A recent study categorized the Madden-Julian Oscillation (MJO) during the boreal winter season into four types including stand, jump, slow eastward propagating, and fast eastward propagating MJO. This study focuses on the stand and jump MJO events. Based on whether their convection penetrates the Maritime Continent (MC), stand and jump MJO are combined as non-penetrating (NP) MJO, while the rest two types are combined as eastward-penetrating (EP) MJO. Results reveal the relative roles of the westward-propagating wave (WPW), as well as the QBO and ENSO, in limiting MJO propagation. Lack of the pre-moistening over the southern sea surface of MC is responsible for NP MJO’s failure to penetrate MC. The active convection of WPW hinders the NP MJO descending branch over the Pacific and therfore leads to the insufficient meridional advective moistening over the southern sea surface of the MC. The overall drying over the MC for jump MJO also comes from the intraseasonal dry amonalies induced by WPW over the western Pacific, which are then advected to MC by the superposed seasonal mean northern easterlies. The independent convection over the Pacific for jump MJO is influenced by a combined effect of the QBO and ENSO. Under the influence of the preferred QBO westerly (QBOW) phase, the tropopause instability of NP MJO is decoupled with its convection. The decoupled tropopause instability propagates eastward into the Pacific, and amplifies the local WPW convection there for jump MJO. For stand MJO, however, the La Nina-like seasonal mean cool sea surface temperature (SST) anmalies confine WPW within the western Pacific. Therefore, the decoupled tropopause instability of stand MJO is out phase of WPW over the central Pacific, and fails to induce an independent convection as a result. The similar diagnosis is further applied to the CESM2 and E3SM CMIP6 historical simulations to investigate the MJO diversity and propagation in climate models. Four MJO types as in the observation are successfully categorized in these two models. The accompanied westward propagating waves over the central-western Pacific for NP MJO events are also evident. However, models suffer from some biases in MJO propagation such as too many NP MJO events than the observation.

Cratons are stable parts of the Earth’s continental lithosphere that have remained largely undeformed for several billion years. These consist of crustal granite-greenstone terrains coupled to roots of strong, buoyant cratonic lithospheric mantle that extend up to several hundreds of kms depth. Due to their stability, cratons preserve a record of the tectonics and the thermal evolution of the mantle in the early Earth. These observations suggest that the highly viscous (strong) character of cratons hampered the viability of early Earth tectonics, thereby affecting mantle convection patterns and cooling. In this study, we investigate the controls of stiff cratons on the initiation of subduction and mantle thermal evolution on the early Earth. Using numerical models, we simulate the effects of strong and buoyant cratons on mantle convection. We vary a set of parameters including (i) width and thickness of cratons, and (ii) viscosity ratio between cratonic lithosphere and cratonic crust. We test initial conditions varying the number of cratons, which is unconstrained for early Earth and associated it to mantle cooling rates. Our preliminary results show that the mantle cooling rate decreases with increasing number of cratons. Because mantle cooling rates affect the early Earth transition from a basaltic drip regime to initiation of subduction, we show that the craton coverage on the early Earth controls the time of onset of plate tectonics. Furthermore, we observe that cratons will remain separate or combine depending on the convective cell size, which is function of mantle cooling.

In this study, we used the potential vorticity (PV) diagnosis technique to investigate the key factors that affect the track forecasts of Hurricane Maria (2017) in the NCEP GFS v14, ECMWF IFS and GFDL SHiELD models. In Chen et al. (2019), it showed that a slow bias of Maria’s translation speed in the IFS 5-day forecasts was significantly improved by GFDL SHiELD with IFS initial conditions (SHiELD_IFS). Our results found that the slow moving bias in the IFS is mainly due to a strong, westerly steering flow contribution from a cutoff low from the northeast quadrant and another low system from the southwest quadrant of Maria. On the other hand, the SHiELD_IFS improves on the IFS by better simulating the strength of the Bermuda High, and low systems in the southwest, northwest, and northeast quadrants allowing for better track alignment with observations. We also found that the northward track bias of Maria in the legacy GFS and SHiELD with the GFS initial conditions (SHiELD_GFS) was associated with a weaker Continental High which contributed less northerly steering flow compared to that in the IFS and SHiELD_IFS. Furthermore, the Bermuda High was relatively weak in the SHiELD_GFS, while the two low systems in the northwest and northeast quadrants contributed steering flow opposing Maria’s moving direction, causing a slowdown of translation speed of Maria in the SHiELD_GFS. By performing this piecewise potential vorticity diagnosis on all of the storms in the 2017 North Atlantic Hurricane Season, we could possibly identify the key elements that generate the biases in TC track forecasts in these models.

