Floods are convincingly the most frequent and widespread natural hazard across the world. With an ample amount of literature forecasting increase in its frequency and magnitude further in the future, highly credible and efficient algorithms and tools are crucial for real-time flood monitoring. In this study, a highly efficient tool, Multi-Mission Flood Mapper, has been developed to delineate flood inundation extent without any human intervention from SAR images captured by multiple microwave SAR satellite missions, including ALOS PALSAR CEOS, ALOS 2 CEOS, COSMO-SkyMed, ENVISAT ASAR, ERS 1/2 CEOS, ERS 1/2 SAR(.E1, .E2), ICEYE, JERS CEOS, KOMPSAT-5, PAZ, RADARSAT-1 & -2, RCM, SAOCOM, SeaSat, Sentinel-1, TerraSAR-X, and TanDEM-X. The efficacy of the developed tool is assessed by performing a test on a significant number of flood events in India having diverse flooding patterns and landforms. To manifest the performance of the tool, the step-by-step processing at the backend of the tool is discussed in detail in this study by taking a flood event along the Ganga River in India as a case study. The algorithm of the tool includes various processing steps: pre-processing that incorporate applying orbit file, calibrate to sigma naught, speckle filtering, terrain correction and linear to decibel conversion; thematic analysis that involves multi-segmentation and Otsu’s thresholding techniques; post-processing that consists of the elimination of hill shadows, applying majority filter, and masking out permanent water bodies. Thus derived flood inundation layer is observed to be highly accurate compared to the master image. The total time taken by the tool for processing is about 4 minutes for the given image. The developed tool would be beneficial for rapid flood inundation map generation on a timely basis for flood monitoring and relief management during a disaster. In addition, the flood inundation layers can also be used for calibration/validation of hydrological/hydraulic models, geospatial planning, and generating flood hazard maps. Also, the Multi-Mission Flood Mapper tool is facilitated with a user-friendly Graphical User Interface (GUI), making it look simple and easy to use.

Diverse, complex data are a significant component of Earth Science’s “big data” challenge. Some earth science data, like remote sensing observations, are well understood, are uniformly structured, and have well-developed standards that are adopted broadly within the scientific community. Unfortunately, for other types of Earth Science data, like ecological, geochemical and hydrological observations, few standards exist and their adoption is limited. The synthesis challenge is compounded in interdisciplinary projects in which many disciplines, each with their own cultures, must synthesize data to solve cutting edge research questions. Data synthesis for research analysis is a common, resource intensive bottleneck in data management workflows. We have faced this challenge in several U.S. Department of Energy research projects in which data synthesis is essential to addressing the science. These projects include AmeriFlux, Next Generation Ecosystem Experiment (NGEE) - Tropics, Watershed Function Science Focus Area, Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE), and a DOE Early Career project using data-driven approaches to predict water quality. In these projects, we have taken a range of approaches to support (meta)data synthesis. At one end of the spectrum, data providers apply well-defined standards or reporting formats before sharing their data, and at the other, data users apply standards after data acquisition. As these projects continue to evolve, we have gained insights from these experiences, including advantages and disadvantages, how project history and resources led to choice of approach, and enabled data harmonization. In this talk, we discuss the pros and cons of the various approaches, and also present flexible applications of standards to support diverse needs when dealing with complex data.

Greenhouse gas (GHG) emission metrics, that is, conversion factors to evaluate the emissions of non-CO2 climate forcers on a common scale with CO2, serve crucial functions upon the implementation of the Paris Agreement. While different metrics have been proposed, they have not been investigated under a range of pathways, including those significantly overshooting the temperature targets of the Paris Agreement. Here we show that cost-effective metrics that minimize the overall cost of climate mitigation are time-dependent, primarily determined by the period remaining before the eventual stabilization, and strongly influenced by temperature overshoot. Our study suggests that flexibility should be maintained to adapt the choice of metrics in time as the future unfolds, if cost-effectiveness is a key consideration for global climate policy, instead of hardwiring the 100-year Global Warming Potential (GWP100) as a permanent feature of the Paris Agreement implementation as is currently under negotiation.

