If the university can be thought of as an incubator for ideas and thought leadership, then each department is a learning ecosystem unto itself. The IDEEAS (Inclusion, Diversity, and Equity in Earth and Atmospheric Sciences) Working Group formed organically in Cornell’s Earth and Atmospheric Sciences department as a grassroots group with a desire to improve the department ecosystem. Self-selected from the full cross-section of the department, our members comprise students, staff, researchers, faculty, and emeriti. IDEEAS is a non-hierarchical group within the very hierarchical setting of academia, and our work provides a model for disrupting traditional power structures while leveraging their influence to reimagine how an academic unit could and should function. IDEEAS is not a committee; we are a collective. We believe that, irrespective of rank or role, every member of the department community has the capacity to practice leadership. As such, we lead by action. Each IDEEAS project or initiative is organized around an action team, who collectively carry out a community-informed vision of the culture we would like to co-create with the rest of the department. Our commitment to collective leadership empowers constituencies (e.g., students, non-academic staff, post-docs) who have traditionally lacked a pathway to provide input or participate in department-level decision making. IDEEAS is developing formal channels of communication between the group and department leadership in an effort to develop a sustainable ecosystem that will outlive its founders. IDEEAS events combine community building and intentional learning opportunities to promote critical reflection and foster connections. Events included a well-attended kickoff party with facilitated conversation that drew 56 attendees (~40% of the department), and community conversations about implicit bias and structural racism. IDEEAS organizers have been critically responsive during ongoing COVID19 isolation, providing numerous opportunities for social connection and using the disruption as a catalyst to cultivate connection and build community resilience that will outlast the pandemic. We invite discussion and collaboration with those engaged in similar justice, equity, diversity, and inclusion work in the geosciences.

A unique event in 2020 was the nearly complete lack of ice cover on the shallow Kara Sea and Laptev Sea continental shelves at the end of October – a necessary antecedent to hypothesized continental-shelf clathrate methane releases’ runaway-warming feedback (not well-addressed in AR5). Although the current state of coupled Atmosphere/Ocean General Circulation Models (AOGCMs) is such that they cannot reliably predict resulting weather-pattern changes in detail, a potential lower-bound for the overall impact of such releases can be explored on a decadal scale; impact is an important factor in risk-assessment. Previous work* with a prescribed-ocean, atmospheric circulation model suggested a 0.01 C global temperature increase per gigaton of additional atmospheric methane burden. The inclusion of a coupled dynamic ocean model in the present work resulted in a tripling of that estimate to about 0.03 C global warming per gigaton increase in atmospheric methane burden. Additionally, much larger Arctic responses were observed when specifying model changes chosen to approximate increased cloud brightness over the Arctic ocean. In this work, the AR5 RCP8.5 GHG scenario was chosen as the baseline in order to most faithfully represent current and anticipated trends over the relatively short time period investigated here (i.e., 2020 – 2040). After an extended model spin-up, the model global mean temperature was constant for two decades of integration using a constant model-year (2019), and closely reproduced the mean warming rate reported for this time period in the AR5 data when run in baseline RCP8.5 transient-mode over the subject time period. As is typical for the AOGCM used in this work (AR5 GISS ModelE2.0 07.50.01), the modeled Arctic sea-ice losses were less than is currently observed in the real-world; hence, the results presented here are regarded as no more than a lower bound to the magnitude of expected climatic responses to such a release. *[essoar.org/doi/10.1002/essoar.10503094.1 (2019)]

Micro-scale models are important to assess processes in complex domains, for example cities. The most common data standard for atmospheric model output data are the CF-conventions, a data standard for netCDF files, but this standard is not adapted to the model output of micro-scale models. As a part of the project AtMoDat (Atmospheric Model Data) we want to develop a model data standard for obstacle resolving models (ORM), including the additional variables (i.e. building structures, wall temperatures) used by these models. In order to involve the micro-scale modeller community in this process, a web based survey was developed and distributed in the modeller community via conferences and email. With this survey we want to find out which micro-scale ORMs are currently in use, their model specifics (e.g. used grid, coordinate system), and the handling of the model result data. Furthermore, the survey provides the opportunity to include suggestions and ideas, what we should consider in the development of the standard. Between September 2019 and July 2020, the survey was accessed 29 times, but only 12 surveys were completed. The finished surveys refer to eight different models and their corresponding model information. Results show that these different models use different output formats and processing tools, which results in different model result handling routines. The participants suggested to use the netCDF data format and to provide information on model initialization, model settings and model input along with the model output data. This would enable an easier intercomparison between different models and repetition of model simulations. Standardized model output and variable names would also enhance the development of shared routines for the analysis of micro-scale model data and a better findability of the data with search engines. This survey will remain open with regular assessments of contents (i.e. November 2020, May 2021; https://uhh.de/orm-survey).

