Biogenic emission models are developed on the foundation of leaf physiological processes and driven by a set of physical and biological factors. To estimate emissions online, many studies used weather forecasting models coupled with simple biogenic emission algorithms, in which the canopy physiological parameters were neglected or oversimplified. In this study, the land surface scheme CLM4 (Community Land Model version 4) coupled in the advanced Weather Research and Forecasting model (WRF) was used to determine canopy physiological parameters. The MEGAN (Model of Emissions of Gases and Aerosols from Nature) algorithms embedded in CLM4 scheme used these parameters to estimate biogenic emissions. The emission estimated by using leaf temperature in our study were about 23% higher than that based on air temperature as in the previous methods. Compared with studies neglecting shaded canopy, the separate treatments of sunlit and shaded leaves in this study lowered the estimations by a factor of 2 through decreasing diffuse radiaton absorbed by sunlit canopy. Dynamic weather history was used in our study to replace the fixed values in the original MEGAN-CLM4 code. An emission inventory of isoprene and monoterpenes in China was established for the year 2018. The estimates were evaluated against field measurements. Generally, the coupled model produced a reasonable simulation in both emission budgets and spatiotemporal distribution of biogenic emissions.

Seismic sensors and seismic imaging have been widely used to monitor the geophysical properties of the subsurface. As subsurface engineering techniques advance, more precise monitoring systems are required. Seismic event catalogs and seismic velocity structures are two of the major outputs of seismic monitoring systems. Although seismic event catalogs and velocity structure are often studied separately, published reports suggest constraining them simultaneously can lead to better results. We conducted a double-difference seismic tomography analysis to constrain both the seismic event locations and the 3D seismic velocity structure. Passive seismic data collected from a geothermal research project in Lead, South Dakota were used to image a 3D volume on the scale of tens of meters. Specifically, around 18,500 P-wave and 8,900 S-wave arrival times from 1,874 seismic events were used. Checkerboard tests showed that the observed data can image the seismically active region well. We compared tomography results with fixed seismic event locations against those with updated event locations. Tomography results with updated event locations showed better fits to the observations and improved the seismic event catalog, showing sharper patterns compared to the original one. These patterns helped us monitor the seismically active fractures since the seismic events were mostly due to hydraulic stimulations. Two parallel fractures revealed by the updated seismic event catalog spatially correlated with independent borehole temperature observations. The average seismic velocity values of the well-constrained volume agreed to the first order with core sample measurements and active-source seismic surveys.

In this work we examine variations in mantle uplift associated with large lunar impact craters and basins between major terranes. We analyze the Bouguer gravity anomalies of 100–650-km diameter lunar impact craters using Gravity Recovery and Interior Laboratory (GRAIL) observations and the Lunar Orbiter Laser Altimeter (LOLA) crater database. The Bouguer gravity anomalies of 324 large impact craters analyzed herein are primarily controlled by the uplifted crust-mantle (Moho) interface in the central region of these impact craters, although post-impact mare deposits contribute to the gravity anomalies of some individual craters. The central uplift of the Moho interface is primarily controlled by impact energy and increases to ~ 30 km for a 650-km crater. Further analyses of craters in the Feldspathic Highlands Terrane (FHT) with varied crustal thickness () reveal that the onset crater diameter () with an uplifted Moho interface is dependent on the local : ~ 146+1.1(in a unit of km). This equation also provides a quantification of the depth-dependent attenuation of impact-induced structural uplift, using the Moho uplift as a proxy for structural uplift. Moho uplift of large craters in the hotter South Pole-Aitken Terrane (SPA) is not statistically different from FHT craters, consistent with the expected thermal difference between these terrains during the pre-Nectarian period.

