Cloud microphysics plays important role on the storm dynamics. To investigate the impact of advanced microphysics schemes, using single and double moment (WSM6/WDM6) schemes, numerical simulations are conducted for a severe convective system that formed over the Korean Peninsula. Spatial rainfall distribution and pattern correlation associated with the convective system are improved in WDM6. During developing stage of the system, the distribution of total hydrometeors is larger in WDM6 compared to WSM6. Along with mixing ratio of hydrometeors (cloud, rain, graupel, snow and ice),number concentration of cloud and rainwater are also predicted in WDM6. To understand the differences in vertical representation of cloud hydrometeors between the schemes, rain number concentration (Nr) from WSM6 is also computed using particle density to compare with Nr readily available in WDM6.Varied vertical distribution, and large differences in rain number concentration, rain particle mass are evident between the schemes. Inclusion of number concentration of rain and cloud, CCN along with mixing ratio of different hydrometers have improved the storm morphology in WDM6. Inorder to investigate the cloud-aerosol interactions, numerical simulation has been conducted using an increase in CCN(aerosol) in WDM6 which has shown an improved rainfall distribution with intense hydrometer distribution. The latent heating (LH) rates of different phase change processes (condensation, evaporation, freezing, melting, sublimation and deposition) are also computed using various transformation rate terms in the microphysics modules. It is inferred that the change in aerosol has increased the LH of evaporation and freezing and affected the warming and cooling processes, cloud vertical distribution and subsequent rainfall.

Computational models of flood inundation, precipitation-runoff, and groundwater have traditionally been developed within their individual scientific fields. Increasingly, there is a desire and need to couple these models into an integrated system to solve complex problems and aid studies in water resources; for example, the impact of land-use change or climate variability on surface and subsurface flow in watersheds could be simulated by linking a precipitation-runoff model to a groundwater model. In this collaborative project, we factored the U.S. Geological Survey (USGS) Precipitation-Runoff Modeling System (PRMS) into four independent process components: surface, soil, groundwater, and streamflow. Each process component, written in Fortran, has a Basic Model Interface (BMI), which gives the model a standardized set of functions allowing it to be queried, modified, and updated in time. When compiled through Cython, the BMI-equipped components become Python packages, and can then be imported into Python with the Python Modeling Toolkit (pymt), which provides a framework and tools for running and coupling models. The addition of a Python interface for PRMS makes it easier to use, especially for researchers lacking experience in compiling and linking Fortran code, and pymt provides an easy collaboration platform for developing and prototyping complex integrated models. In the next phase of the project, we developed a Python package for a data service to access gridMET climatological data distributed over the web by the University of Idaho. The data service has a BMI, so it can be used directly with pymt for model-data coupling. Finally, using pymt, we coupled the PRMS process components and drove the coupled system with climate data from the gridMET data component. As a simple test, we were able to reproduce the results from running the standalone PRMS model. (The figure shows that outflow for the last stream segment in the coupled model system equals that from standalone PRMS.) This project was a fruitful collaboration between USGS and University of Colorado researchers, showing that research and operational models written in different languages can be wrapped in Python and coupled in an integrated modeling framework, making them more easily accessible for a new generation of researchers.