Urban Green Spaces (UGSs) are proving to be most important part of urban area of a city. These green spaces not only provide psychological comfort to humans but also affect heat impact on city to a vast level. In a developing country like India, where urban growth is happening in a very fast and haphazard manner a little consideration is given to Green Spaces in city. Such a study has been conducted over Lucknow metro-city of Uttar Pradesh state of India. In this study, the relation between Land Use\Land Cover (LULC) and Land surface temperature (LST) has been tried to find. Using Landsat-8 OLI data (Band 3, 4 & 5) and Maximum Likelihood Classification algorithm, 6 Land Use classes are obtained, which are “Built-up”, “Vegetation”, “Shrub”, “Water”, “Fallow Land” and “Other”. In addition to these 4 Land Cover Indices namely Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Barren Index (NDBaI) are also generated for same areas. LST is obtained using TIRS data (Band 10 & 11) and Split Window Algorithm by Radiative Transfer Equation. Ancillary data is used for digitization of 5 assembly constituencies (ACs) of Lucknow parliamentary constituency (PC) of Lucknow district namely, Lucknow Cantonment, Lucknow North, Lucknow East, Lucknow West and Lucknow Central has been done. River and Canal passing through these areas, are also considered in Urban Green Spaces as per Urban Atlas code 14100. 250m radius buffer is generated around Built-up pixels for analysis of impact of UGSs on LST. Cantonment and East Lucknow area having highest amount of UGSs in terms of “Vegetation”, “Shrub” & “Water” pixels due to presence of Forest area in both ACs. It is found that LST is positively related with all indices except for NDVI with strong negative correlation and R2 of 0.47 and highest R2 of 0.53 with NDBI. Among all 5 ACs best correlation between all 4 LC and LST values is found in Lucknow East AC with R2 > 0.64 for NDVI, NDWI and NDBI. Lucknow East AC is having least LST but there is very little difference between LST values of Built-up pixels having minimum UGSs present in 250m radius buffer around built-ups and Built-up pixels having no UGSs around. AC Lucknow Central is having 1 °C difference in LST values of such different Built-ups.

A slope at Barry Arm, in Alaska’s Prince William Sound, is deforming at a varying rate up to tens of meters per year above a retreating glacier and deep fjord that is a popular recreational destination. If the estimated 500 million cubic meters of unstable material on this slope were to fail catastrophically, the impact of the landslide with the ocean would produce a tsunami that would not only endanger those in its immediate vicinity, but likely also those in more distant areas such as the port of Whittier, 50 km away. The discovery of this threat was happenstance, and the response so far has been cobbled together from over a dozen existing grants and programs. Remotely sensed imagery could have revealed this hazard a decade ago, but nobody was looking, highlighting our lack of coordination and preparedness for this growing hazard driven by climate change. As glaciers retreat, they can simultaneously destabilize mountain slopes and expose deep waters below, creating the potential for destructive tsunamis. The settings where this risk might occur are easily identified, but more difficult to assess and monitor. Unlike for volcanoes, active faults, landslides, and tectonic tsunamis, the US has conducted no systematic assessment of tsunamis generated by subaerial landslides, nor has the US established methods for monitoring or issuing warnings for such tsunamis. The U.S. National Tsunami Warning Center relies on seismic signals and sea-level measurements to issue warnings; however, landslides are more difficult to detect than earthquakes, and the resultant tsunamis often would reach vulnerable populations and infrastructure before water level gages could help estimate the magnitude of the tsunami. Also, integrating precursory motion and other clues of an impending slope failure into a tsunami warning system has only been done outside the US (e.g Norway: Blikra et al., 2012). Barry Arm is a dramatic case study highlighting these challenges and may provide a model for mitigating the threat of tsunamis generated by subaerial landslides enabled by glacial retreat elsewhere.