Drought is probably the most complex among natural hazards to assess its effects. While most drought indicators and risk assessments are developed around agricultural or water shortages effects of drought, its effect on human health is highly understudied because of its unclear and complicated path towards physical and mental health effects. This study assesses the health risk of the latest decadal drought over the US counties by spatially superimposing several proxy variables of counties’ health vulnerabilities over their drought levels. We have used different variations of Local Moran’s I statistics to assess the spatial distribution of drought-vulnerability in two five-year study periods (2010-2014 and 2015-2019) and their differences. Our results show large clusters of significant risk increase in the west due to increases in both vulnerability and hazard indicators in the second study period. Since the used vulnerability variables include indicators of agriculture, drinking water, and socioeconomic prosperities, the results of this study can help researchers and policymakers in these areas to distinguish areas in need of higher attention for interdisciplinary study and planning in national or regional scales.

Wildfire emissions can vary substantially between different inventories due to the assumptions made in the emission creation process, including the defined vegetation type, fire detection, fuel loading, fraction of vegetation burned and emissions factors. Here, we focus on the uncertainty in emission factors and the resulting impact on modeled composition. We use the Community Atmosphere Model with chemistry (CAM-chem) to simulate 2014 atmospheric composition and focus on carbon monoxide (CO), a trace gas emitted from incomplete combustion and also produced from secondary oxidation of volatile organic compounds (VOCs). Fire is a major source of atmospheric CO and VOCs. Multiple simulations are compared, from an ensemble using four fire emission inventories (CMIP6/GFED4s, QFED2.5, GFAS1.2 and FINN1.5) and a range of sensitivity tests based on CO and VOC emission factor uncertainties. We compare model output and evaluate against CO observations from the Measurements of Pollution in the Troposphere (MOPITT) satellite-based instrument. For some regions, emission factor uncertainty spans the results found by using different inventories. Finally, we use modeled ozone (O3) to investigate how emission factor uncertainty influences the atmospheric oxidative environment. Overall, accounting for emission factor uncertainty lends a range of uncertainty to simulated results.

Recent geological observations imply that slow slip events (SSEs) occur in fault zones with a finite thickness of ~100s of meters. The bulk matrix of the fault zone deforms viscously, while pervasive frictional surfaces are distributed in the viscous matrix. In this theoretical study, we investigate the rupture behaviors of a frictional-viscous-mixing fault model and explore its potential to generate a single SSE. To simultaneously consider both the 10s-kilometer-scale rupture propagations and the 100s-meter-scale “frictional-viscous” features in the same model, we treat a fault zone as a zero-thickness “surface” embedded in an elastic medium. The “frictional-viscous” characteristics are parameterized into a constitutive relation, or “friction law”, where fault strength is partitioned into a frictional and a viscous component. For simplicity, the frictional strength is set to be slip weakening, while the viscous strength increases linearly as the bulk shear rate (slip rate) increases. We explore the rupture behaviors of the above model both analytically and numerically. We find that: 1. Final slip is proportional to the static stress drop and slipping area length, as in fast earthquakes. Peak slip rate increases with dynamic frictional stress drop, while a high viscous coefficient can significantly reduce slip rate, leading to slow slip behaviors. 2. Rupture propagation speed is mainly controlled by the radiation damping factor and viscous coefficient and can be significantly reduced compared to typical fast earthquakes when the viscous coefficient is high. 3. The slip rate decay time increases with the viscous coefficient and slipping area length, which eventually predicts M~T^3 scaling. When frictional stress drop is ~1MPa and the viscous coefficient is smaller than the radiation damping factor μ/(2β), the above models predict the fast slip behavior of regular fast earthquakes. Our model predicts slow slip behaviors in a wide range of parameter space when stress drop is low and viscous coefficient is high. In particular, a frictional strength drop of ~10 kPa and viscous coefficient of 10^4-10^5 μ/(2β) can simultaneously explain many independent characteristic rupture parameters of SSEs. Our model can be further tested with future geophysical, geological, and experimental data.