Like most NSF-funded Research Experience for Undergraduates (REU) programs, the REU on Sustainable Land and Water Resources (REU SLAWR) had to choose between a virtual experience in summer 2020, or cancellation of the program due to the Covid19 pandemic. The REU SLAWR was restructured into a modular online program designed to meet the same program goals that have shaped the REU SLAWR over the past 11 years. Using program evaluations from 2011-2019, the authors will compare the results from 2020 to build knowledge on how the REU experience in 2020 was differently structured to meet the need for a virtual program, the impact this had on participant and mentor outcomes, and what can be learned for future REU programs. This provides valuable information for creating accessibility to the REU experience. The REU usually takes place at three locations (Salish Kootenai College, MT; at the Univ. of Minnesota in Mpls. and Duluth, MN). The program is centered on tribally-focused Community-based Participatory Research (CBPR), and is a place-based REU. The REU SLAWR has always incorporated a virtual experience designed to create cross-team socialization, community-building, and widen participants’ interest and knowledge about projects incorporating tribal CBPR. Summer 2020 immersed students, mentors, and tribal partners in a virtual learning environment. The PIs explored new methods for running an REU with virtual technology that will be incorporated in future programs for richer cross-team collaboration. A focus of the REU SLAWR has been to increase participants’ abilities to work on diverse teams. Collaborating virtually across distances is a skill all researchers need. Training in this can benefit next-generation researchers and STEM workers. One aspect of concern and interest is the impact of redesigning the research projects to make them possible to conduct in a virtual space. The projects were fundamentally different than previous years, with many focusing on using pre-existing data. While there were negative impacts in some aspects of building research skills (i.e., little exposure to field or lab work), other aspects (i.e., computational modeling, communicating science) showed gains. The authors explore both the limits and possibilities inherent in virtual collaboration in research for undergraduate students.

A fire-spotting parameterization was developed for the WRF-Fire component of the WRF model version 4.0.1. The parameterization uses a Lagrangian particle transport framework and is coupled to the fire component of the WRF-ARW model as an independent Fortran module. When fires are active, the fire-spotting module identifies areas at risk of fire spotting by modeling transport and physical processes of individual firebrands released from fire locations. Firebrands are released at varying heights, from locations with higher emission potential, defined as a function of fire rate of spread and fuel load. Firebrands are transported with the atmospheric flow, and physical properties (temperature, mass, and terminal velocity) are updated at the default model timestep. The particles may either burnout before settling or deposit at a grid point when carried below a specified height threshold. The number and spatial distribution of deposited firebrands correspond to the flow-dependent risk component of new fire ignitions due to fire spotting. The flow-dependent component is then combined with the risk associated with local fuel properties (load and moisture) to yield the fire spotting spatial likelihood. In this presentation, the fire-spotting parameterization is assessed through a qualitative analysis of wildfires in Colorado. Uncertainties in fire ignition observations, used to initialize fires in the WRF-Fire model, often limit the ability to accurately model fire area, which in turn controls the firebrands’ emission location. Limited spotting observations are also a challenge to an objective verification of the module skill. We expect that the most recent remote sensing products will improve the representation of surface properties and accuracy of ignition parameters for WRF-Fire, which will directly transfer to the fire-spotting module capability. Direct enhancements to the parameterization may be incorporated into the module as laboratory experiments and field campaigns provide data to improve our ability to model firebrands’ initial properties (e.g. firebrand size and ejection height) and physical processes (burnout and terminal velocity).

Extreme environmental events have become a major interest of ecologists. Commonly, extreme climatic events are identified based on “changes in the mean conditions” over a discrete period with respect to the longer-term climatology. In this study we aim to: 1) define a different type of extreme event, i.e. weather extreme events: an event with extreme deviation from an expected value (calculated based on past weather conditions) and 2) quantify ecosystem resistance, recovery, and resilience in response to these shock events based on changes in net ecosystem productivity (NEP) measured over 16 years (2004 – 2019), in a montane mixed forest in Switzerland (CH-LAE, Lägeren). In addition to the identification of the physiological extreme events, we test the hypothesis that extremes associated with continuously varying environmental conditions can modify the physiological functionality of a forest ecosystem. We calculated weather extreme;based on half-hourly measurements of atmospheric water demand (i.e. vapor pressure deficit, VPD) measured alongside eddy covariance flux measurements. Between 2004 and 2019, we found 185 such physiological extreme events (VPD-extreme), ranging from one to seven days, that occurred about 27 % in spring and 68 % in summer. On average NEP decreased by 25% during these VPD-extreme days compared to the normal-VPD day before, resulting in mean resistance (NEPextreme/NEPpre-extreme) of 0.75. Mean recovery (NEPpost-extreme/NEEextreme) was about 0.85, indicating about a 15% decrease in NEP on days after the extreme events compared to before. There was no significant trend in resistance, recovery, and resilience over the 16 years.  Finally, decreased functionality during these VPD-extreme days events confirms our hypothesis. Our approach of looking at the forest response to extreme events is independent of “changes of mean conditions from long-term climatology” and focuses on the ability of the ecosystem to maintain functionality within the realm of “continuous environmental variability”. Identification of physiologically-relevant climatic extremes and testing the legacy effect from those events is a crucial requirement for understanding the future response of forests to climate change.