Waterways such as the Hudson River play an integral role in agriculture, health, transportation, recreation, energy, and sustaining biodiversity. Although water pollution in New York Harbor has been extensively studied, the reduction of millions of commuters during the COVID-19 lockdown presents an unprecedented opportunity to study human impact on water quality. We used remote sensing data to assess how the COVID-19 lockdown impacted water quality in New York Harbor, particularly in areas near Combined Sewer Outfalls (CSOs). This technique has previously been used to measure water quality in the Hudson River. We used ACOLITE to process Landsat-8 and Sentinel-2 images from 2015-2020. The algorithms “t_nechad”, “spm_nechad”, and “kdpar_qaasw” were used to measure turbidity, and “chl_oc2”, “chl_oc3”, “chl_re_moses3b”, “chl_re_moses740”, and “chl_re_mishra” to measure chlorophyll concentration. After uploading processed images into SeaDAS, we extracted values from pixels corresponding to Department of Environmental Protection (DEP) field sites. At these sites, the DEP measures Total Suspended Solids and Chlorophyll A Concentration using optical turbidity sensors and fluorometers, respectively. By comparing pixel values with DEP data we determined that the chlorophyll algorithms did not produce accurate readings of chlorophyll concentration in New York Harbor. We focused on analyzing turbidity at five DEP sites, four of which were located around wastewater treatment plants, to assess any CSO-induced changes in water quality. The frequency of usable satellite data from 2020 was severely limited by cloudiness, so we combined Landsat-8 and Sentinel-2 turbidity measurements (R = 0.8685) to form time series for each site. We expected to see a decrease in turbidity during the lockdown period, due to a decrease in sewage from office buildings. However, turbidity strongly fluctuated throughout all years with no discernable temporal pattern, and we could not distinguish between 2020 measurements and seasonal patterns. Thus, preliminary analysis shows that there was no significant variation in water turbidity due to the COVID-19 lockdown.

In coral reef studies, mass spectrometry methods are widely applied to determine geochemical proxies in corals as a tool to evaluate seawater changes. As the coral grows, its skeleton forms annual bands similar to the growth rings found in trees. The density of the calcium carbonate skeletons changes as the water temperature, light, and nutrient conditions change. The elements stored within these bands can provide insight into the changing conditions of seawater over the entire lifetime of the coral, and serve as useful environmental records. Corals incorporate trace elements that can be precisely measured using high-resolution techniques, such as Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS). This analytical tool offers high levels of precision to determine the distribution of trace elements along the annual bands of coral skeletons. This approach can serve to monitor fixed-point time-series for water quality research, as well as large-scale observations in ocean science. Ultimately, this procedure can be applied to reconstruct past climate oscillation episodes and/or to quantify the impacts of marine pollution on coral reefs. The benefits of techno-scientific aspects of new and established mass spectrometry applications in coral reef research hold great promise that may continue to be improved in future studies. Given the current climate crisis, this issue requires accurate measurements to increase our understanding on the impacts that have become more frequent and intense.

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.

Concurrent temperature and precipitation extremes during Indian summer monsoon generally have signicant effects on agriculture, society and ecosystems. Due to climate change, frequency and spatial extent of concurrent extremes have changed, and there is a need to advance our understanding in this domain. Quantication of individual extremes (temperature and precipitation) during the summer monsoon season and its teleconnections to climate indices have been studied comprehensively. But, less attention is devoted to the quantication of concurrent extremes and its teleconnections to climate indices. In this study, concurrent extremes (dry/hot and wet/cold) based on mean monthly temperature and total monthly precipitation during the Indian summer season from 1951 to 2019 over the Indian mainland are investigated. Next, the study uses wavelet coherence analysis to unravel the teleconnections of the spatial extent of concurrent extremes to climate indices (Nino 3.4, WEIO SST and SEEIO SST). Results show that the frequency of wet/hot concurrent extremes has increased signicantly, while the frequency of wet/cold concurrent has decreased for the time window 1985 to 2019 relative to 1951-1984. Also, a statistically signicant increase (decrease) in the spatial extent exists in concurrent dry/hot (wet/cold) extremes during the July, August and September months. The ndings of this study could advance our understanding of changes in concurrent extremes during the Indian summer monsoon due to climate change.