Several studies show anthropogenic aerosols (AAs) can perturb large-scale atmospheric circulation, as well as precipitation associated with the tropical rain belt and monsoons of the Northern Hemisphere, including the South and East Asian monsoons, as well as the West African monsoon. In the Northern Hemisphere mid-latitudes, however, the impact of AAs on regional climate and precipitation remains uncertain. This work investigates the influence of AAs on wintertime precipitation along the west coast of North America using the newest climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6). Over much of the 20th century, particularly from 1930-1975 when U.S. and European AA emissions rapidly increased, models show a robust wintertime precipitation dipole pattern along the Pacific Coast, with wetting southward of 40°N and drying to the north. This dipole pattern is consistent with a deepening of the Aleutian Low and a southeastward shift of the east Pacific jet stream, which in turn is associated with a southeastward displacement of the region of maximum baroclinicity. These changes in baroclinicity are related to strong aerosol-induced tropospheric cooling that maximizes at 40°N over the east Pacific. This strong cooling is consistent with aerosol-induced decreases in top-of-the-atmosphere radiation and changes in clouds, including decreases in cloud top effective radius and increases in cloud cover. Consistently, in response to 21st-century AA reductions an opposite hydro-dynamic dipole pattern occurs, including drying southward of 40°N and wetting to the north. Although uncertainties remain, including possible overestimation of aerosol forcing, our results suggest aerosols can perturb precipitation along the west coast of North America. However, the dipole pattern under AA forcing is partially offset by GHGs, resulting in a muted dipole response in all-forcing simulations.

We present a methodology that allows real-time simulation of the geoelectric field (GEF) spatiotemporal evolution in a given 3-D conductivity model of the Earth based on continuously augmented inducing source data. The presented concept is validated using Fennoscandia as a test region. The choice of Fennoscandia is motivated by several reasons. First, it is a high latitude region, where the GEF is expected to be particularly large. Second, there exists a 3-D ground electrical conductivity model of the region. Third, the regional magnetometer network, IMAGE, allows us to build a realistic model of the source for a given geomagnetic disturbance. Taking the 7-8 September 2017 geomagnetic storm as a space weather event, we show that real-time high-resolution 3-D modeling of the GEF is feasible and requires only a few tens of seconds.

Scalar parameters of various aspects of magnetic reconnection offer easily comparable measures of the characteristics of the diffusion regions surrounding X-lines in a way arguably more general than other attributes which may depend on the coordinate system used. We performed a principal component analysis (PCA) of several scalar parameters including measures of relative magnetic field line (MFL) curvature, ion agyrotropy, energy conversion rate, electric field, and current density in the time spans surrounding and including independently identified ion diffusion regions (IDRs) observed by MMS in the geomagnetic tail. We find that relative MFL curvature is the most bold indicator of an IDR encounter while some more traditional measures vary comparatively little between IDRs and the regions adjacent to them when averaged across events. More granular statistics are also presented and discussed.

Groundwater is critical in sustaining streamflow, especially in mountain catchments, because of its ability to supply baseflow in the absence of precipitation. In water-limited arid and semi-arid mountain environments, the need to characterize groundwater recharge and discharge has grown in tandem with demands to effectively manage current and future water resources. However, studying groundwater is challenging in complex terrain due to limited field measurements. Nearly a decade of monitoring data collection at Gordon Gulch in the Colorado Front Range provides a unique opportunity to study such an environment. The field data is used to parameterize and calibrate a groundwater flow model (MODFLOW-NWT). Model results reveal spatial and temporal patterns in groundwater recharge and discharge to the stream. Groundwater is recharged primarily by one to two recharge events each year, driven by spring snowmelt and rain. The majority of groundwater recharge occurs in upper Gordon Gulch and is stored in saprolite and weathered bedrock. Groundwater is discharged to the stream via long, deep flowpaths sourced from upper Gordon Gulch and short, shallow flowpaths from soil and saprolite in lower Gordon Gulch. Using Gordon Gulch as a case study, this model and data analysis contribute to a larger effort to understand and constrain the mechanisms driving groundwater recharge and groundwater-stream exchanges in semi-arid, montane environments.