Being an agent of environmental hazard, wild fire is a major concern, especially in Mediterranean countries, where warm and dry summers may lead to high levels of vegetation stress. Vegetation fire, whether caused naturally or by humans, its widespread and occurrence depends on land cover (LC) type and state, climate and meteorological conditions, land use. The main concern of the study is quantification the physical processes related to vulnerability of biomass burning across the climatic gradient for Eastern Mediterranean Europe (Bulgaria) using satellite information, ground observations and model outputs for land surface state. Sixteen years (2004-2019) satellite data from LSASAF FRP-PIXEL product at MSG resolution were processed in order to determine the distribution of vegetation fires for the most risky period July-September. The work examines the relationships of wildfire statistics (number of fires and radiative energy released) to surface temperature (according LSASAF LST product), and soil moisture availability, SMA (according SVAT model) as energy-moisture related climate variables. Regression analyses for the main aggregated vegetation types (forest, shrubs, cultivated) reveal statistically significant dependences: - In short-term climatic aspect: a high correlation between monthly mean positive LST anomalies and fire activity as well as negative correlation between SMA anomalies and fire activity; - In long-term climatic aspect: the contribution of different LC types for fire activity is considered. Identified specific fire regime with location of spots with highest fire activity provides knowledge about fire-climate relations on a regional scale, which is important to define more risky regions as well as for projection the effects of global environmental change.

Southern California has seen a resurgence of winegrowing regions in the past few decades, however the future of winegrape climatic suitability in the area has not been exhaustively explored. This study evaluated the future climate suitability for the cultivation of winegrape and potential global warming impacts on southern California’s winegrowing regions through a series of high-resolution surface air temperature and precipitation projections obtained with the WRF-SSIB regional climate model. Results reveal that by mid-21st-century the surface air temperature will increase by approximately 1.2 °C, while average precipitation will decrease by as much as 11% in the southern winegrowing areas under the Intergovernmental Panel on Climate Change high greenhouse-gas emissions scenario. Evaluation of bioclimatic suitability indices indicate increases in heat accumulation for all major winegrowing areas; including an increase of about 10% in growing-degree day, while morning low temperatures in September may experience increases of approximately 11% in the future, thus impacting negatively the ripening stage of grapevines and leading to changes in wine composition and quality. Additionally, the extent of areas classified under the cool to warm climate suitability categories could decrease by nearly 42% in the study area by 2050. Conditions in southern California are already warm and dry for viticulture and continuing heat accumulation increase, along with rainfall reduction, could potentially place additional stress to winegrape crop in the area, including advanced phenological timing and moisture deficit stress that could lead to decreases in yield. The projected decline in viticulture suitability highlights the need for adaptive capacity within this sector to mitigate the impacts of global warming. Possible mitigating strategies include planting hotter climate grape varieties, moving vineyards to regions that are more suitable in the future, and adopting dry-farming techniques.