Physiological dynamics at plant level are essential but also challenging for precision agriculture applications linked to plant phenotyping. In this study, we explore not only the spatial dynamics of corn in field conditions but also their temporal analysis via skeleton reconstruction of individual plants as a shape descriptor. For this purpose, an optimized approach for high-throughput was developed by point cloud data derived from UAS imagery. The curve-skeleton extraction is calculated based on a constrained Laplacian smoothing algorithm. The experimental setup was performed at the Indiana Corn and Soybean Innovation Center at the Agronomy Center for Research and Education (ACRE) in West Lafayette, Indiana, USA. On July 27th and August 3rd of 2021, two flights were performed over a trial with more than 200 maize plants using a custom designed UAS platform with a Sony Alpha ILCE-7R photogrammetric sensor. RGB images were processed by a standard photogrammetric pipeline by Structure from Motion (SfM) to get a scaled 3D point cloud of the individual corn. Filtering techniques and labeling algorithms were joined together to reconstruct a robust and accurate skeleton of individual maize. Therefore, significant traits such as number, length, growth angle and elongation rate of leaves and stem can be easily extracted. Height variations computed from the skeleton at the two dates show a coefficient of correlation with on-field measurements better than 92%. Our experimental outcomes demonstrate the UAS-data’s ability to provide practical information to efficiently select phenotypes in plant breeding programs.

We quantify the shape of hooked hairs which is a newly observed phenotype of epidermal cell extensions [1] in the common bean genotype L88-57 (Phaseolus vulgaris). The hooked hairs emerge below-ground before the root hairs and have a distinct ‘hooking’ morphology. We generated a dataset capturing their full distribution under the microscope within 3-5 days of germination. We quantify their shape by a novel computational pipeline that can automatically phenotype morphology. Our phenotyping pipeline quantifies traits like length, curvature, perimeter, area, and ‘hooking.’ Our objective is to quantify their response to nutrient stress to determine the function of hooked hairs in common bean during early development. We used the pipeline for analyzing our dataset of hydroponically grown beans and observed statistically significant responses compared to the control for length, curvature, perimeter, and area to nitrogen (p<0.001**) and phosphorus (p<0.001**) stress treatments. The calculation of ‘hooking’ for our dataset is still ongoing. We are simultaneously developing a landmark-free method for the two-dimensional shape analysis of our dataset and believe that our phenotyping efforts will enable the high-throughput analysis of morphological root hair traits for any plant species.

Managing river temperature in highly urbanized stream systems is critical for maintaining aquatic ecosystems and associated beneficial uses. Elevated river temperatures arise from warm surface inflows from impervious areas, channelization, the absence of riparian forests, and the lack of groundwater-surface water interactions. In the current work, we utilize a mechanistic river temperature model, i-Tree Cool River, to evaluate the cooling impacts of alternative ecological restoration scenarios: a) shading effects of tree planting in riparian areas and b) alternative streambed materials. The model was calibrated and validated on a 4.2 km reach of the Compton Creek in the Los Angeles (LA) River watershed, California, for low and high flow periods. The Arroyo Chub and Stickleback were considered the target species for analyzing thermal habitat suitability. River temperature simulations showed that like the ambient air temperature. The thermal response of the river in high flow periods was a function of upstream river temperature , where in low flow periods river water temperature was most affected by the tested restoration scenarios. Tree planting in the riparian corridor decreased the median thermal metrics: Max Weekly Max, Max Weekly Average, and Min Weekly Min Temperatures by an average of 3 ℃ (13%) to 20.4 ℃, 19.7 ℃, and 17.8 ℃, respectively. Using limecrete as an alternative bed material to the current concrete bottom decreased the median thermal metrics by an average of 0.9 ℃ (4%) to 22.7 ℃, 22 ℃, and 19 ℃, respectively. Combining the two scenarios decreased the river temperature metrics by an average of 4 ℃ (18%) to 18.2 ℃. Besides riparian vegetation, altering bed material is an impactful option in case of groundwater contamination and if channelized urban corridors lack the substrate to support vegetation. The use of ecological restoration scenarios resulted in summertime temperatures were within the documented spawning temperature thresholds and therefore temperature would not be a limiting factor in the potential reintroduction of the Arroyo Chub and Stickleback to Compton Creek. This tributary could be considered as a potential refuge and improved fish habitat in the LA basin during low flow periods.