Statistical studies of the properties of different plasma regions, such as the magnetosheath and outer magnetosphere found near the boundaries of planetary magnetospheres, require knowledge of boundary (bow shock and magnetopause) crossings for purposes of classification. These are commonly detected by visual inspection of the magnetic field and / or particle data sampled by the relevant spacecraft. Automation of this type of activity would thus improve the efficiency of boundary and region studies, which benefit from large crossing datasets, and could also have implications for future development of onboard data-processing protocols in the pre-downlink stage. The Cassini mission at Saturn (2004-2017) provided an invaluable dataset for testing the viability of automated boundary classification. The training dataset consists of BS and MP crossings for the time period 2004 to 2016 (Jackman et al. (2019)). We have employed a series of techniques which involve pre-processing the calibrated magnetometer data, unsupervised training of a LSTM recurrent neural network on magnetometer data to filter magnetosheath regions where crossings are most likely to be found, isolating large rotations in magnetic field using minimum variance analysis (MVA), feature engineering such as magnetic field strength ratio either side of the field rotation to form a ‘feature vector’ for each candidate, and finally applying a gradient-boosting decision-tree-based algorithm to predict the probability that a given interval of data contains the signature of a bow shock (BS), a magnetopause (MP), or None (not a boundary crossing). The resulting model performs better on bow shock events, with a precision (fraction of true events in the retrieved sample) and recall (fraction of the total true events which were retrieved) of ~86% and ~90% respectively, as compared to ~50% and ~68% for the MP. The ongoing work focuses on augmenting the feature space for improved classification of MP, based on a magnetic pressure model of MP crossings derived using a local pressure balance condition (e.g. Pilkington et al. 2015) and using the distinct energetic particle flux changes across the MP in MIMI data (e.g. Liou et al. 2021). We expect that these promising new features will help us to better constrain the retrieval of candidate events which are true MP crossings.

The Arctic Ocean's Wandel Sea is the easternmost sector of the Last Ice Area, where thick, old sea ice is expected to endure longer than elsewhere. Nevertheless, in August 2020 the area experienced record-low sea ice concentration. Here we use satellite data and sea ice model experiments to determine what caused this record sea ice minimum. In our simulations there was a multi-year sea-ice thinning trend due to climate change. Natural climate variability expressed as wind-forced ice advection and subsequent melt added to this trend. In spring 2020, the Wandel Sea had a mixture of both thin and---unusual for recent years---thick ice, but this thick ice was not sufficiently widespread to prevent the summer sea ice concentration minimum. With continued thinning, more frequent low summer sea ice events are expected. We suggest that the Last Ice Area, an important refuge for ice-dependent species, is less resilient to warming than previously thought.

Ensuring the stability of food production is essential for adapting food systems to rising climate variability. Food production instability is determined by changes in yield, planted area, and the ratio between planted and harvested area. Yet most research has focused on evaluating and improving the response of crop yields to climate fluctuations, and there remains a poor understanding of whether and to what extent changes in planted and harvested areas affect crop production stability. Here we use the example of corn in the United States, the country’s most widely produced crop, to evaluate the relative importance of disruptions in yield, harvested area, and the planted: harvested area ratio in contributing to production instability. We apply a new time-series shock detection approach to data covering 2511 counties from the years 1970 to 2020. We find that disruptions in yield, ratio, and planted area explain 34%, 32%, and 20% of total production instabilities, respectively. Considering multiple shocks could happen simultaneously, 48% of the production fluctuations coincided with area (either ratio or planted area) instabilities. In terms of shocks (negative disruptions), the proportion of production shocks occurring concurrently with area shocks rises to 54%, and with yield shocks rise to 45%. The greater impact on production shocks confirms the risk of area shocks to production fluctuation and food security. Based on correlation analysis between the county level ratio shocks and the frequencies of six natural disasters, we show that ratio shocks are significantly correlated with the occurrence of flood, drought, and hail (P<0.001). These findings suggest that fluctuations in planted and harvested area may determine production instability more frequently than yield and that decisions about cropping patterns can thus play a crucial role in stabilizing food production in the face of climate variability.

