We examine the effects of the submesoscale in mediating the response to projected warming of phytoplankton new production and export using idealized biogeochemical tracers in a high-resolution regional model of the Porcupine Abyssal Plain region of the North Atlantic. We quantify submesoscale effects by comparing our control run to an integration in which submesoscale motions have been suppressed using increased viscosity. The warming climate over the 21st century reduces resolved submesoscale activity by a factor of 2-3. Annual new production is slightly reduced by submesoscale motions in a climate representative of the early 21st-century and slightly increased by submesoscale motions in a climate representative of the late 21st-century. Resolving the submesoscale, however, does not strongly impact the projected reduction in annual production under representative warming. Organic carbon export from the surface ocean includes both direct sinking of detritus (the biological gravitational pump) and advective transport mediated pathways; the sinking component is larger than advectively mediated transport by up to an order of magnitude across a wide range of imposed sinking rates. Submesoscales are responsible for most of the advective carbon export, however, which is thus largely reduced by a warming climate. In summary, our results demonstrate that resolving more of the submesoscale has a modest effect on present-day new production, a small effect on simulated reductions in new production under global warming, and a large effect on advectively-mediated export fluxes.

Insight and other observations of the Martian surface at different locations have recorded the diurnal variation in surface pressure (Ps) with two rapid fluctuations that occur at dawn and dusk (around LT0800 and LT2000). These short-period surface pressure perturbations at specific local times are typically observed near Martian equinox. Similar phase-locked surface pressure fluctuations over most areas of the middle and low latitudes are simulated by the Martian General Circulation Model at the Dynamic Meteorology Laboratory (LMD). This phenomenon is thus likely to be global rather than local. By reconstructing the surface pressure variation from the horizontal mass flux, the pressure fluctuations in a sol can be attributed to the diurnal variation in the horizontal wind divergence and convergence in the Martain tropical troposphere in the GCM simulations. The background diurnal variation in Ps is related to the diurnal migrating tidal wind, while the enhanced convergence due to the overlap of the 4-hour and 6-hour tides before LT0800 and LT2000 is responsible for the Ps peaks occurring at dawn and twilgith. Although the amplitudes of the 4-hour and 6-hour tides are smaller than those of diurnal tides, the phases of these tides remain similar in the Martain troposphere, which suggests that the convergences and divergences due to 4 h/6 h tidal winds at different altitudes are in phase and together create a mass flux comparable to that induced by diurnal/semidiurnal components and lead to rapid pressure fluctuations.

The various extreme weather events that occurred globally in 2021, from Europe to China to North America, served as yet another reminder that robust strategies for climate adaptation are crucial at a time of rapid global warming. Building resilient communities and lessening the impact that natural disasters have on vulnerable infrastructure can be aided by automated systems driven by machine learning algorithms trained on Earth observation data. When deployed, computer vision models can analyze satellite imagery in real time and inform decision makers and nongovernmental organizations about the timely and targeted allocation of resources and humanitarian aid personnel to affected areas. Here, we overview several specific 2021 extreme events and the factors that caused the loss of life, damage to infrastructure, and economic loss. The events surveyed include flooding in Germany, wildfires in Greece, and Hurricane Ida in the Eastern United States. Taking this information into account, we further discuss barriers to the large-scale deployment of current machine learning technologies, especially models trained on Earth observation data. We examine the limitations of satellite imagery and big data applications in detecting damage and building collapse and how Interferometric Synthetic Aperture Radar (InSAR) can be a tool to resolve existing issues. The aim of this work is to understand why many state-of-the-art models being developed have not yet been successfully and extensively deployed in the real world and to foster discussion about optimizing the use of deep learning technology to save lives and lead effective disaster management efforts.