The 2020 Monte Cristo Earthquake sequence in western Nevada began with a M6.5 shock on 5/15/20, and was the largest to occur in Nevada since 1954. The event exhibited left-lateral slip along an eastward extension of the Candelaria fault and extensive distributed surface faulting in the epicentral area. Groundwater monitoring and strain analysis were conducted to evaluate hydrochemical effects on the regional groundwater systems following the initial event. Physio-chemical monitoring, (started on 5/16 and still ongoing) includes measurements of temperature (temp), pH, specific conductance (SpC), flow rate, alkalinity and collection of samples for major ions and trace element analysis. Since sites had not been monitored prior to the initial shock, measurements were evaluated against a year of post-event data to gauge response to seismicity. Four sites were monitored: a well from Columbus Marsh (CM) located 5 km from the epicenter; an artesian thermal well from Fish Lake Valley (FL); a well at Willow Ranch (WR) tapping cool water above the FL waters; and a spring along Mina Dump Road (MD) located 15 km north of the Candelaria fault on the Benton Springs Fault. GPS and InSAR measurements were used to create a model of the slip from which we estimated coseismic strain at each sampling location. All but one sample site, MD, experienced positive dilation and CM experienced the greatest amount of strain (15-17 microstrains). Hydrologic and chemical changes were observed following the initial shock, varying between sites. CM had significantly lower SpC values in the week following the event, as well as changes in major ion composition. Other sites showed minor changes; MD showed fluctuations in pH values and FL experienced a slight drop in temp. These waters showed minimal changes in major ions and trace elemental composition. Clear responses were observed throughout three >M5 aftershocks (6/30/20, 11/13/20, and 12/1/20), especially in SpC and alkalinity. A remarkable change in elemental concentration (an increase in Ca, K, SO4, Fe, and decrease in Na, Cl, Li, and Ba) was observed in CM. WR experienced a transient increase in temp measured two weeks prior to the 11/13/20 earthquake. Strain analyses of the smaller (>M5) events are planned to further evaluate observed responses and to clarify factors affecting groundwater response.

The Delaware River is a major freshwater supplier of New York City (NYC). Nearly half of NYC drinking water is supplied by inter-basin transfer of surface water stored in reservoirs within the upper reaches of the Delaware River. In its lower reaches, the Delaware River is a tidal estuary, and upstream freshwater discharge provides a critical control on estuary salinity. During the record 1950–1960’s drought, NYC water withdrawals exacerbated low flows. Estuary salinity reached levels that threatened freshwater intakes and groundwater recharge, resulting in legal action and Supreme Court decrees. We revisit this classic case study in coupled human and natural systems using the Energy Exascale Earth System Model (E3SM). The E3SM water management sub-model is updated to include inter-basin water transfer and reservoir-specific operating rules. Model simulations are developed to investigate competition between NYC water demand and in-stream flow targets needed to maintain estuary salinity within regulatory guidelines under historic and future climate. To our knowledge, this is a first demonstration of an Earth System Model simulation with inter-basin water transfer, which, in this study area, provides water for nearly five million people living outside the Delaware River basin in New York City and New Jersey.

Seismology data is overgrowing and is outpacing the development of processing algorithms. This tremendous increase in high-quality data can help better understand the earthquake processes related to the geology of active seismic regions such as the Hindu Kush. Most traditional detection algorithms are computationally inefficient compared to the amount of seismological data available and fail to detect low magnitude and noisy events. Deep learning algorithms are known for their applicability on large datasets with less runtime. Event detection and phase detection can be considered a supervised deep-learning problem quite similar to image recognition. In this study, we have implemented a hierarchical attention mechanism-based deep learning model for simultaneously phase picking and earthquake detection. This model is trained using Stanford Earthquake Dataset (STEAD), a globally disturbed labeled seismic dataset. We used this trained deep learning model to detect earthquake signals and pick P and S phases in the Hindu Kush - Pamir region for twelve months of continuous data spread across 83 different stations. A rigorous selection criterion based on detection, P and S phase probabilities, and other parameters has been used to associate the phases from different stations and to locate the earthquake. Our model detected almost seve times more earthquakes than previously existed in the catalog. The algorithm picked P and S phases with a high level of precision, comparable to human analyst picks. Furthermore, pinpointing these events allowed us to define the S-shaped seismic zone in the Pamir-Hindu Kush region and better comprehend the deformation caused by Eurasian- Indian plate motion.

