One of the key measurements from the Clouds and the Earth’s Radiant Energy System (CERES) satellite is Earth emitted or longwave (LW) radiation. The CERES Ocean Validation Experiment (COVE), located at Chesapeake Light Station, approximately 25 kilometers east of Virginia Beach, Virginia (coordinates: 36.90N, 75.71W) had provided surface validation for the CERES satellite measurements for many years. Upwelling LW radiation was one of the measurements made at COVE but was complicated due to the Light Station tower being in the upwelling LW instruments field of view. According to our estimates, the Light Station tower alters 15% of the upwelling LW radiation. An unwanted consequence of the tower being in the field of view was the tower radiating effect, particularly noticeable on clear, sunny days. During these days, the tower would radiate extra heat energy by as much as 3% (15 W/m^2) that was measured by the upwelling LW instrument. COVE follows the Baseline Surface Radiation Network requirements and their target uncertainty is 2%. To resolve this issue, we obtain a different upwelling longwave value using data from an infrared radiation thermometer (IRT) and a pyrgeometer that retrieves sea surface temperature (SST) and downwelling longwave respectively. Using an IRT allows conversion from SST to a water emission value and the pyrgeometer provides the reflected flux of the downward longwave radiation. By determining the extent of the undesirable obstruction in the field of view of the upwelling longwave instrument and determining its emissivity could allow others with similar issues to obtain the proper values of upwelling longwave measurements.

The hydrologic cycle is a complex and dynamic system of interacting processes. Hydrologists seeking to understand and predict these systems develop models of varying complexity, and compare their output to observations to evaluate their performance or diagnose shortcomings within the models. Often, these analyses take into account only single variables or isolated aspects of the hydrologic system. To explore how process interactions affect model performance we have developed a general framework based on information theory and conditional probabilities. We compare how conditional mutual information and mean square errors are related in a variety of hydrometeorological conditions. By exploring different regions of phase space we can quantify model strengths and weaknesses in terms of both process accuracy as well as classical performance. By considering a range of conditions we can evaluate and compare models outside of their average behavior. We apply this analysis to physically-based models (based on SUMMA), statistical models, and observations from FluxNet towers at a number of hydro-climatically diverse sites. By focusing on how the turbulent heat fluxes are affected by shortwave radiation, air temperature, and relative humidity we go beyond simple error metrics and are able to reason about model behavior in a physically motivated way. We find that the statistically based models, while showing better performance in the mean field, often do not represent the underlying physics as well as the physically based models. The statistically based model’s over-reliance on shortwave radiation inputs limits their ability to reproduce more complex phenomena.

The 2016 Amatrice-Norcia seismic sequence in central Italy activated a system of normal faults in the central Apennines and ruptured the surface along the Monte Vettore normal fault. Due to the complex rupture behavior, including antithetic faults and the proposed reactivation of an old thrust front, the Amatrice-Norcia seismic sequence offers a unique opportunity to study the relationship between fault complexity, surface ruptures, and earthquake source properties. Here, we focus on the first two months of the Amatrice-Norcia seismic sequence, including the 30 October 2016 Mw 6.5 mainshock near Norcia and more than 25000 aftershocks. Using continuous waveform data from 94 seismic stations with epicentral distances of up to ~100 km, we estimate source parameters of all cataloged earthquakes that exceed specific quality control criteria in a time period ranging from 24 October – 29 November 2016. Displacement spectral corner frequency and seismic moment values are fit using individual earthquake spectra, and corner frequency estimates are refined using spectral ratios. Constrained spectral parameters then provide input for static stress drop estimates based on a circular crack model. Preliminary results suggest the majority of earthquakes have static stress drop values between 1 and 10 MPa and self-similar scaling. Due to the high quality and quantity of available data, including precise earthquake locations, manually reviewed phase arrivals, and detailed mapping of surface ruptures, the Amatrice-Norcia earthquake sequence represents an opportunity to link earthquake source parameters to geological structures and surface rupture complexity. Preliminary results show correlations between high stress drop values and areas with increasing fault complexity, such as fault intersections at depth (inferred from precise earthquake hypocenters) or the mapped tip of the Monte Vettore normal fault, relative to other fault patches with fewer intersections or mapped surface trace terminations. Future work will examine whether the correlation of stress drop and fault complexity holds using refined stress drop estimates obtained using spectral ratio approaches.