The water cycle is an important component of the earth system and it plays a key role in many facets of society, including energy production, agriculture, and human health and safety. In this study, the Energy Exascale Earth System Model version 1 (E3SMv1) is run with low-resolution (roughly 110 km) and high-resolution (roughly 25 km) configurations — as established by the High Resolution Model Intercomparison Project protocol — to evaluate the atmospheric and terrestrial water budgets over the conterminous United States (CONUS) at the large watershed scale. The water cycle slows down in the HR experiment relative to the LR, with decreasing fluxes of precipitation, evapotranspiration, atmospheric moisture convergence, and runoff. The reductions in these terms exacerbate biases for some watersheds, while reducing them in others. For example, precipitation biases are exacerbated at HR over the Eastern and Central CONUS watersheds, while precipitation biases are reduced at HR over the Western CONUS watersheds. The most pronounced changes to the water cycle come from reductions in precipitation and evapotranspiration, the latter of which results from decreases in evaporative fraction. While the HR simulation is warmer than the LR, moisture convergence decreases despite the increased atmospheric water vapor, suggesting circulation biases are an important factor. Additional exploratory metrics show improvements to water cycle extremes (both in precipitation and streamflow), fractional contributions of different storm types to total precipitation, and mountain snowpack.

In this study we introduce a pair of idealized tracers to quantify how changes in physical advection and mixing under climate change affect the nutrient supply, new production, and particulate export rates. The low cost and simplicity of these tracers allows us to explore the sensitivity of the model biogeochemistry, and in particular its response to a changing physical environment, to the choice of model parameters. Using CESM2.1 with active ocean and ice only, at nominal one-degree resolution, under initial conditions and forcing representative of 2000 and 2100, our idealized nutrient and particulate are within the spread of nitrate and export from CMIP5 models. The simple form of the tracers allows us to identify the physical controls on the changing rates of supply, production, and export throughout the year, which together form the different seasonal cycles. We find that the ocean basins with the largest changes in the seasonal cycle over the 21st century are the North Atlantic, the Arctic, and the eastern tropical Pacific. We present results comparing the controls across basins, focusing on shifts in the timing of deepening mixed layers and maximum production rate in the northern North Atlantic through the Arctic, and changes in the spatial and temporal patterns of vertical advective exchange in the tropics and subtropics of the Pacific and Indian Oceans. In both cases we discuss how much these changes depend on the biogeochemical model parameter values.

Stable carbon (δ¹³C) and oxygen (δ¹⁸O) isotope measurements in lacustrine ostracodes are widely used to infer past climatic conditions. Previous work has used individual ostracode valves to resolve seasonal and subdecadal climate signals, yet environmental controls on geochemical variability within co-occurring specimens from modern samples are poorly constrained. Here we focus on individual ostracode valves in modern-aged Lake Turkana sediments, an alkaline desert lake in tropical East Africa. We present individual ostracode valve analyses (IOVA) of δ¹³C and δ¹⁸O measurements (n = 329) of extant species Sclerocypris clavularis from 17 sites spanning the entire lake (n-avg ~19 specimens per site). We demonstrate that the pooled statistics of individual valve measurements at each site overcome inter-specimen isotopic variance and are driven by hydrological variability in the lake. Mean IOVA-δ¹³C and -δ¹⁸O across the sites exhibit strong spatial trends with higher values at more southerly latitudes, modulated by distance from the inflow of the Omo River. Whereas the latitudinal δ¹³C gradient reflects low riverine δ¹³C and decreasing lacustrine productivity towards the southern part of the lake, the δ¹⁸O gradient is controlled by evaporation superimposed on the waning influence of low-δ¹⁸O Omo River waters, sourced from the Ethiopian highlands. We show that ostracode δ¹⁸Oproximal to Omo River inflow is deposited under near-equilibrium conditions and that inter-specimen δ¹⁸O variability across the basin is consistent with observed temperature and lake water δ¹⁸O variability. IOVA can provide skillful constraints on high-frequency paleoenvironmental signals and, in Omo-Turkana sediments, yield quantitative insights into East African paleohydrology.

Proxy reconstructions from the mid-Holocene (MH: 6,000 years ago) indicate an intensification of the West African Monsoon and a weakening of the South American Monsoon, primarily resulting from orbitally-driven insolation changes. However, model studies that account for MH orbital configurations and greenhouse gas concentrations can only partially reproduce these changes. Most model studies do not account for the remarkable vegetation changes that occurred during the MH, in particular over the Sahara, precluding realistic simulations of the period. Here, we study precipitation changes over northern Africa and South America using four fully coupled global climate models by accounting for the Saharan greening. Incorporating the Green Sahara amplifies orbitally-driven changes over both regions, and leads to an improvement in proxy-model agreement. Our work highlights the local and remote impacts of vegetation and the importance of considering vegetation changes in the Sahara when studying and modelling global climate.