The Space Physics Data Facility (SPDF https://spdf.gsfc.nasa.gov), as the non-solar NASA Heliophysics active final archive, works with current operating missions and the heliophysics community to ingest, preserve and serve a wide range of past and current public science-quality data from the mesosphere into the furthest reach of deep-space exploration. SPDF facilitates scientific analysis of multi-instrument and multi-mission datasets to enhance the science return of the many missions. SPDF develops and maintains the Common Data Format (CDF) and the associated ISTP/SPDF metadata guidelines. SPDF services include CDAWeb, which supports both survey and burst mode data with graphics, listings and data superset/subset functions. All public data held by SPDF are also available for direct file download by HTTPS or FTPS links from the SPDF home page (https://spdf.gsfc.nasa.gov). SPDF is currently receiving and serving from missions including Parker Solar Probe, MMS, Van Allen Probes, THEMIS/ARTEMIS, GOLD, ACE, Cluster, IBEX, Voyager, Geotail, Wind and many others, and >120 Ground-Based investigations. SPDF recently added support for ARASE/ERG and MAVEN as supplementary access at the requests of those missions, and is expecting Solar Orbiter and ICON data. SPDF also operates the multi-mission orbit displays and query services of SSCWeb and the Java-based 4D Orbit Viewer, as well as the Heliophysics Data Portal (HDP) discipline-wide data inventory and access service, and OMNIWeb and COHOWeb for near-Earth and deep-space solar wind plasma, magnetic field, and energetic particle database, respectively.

Basaltic melts are produced when convection adiabatically brings deep and hot mantle to lower pressures. Such primary melts were extracted from the mantle of Mars, crystallized near the surface and progressively built the Martian crust. This process displaced a large fraction of the heat producing elements from the mantle to the crust and created an insulating layer that slowed down further cooling of the mantle. The complex crust-mantle system controlled many aspects of the geologic history of Mars, including the development of an atmosphere and whether conditions favorable to life could have existed. Our knowledge of the mineralogy, chemical composition and physical properties of the crust of Mars is rapidly expanding. Global geodynamical models can be used to interpret the available data and constrain the processes of crust-mantle differentiation. However, existing models still treat melting in a simplified way. For example, the degree of melting is often assumed to increase linearly above the solidus temperature, while the density of the residue is assumed to decrease linearly. Calculating the density of the residual mantle more accurately is critical because the compositional buoyancy that develops during partial melting fundamentally modifies mantle dynamics. Here, we present an improved parametrization of partial melting of the Martian mantle, which will be combined with the convection code Gaia. We created a new empirical model of melting that calculates the composition of the extracted melts and, when combined to thermodynamic models (e.g., Perple_X), the density of the corresponding residual mantle. Another advantage of the new melting parametrization is that the major-element composition of partial melts can be tracked and used to constrain the petrogenesis of surface rocks. Preliminary results will be compared to available Martian rocks believed to represent primary mantle melts or melts affected by minor fractional crystallization.

Policies to regulate severe surface ozone pollution in cities in India are challenging to develop, due to the complex dependence on precursor emissions of volatile organic compounds (VOCs) and nitrogen oxides (NOx), non-linear chemistry leading to ozone formation, and very limited spatial and temporal surface air quality monitoring. Ratios of space-based observations of formaldehyde (HCHO), an intermediate oxidation product of VOCs, and nitrogen dioxide (NO2) have been used to characterize the sensitivity of surface ozone production to precursor emissions of VOCs and NOx, but interpretation of these depends on the local oxidation regime. Here we develop an improved approach in which we discretize the data into background HCHO due to methane and other long-lived VOCs (regression intercept) and the local relationship (regression slope) between HCHO associated with reactive VOCs and NO2. We apply this to TROPOMI HCHO and NO2 tropospheric columns oversampled to higher spatial resolution than the native pixel resolution of the instrument over the ten most populous cities in India. We use GEOS-Chem to characterize the ozone production regimes and then apply this updated interpretation of the relationship between HCHO and NO2 to the oversampled TROPOMI columns to identify the most effective strategies for regulating ozone and whether these should vary seasonally and spatially.

