of subalpine forests is crucial for assessing the health of these sinks. The Niwot Ridge Long Term Ecological Research station, located at 3000 m amsl in the southern Rocky Mountains of Colorado, receives just over 1 m of annual precipitation mostly as snow, supporting a persistent seasonal snowpack in alpine and subalpine ecosystems. Previous studies show that longer growing season length is correlated with shallower snowpack, earlier spring onset and reduced net CO2 uptake. Co-located sensors provide over 20 years of continuous SWE and eddy covariance (EC) data, allowing for robust direct comparison of snow and carbon phenomena in a high-elevation catchment. Linear regression and time series analysis was performed on snowmelt, meteorological, phenological and ecosystem productivity variables. Peak productivity is correlated with peak SWE (R2=0.54) and further correlated with snowmelt disappearance (R2=0.38) and the timing of spring growth onset (R2=0.30). Timing of both peak productivity and spring growth onset are correlated with snowmelt and meteorological variables. A multivariable regression of meteorological variables, timing of spring growth onset, a temporal trend, and snowmelt rate and explains 94% of interannual variability in the timing of peak forest productivity. These results develop support and introduce new evidence for the existing studies of Niwot Ridge ecohydrology. Future work will investigate the meteorological and hydrological record extending back to 1979 and the long-term trends in snowmelt and forest productivity.

Lake Magadi is a saline soda lake in East African Rift Valley (Kenya). It is fed by perennial warm and hot saline springs. Na+-HCO3- type dilute inflows evolve into Lake Magadi brines rich in Na+, CO3 (2-), Cl-, HCO3- and SO4 (2-) and depleted in Ca2+ and Mg2+. The pH, CO3 (2-), and SiO2 content of these brines reach 11.5, 109000 ppm, and 1440 ppm respectively. Evaporative concentration coupled with mineral precipitation and fractional dissolution is thought to be the main process responsible for the stepwise evolution between dilute inflows and brines. In order to understand the details of the precipitation kinetics, we have performed simulations of mineral precipitation sequences and the resulting hydrochemical evolution during evaporation under different partial pressure of CO2 (pCO2) and temperature by using EQL-EVP program. In addition, we have performed laboratory precipitation experiments. The crystallization sequence was monitored by using in situ video microscopy and in situ and ex situ X-ray diffraction and Raman spectroscopy. The precipitation sequence was also monitored by scanning electron microscopy coupled with energy dispersive x-ray analysis. Trace amounts of magnesite, calcite, and pirssonite precipitate at the beginning. Magnesium silicate precipitate at low pCO2 (<-2.5) by redissolution of magnesite. Pirssonite forms from calcite dissolution at low pCO2. The rise in temperature highly delayed amorphous silica precipitation. Trona was the second precipitate. At low temperature-high pCO2, nahcolite precipitates at the second place whereas at high temperature-low pCO2, thermonatrite forms instead of trona. Halite is the third in the precipitation sequence. Burkeite (pCO2 of -3 to -4.5) and thenardite (pCO2 of -2 to -2.5) are the fourth in the sequence, which upon redissolution form glaserite. Sylvite, kalicinite, and villiaumite form at the end. Evaporation linearly raises the solute concentration until saturation of Na-CO3-HCO3 minerals and halite, which upon precipitation deplete solute content. Glaserite is a minor phase depleting K+ and SO4 (2-). The combination of modeling based on a kinetic approach and in situ mineralogical analysis is a powerful tool to understand mineral assemblages and kinetic precipitation pathways in soda lakes.

The discrete element method (DEM) can provide detailed descriptions of sea ice dynamics that explicitly model floes and discontinuities in the ice, which can be challenging to represent accurately with current models. However, floe-scale stresses that inform lead formation in sea ice are difficult to calculate in current DEM implementations. In this paper, we use the ParticLS software library to develop a DEM that models the sea ice as a collection of discrete rigid particles that are initially bonded together using a cohesive beam model that approximates the response of an Euler-Bernoulli beam located between particle centroids. Ice fracture and lead formation are determined based on the value of a non-local Cauchy stress state around each particle and a Mohr-Coulomb fracture model. Therefore, large ice floes are modeled as continuous objects made up of many bonded particles that can interact with each other, deform, and fracture. We generate particle configurations by discretizing the ice in MODIS satellite imagery into polygonal floes that fill the observed ice shape and extent. The model is tested on ice advecting through an idealized channel and through Nares Strait. The results indicate that the bonded DEM model is capable of qualitatively capturing the dynamic sea ice patterns through constrictions such as ice bridges, arch kinematic features, and lead formation. In addition, we apply spatial and temporal scaling analyses to illustrate the model’s ability to capture heterogeneity and intermittency in the simulated ice deformation.

Hydraulic fracturing enables hydrocarbon production from unconventional reservoirs. Mapping induced seismicity around newly created fractures is crucial for understanding the reservoir response and increasing the efficiency of operations. Distributed acoustic sensing (DAS) provides a large amount of high spatial resolution microseismic data acquired along the entire length of horizontal wells. We focus on the observed reflected S-waves and develop a new methodology to image induced fractures acting as reflectors in the media surrounding the events and monitoring fiber. The workflow consists of DAS data preprocessing, event location, wavefield separation, raytracing-based imaging, and image postprocessing. The comparison of the resulting fracture images with low-frequency DAS signals with fracture hits corroborates that the reflections are from fractures created by stimulation. The fracture imaging algorithm can be used for real-time mapping of fractures and tracking fracture changes in time. It leads to a better understanding of the reservoir response to hydraulic fracturing stimulation.