Diurnal tidal currents are the dominate contributors to diapycnal mixing in many regions along the pathways for warm Atlantic Water (AW) circulating within the Arctic Ocean along the continental slope. This mixing diffuses AW heat and salt into the cooler and fresher surroundings, including the upper ocean where ocean heat fluxes play a role in the stability of the ice pack. The strongest diurnal currents are associated with topographically-trapped vorticity waves, which are sensitive to stratification and mean flow. In models, these waves are also sensitive to choices for forcing and geometry. Sensitivity to background conditions implies that tidal currents and mixing will change as the Arctic evolves towards a new climate state. Here, as a first step towards understanding how diurnal tidal currents might change in a future Arctic Ocean, we describe results from a suite of high-resolution (dx=2 km) 2-D and 3-D models for Arctic diurnal tides, focusing on their currents at locations along the AW pathways. We first demonstrate that accurate representation of barotropic diurnal tides requires forcing with both open boundary conditions and the direct potential tide. Next, we use 3-D models with realistic, ocean background stratification and mean flow to describe the annual cycle of depth-averaged diurnal tidal currents. Finally, we investigate the baroclinic structure of diurnally forced waves including the generation of harmonics (semidiurnal and higher) that can contribute to mixing within the water column. Our results show that tides should be explicitly included in ocean and coupled predictive models for the Arctic to represent the feedbacks between tidal energetics and ocean mean state via mixing.

Important progress has been made in recent years in characterizing surface soil moisture (SSM) at regional scales, through remote sensing estimates and the implementation of new in situ networks. Each of these sources of information has intrinsic features, such as the dynamic range of the SSM and the temporal frequency of acquisition. Another relevant factor is the period of data availability. Improving the knowledge of the limitations and biases of these features is crucial to increase the potential and the consistency of data sources validations. As a case of study we considered an agricultural area in the Argentinean Pampas, characterized by a sub-humid climate with a marked seasonal dynamic. It also holds a synchronized cropping rhythm and is subject to flooding and waterlogging that can last from days to months. The features mentioned above and considering that the region is almost devoid of irrigation, offer a natural laboratory that is distinguished by a wide dynamic range of SSM conditions. In this context, we analyze and expose different sources of SSM data gaps over long periods of time, using information from in situ stations and from the SMOS and SMAP satellite systems, during 2015-2019. We found SMAP data gaps resulting from the filtering of high SSM signals that are not spurious but typical for this flood-prone region. Reports from national institutions and comparison with other data sources allowed us to identify that high soil water content in the same period in which the data gaps occurred. In a different way, the SMOS register has a low-frequency range of data due to radio frequency interference over the study area. This data gap occurs during a long-anomalously wet period and it is relevant to take it into account when analyzing SMOS data for the full period. Our study shows the importance of using multiple sources of information and the relevance of examining the availability of data.

COVID-19 is a deadly pandemic affecting cities in the US, which slows down life and has affected many different sectors of our daily lives such as business, travel, hospitality, and education. With the growing COVID-19 cases, some of the states have issued advisories to the people to stay home and keep social distancing. It has been observed that people have followed the social distancing regulations in some states, whereas in others the regulations were not heeded. We have analyzed various atmospheric parameters over the capital cities of the US during the COVID-19 lockdown. We have carried out an analysis of the daily Aerosol Optical Depth (AOD) and Nitrogen Dioxide (NO2) data derived from the MODIS Terra, Aqua, and AIRS satellite, which could provide information about the decline in traffic emissions during the period January – June 2020. Comparison of this data with the same period of 2019 shows a decline in AOD and NO2 which can be due to the fact that some of the US capitals have strictly followed social distancing and some of the states ignored and as a result the COVID-19 cases have surged in many cities. We have studied PM2.5 using the published data from the Environmental Protection Agency (EPA) and the total column of ozone retrieved from OMI AURA satellite. A decline in the particle matter (PM2.5) and observed an increase in the total column of ozone. The COVID-19 lockdown shows an improvement in air quality, good for human health but on the other hand, lockdown has seriously impacted the day to day life.