We present a new metric for braiding intensity to characterize multi-thread systems (e.g., braided and anastomosing rivers) called the Entropic Braiding Index, eBI. This metric is a generalization of the widely used Braiding Index (BI) which is simply the average count of intercepted channels per cross-section. The co-existence of diverse channels (widely different widths and discharges) within river cross-sections distorts the information conveyed by BI, since its value does not reflect the diversity and natural variability of the system. Moreover, the fact that BI is extremely sensitive to resolution (BI increases at higher resolution as smaller scale channels can be resolved) challenges its applicability. eBI, addresses these main drawbacks of BI. eBI is rooted in the concept of Shannon Entropy, and its value can be intuitively interpreted as the equivalent number of equally important (in terms of discharge) channels per cross-section. Thus, if the channels observed in a multi-thread system are all carrying the same amount of discharge, eBI has the same value of BI. On the other hand, if a very dominant channel in terms of discharge co-exists with much smaller channels, eBI would take a value slightly larger than 1 (note that the actual value would depend on the number of small channels and their relative size with respect to the dominant channel). We present a comparative study of BI and eBI for different multi-thread rivers obtained from numerical simulations and remote-sensing data and for different discharge stages. We also provide evidence of the robustness of eBI in contrast to BI when a given river system is studied under different resolutions. Finally, we explore the potential of eBI as a metric to characterize different types of multi-thread systems and their stability.

Scattering of longwave radiation by cloud particles has been regarded unimportant and hence commonly neglected in global climate models. However, it has been demonstrated by recent studies that cloud longwave scattering plays an unignorable role in modulating the energy budget of the Earth System. Offline radiative transfer calculation showed that excluding cloud longwave scattering could overestimate outgoing longwave radiation and underestimate downward irradiance to the surface, and thus impose excessive cooling onto the atmosphere column. How this physical process interacts with other processes in the Arctic climate system, however, has not been thoroughly evaluated yet. Given the fact that the melting of ice and snow that cover the vast surface of the Arctic region is sensitive to energy budget, and such melting may trigger further feedback mechanisms, the neglection of cloud longwave scattering could bias the regional climate simulations to a considerable extent. We have incorporated cloud longwave scattering into the NCAR CESM and the DoE E3SM and this study analyzed the impact on the simulated polar climates in both earth system models. Cloud longwave scattering leads to a warmer surface air temperature in both models, especially over the wintertime. A detailed surface energy budget analysis is performed, for both the mean state and the temporal variability. Preliminary results suggest that the leading change is downward longwave flux and upward longwave flux, followed by the changes of turbulent heat flux. How the longwave scattering treatments can couple with cloud microphysics and precipitation physics to affect Arctic precipitation is further explored.

Researchers using climate data are facing challenge to analyze the data they need. Data volumes are increasing very rapidly, and the ability to download all needed data is often not a possibility anymore. A platform called climate4impact (C4I) has been designed and developed to enhance the use of research data, to support researchers with analytics and to support other climate portals. It is currently under development within the European Project IS-ENES3 and builds on previous developments from previous IS-ENES projects, CLIPC and C3S-Magic. C4I offers a front-end and standard services (with APIs) on top of the climate data infrastructure, and it can be visited at https://climate4impact.eu. The current version provides processing services include climate indicator calculations, country based statistics and polygon extraction. C4I makes use of the DKRZ Birdhouse framework, which is an extendable and modular processing framework based on PyWPS. Data is obtained from various ESGF nodes using secure OpenDAP. C4I provides a personal basket where users can upload their own data and do research with the provided tools. The software is open, reusable, modular and packaged. Components are available via docker containers to enable easy re-use. The on-demand calculations are taking place on the front-end server, and this is not scalable and can lead to performance problems. Within the DARE project, delegation of the calculations on the DARE Platform using the DARE API has been implemented and tested in a prototype, using EUDAT B2DROP as an intermediate storage service. It is to be noted that the DARE Platform as well as the EUDAT B2 Services should be interoperable with the European Open Science Cloud (EOSC). This prototype service delegation will be made operational during the upcoming year. In the IS-ENES3 project, the web portal will be redesigned with a completely new architecture using a micro-services and containerized approach, building on experience gained during the previous projects. The next version of the portal will be built using the React framework, which allows for creating large web applications which can change data, without reloading the page. We are actively seeking input from current as well as potential users at this time, to make the next version of C4I useful to as many people as possible. The material presented here is made possible because the IS-ENES3 project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°824084.