The Smart Rock is a submersible sensor suite that monitors temperature, pressure (water depth), turbidity, and electrical conductivity. The sensor suite can be deployed in streams for 3-6 months at a time taking data every 20 minutes. The frequency of data collection can be changed which will affect battery life. Smart Rock assembly has been streamlined to make assembly and programming as accessible as possible. We want water sensing to be affordable and accessible to water scientists around the world. Our goal with the Smart Rock remains to develop an affordable, user friendly, low cost, and accessible for our different users needs. For those familiar with previous versions of the Smart Rock let me cover what has changed with this latest revision. We have designed our own electrical conductivity sensor with increased control for the user to change the range and resolution of the sensor. The enclosure has shrunk with a new selection of batteries doubling the capacity of the previous version’s battery. We worked on stabilizing the code and simplifying the operation. You can now control the unit’s settings via a config file on the SD card. No more Arduino reprogramming to change settings.

An understanding of fluid flow and transport in porous media is crucial in the development of better oil and gas recovery processes. With the emergence of parallel computing, today this is achieved more efficiently through direct numerical simulations of microscopic flow and transport. In order for this to be done, porous media models have to be created and multi-phase flow must be simulated. The Lattice Boltzmann Method (LBM) is a flexible computational tool that allows one to simulate fluid flow in complex heterogeneous media. It treats flow as the collective dynamics of pseudo particles and obtains a macroscopic equivalent to the Navier Stokes equations by approximating collision and propagation. For this research, the Rothman and Keller Lattice Boltzmann Method (LBM) was used to simulate two-phase fluid flow in two-dimensional porous media structures. This color gradient method can simulate different wettability and large viscosity ratios with ease and accuracy using a vectorized 2-Dimensional LBM code. Nine different artificial two-dimensional porous media across three porosity values (60%, 65%, and 70%) were created. This was done to understand the influence of pore structure and arrangement on fluid flow for porous medias with the same porosity value. A total of 81 simulations were conducted in which a “red” fluid was injected in a porous medium that was initially saturated with a “blue” fluid of a different viscosity. Different wetting angles and viscosity ratios were used for the simulation to understand their influence on the flow morphology. The result showed that the viscous fingers for the wetting fluid (𝜃 = 0◦) were somewhat broader and more rounded relative to the fingers of the non-wetting fluid (𝜃 = 180◦). It also showed that the recovery factor benefits from higher porosity values. Observing the flow patterns from the simulations showed that the flow morphology of porous medias with the same porosity are similar irrespective of the pore arrangement and structure. The results from this experiment show that with increased viscosity ratio, the recovery ratio is higher, which means more production.

Methane emissions from freshwater, mineral-soil wetlands represent an important portion of the global greenhouse gas budgets. We use long-term observations in Old Woman Creek (OWC), an estuarine wetland at the coast of Lake Erie. OWC is characterized by a fluctuating water level controlled by a natural sand barrier. OWC water levels are high when the barrier is closed. When it breaks, OWC is directly connected to the lake. Long term water level rise of Lake Erie provides a trend of water level at OWC. These changes to hydrology drive changes to the ecology. In OWC the dominant eco-hydrological patch types are mudflats, cattails (Typha), floating-leaf vegetation (Lotus, water lily), and open water. The seasonal and long term changes to water level lead to dynamic changes in the patch type composition, and as OWC gets deeper, mudflats and cattails give way to open water and floating-leaf vegetation. We developed an approach to classify the eco-hydrological patch type from remote sensing images. We used seasonal time series of NDVI from HLS (a composite dataset of Sentinel and Landsat). These time series were classified to patch types according to their similarity to the seasonal NDVI profiles of pixels identified and ground-truthed as pure pixels of a specific patch type. We then used the DG-SWEM high resolution hydrodynamic model to simulate the flow velocity throughout the wetland. Combining the eco-hydrological patch locations and the high-resolution flow simulation allowed for the calculation of an effective patch-type-level residence time. We found differences in residence times between the different patch types. We measured the relations between methane flux and CO2 uptake at the whole wetland scale, the vegetation patch scale, and directly from leaves. We found different methane-CO2 relations among the floating leaved and emergent species. Phenological transitions throughout the growing season continued to make an important effect only in Typha. Our observations represent a valuable foundation towards a more robust models of methane fluxes in wetlands at the resolution of within-wetland vegetation patch type, and resolving the effects of seasonal and within-season vegetation phenology in ecosystem-scale models.