Particulate inorganic carbon (PIC) indicates CaCO3, which is produced biologically and constitutes about a quarter of total marine sediments. It is produced by coral reefs, foraminifera, coccolithophores and some macrophytic algae (Balch et al., 2005). The dominating calcifiers in the oceans are the coccolithophores, producing roughly half of the global ocean carbonates. They also cover 20 % of the blooming phytoplankton (Krumhardt et al., 2017). An algorithm has been developed to estimate the PIC concentrations from satellites. The data is collected by MODIS Aqua and SeaWiFS. Observational studies show mixed results for coccolithophore abundance as both increase/decrease in various places. However, it can be agreed that anthropogenic climate change has a significant impact on the PIC, mainly due to global warming and ocean acidification.

Permafrost active layer thickness (ALT) is a sensitive indicator of permafrost response to climate change. In recent decades, ALT has increased at sites across the Arctic, concurrent with observed increases in annual minimum streamflow (baseflow). The trends in ALT and baseflow are thought to be linked via: 1) increased soil water storage capacity due to an increased active layer, and 2) enhanced soil water mobility within a more continuous active layer, both of which support higher baseflow in Arctic rivers. One approach to analyzing these changes in ALT and baseflow is to use baseflow recession analysis, which is a classical method in hydrology that relates groundwater storage S to baseflow Q with a power law-like relationship Q = aSb. For the special case of a linear reservoir (b=1.0), the baseflow recession method has been extended to quantify changes in ALT from streamflow measurements alone. We test this approach at sites across the North American Arctic and find that catchments underlain by permafrost behave as nonlinear reservoirs, with scaling exponents b~1.5–3.0, undermining the key assumption of linearity that is commonly applied in this method. Despite this limitation, trends in a provide insight into the relationship between changing ALT and changing Arctic baseflow. Although care should be taken to ensure the theoretical assumptions are met, baseflow recession analysis shows promise as an empirical approach to constrain modeled permafrost change at the river basin scale.

The Canadian Arctic Archipelago (CAA) is vulnerable to climate warming, and with over 300 tidewater glaciers, is a hotspot for enhanced glacial retreat and meltwater runoff to the ocean. In contrast to Greenlandic and Antarctic systems, CAA glaciers and their impact on the marine environment remain largely unexplored. Here we investigate how CAA glaciers impact nutrient delivery to surface waters. We compare water column properties in the nearshore coastal zone along a continuum of locations, spanning those with glaciers (glacierized) to those without (non-glacierized), in Jones Sound, eastern CAA. We find that surface waters of glacierized regions contain significantly more macronutrients (nitrogen, silica, phosphorus) and micronutrients (iron, manganese) than their non-glacierized counterparts. Water column structure and chemical composition suggest that macronutrient enrichments are a result of upwelling induced by rising submarine discharge plumes, while micronutrient enrichments are driven directly by glacial discharge. Generally, the strength of upwelling and associated macronutrient delivery scales with tidewater discharge volume. Glacier-driven delivery of the limiting macronutrient, nitrate, is of particular importance for local productivity, while metal delivery may have consequences for regional micronutrient cycling given Jones Sound’s important role in modifying water masses flowing into the North Atlantic. Finally, we use the natural variability in glacier characteristics observed in Jones Sound to consider how nutrient delivery may be affected as glaciers retreat. The impacts of melting glaciers on marine ecosystems through both these mechanisms will likely be amplified with increased meltwater fluxes in the short-term, but eventually muted as CAA ice masses diminish.

The agricultural sector is a major consumer of water for irrigation purposes. Precise quantification of irrigation water requirements will help in achieving sustainable water management. Root water uptake (RWU) is driven by transpiration demand exerted by crops and is influenced by factors such as weather and leaf area index (LAI). The root water uptake at a particular depth in the root zone depends on the local moisture content, soil type, and root density. Under certain conditions, lack of availability of soil moisture at a certain depth may be compensated by more water being drawn into the roots from wetter portions of the soil. To incorporate this mechanism, this study presents a compensated root water uptake model (CRWU) with application to maize crops grown in Indian climatic conditions. Irrigation field experiments were conducted at seven field plots with the same soil by adjusting the irrigation supplied to simulate different degrees of water stress. Daily soil moisture data recorded at different depths in the root zone, crop yield and biomass data help in assessing model performance and identifying a critical water stress index that governs the extent of compensation in RWU.