Through the PolarTREC program that pairs US educators with field researchers in polar regions, our team has been collaborating on K-12 and undergraduate curriculum development and outreach activities on Arctic amplification of climate change. We have created new lesson plans and activities focused on how organic carbon from thawing permafrost in the Arctic is turned into carbon dioxide, a greenhouse gas that amplifies climate change. This presentation will cover our collaboration to bring this knowledge and experience to high school science students through classroom activities and projects. The focus will be laboratory activities designed for the chemistry classroom: use of spectrophotometry to assess degree of photobleaching in organic samples and evaluation of data from high resolution mass spectrometry to characterize complex organic mixtures. We will also review lessons learned from our efforts to promote enthusiasm for polar science within the general public and discuss the benefits of the PolarTREC program to researchers, educators, students, and the public.

Climate mitigation planning requires accurate information on forest carbon dynamics. Forest carbon monitoring and modeling systems need to step beyond the traditional Monitoring, Reporting, and Verification (MRV) framework of current forest cover and carbon stock. They should be able to project potential future carbon stocks with high accuracy and high spatial resolution over large policy-relevant spatial domains. Previous efforts have demonstrated the possibility and value of combining a process-based ecosystem model (Ecosystem Demography, ED), high-resolution (1-meter) lidar and NAIP data, field inventory data, and meteorology and soil properties in a prototype carbon monitoring and modeling system developed for the state of Maryland. Here we present recent work on expanding the Maryland prototype to a 10x larger domain, namely the Regional Greenhouse Gas Initiative (RGGI+) domain consisting of the states of Maryland, Delaware, Pennsylvania, New York, New Jersey, Rhode Island, Connecticut, Massachusetts, Vermont, New Hampshire, and Maine. The system expansion includes an updated version of the ED ecosystem model, improved initialization strategy, and expanded Cal/val approach. High-resolution wall-to-wall maps of current aboveground carbon, carbon sequestration potential, carbon sequestration potential gap, and time to reach sequestration potential are provided at 90m resolution across the RGGI+ domain. Total forest aboveground carbon sequestration potential gap is estimated to be over 2,300 Tg C for the RGGI+ region, about 1.5 times of contemporary aboveground carbon stock. States and counties exhibit variations in carbon sequestration potential gap, implying different policy planning for future afforestation/reforestation and forest conservation activities. Here we present the details of this new carbon monitoring and modeling system as well as regional results, including evaluations of our estimates against USFS Forest Inventory and Analysis (FIA) data, multiple wall-to-wall AGB maps, and state-wide and county-wide future carbon sequestration potential over time.

Why would hundreds of scientists from around the world freeze a ship in Arctic sea ice for an entire year, braving subzero temperatures and months of polar darkness? This may sound like a fictional adventure movie plot, but from September 2019 through October 2020, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) Arctic research expedition did just this. Currently, the Arctic is warming twice as fast as the global average (a phenomenon known as Arctic amplification) and due to a lack of observations, there is considerable uncertainty in climate models projecting the Arctic climate of the future. The MOSAiC expedition aims to better understand the changing Arctic climate system by gathering data from ground zero over a full seasonal cycle to augment satellite observation data. Using the expedition as an engagement hook, scientists and curriculum developers developed a high school earth science curriculum anchored by the phenomenon that climate scientists are actively trying to explain: Arctic amplification. The curriculum follows the model-based inquiry instructional framework where each lesson provides students with learning experiences (e.g., virtual reality tours of MOSAiC field sites, analyzing authentic Arctic satellite datasets) that relate back to the phenomena. Focusing on explaining natural phenomena provides an authentic context for students to learn and apply scientific understanding, which research shows can help engage students in NGSS scientific practices. Here we present an overview of the learning sequence using refinement of mental models throughout the unit and present preliminary results from pre-post assessments from two educator workshops (~100 teachers) that show that participants’ understanding of Earth’s climate system improved significantly after engaging with the curriculum. Based on these results, we expect this curriculum to be an important tool in engaging students in Earth’s systems thinking.