During recent droughts in North Carolina, various audiences have articulated needs for information that explains current or anticipated impacts, droughts’ geographic extent and timing, and how the State monitors drought. This is despite there being a regular process in place to evaluate statewide conditions and seemingly abundant information available through federal, state, and local agency websites; media outlets; and other channels. This presentation provides findings from a research project designed to improve the availability, understandability, and usability of drought communications products for North Carolina audiences, focusing on the US Drought Monitor map of North Carolina as an example. The North Carolina Drought Management Advisory Council (DMAC) technical committee has met weekly to assess drought conditions since the 1990s and has recommended the state’s drought designations to the US Drought Monitor since 2000. The DMAC recommendations typically align with the weekly USDM map. Through surveys, focus groups, usability studies, and other engagement methods, we collected information from groups such as extension agents and water utility staff about 1) their communications preferences - resources that are concise, easily readable, and readily shareable through email, listservs, and social media - and 2) infographic prototypes created to address those preferences. User feedback on the prototypes informed iterative refinements to their content and design and provided information about their potential use for communications and management decisions. Ultimately, understanding the monitoring process and how drought designations are made was a key factor affecting the extent to which extension and other communication professionals apply, share, and value the information produced by monitoring groups and scientific agencies. This research suggests that addressing transparency questions can support efforts to communicate complex problems, such as drought.

Endogeneity or reverse causality can lead to biased estimation of effects and to inaccurate interpretations of cause and effect water-use relationships. However, biased estimations of the effects of designed interventions on water–related behaviour are rarely discussed. This study investigates the endogeneity of psychological factors in water-related behaviour using an instrument variable (IV) approach. Data from eight household water treatment (HWT) studies in Asia, Africa, and South America are utilized. A combination of several socio-economic characteristics, such as education and accessibility, is used as a control variable and three psychological factors, i.e., perception of risk, attitude, and social norms, are used as predictors of the adoption of HWT. Variables related to country level institutional quality are used as the IVs to remove the effects of reverse causality of households having adopted HWT on the psychological factors. Results show that psychological factors and HWT adoption indeed co-evolve, resulting in the emergence of reverse causalities, indicating that endogeneity exists in the water-related behaviour. Institutional quality indicators are found to be valid instruments for psychological factors such as attitude and norms towards HWT, but not households’ perception of risk in context of HWT. This suggests that institutional performance “heavily” influences households’ attitude and norms regarding water-related behaviour. The effects of households’ attitude and norms on HWT behaviour are underestimated by 59 and 40%, respectively, if the feedback effects of adoption behaviour on psychological factors are not considered. Further, households’ experience of using water-related technologies and external nudges, such as from policy makers, are more important drivers of the behaviour compared to risk perception. Despite the challenge of finding valid instruments for psychological factors, the study recommends that the effects of reverse causality addressed here need to be considered in future water-related behavioural studies. The conventional regression analysis approaches that ignore such effects are no longer suggested for analysing water-related behaviour.

Marine microplastics (< 5 mm) pollution is a growing problem affecting coastal communities, marine ecosystems, marine life and human health. Microplastics have been detected throughout the entire global ocean, and it is hard to escape news of microplastics debris in our oceans. Despite the growing awareness, the management of marine microplastics data, from large-scale surveys along the coast and open ocean, to effects of microplastics on planktonic communities, lags far behind the needs of the scientific, education, and decision maker communities. The lack of large-scale, long-term comprehensive data on microplastics makes it difficult to completely understand the sources, distribution and impacts of microplastics, as well as forecasting their trajectory and aggregation under current and future climates. The National Oceanic and Atmospheric Administration’s (NOAA) National Centers for Environmental Information (NCEI), which is regarded as the nation’s environmental data steward, is spearheading efforts to create a global database on microplastics. As a first step, we are currently collating microplastics data from across the world into a primary marine debris data warehouse. The ultimate goal is to develop a one-stop repository where data on all types of microplastics are aggregated, archived, and served to the community in a consistent and reliable manner. Together with other NCEI databases, such as the Global Ocean Current Database, World Ocean Database, and the Surface Underway Marine Database, stakeholders will be able to access and assess data for their needs, thereby enhancing our capacity to understand and manage the problem of microplastics pollution. The microplastics database will be freely accessible and maintained with newly received data from global stakeholders.