Accurate and high-resolution air temperature prediction is important in many different applications. Hourly air temperature forecasting in mountainous areas is necessary and important because mountainous areas are becoming increasingly important areas of human activities. At present, scientists successfully employ numerical weather prediction (NWP) models, such as the WRF model, to achieve reliable forecasts. However, air temperature forecasting and modeling over complex geographical zones are difficult tasks. The WRF model is a mesoscale model and does not adequately account for the influence of terrain on the air temperature. It is important to downscale larger-scale models to a much finer scale. In this paper, a statistical temperature downscaling method based on geographically weighted regression (GWR) and diurnal temperature cycle (DTC) models is proposed. A statistical downscaling scheme is designed to forecast the hourly air temperature, at a 30-m spatial resolution, up to 24 h in advance. Compared to WRF simulations, RMSE of the combined downscaling model decreased 1.01 ℃ at the automatic weather station level and 0.80 ℃ at the spatial level, and MAE decreased by 0.81 ℃ and 0.69 ℃, respectively, at these two levels. The results reveal that the combined downscaling model performs very well in correcting and downscaling the air temperature in WRF simulations in mountainous areas.

Paleomagnetic poles used to compute apparent polar wander paths (APWPs) are strongly dispersed, which was recently shown to cause a large fraction (>50%) of these poles to be statistically distinct from the APWP to which they contributed, suggesting that current statistical approaches overestimate paleomagnetic resolution. Here, we analyze why coeval paleopoles are so dispersed, using the paleopoles behind the most recent global APWP and a compilation of paleomagnetic data obtained from <10 Ma volcanic rocks (PSV10). We find that paleopoles derived from sedimentary rocks, or from data sets underrepresenting paleosecular variation (PSV), are more dispersed and more frequently displaced. We show that paleopoles based on a smaller number of paleomagnetic sites are more dispersed than poles based on larger data sets, revealing that the degree to which PSV is averaged is an important contributor to the pole dispersion. We identify as fundamental problem, however, that the amount of paleomagnetic data used to calculate a paleopole, and thus the dispersion of coeval paleopoles, is essentially arbitrary. We therefore explore a different approach in which reference poles of APWPs are calculated from site-level data instead of paleopoles, thereby assigning larger weight to larger data sets. We introduce a bootstrap-based method for comparing a collection of paleomagnetic data with a reference data set on the same hierarchical level, whereby the uncertainty is weighted against the number of paleomagnetic sites. Finally, our study highlights that demonstrating smaller tectonic displacements requires larger paleomagnetic data sets, and that such data sets can strongly improve future APWPs.

Mixed-phase clouds (MPCs), which consist of both supercooled cloud droplets and ice crystals, play an important role in the Earth’s radiative energy budget and hydrological cycle. In particular, the fraction of ice crystals in MPCs determines their radiative effects, precipitation formation and lifetime. In order for ice crystals to form in MPCs, ice-nucleating particles (INPs) are required. However, a large-scale relationship between INPs and ice initiation in clouds has yet to be observed. By analyzing satellite observations of the typical transition temperature (T*) where MPCs become more frequent than liquid clouds, we constrain the importance of INPs in MPC formation. We find that over the Arctic and Southern Ocean, snow and sea ice cover significantly reduces T*. This indicates that the availability of INPs is essential in controlling cloud phase evolution and that local sources of INPs in the high-latitudes play a key role in the formation of MPCs.

We present here an in-depth analysis of one time interval when quasi-linear mirror mode structures were detected by magnetic field and plasma measurements as observed by the NASA/Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft. We employ ion and electron spectrometers in tandem to support the magnetic field measurements and confirm that the signatures are indeed mirror modes. Wedged against the magnetic pile-up boundary, the low-frequency signatures lasted on average ~10 s with corresponding sizes of the order of 15-30 upstream solar wind proton thermal gyroradii, or 10-20 proton gyroradii in the immediate wake of the quasi-perpendicular bow shock. Their peak-to-peak amplitudes were of the order of 30-35 nT with respect to the background field, and appeared as a mixture of dips and peaks, suggesting that they may have been at different stages in their evolution. Situated in a marginally stable plasma with β|| ~ 1, we hypothesise that these so-called magnetic bottles, containing a relatively higher energy and denser ion population with respect to the background plasma, were formed upstream of the spacecraft behind the quasi-perpendicular shock. These signatures are very reminiscent of magnetic bottles found at other unmagnetised objects such as Venus and comets, also interpreted as mirror modes. Our case study constitutes the first unambiguous detection of mirror modes around Mars, which had up until now only been surmised because of the lack of high-temporal resolution plasma measurements.