The NASA Mars 2020 Perseverance rover mission will collect a suite of scientifically compelling samples for return to Earth. On the basis of orbital data, the Mars 2020 science team* identified two notional sample caches to study (1) the geology of Jezero crater, collected during the prime mission and (2) the ancient crust outside of Jezero crater, collected during a possible extended mission. Jezero crater geology consists of well-preserved, Early Hesperian to Late Noachian deltaic and lacustrine deposits sourced from a river system that drained Noachian terrain. The crater floor comprises at least two distinct units of sedimentary or volcanic origin whose relationship to the deltaic deposits is presently unclear. Remotely-sensed data reveal signatures of carbonate+olivine and clay minerals within crater floor and crater margin units. Samples from within Jezero that comprise the prime mission notional sample collection thus include: crater floor units; fine- and coarse-grained delta facies, the former with potential to preserve organic matter and/or biosignatures, the latter to possibly constrain the type and timing of sediment deposition; chemical sediments with the potential to preserve biosignatures; a sample of crater rim bedrock; and at least one sample of regolith. The region of southern Nili Planum, directly outside the western rim of Jezero crater, is geologically distinct from Jezero crater and contains diverse Early or even Pre-Noachian lithologies, that may contain records of early planetary differentiation, magnetism, paleoclimate and habitability. The notional sample collection from this region will include: layered and other basement rocks; megabreccias, which may represent blocks of (pre-)Noachian crust; basement-hosted hydrothermal fracture fill; olivine+carbonate rocks that are regionally significant and may be related to units within Jezero crater; and mafic cap unit rocks. The samples described are notional and may change with ongoing surface investigations. However, the samples we anticipate collecting align well with community priorities for Mars exploration, addressing geologic diversity, potential ancient biologic activity on Mars, planetary evolution, volatiles, and human health hazards. *Many other Mars 2020 team members were involved in this planning

In recent decades, there is a broad consensus in the literature that heat-related mortality overall has declined relative to the magnitude of the heat event. This said, as society is aging and the climate is warming, it is uncertain that this trajectory can be sustained moving forward, particularly as historically rare events become more common. To explore more recent trends, using a recently extended data set, we explored trends in anomalous mortality associated with extreme heat event days for the period 1975-2018 across the largest 107 metropolitan areas of the United States. We defined heat using an excess heat factor, and once events were identified, used a distributed-lag nonlinear model (DLNM) to assess mortality response over a cumulative 10-day period. In addition to total mortality, we also assessed subsets of those 65+ and 45-64, each of which were subdivided by sex. Results indicate that, overall, heat-related mortality associated with any given heat event day is decreasing. The most substantive decreases in mortality are those 65 and older, which may be associated with greater awareness as well as that population being the target of most intervention systems. Indeed, in many locations heat-related mortality among women 65 and older is no longer statistically significant. In contrast, while overall rates are lower, such trends are not seen in those aged 45-64. In particular, there is an increase in mortality among men 45–64 of 11.3 deaths per year across the US, most concentrated in southern and southwestern US cities. Overall, however, the general decreases in heat related mortality are being offset somewhat by the increase in heat event days, particularly since 2010. Given the impacts of the heat events since 2018 over the US West in particular, it is clear that heat-related mortality is not something confined to the past.