The NOAA Physical Sciences Laboratory produces the Global Ensemble Forecasting System (GEFS) which comprises 11 ensemble members (1 control and 10 perturbation runs) for over a 36-year period (December 1984 to present), with forecasts initialized every day for the next 16 days (first 8-day forecasts obtained from a high-resolution grid and the next 8-day forecasts from a low-resolution grid). The system provides 36 variables related to a wide range of hydrometeorological processes. In this study, we assess the predictability of precipitation within the context of statistical downscaling using a minimum set of predictor variables (precipitation and temperature). We use feedforward backpropagation neural networks with a suite of training algorithms to determine which variables (features) are of most relevance at different forecast lead times. The outcome of this study will significantly benefit short-term flood forecasting using GEFS data.

In the first several hours following an earthquake, municipalities are often forced to rely upon reports from first responders, reconnaissance along disrupted roadways by emergency personnel, or wait for aerial surveillance and remote sensing. The latter is expected to take at least 12 hours, a crucial period following a major earthquake in which situational awareness can be greatly improved using existing seismic risk modelling tools. This work presents a new initiative to develop a rapid disaster modelling protocol for earthquakes in British Columbia (BC). We explore best practices and the feasibility of using immediately available seismic data in the existing OpenQuake Canada framework to model the impacts to people, the built environment, and the economy from an earthquake in near real-time. The current prototype integrates observed ground motion data from regional strong motion seismometers, like the BC Smart Infrastructure Monitoring System, with physical exposure data from Natural Resources Canada’s Human Settlement Layer to report on key metrics for early response: collapsed buildings, entrapment injuries, hospital demand surge, roadway debris which may block response, and immediate mass care needs like shelter requirements. These indicators will be ported to the British Columbia Common Operating Picture Portal, the online situational awareness and mapping platform for authoritative, collaborative and coordinated distribution of emergency management information in the province. These outputs could be made available within tens of minutes of the earthquake occurring, potentially affording emergency managers the opportunity to best direct resources to save lives and reduce suffering.

Near-Earth asteroids and meteoroids constitute various levels of impact danger to our planet. On the one end, billions of events associated with small-sized meteoroids have resulted in trivial effects. On the other end, the occurrences of large-sized asteroidal collisions that can cause mass extinctions and may wipe out the modern human civilization are extremely rare. In addition, large near-Earth asteroids are being monitored constantly for accurate and precise predictions of potential hazardous visits to our planet. However, small asteroids and large meteoroids can still often go under the radar and cause bolide explosions with potential of significant damage to communities on the ground. To facilitate management of bolide hazard, a number of scholarly works have been dedicated to estimation of frequencies of bolide events from a global perspective for planetary defense and mitigation. Nevertheless, few of the existing bolide frequency models were developed for local hazard management. In this presentation, the author introduces two recently developed frequency models for local management of bolide hazard. The first one, called the Dome model, computes the expected frequency of bolide explosions within a dome-shaped volume around a location. The second one, called the Coffee Cup model, is for a column-shaped volume above an area. Both models are based on empirical calibrations with historical data on energy, latitude, altitude, and frequency of bolide events. The modeling results indicate a linearly decreasing trend of frequency of bolide events from south to north latitudinally around the globe. The presented models can be applied to any location or area on Earth, including the entire surface of the planet.