It has been widely recognized that tropical cyclone (TC) genesis requires favorable large-scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large-scale environmental favorabilities in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relation between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer-learning technique to train ensembles of CNNs first on suites of high-resolution climate simulations with realistic seasonal TC activities and large-scale environmental conditions, and then subsequently on the state-of-the-art reanalysis from 1950 to 2019. Our CNNs can remarkably reproduce the historical TC records, and yields significant seasonal prediction skills when the large-scale environmental inputs are provided by operational climate forecasts. Furthermore, by forcing the ensemble CNNs with 20th century reanalysis products and phase 6 of the Coupled Model Intercomparison Project (CMIP6) experiments, we attempted to investigate TC variabilities and their changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our dynamic projections using TC-permitting high-resolution coupled climate model.

Solar-induced fluorescence (SIF) shows enormous promise as a proxy for photosynthesis and as a tool for modeling variability in gross primary productivity (GPP) and net biosphere exchange (NBE). In this study, we explore the skill of SIF and other vegetation indicators in predicting variability in global atmospheric CO2 observations, and thus global variability in NBE. We do so using a four-year record of global CO2 observations from NASA’s Orbiting Carbon Observatory 2 (OCO-2) satellite and using a geostatistical inverse model. We find that existing SIF products closely correlate with space-time variability in atmospheric CO2 observations in the extra-tropics but show weaker explanatory power across the tropics. In the extra-tropics, all SIF products exhibit greater skill in explaining variability in atmospheric CO2 observations compared to an ensemble of process-based CO2 flux models and other vegetation indicators. Furthermore, we find that using SIF as a predictor variable in the geosatistical inverse model shifts the seasonal cycle of estimated NBE and yields an earlier end to the growing season relative to other vegetation indicators. In tropical biomes, by contrast, the seasonal cycles of SIF products and estimated NBE are out of phase, and existing respiration and biomass burning estimates do not reconcile this discrepancy. Overall, our results highlight several advantages and challenges of using SIF products to help predict global variability in GPP and NBE.

In the present study, using sixty-three and fifty-six years of continuous observations, we investigate the long-term oscillations and residual trends, respectively, in the E- and F-region ionosonde measured parameters over Juliusruh, Europe. Using the Lomb-Scargle periodogram (LSP) long-term variations are estimated before the trend estimation. We found that the amplitude of the annual oscillation is higher than the 11-year solar cycle variation in the critical frequencies of the daytime E (foE) and Es (foEs) layers. A weak semi-annual oscillation is also identified in the foE. In the F-region, except for daytime hmF2, and nighttime foF2, the amplitude of the 11-year solar cycle variation is higher than the annual oscillation. The LSP estimated periods and their corresponding amplitudes are used to construct a model E- and F-region ionospheric parameters that are in good agreement with the observation. The linear trend estimation is derived by applying a least-squares fit analysis to the residuals, subtracting the model from the observation. Except for the daytime foF2, all the other parameters like nighttime foF2, day and nighttime h’F, and hmF2 show a negative trend. Present results suggest that the greenhouse effect is a prime driver for the observed long-term trend in the F-region. Interestingly, weak negative trends in the foE and foEs are found which contradicts an earlier investigation. The present study suggests that the changes in the upper stratospheric ozone and mesosphere wind shear variability could be the main driver for the observed weak negative trends in the foE, and foEs, respectively.

El Niño‐Southern Oscillation (ENSO) is often considered as a source of long-term predictability for extreme events via its teleconnection patterns. However, given that its characteristic cycle varies from two to seven years, it is difficult to obtain statistically significant conclusions based on observational periods spanning only a few decades. To overcome this, we apply the global flood risk modeling framework developed by Carozza and Boudreault to an equivalent of 1600 years of bias-corrected GCM outputs. The results show substantial anomalies in flood occurrences and impacts for El Niño and La Niña when compared to the all-year baseline. We were able to obtain a larger global coverage of statistically significant results than previous studies limited to observational data. Asymmetries in anomalies for both ENSO phases show a larger global influence of El Niño than La Niña on flood hazard and risk.