We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed-layer model with entrainment and detrainment, an Ekman mixed-layer model, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting 3-dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed-layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid-latitude westerly jet and of the Inter-tropical Convergence Zone (ITCZ) in response to sea-ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed-layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.

Preparedness is important for reducing potential losses from various disasters. There are limited studies that investigated the concrete reasons for not adopting a specific preparedness action. This paper fills such a gap using representative national survey data from China. Seven disaster preparedness actions, namely “preparing food and water at home,” “pay attention to disaster-related information,” “making emergency plans,” “being aware of nearest shelters,” “being aware of building code,” “participating in exercise or drills” and “being a volunteer for emergencies” are used as the measure of preparedness behaviors. Overall, the public has adopted more material-related preparedness actions, equipped with fewer awareness activities, and had the least community participation-related preparedness behaviors. The primary reasons for not adopting these actions are “not aware,” “don’t know where to buy or where to reach,” and “the action is not useful, there is no necessary,” while “costly,” “need special knowledge,” “don’t have time,” “need collaboration with others,” “energy-consuming” and “not my responsibility” are the less chose reasons. Besides, trust in government, relocation due to disasters, living in urban areas, and a higher degree of socioeconomic status are positively correlated with higher probabilities of adopting all the seven preparedness activities. These findings highlight the importance of community outreach from emergency management professionals to increase the public’s awareness of preparing for potential disasters. It is necessary to let the general public know the existence of these preparedness actions, and these actions can reduce losses.

The activation of landslides and shallow faults is related to the shear behavior of soft interlayers during groundwater infiltration. Regarding the water sensitivity of clay minerals, the shear behavior of soft interlayers may rely more on weathering and water content than the requirement of shear displacement and normal stress for quartz grains. Here, we present the reduction characteristics of the shear resistance and permeability of mudstone granules considering weathering under dry-wet cycling. Within a shear displacement of 20 mm, the shear mode transformation of the weathered mudstone granules from strain hardening to strain softening was revealed from dry to wet conditions. However, this transition was not observed for unweathered mudstone and weathered sandstone samples. Correspondingly, the permeability perpendicular to the shear zone reduced 10~45 times with increasing normal stress according to post-shear measurements. Because weathered particles exhibited more micropores, the addition of water resulted in mineral separation and generated mud that filled the specimen pores. Thus, the sealing and lubrication effect of the mud decreased the porosity and shear resistance of the soft interlayer, along with increasing particle roundness. This rapid transformation mechanism within a limited displacement reveals the effect of water softening and weathering on the shear behavior of soft interlayers, which helps to understand landslide occurrence and shallow fault activation.

High altitude platforms (HAPs) such as stratospheric balloons and eventually other high altitude, long endurance unmanned vehicles have reached a stage where it is possible to deploy a persistent fleet of aircraft acting as a meteorological observation network for a reasonable cost. Whether directly collecting in situ measurements like winds aloft or via dropsondes or performing remote sensing using, for example, radar or GPS radio occultation, these observation networks can collect measurements which are hard to obtain from other observation platforms and are complementary to other systems. They are also highly autonomous and can be deployed worldwide (and thus can add redundancy to the global forecast system). Because they are mobile, the observation network can be adjusted to collect in situ measurements in the places that are most important to forecasters and scientists. We use simulation of fleets of stratospheric balloons that are navigated by machine learning algorithms that actuate an altitude control system to demonstrate some of the potential constellations that are achievable with HAPs and motivate the greater consideration of an autonomous, persistent HAPs-based meteorological observing network.