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.

Fronts are ubiquitous in the climate system. In the Southern Ocean, fronts delineate water masses, which correspond to upwelling and downwelling branches of the overturning circulation. A robust understanding of Southern Ocean fronts is key to projecting future changes in overturning and the associated air-sea partitioning of heat and carbon. Classically, oceanographers define Southern Ocean fronts as a small number of continuous linear features that encircle Antarctica. However, modern observational and theoretical developments are challenging this traditional framework to accommodate more localized views of fronts [Chapman et al. 2020]. In this work, we present two related methods for calculating fronts from oceanographic data. The first method uses unsupervised classification (specifically, Gaussian Mixture Modeling or GMM) and an interclass metric to define fronts. This approach produces a discontinuous, probabilistic view of front location, emphasising the fact that the boundaries between water masses are not uniformly sharp across the entire Southern Ocean. The second method uses Sobel edge detection to highlight rapid changes [Hjelmervik & Hjelmervik, 2019]. This approach produces a more local view of fronts, with the advantage that it can highlight the movement of individual eddy-like features (such as the Agulhas rings). The fronts detected using the Sobel method are moderately correlated with the magnitude of the velocity field, which is consistent with the theoretically expected spatial coincidence of fronts and jets. We will present our python GitHub repository, which will allow researchers to easily apply these methods to their own datasets. Figure caption Two methods for interpretable front detection. Solid lines represent classical fronts. (a) The “inter-class” metric, which indicates the probability that a grid cell is a boundary between two classes. The classes are defined by GMM of principal component values (PCs) derived from both temperature and salinity. The different colors indicate different class boundaries. (b) Sobel edge detection: approximately the magnitude of the spatial gradient of the PCs divided by each field’s standard deviation, which highlights locations of rapid change.

It is important that we prepare tomorrow’s scientists, decision makers, and communities to address the societal impacts of a changing climate. In order to respond to, manage, and adapt to those changes, citizens of all ages need accurate, up-to-date information, knowledge of the sciences, and analytical skills to make responsible decisions and long-term resiliency plans regarding these challenging topics. The Climate Literacy and Energy Awareness Network (CLEAN, http://cleanet.org) is 1) providing teaching resources for educators through the CLEAN Collection and pedagogical support for teaching climate and energy science; and 2) facilitating a professionally diverse community of climate and energy literacy stakeholders, called the CLEAN Network, to share and leverage efforts to extend the reach and effectiveness of climate and energy education. This presentation will provide an overview of the CLEAN web portal and techniques we have used to market it. We will showcase the CLEAN Collection, which is comprised of 700+ resources (curricula, activities, videos, visualizations, and demonstrations/experiments) that have been reviewed for scientific accuracy, pedagogical effectiveness, and technical quality. Recent activities of the CLEAN Network will be highlighted. We will present findings from our web analytics work, which monitors visitor use of the CLEAN web portal. Through analytics data, we will show evidence of successful CLEAN marketing efforts. The results of our recent pop-up survey, which has been completed by CLEAN visitors from six continents, will also be discussed. Survey results will provide detailed information about how our audiences use the web portal. We anticipate that our insights from the CLEAN network can aid other climate and energy education programs in effectively increasing the visibility of their vital work.

Open source in-situ environmental sensor hardware continues to expand across the geosphere to a variety of applications. These systems typically perform three fundamental tasks: sample sensors at a specified time or period, save data onto retrievable media, switch power to components on and off in between sample cycles to conserve battery energy and increase field operation time. This is commonly accomplished through integrating separate off-the-shelf components into the desired system such as: power relays, SD card hardware, Real-Time Clocks (RTCs), and coin cell batteries. To enable faster prototyping, the Openly Published Environmental Sensing Lab abstracted all of these requirements into a single PCB that can be dropped into any project to achieve these commonly-required capabilities. The hardware is laid out in a “Feather” form factor, a popular configuration in the open-source hardware community, to easily mate with other industry standard products. The onboard RTC acts as an alarm clock that wakes a user-attached micro controller from low-power sleep modes in between sample cycles. By integrating all these components into a single PCB, we save cost while significantly reducing physical system size. The design as well as a suite of code functions that enable the user to configure all the Hypnos board features are detailed. For more information, please visit open-sensing.org/projects.