Factors influencing data reproducibility of fission-track (FT) thermochronology can be summarized into three main categories associated with data acquisition steps. (1) Sample preparation involves mineral separation, mounting, polishing and etching; (2) data revelation relates to instrumentation (microscope, LAICPMS, etc.) and software settings; and (3) execution depends on feature selection by the analyst. Previous committee reports and studies (Hurford A.J. 1990; Ketcham et al. 2009; Ketcham et al. 2015; Ketcham et al. 2018) have contributed significant insights into the reproducibility of fission-track data by comparing length and age measurements produced by several laboratories using their own preparation and revelation procedures. A recent attempt to isolate analyst-specific factors in length measurement using an image-based approach (Tamer et al. 2019) found that when two analysts observe the same feature and agree it is a valid track, measurement reproducibility was very good, though impacted by etching. Dispersion of individual length measurements was 0.7-1.0 µm (2 for weaker etching and 0.5-0.8 µm for stronger etching, but mean lengths were always within 0.1 µm of each other. Where the analysts disagreed more significantly, however, was in finding tracks and evaluating whether they were valid, sufficiently clear, and sufficiently etched for measurement, which led to differences of up to ~0.8 µm in mean track length. This study builds on the image-based approach to encompass more aspects of the measurement process and increase the number of analysts being compared. We will look at confined track selection in greater detail, and also study analyst decisions behind age determination, including the selection of the region of interest for counting, and identification of grain-surface features as tracks appropriate for counting. Reflected and transmitted light image stacks for 41 grains and graticules are available on a cloud platform Participants will carry out analyses of these images using their preferred approach, e.g. suitable analytical software, manual measurements or AI-based analysis. A limited license for FastTracks (v3.2) will be available for those who would like to participate but do not have measurement software. Analysts are asked to fill out a questionnaire about their fission track experience, conduct track density estimations, confined track length and Dpar measurements, and especially provide comments on all grains being analyzed or skipped. FastTracks users are asked to send the .xml files produced by the software, while other participants are asked to submit the results using a template. The results will be entirely anonymous unless the analyst states otherwise. The deadline for the submission of the results is June 1st, 2022. The results will be shared on 18th International Conference on Thermochronology.

The subduction zone of the Cocos Plate beneath Southern Mexico has major variations in the megathrust geometry and behavior. The subduction segment beneath the Oaxaca state of Mexico has relatively frequent large earthquakes on the shallow part of the megathrust and within the subducting slab, and it also has large aseismic slow-slip events. The slab geometry under Oaxaca includes part of the subhorizontal “flat-slab” zone extending far from the trench beneath southern Mexico and the beginning of its transition to more regular subduction geometry to the southeast. We study the rupture of the 16 February 2018 Mw 7.2 Pinotepa earthquake near Pinotepa Nacional in Oaxaca that was a thrust event on the subduction interface. The Pinotepa earthquake was about 350 km away from the 8 September 2017 Mw 8.2 Tehuantepec earthquake in the subducting slab offshore Oaxaca and Chiapas; it was in an area of Coulomb stress decrease from the M8.2 quake, so it seems unlikely to be a regular aftershock and was not triggered by the static stress change. Geodetic measurements from interferometric analysis of synthetic aperture radar (InSAR) and time-series analysis of GPS station data constrain finite-fault slip models for the M7.2 Pinotepa earthquake. We analyzed InSAR data from Copernicus Sentinel-1A and -1B satellites and JAXA ALOS-2 satellite. Our Bayesian (AlTar) static slip model for the Pinotepa earthquake shows all of the slip confined to a very small (10-20 km diameter) rupture, similar to some early seismic waveform fits. The Pinotepa earthquake ruptured a portion of the Cocos megathrust that has been previously mapped as partially coupled and shows that at least small asperities in that zone of the subduction interface are fully coupled and fail in high-stress drop earthquakes. The previous 2012 Mw 7.4 Ometepec earthquake is another example of asperity in the partially coupled zone but was not imaged by InSAR so the rupture extent is not so well constrained. The preliminary NEIC epicenter for the Pinotepa earthquake was about 40 km away (NE) from the rupture imaged by InSAR, but the NEIC updated epicenter and Mexican SSN location are closer. Preliminary analysis of GPS data after the Pinotepa earthquake indicates rapid afterslip on the megathrust in the region of coseismic slip. Atmospheric noise masks the postseismic signal on early InSAR data.