Atmospheric drag describes the main perturbing force of the atmosphere on the orbital trajectories of near-Earth orbiting satellites. The ability to accurately model atmospheric drag is critical for precise satellite orbit determination and collision avoidance. Assuming we know atmospheric winds and satellite velocity, area and mass, the primary sources of uncertainty in atmospheric drag include mass density of the space environment and the spacecraft drag coefficient, CD. Historically, much of the focus has been on physically or empirically estimating mass density, while CD is treated as a fitting parameter or fixed value. Presently, CD can be physically modeled through energy and momentum exchange processes between the atmospheric gas particles and the satellite surface. However, physical CD models rely on assumptions regarding the scattering and adsorption of atmospheric particles, and these responses are driven by atmospheric composition and temperature. Modifications to these assumptions can cause CD to change by up to ~40%. The nature and magnitude of these changes also depend on the shape of the spacecraft. We can check the consistency of the CD model assumptions by comparing densities derived from satellite drag measurements and computed CD values for satellites of different shapes orbiting in the same space environment. Since all of the satellites should see the same density, offsets in the derived densities should be attributable to inconsistencies in the CD model. Adjusting the CD model scattering assumptions can improve derived density consistency among the different satellites and inform the physics behind CD modeling. In turn, these efforts will help to reduce uncertainty in CD, leading to improved atmospheric drag estimates.

Magnetic reconnection is responsible for the major reconfigurations of the magnetosphere that lead to energy transport and deposition into the ionosphere. The fast rate at which magnetic energy is converted to plasma kinetic energy is likely enabled by the polarization Hall electric field that results from the separation of ions and electrons at small scales. Signatures of Hall fields have played a key role in identifying and studying reconnection, but the density of accumulated charge has not been quantified. We use the 4-point measurements of the Magnetospheric Multiscale mission to compute the divergence of the electric field and present the first observations of charge density in the diffusion region of magnetic reconnection. We show how it ties into the Hall system, discuss measurement uncertainties, analyze quality estimates, and make comparisons to 2D simulations. Charge density is briefly presented for other phenomena, and ranges from 2% or less of the background plasma density for magnetic reconnection and electron-scale magnetic holes and peaks to upwards of 4% for electron phase space holes.

Warm and dry föhn winds on the lee side of the Antarctic Peninsula (AP) mountain range cause surface melt that can destabilize vulnerable ice shelves. Topographic funneling of these winds through mountain passes and canyons leads to localized wind-induced melt which is difficult to identify without direct measurements. Our Föhn Detection Algorithm (FonDA) identifies the surface Föhn signature using data from twelve Automatic Weather Stations on the AP and uses machine learning to detect föhn in 5km Regional Atmospheric Climate Model 2 (RACMO2.3p2) output and ERA5 reanalysis data. We estimate and compare the climatology and impact of föhns on the AP surface energy budget, surface melt pattern, and melt quantity from 1979-2018. We show that föhn-induced melt is strongest at the eastern base of the AP and the northern portion of the Larsen C ice shelf. We identify previously unknown wind-induced melt possibly katabatic in nature on the Wilkins and George VI ice shelves. Neither RACMO2 nor ERA5 datasets show a significant increase in föhn melt thus far despite a more positive Southern Annular Mode and increasing surface temperatures. The warming climate and associated southward shift of westerly winds on the AP suggest a likely increase in the wind-induced melt that can densify firn, form melt ponds, and weaken ice shelf stability, however that trend remains insignificant for the past 40 years.