Stream water temperature (T) is a variable of critical importance and decision-making relevance to aquatic ecosystems, energy production, and human’s interaction with the river system. Here, we propose a basin-centric stream water temperature model based on the long short-term memory (LSTM) model trained over hundreds of basins over continental United States, providing a first continental-scale benchmark on this problem. This model was fed by atmospheric forcing data, static catchment attributes and optionally observed or simulated discharge data. The model achieved a high performance, delivering a high median root-mean-squared-error (RMSE) for the groups with extensive, intermediate and scarce temperature measurements, respectively. The median Nash Sutcliffe model efficiency coefficients were above 0.97 for all groups and above 0.91 after air temperature was subtracted, showing the model to capture most of the temporal dynamics. Reservoirs have a substantial impact on the pattern of water temperature and negative influence the model performance. The median RMSE was 0.69 and 0.99 for sites without major dams and with major dams, respectively, in groups with data availability larger than 90%. Additional experiments showed that observed or simulated streamflow data is useful as an input for basins without major dams but may increase prediction bias otherwise. Our results suggest a strong mapping exists between basin-averaged forcings variables and attributes and water temperature, but local measurements can strongly improve the model. This work provides the first benchmark and significant insights for future effort. However, challenges remain for basins with large dams which can be targeted in the future when more information of withdrawal timing and water ponding time were accessible.

The use of an objective seasonal forecast procedure, defined as a traceable, reproducible, and well-documented set of steps that allows the quantification of forecast quality, are preferred and recommended by the World Meteorological Organization in their recent seasonal forecast guidance. However, National Meteorological Services (NMS) in African countries have been issuing seasonal rainfall forecasts using a subjective consensus process, which is based on meteorologists’ experiences using Global Producing Center’s (GPCs) outputs and other available information. A systematic general objective approach named as NextGen (Next Generation) forecasting system is being developed for some East African countries as part of implementing or strengthening ENACTS (Enhancing National Climate Services; https://iri.columbia.edu/resources/enacts/) initiative as well implementing Columbia University’s World Project “Adapting Agriculture to Climate Today, for Tomorrow” (ACToday; https://iri.columbia.edu/actoday/) project. This new forecast system is based on a calibrated multi-model ensemble (CMME) process using state-of-the-art general circulation models (GCM) from the North American Multi-Model Ensemble project. A canonical-correlation-analysis-based regression is used to calibrate the raw outputs from the GCMs; then the individually-calibrated GCMs are combined with equal weight to make a final CMME prediction. In addition to traditional tercile probability forecasts, NextGen also provides a more flexible format that enables users to extract information for those parts of the forecast distribution of the greatest interest to them in the decision-making process. Therefore, NextGen enables NMS to generate and deliver targeted climate information products relevant to the needs of decision-makers at multiple levels. The NextGen forecast system has so far been implemented in Ethiopia, Rwanda, Zambia, Malawi and Tanzania, and planned to be implemented in more countries in the near future. In this study, we describe the co-design, co-development, and skill assessment of this NextGen system.

Previous study has shown that atmospheric conductivity variations are larger in amplitude than can be explained by current models. Several possible explanations for these variations have been proposed. However, recent conductivity research has been sparse, and no definitive explanations have been found. The latest iteration of the Undergraduate Student Instrument Project (USIP) at University of Houston seeks to add to previous work and explore possible contributing factors to these unexplained variations. To achieve this goal, the Conductivity team within USIP is designing and constructing an instrument that will be launched in Alaska. The data collected will be compared to measurements by other instruments launched during the same project. These include instruments studying microplastics, high energy particles, VLF waves, and gaseous compounds. In addition to contributing to the effort to understand the global electric circuit, the Conductivity team hopes to develop a low-cost kit that can be used by school groups to collect their own conductivity data. In the face of the COVID-19 pandemic, this student-led research project has overcome the challenges of distance, disrupted schedules, and uncertain funding by fostering a learning and working environment that can adapt to a variety of situations. In response to these challenges, USIP shifted towards the virtual resources that were already in use as a supplement to in-person work. The team uses Microsoft Teams and Zoom for virtual meetings, Slack for regular communication between members, and email to regularly coordinate with resources outside of the students in the research group. Diligent communication and adaptable budget, construction, and organization planning have been critical to maintaining the momentum of this project. Although the future remains uncertain, the Conductivity team continues to hope and prepare for the originally planned March 2021 launch.