Melt ponds play an important role in the seasonal evolution of Arctic sea ice. During the melt season, snow atop the sea ice begins to metamorphose and melt, forming ponds on the ice. These ponds reduce the albedo of the surface, allowing for increased solar energy absorption and thus further melting of snow and ice. Analyzing the spatial distribution and temporal evolution of melt ponds helps us understand the sea ice processes that occur during the summer melt season. It has been shown that the inclusion of melt pond parameters in sea ice models increases the skill of predicting the summer sea ice minimum extent. Previous studies have used remote sensing imagery to characterize surface features and calculate melt pond statistics. Here we use new observations of melt ponds obtained by the Digital Mapping System (DMS) flown onboard NASA Operation IceBridge (OIB) during two Arctic summer melt campaigns which surveyed thousands of kilometers of sea ice and resulted in more than 45,000 images. One campaign was conducted in the Beaufort Sea (July 2016), and one in the Lincoln Sea and the Arctic Ocean north of Greenland (July 2017). Using these data we expect to advance our understanding of the differences and similarities between melt pond features on young, thin sea ice seen in the Beaufort Sea versus those on multi-year ice. We have developed a pixel-based classification scheme by considering the different RGB spectral values associated with each surface type. We identify four sea ice surface types (level ice, rubbled ice, open water, and melt ponds). The classification scheme enables the calculation of parameters including melt pond fraction, ice concentration, melt pond area, and melt pond dimensions. We compare results with data from the Airborne Topographic Mapper (ATM), a laser altimeter also operated during these OIB missions. Given the extent over which the OIB data are available, regional information may be derived. Leveraging existing satellite data products, we examine whether the high-resolution airborne statistics are representative of the region and can be scaled up for comparison against satellite-derived parameters such as ice concentration and extent.

Current estimates of the impact of an increase in greenhouse gas concentrations on global warming, including by the IPCC and in General Circulation Models, are based on radiative forcing. Two recently published formulations of the theoretical foundation for radiative forcing are reviewed. Radiative forcing at the tropopause is calculated by assuming that the absorption of terrestrial radiation by greenhouse gases is determined by their spectral properties, using a radiative transmittance function based on the line strength and line shape of the absorption lines and the vertical optical mass, whilst, under conditions of local thermodynamic equilibrium, the emission of radiation at each layer of the atmosphere is given by the Planck blackbody function at the local atmospheric temperature. Radiative forcing is given by the net change in radiative flux at the troposphere due to an increase in greenhouse gases. Climate change is seen to take place when the system responds to restore the radiative equilibrium. Without any theoretical foundation, a linear relationship between the change in surface temperature in °C and radiative forcing is assumed. Here, IPCC 2013’s estimate of radiative forcing of 2.83 W/m2 due to the increase in greenhouse gases from 1750 to 2011 is used to calculate the resulting change in radiative flux at the Earth’s surface under reasonable assumptions, and the Stefan-Boltzmann law is applied to calculate the change in surface temperature of between 0.8 and 1.0 °C. This represents a climate sensitivity of around 0.32 °C/(W/m2), about one third of the climate sensitivity of 1.0 °C/(W/m2) used by IPCC 2013 that was obtained from the mean regression-based values of 30 climate models.

Considering the theme for AbSciCon 2019: “Understanding and Enabling the Search for Life on Worlds Near and Far”, it is worth to set the emphasis on ferric minerals and show that their formation in the absence of oxygen does not require the necessary presence of microorganisms but can occur during the alkaline interaction of ferrous silicates rocks with water in conditions of temperature and pressure near the critical point. The results show that molecules of life can form in a path which is concomitant to this specific water-rock interaction and that organic matter of biological interest can form inside inclusions in the produced minerals. The knowledge about the formation of ferric iron in anoxic alkaline conditions may be important for the understanding of the Earth oxygenation and of extraterrestrial objects such as Enceladus. It is concluded that the search for the molecules of life may be connected to the search of amorphous silica, quartz, ferric oxides, amorphous and crystalline ferric silicates, in association with siderite. The observation of ferric minerals on early Earth and extraterrestrial objects does not mean that life had already emerged at the time of formation of the minerals.

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)]