Axial Seamount is the most active submarine volcano in the NE Pacific Ocean, and is monitored by instruments connected to a cabled observatory (the US Ocean Observatories Initiative Cabled Array), supplemented by autonomous battery-powered instruments on the seafloor (at ~1500 m depth). Axial is a basaltic hot spot volcano superimposed on the Juan de Fuca spreading ridge, giving it a robust and apparently continuous magma supply. It has had three effusive eruptions in the last 21 years: in 1998, 2011, and 2015. Deformation measurements have been conducted at Axial Seamount since the late 1980’s with bottom pressure recorders (BPRs) that can detect vertical movements of the seafloor with a resolution of ~1 cm. This monitoring has produced a 22+ year time-series including co-eruption rapid deflation events of 2.5-3.2 meters, separated by continuous gradual inter-eruption inflation at variable rates between 15-80 cm/yr. The overall pattern appears to be inflation-predictable, with eruptions triggered at or near a critical level of inflation. Using this pattern, the 2015 eruption was successfully forecast within a one-year time window, 7 months in advance. As of December 2019, Axial Seamount has re-inflated 1.98 m (~78%) of the 2.54 m it deflated during the 2015 eruption. We are exploring several methods to forecast the next eruption, including daily extrapolation of the average rate of inflation from OOI BPR data during the last 3 months forward in time until it intersects the threshold reached before the 2015 eruption. Using this method with the difference in inflation between two OOI BPR instruments located 3.5 km apart removes noise from tidal residuals and oceanographic signals that are common to both instruments. This method suggests the next eruption is likely between 2020 and 2024. However, this simple method is complicated by uncertainties in the next inflation threshold (the volcano inflated 20 cm higher before the 2015 eruption compared to 2011), changes in the rate of inflation with time, and by intermittent pauses in the inflation (and seismicity) observed since 2015 that have lasted from a week to several months.

Wireless sensor networks for environmental monitoring are becoming a common tool for researchers across many of the field sciences. However, managing these systems is still an emerging issue. Internet of Things (IoT) technologies have provided many tools for creating big data solutions to these issues, but the industry is at cross-purposes with scientists. The big data approach is to collect massive amounts of data then throw out the anomalous points. For environmental monitoring, we need to archive and curate all the data as a permanent record of rapid environmental change. To achieve this, we need to combine IoT with museum curation sensibility. Dendra is cyberinfrastructure for real-time sensor data storage, retrieval, management, and curation for the field sciences. It is a cloud-based, multi-organizational system, designed to support massive permanent monitoring efforts (https://dendra.science). The name is derived from dendritic networks, such as river networks. Environmental monitoring performs in a similar manner, pulling data from the earth’s surface to a single location. To curate streaming data, we developed a dynamic data versioning system. A field scientist reports invalid data from the field via mobile phone, the annotation is approved by curator, and is instantly applied to all data accessed. Data is only modified on extract. This allows us to pull data from any time in the past with the edits and calibrations of that time. Networked data logger integration works with LoggerNet, GOES satellite, and soon Iridium satellite. Dendra is hosted on NSF’s XSEDE Jetstream cloud service. The system is designed as a set of microservices that interact through as set of persistent ques (NATS). Server-side javascript with Node.js is the primary development language. A data abstraction layer allows for multiple time-series databases (InfluxDB, MySQL, etc) to be accessed, even for a single instrument over time and reassembled as a single datastream. Access is via REST API & website. Dendra is used and supported by: Eel River Critical Zone Observatory (23 stations) in Mendocino, California; the University of California Natural Reserve System (25 stations); the Moore Foundation funded California Heartbeat Initiative (4 stations, 10 mobile, 40 planned).

From inside the stemflow research community, the past decade’s progress might look great: 1) the number of papers published on stemflow per year has doubled; 2) citations of stemflow publications have more than doubled; and 3) the number of research sites monitoring stemflow is on the rise. However, from a broader perspective, a brief Web of Science bibliometric analysis of the past decade reveals issues with these trends: 1) annual publication numbers have increased year-to-year for most topics in natural science, but stemflow publication trends are lower than related and broader disciplines; 2) self-citation is significantly higher for stemflow research than other disciplines (e.g., 26% compared to 2% for all hydrology); and, most importantly, 3) we may have more stemflow data, but we still lack a clear understanding of stemflow’s mechanistic importance. This creates ambiguities as to whether and how stemflow processes can be incorporated into hydrological models and concepts. In this presentation, I argue that we should progress from using metrics that are exclusively used by those who work on stemflow (e.g., unitless metrics such as funneling and enrichment ratios) towards using scaled flux-per-unit-area metrics that may support better integration into hydrological and ecological models (e.g., water or chemical yield per unit canopy area). While the magnitudes of funneling and enrichment ratios from individual plants have effectively conveyed to broader audiences the possibility for stemflow to play important roles in ecosystem functioning, I argue that we need to now move onto mechanistic investigations of stemflow’s impact on specific processes at ecohydrologically relevant scales. Dimensionless (often individual plant-scale) funneling-type metrics may not be useful in this regard, as they tell us nothing about where stemflow goes or what roles stemflow may play in the ecosystem. They also rely on an estimate of infiltration area, which has rarely been observed to date. Their use is further limited to falling liquid-phase rain, which prevents comparison of stemflow observations/processes under occult precipitation (fog, condensation) or mixed and solid-phase precipitation (snow, rime, etc). Please view the “Make Stemflow Unit-ed Again” companion video on YouTube: https://youtu.be/4vPk9m45V0c

The Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2 offers a new remote sensing capability to measure complex sea ice surface topography. We demonstrate the retrieval of six sea ice parameters from ICESat-2/ATLAS data: surface roughness, ridge height, ridge frequency, melt pond depth, floe size distribution and lead frequency. Our results establish that these properties can be observed in high fidelity, across broad geographic regions and ice conditions. We resolve features as narrow as 7 m, and achieve a vertical height precision of 0.01 m, representing a significant advance in resolution over previous satellite altimeters. ICESat-2 employs a year-round observation strategy spanning all seasons, across both the Arctic and Southern Oceans. Because of its higher resolution, coupled with the spatial and temporal extent of data acquisition, ICESat-2 observations may be used to investigate time-varying, dynamic and thermodynamic sea ice processes.

The current technology used by the Extratropical Surge and Tide Operational Forecast System (ESTOFS) on the East of the US and Gulf of Mexico coasts uses a sub-optimal unstructured grid, that over-resolves some straight portions of the coastline, under-resolves complex estuaries and coastal features, and employs roughly uniform resolution depending on the different water depths. The ESTOFS model is very efficient in terms of computational run time because it was designed for operational use, but accuracy is sub-optimal as the details of the complex inland water bodies is not captured with the 200 m minimum mesh resolution. ADCIRC is a robust high-fidelity depth-integrated model, widely used for the coastal community, including ESTOFS, for tides, storm surge, and wave-induced coastal setup. ADCIRC is a continuous-Galerkin based finite element unstructured grid framework that allows using meshes with a heterogeneous resolution to better represent the complexity of the ocean, shelf and nearshore regions. Recent advances on mesh generation tools now allow generating replicable high-resolution grids in times much shorter than the hand-edited processes used to develop the current version of ESTOFS. This opens the opportunity to study the effect of the different resolutions to represent topo-bathymetric and far inland water body features, in order to reduce the computational cost and improve the accuracy of the models. Thus, the objective of this research is to develop an ADCIRC-based model to accurately and efficiently simulate the dynamics of the ocean and riverine system in the Atlantic coast of the US and Gulf of Mexico for tide/storm predictions. The new ADCIRC-based model will incorporate a representation of the riverine system far up to the point where the ocean has no effect on water levels, efficiently use the resolution to reduce the minimum grid-size from 250 m to 50 m, with no significant increase in the number of nodes, and will combine pseudo-quadrilateral elements to efficiently represent narrow channels. This new generation of ESTOFS will represent a significant enhancement of the current technology for tides and storm surge prediction, but also will set up the required conditions for future approaches focused on coupling inland hydrology to the coastal modeling.

We use numerical solutions of the Richard’s Equations for 3D porous media to investigate the influence of agricultural subsurface drainage as a hydrologic process and its effect on the hydrologic regime of a watershed. Specifically, we determine the relation between subsurface seepage and subsurface storage in hillslopes with (drained) and without (undrained) subsurface drainage. Simulations are performed in Hydrus3D and the output is analyzed with MATLAB’s curve fitting tools, to create simple ordinary differential equations that represent the relationship between subsurface flow and subsurface storage for hillslopes of varying topographical gradients and shapes. We have determined an ‘activation point’ below which the seepage/storage relationship is roughly linear, and above which the drained and undrained simulations behave according to different nonlinear functional forms. Although the seepage/storage relationship of flat hillslopes have parametric consistencies independent of the hillslope gradient, the addition of curvature increases the complexity. In this work, we describe approximations to account for curved hillslopes. From our formulation, subsurface flow for varying hillslopes can be approximated using only the water storage and the topography of the hillslope. Reducing the system from partial differential equations (Hydrus) to ordinary differential equations improves scalability of the model. Simplified equations are used to study the consequences of large-scale changes in agricultural landscapes due to subsurface drainage.