High altitude ecosystems react sensitively to climatic changes. To evaluate hydroclimate dynamics and ecosystem responses since the late glacial in a typical glacier influenced, high-altitude, lacustrine ecosystem, we analysed concentrations and hydrogen isotopic composition of different aquatic (alkenones, nC23:1, HBIs) and terrestrial (nC31) biomarker groups in Hala Hu. The lake is located on the northern margin of the Tibetan Plateau, i.e. in the transitioning zone between the East Asian summer monsoon (EASM) and westerlies influence. According to species association of ostracods, it experienced a lake level rise of ca. 45 m during the late glacial and early Holocene and reached its highest stand during early mid-Holocene (8 - 6.1 cal. kyr BP) followed by lake level decline. The organic geochemical data reveal asynchronous responses of the aquatic and terrestrial ecosystem in Hala Hu and its catchment to hydroclimatic forcing. We interpret δD values of terrestrial biomarkers as indicative for δD of growing season precipitation, with a potential contribution of surface meltwater runoff. Hence, most negative values during 9 - 7 cal. kyr BP indicate changes in moisture source, possibly related to EASM intensification. In contrast, rapid appearance and disappearance of aquatic biomarkers and shifts in theirs δD values between ca. 8 – 5 kyr BP, revealed distinct mid-Holocene aquatic regime-shifts and changes in lake hydrology. Maxima of terrestrial biomarker concentrations also occurred between 8 - 5 cal. kyr BP, suggesting expansion of terrestrial grasses and eventually increased biomarker flux via surface runoff. These results give evidence that neither shifts of vapour source nor increased precipitation amounts were the major triggers of ecosystem responses. Instead we hypothesize, that warmer temperatures during the Holocene climate optimum, receding glaciers and consequently increased meltwater discharge into the lake, caused the pronounced ecological changes. The rapid regime-shifts within phytoplankton communities in the mid-Holocene illustrate the sensitivity of high altitude mountain ecosystems within the marginal Asian monsoon region to climatic changes. This is of relevance in context with future changes as response to global warming.

On 11 March 2020, the World Health Organization (WHO) declared Coronavirus disease (COVID-19) as a pandemic. The announcement had a cascading effect as countries around the world rushed to declare various states of emergencies. Canada was no exception. All Canadian provinces and territories implemented some health emergency measures to check the spread of COVID-19. This provided an opportunity to study the changes in seismic vibrations registered by the land-based seismic stations before, during, and after the lockdown. I analyzed continuous seismic data for 6 Canadian cities: Calgary (Alberta), Edmonton (Alberta), Montreal (Quebec), Ottawa (Ontario), Toronto (Ontario), and Yellowknife (Northwest Territories). These cities represent the wide geographical spread of Canada. The source of data for the study was seismic stations run by the Canadian National Seismograph Network (CNSN). Data available freely on the Incorporated Research Institutions for Seismology (IRIS) website was used. Python and ObsPy were used to load and convert raw data into Probabilistic Power Spectral Densities (PPSDs). The seismic vibrations in the PPSDs that fell between 0.1 HZ and 20 HZ were extracted and averaged for every two weeks period to determine the trend of seismic vibrations. The lockdown had an impact on seismic vibrations in almost all the cities I analyzed. Except for Ottawa, the seismic vibrations decreased between 14% - 44% with the biggest decrease in Yellowknife in the Northwest Territories. In the 3 densely packed cities of the population over 1 million - Toronto, Montreal, and Calgary, the seismic vibrations dropped by over 30%. In the case of Ottawa, the seismic vibrations increased by 8%. As not all seismic stations were equally close to the cities, they were not equally sensitive to changes in human activities. Furthermore, while lockdown happened in all the cities selected for the study, the strictness enforced and the participation of people in the lockdown varied. Many cities extended the lockdown without any change while others extended the lockdown with a loosening of restrictions. All these differences induced variations in the study. Finally, a comprehensive online training module was created using Jupyter notebooks to allow researchers to analyse lockdown data from other seismic stations.