Climate models still need to be improved in their capability of reproducing the present climate at both global and regional scale. The assessment of their performance depends on the datasets used as comparators. Reanalysis and gridded (homogenized or not homogenized) observational datasets have been frequently used for this purpose. However, none of these can be considered a reference dataset. Here, for the first time, using in-situ measurements from NOAA U.S. Climate Reference Network (USCRN), a network of 139 stations with high-quality instruments deployed across the continental U.S, daily temperature, and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA-Interim) involved in NA-CORDEX are assessed. The assessment is extended also to the most recent and modern widely used reanalysis (ERA5, ERA-Interim, MERRA2, NARR) and gridded observational datasets (Daymet, PRISM, Livneh, CPC). Results show that biases for the different datasets are mainly seasonal and subregional dependent. On average, reanalysis and in-situ-based datasets are generally warmer than USCRN year-round, while models are colder (warmer) in winter (summer). In-situ-based datasets provide the best performance in most of the CONUS regions compared to reanalysis and models, but still have biases in regions such as the Midwest mountains and the Northwestern Pacific. Results also highlight that reanalysis does not outperform RCMs in most of the U.S. subregions. Likewise, for both reanalysis and models, temperature and precipitation biases are also significantly depending on the orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis.

We test the application of a rare event simulation algorithm to accelerate the sampling of extreme winter rainfall over Europe in a climate model. The genealogical particle analysis algorithm, an ensemble method that interrupts the simulation at intermediate times to clone realizations in which an extreme event is developing, is applied to the intermediate complexity general circulation model PlaSim. We show that the algorithm strongly reduces the numerical effort required to estimate probabilities of extremes, demonstrating the potential of rare event simulation of seasonal precipitation extremes.

Based on the long-term climatological data from Ny Alesund, Svalbard Airport – Longyearbyen and Hornsund Polish Polar Station, we undertook an analysis of drought indices on West Spitsbergen Island, Svalbard for the period 1979-2019. The features and causes of spatio-temporal variability of atmospheric drought on Svalbard were identified, as expressed by the Standardised Precipitation Evapotranspiration Index (SPEI). It was possible to indicate several-years long periods with the SPEI indicating a domination of drought or wet conditions. Long-term variability of annual and half-year (May-October) values of SPEI showed a prevalence of droughts in the 80-ties and in the first decade of the 21st century while wet seasons were frequent in the 90-ties and in the second decade of the 21st century. Seasonal SPEIs were characteristic of great inter-annual variability. In MAM and JJA droughts were more frequent after 2000; in the same period in SON and DJF, the frequency of wet seasons increased. The most remarkable changes in the scale of the entire research period were estimated for autumn where negative values of SPEI occur more often in the first part of the period and positive values dominate in the last 20 years. The long-term course of the variables in subsequent seasons between 1979-2019 indicates strong relationships between the SPEI drought index and anomalies of precipitable water and somewhat weaker relationships with anomalies of sea level pressure.

Deforestation alters the exchange of heat, moisture, and momentum between the Earth's surface and the atmosphere, which can significantly affect the surface energy balance and water budget. However, changes in surface heat fluxes in response to deforestation are diverse among multi-model simulations. Changes in surface heat fluxes may lead to further energy partitioning and different land-atmosphere interactions. This study explores factors that might cause different changes in surface fluxes under tropical deforestation. The mediating effect of the Bowen ratio on changes in turbulent surface fluxes in response to the removal of tropical rainforests is examined with the Community Earth System Model of the National Center for Atmospheric Research. Different flux partitioning in the mean state of the Bowen ratio is associated with various flux changes under deforestation. When the mean Bowen ratio is smaller, deforestation tends to increase sensible heat fluxes and reduce latent heat fluxes. Our research further indicates that the simulated mean-state Bowen ratios in the Land Use Model Intercomparison Project model archive might modulate changes in surface heat fluxes that provide some clues for the land surface model developments.

The debate over the historical and future evolution of the Atlantic Meridional Overturning Circulation (AMOC) has united scientists around a single topic, but this community has yet to unite around a single definition of the AMOC. In an effort to focus the debate around dynamics rather than semantics, we recommend that the community universally adopt a definition of the AMOC in density coordinates. We present evidence that the traditional depth space definition is insufficient at capturing elements of this circulation, especially at high latitudes where the northward and southward limbs of the AMOC are separated horizontally rather than vertically. Instead, the AMOC in density coordinates more realistically captures the water mass transformation process at high latitudes, shifts the maximum AMOC from the subtropical to the subpolar North Atlantic where the majority of the deep waters are formed, and depicts the peak in meridional heat transport associated with the subtropical gyre.