The University of Houston’s USIP (Undergraduate Student Instrument Project) Remote Sensing team is designing and building an airborne LiDAR with the intention of using it to 3D map landslides and for possible mineral exploration. The maps will be used to conduct landslide analyses and failure predictions while the magnetometer feedback will be used to determine the presence of possible metallic minerals in the area. The LiDAR will employ the use of two lasers with different output wavelengths; 1550nm will be used for typical terrain mapping and 532nm will be used for snow depth reading to extrapolate the underlying terrain characteristics. Trade studies are currently underway for the lasers, sensors, IMU’s and magnetometers. It is planned for the LiDAR to collect data near Fairbanks, Alaska, with further research into potential study sites being conducted. The scan pattern is still being decided on with the most likely option being a circular scan for the 532nm laser and a zig zag pattern for the 1550nm laser both set at a maximum scan angle of 20 degrees. The Remote Sensing team has been facing unforeseen obstacles due to the nature of the COVID-19 pandemic. Upon initial lockdown, weekly scheduled in person meetings and lab work were prohibited. After the first week, though, new mediums of communication were established. The USIP group decided to conduct online meetings through Microsoft Teams and use Slack for text-based communication outside of meetings. Unfortunately, lockdown and COVID chaos brought psychological issues to group members that can be difficult to overcome. This included high stress levels caused by the chaotic events as well as isolation-induced depression. After some deliberation amongst all USIP groups, it was decided to occasionally hold non work-related meetings through Microsoft Teams. This reduced some of the isolation depression by relaxing, talking, and eating pizza. After lockdown restrictions were lifted, the university had begun preventing the previous number of students from being in the lab at once. On top of that, individuals were understandably hesitant to go to the lab, often opting out. Despite this, small groups of USIP students have been going to the labs to clean, disinfect, and get the lab ready for work. New lab procedures have also been created to adhere to social distancing norms.

The InSight mission [1] landed in November 2018 in the Elysium Planitia region [2] bringing the first geophysical observatory to Mars. Since February 2019 the seismometer SEIS [3] has continuously recorded Mars’ seismic activity, and a list of the seismic events is available in the InSight Marsquake Service catalog [4]. In this study, we predict present-day seismic velocities in the Martian interior using the 3D thermal evolution models of [5]. We then use the 3D velocity distributions to interpret seismic observations recorded by InSight. Our analysis is focused on the two high quality events S0173a and S0235b. Both have distinguishable P- and S-wave arrivals and are thought to originate in Cerberus Fossae [6], a potentially active fault system [7]. Our results show that models with a crust containing more than half of the total amount of heat producing elements (HPE) of the bulk of Mars lead to large variations of the seismic velocities in the lithosphere. A seismic velocity pattern similar to the crustal thickness structure is observed at depths larger than 400 km for cases with cold and thick lithospheres. Models, with less than 20% of the total HPE in the crust have thinner lithospheres with shallower but more prominent low velocity zones. The latter, lead to shadow zones that are incompatible with the observed P- and S-wave arrivals of seismic events occurring in Cerberus Fossae, in 20° - 40° epicentral distance. We therefore expect that future high-quality seismic events have the potential to further constrain the amount of HPE in the Martian crust. Future work will combine the seismic velocities distribution calculated in this study with modeling of seismic wave propagation [8, 9]. This will help to assess the effects of a 3D thermal structure on the waveforms and provide a powerful framework for the interpretation of InSight’s seismic data. [1] Banerdt et al., Nat. Geo. 2020; [2] Golombek et al., Nat. Comm. 2020, [3] Lognnoné et al., Nat. Geo. 2020, [4] InSight MQS, Mars Seismic Catalogue, InSight Mission V3, 2020, https://doi.org/10.12686/A8, [5] Plesa et al., GRL 2018, [6] Giardini et al., Nat. Geo. 2020, [7] Taylor et al., JGR 2013, [8] Bozdag et al., SSR 2017, [9] Komatitsch & Tromp, GJI 2002.