Climate mitigation and forest management require accurate information on carbon stocks, fluxes, and potential future sequestration potential. Previous large-scale estimates have substantial uncertainties arising from lack of data, heterogeneity of forest structure, and modeling limitations. However, recent local-to-regional studies suggest that combination of lidar-derived canopy height with an advanced 3-D ecosystem model that explicitly tracks vegetation height (i.e. Ecosystem Demography, ED) can reduce uncertainties and provide mapped estimates of these quantities at high-spatial resolution over policy relevant domains. Extending this approach to the global scale requires both a source of global lidar data height data and a global height structured ecosystem model. The NASA GEDI mission provides precise measurements of forest canopy height and vertical structure with great potential for global carbon cycle modelling. Here we present recent development and calibration of ED-global (v1.0) and its evaluation simulations against heterogeneous sources of satellite observations and field measurements. ED-global estimates of vegetation carbon stocks and fluxes, vegetation distribution and structure will be examined across various temporal and spatial scales from seasonal to inter-annual and also from grid cell to biome. The developed ED-global will serve as base model of NASA’s GEDI mission to answer the key science questions: What is the carbon balance of Earth’s forests? And how will the land surface mitigate atmospheric CO2 in the future?

In this study, the changes in the occurrence of marine fog over the summer North Pacific in warmer sea surface temperature (SST) or increased CO2 climates were investigated based on atmospheric model simulations by using the fifth phase of the Climate Model Intercomparison Project (CMIP5) multimodel data. Initially, the marine fog representation in CMIP5 multimodels was briefly evaluated globally. We found that the simulated marine fog occurrence was represented relatively well in boreal summer but poorly in other seasons. The results indicated that the changes in the North Pacific high‐pressure system accompanied by changes in horizontal wind patterns control the changes in marine fog occurrence in the North Pacific. The magnitude of contrasting pair changes in marine fog occurrence in the western and eastern North Pacific are primarily determined by the magnitude of changes in the North Pacific high‐pressure system. Global‐scale changes in the vertical profiles of the atmosphere (stability changes) can also affect the marine fog changes. These changes in marine fog over the North Pacific were consistent among most CMIP5 models.

Synoptic-scale cyclonic vortices produce abundant rainfall in South Asia, where these low pressure systems (LPS) are traditionally categorized as monsoon lows, monsoon depressions, and more intense cyclonic storms. The India Meteorological Department has tracked monsoon depressions for over a century, finding a large decline in the number of those storms in recent decades; their tracking methods, however, seem to have changed over time and do not include monsoon lows, which can produce intense rainfall despite their weak winds. This study presents a fast and objective tracking algorithm that can identify monsoon LPS in high-resolution datasets with a variety of grid structures. A sensitivity analysis has been performed to select a set of atmospheric variables and their corresponding thresholds for optimal tracking of LPS. Approximately 250 combinations of variables and thresholds are used to identify LPS over roughly a decade (the training period) in each of four atmospheric reanalyses, and these combinations are ranked using a skill score that compares the reanalyses with each other and with a preexisting track dataset that was compiled by subjective identification of LPS. This procedure finds the streamfunction of the 850 hPa horizontal wind to be the best variable for tracking LPS. The streamfunction is smoother than the vorticity field and represents the complete non-divergent component of the wind even when the flow is not geostrophic, unlike the geopotential height or sea level pressure. Using this tracking algorithm, LPS statistics are then computed in five reanalysis products that each span at least 40 years, with a primary goal being to determine whether the large decrease in monsoon depressions seen in the India Meteorological Department track dataset since the 1970s can be found in any reanalysis. This trend assessment is particularly relevant for the ERA5 reanalysis, which extends back to 1950 and which contains explicit climate forcings. In addition to secular trends, this study assesses the decadal variation of LPS, as well as interannual changes in LPS activity that are associated with the El Niño-Southern Oscillation and the Indian Ocean Dipole.