Addressing questions of equitable contributions to emission reductions is important to facilitate ambitious global action on climate change within the ambit of the Paris Agreement. Several large developing regions with low historical contributions to global warming have a strong moral claim to a large proportion of the remaining carbon budget. However, this claim needs to be assessed in a context where the remaining carbon budget consistent with the Long-Term Temperature Goal (LTTG) of the Paris Agreement is rapidly diminishing. Here we assess the potential tension between the moral claim to the remaining carbon space by large developing regions with low per capita emissions, and the collective obligation to achieve the goals of the Paris Agreement. Based on scenarios underlying the IPCC’s 6th Assessment Report, we construct a suite of scenarios that combine the following elements: (i) two quantifications of a moral claim to the remaining carbon space by South Asia, and Africa, (ii) a “highest possible emission reduction” effort by developed regions, and (iii) a corresponding range for other developing regions. We find that even the best effort by developed regions cannot compensate for a unilateral claim to the remaining carbon space by South Asia and Africa. This would put the LTTG firmly out of reach unless other developing regions cede their moral claim to emissions space and, like developed regions, pursue highest possible emission reductions. Furthermore, regions such as Latin America would need to provide large-scale negative emissions with potential risks and negative side effects. Our findings raise important questions of perspectives on equity in the context of the Paris Agreement including on the critical importance of climate finance. A failure to provide adequate levels of financial support to compensate large developing regions to emit less than their moral claim will put the Paris Agreement at risk.

The Solar Irradiance Science Team #2 (SIST-2) program is a competitively solicited National Aeronautics and Space Administration (NASA) Earth Science Division (ESD) science research program providing three-year awards beginning in July 2018 to quantify and understand the solar irradiance and its variability. A key motivation for the SIST-2 program is to understand the solar radiation variability and implications for Earth’s climate and atmospheric composition. The purpose for the SIST-2 program is limited to the accurate specification of the incoming solar irradiance into the Earth system considering the 43-year satellite data record as well as proxies to which the satellite record can be tied. The SIST-2 program funded eight research grants to study the variability of the total solar irradiance (TSI) and solar spectral irradiance (SSI) and to develop improved space-based data sets, solar proxies, and variability models of the solar irradiance. The SIST-2 projects are briefly introduced.

Evaluating historical simulations from global climate models (GCMs) remains an important exercise for better understanding future projections of climate change and variability in rapidly warming regions, such as the Arctic. As an alternative approach for comparing climate models and observations, we set up a machine learning classification task using a shallow artificial neural network (ANN). Specifically, we train an ANN on maps of annual mean near-surface temperature in the Arctic from a multi-model large ensemble archive in order to classify which GCM produced each temperature map. After training our ANN on data from the large ensembles, we input annual mean maps of Arctic temperature from observational reanalysis and sort the prediction output according to increasing values of the ANN’s confidence for each GCM class. To attempt to understand how the ANN is classifying each temperature map with a GCM, we leverage a feature attribution method from explainable artificial intelligence. By comparing composites from the attribution method for every GCM classification, we find that the ANN is learning regional temperature patterns in the Arctic that are unique to each GCM relative to the multi-model mean ensemble. In agreement with recent studies, we show that ANNs can be useful tools for extracting regional climate signals in GCMs and observations.

The ice stream geometry and large ice surface velocities at the onset region of the Northeast Greenland Ice Stream (NEGIS) are not yet well reproduced by ice sheet models. The quantification of basal sliding and a parametrisation of basal conditions remains a major gap. In this study, we assess the basal conditions of the onset region of the NEGIS in a systematic analysis of airborne ultra-wideband radar data. We evaluate basal roughness and basal return echoes in the context of the current ice stream geometry and ice surface velocity. We observe a change from a smooth to a rougher bed where the ice stream widens, and a distinct roughness anisotropy, indicating a preferred orientation of subglacial structures. In the upstream region, the excess ice mass flux through the shear margins is evacuated by ice flow acceleration and along-flow stretching of the ice. At the downstream part, the generally rougher bed topography correlates with a decrease in flow acceleration and lateral variations in ice surface velocity. Together with basal water routing pathways, this hints to two different zones in this part of the NEGIS: the upstream region collecting water, with a reduced basal traction and downstream, where the ice stream is slowing down and is widening on a rougher bed, with a distribution of basal water towards the shear margins. Our findings support the hypothesis that the NEGIS is strongly interconnected to the subglacial water system in its onset region, but also to the subglacial substrate and morphology.

Dolomite (CaMg(CO3)2) forms in minor quantities in modern environments yet comprises most of the Precambrian carbonate rock record. Precambrian dolomites are often fine-grained and fabric-retentive and are interpreted to have precipitated as primary cements or formed as early diagenetic replacements of CaCO3. Detailed physical and chemical characterization of these dolomites could inform their origin and relevance for paleoenvironmental reconstruction. Here, we use synchrotron radiation to produce a nanometer-resolution crystal orientation map of one exquisitely-preserved ooid deposited at the onset of the Shuram carbon isotope excursion (~574 Ma). The crystal orientation map reveals small (~10μm) acicular, radially-oriented crystals grouped into bundles of similarly-oriented crystals with varying optical properties. We interpret that this dolomite formed via primary, spherulitic precipitation during ooid growth in shallow marine waters. This result provides additional evidence that the physicochemical properties of late Precambrian oceans promoted dolomite precipitation and supports a primary origin for the Shuram excursion.

Evidence based on sparse tree-ring data suggests a severe sustained drought occurred in the 2nd century CE that could have rivaled medieval period droughts in the Colorado River basin (Gangopadhyay et al. 2022). Most of these tree-ring data have been used in gridded drought reconstructions (Cook et al., 2010) which extend back to 1 CE over an area that includes the intermountain western US. However, the 2nd century drought has not been highlighted in prior studies given the sparseness of the data available for this time period. A new reconstruction of Colorado River flow based on these data documents a notably severe and sustained drought over much of the 2nd century (Gangopadhyay et al. 2022). While this reconstruction suggests that the drought exceeds the severity and duration of any drought in the past 2000 years, a complete assessment of the 2nd century drought is challenging due to the sparseness of data. In this poster presentation, we describe the tree-ring data available, along with other proxy data that provide evidence for the 2nd century drought and support its severity. In our conclusions, we discuss outstanding questions and thoughts for further work.

Model simulations of past climates are increasingly found to compare well with proxy data at a global scale, but regional discrepancies remain. A persistent issue in modeling past greenhouse climates has been the temperature difference between equatorial and (sub-)polar regions, which is typically much larger in simulations than proxy data suggest. Particularly in the Eocene, multiple temperature proxies suggest extreme warmth in the southwest Pacific Ocean, where model simulations consistently suggest temperate conditions. Here we present new global ocean model simulations at 0.1° horizontal resolution for the middle-late Eocene. The eddies in the high-resolution model affect poleward heat transport and local time-mean flow in critical regions compared to the non-eddying flow in the standard low-resolution simulations. As a result, the high-resolution simulations produce higher surface temperatures near Antarctica and lower surface temperatures near the equator compared to the low-resolution simulations, leading to better correspondence with proxy reconstructions. Crucially, the high-resolution simulations are also much more consistent with biogeographic patterns in endemic-Antarctic and low-latitude-derived plankton, and thus resolve the long-standing discrepancy of warm subpolar ocean temperatures and isolating polar gyre circulation. The results imply that strongly eddying model simulations are required to reconcile discrepancies between regional proxy data and models, and demonstrate the importance of accurate regional paleobathymetry for proxy-model comparisons.

The fastest projected rates of sea level rise appear in models which include “the marine ice cliff instability (MICI),” a hypothesized but mostly unobserved process defined by rapid, brittle failure of terminal ice cliffs that outpaces viscous relaxation and ice-shelf formation. Crane Glacier’s response to the Larsen B Ice Shelf collapse has been invoked as evidence of MICI, but sparse data coverage of that event in space and time has hindered interpretation of the processes controlling terminus retreat. Using available remote sensing data, we deconstruct Crane’s retreat, arrest, and regrowth over the last two decades. Much of Crane’s terminus retreat occurred in floating, not grounded ice, but calving accelerated by at least 55% during the 2 years following ice shelf collapse, consistent with a positive geometric feedback. If calving occurred by cliff failure, maximum cliff heights would have been 111 m, only consistent with process models that incorporate damaged ice.