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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Causes of Long-Term Drought in the United States Great Plains

The United States Great Plains (USGP) experienced a number of multi-year droughts during the last century, most notably the droughts of the 1930s and 1950s. This study examines the causes of such droughts using ensembles of long term (1930-1999) simulations carried out with the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1) atmospheric general circulation model (AGCM) forced with observed sea surface temperatures (SSTs). The results show that the model produces long-term (multi-year) variations in the USGP precipitation that are similar to those observed. A correlative analysis suggests that the ensemble mean low frequency (time scales longer than about 6 years) rainfall variations in the USGP are linked to a pan-Pacific pattern of SST variability that is the leading empirical orthogonal function (EOF) in the low frequency SST data. The link between the SST and the Great Plains precipitation is confirmed in idealized AGCM simulations, in which the model is forced by the 2 polarities of the pan-Pacific SST pattern. The idealized simulations further show that it is primarily the tropical part of the SST anomalies that influence the USGP. As such, the USGP tend to have above normal precipitation when the tropical Pacific SSTs are above normal, while there is a tendency for drought when the tropical SSTs are cold. The upper tropospheric response to the pan-Pacific SST EOF shows a global-scale pattern with a strong wave response in the Pacific and a substantial zonally-symmetric component in which USGP pluvial (drought) conditions are associated with reduced (enhanced) heights throughout the extra-tropics. The potential predictability of rainfall in the USGP associated with SSTs is rather modest, with on average about 1/3 of the total low frequency rainfall variance forced by SST anomalies. Further idealized experiments with climatological SST, suggest that the remaining low frequency variance in the USGP precipitation is the result of interactions with soil moisture. In particular, simulations with soil moisture feedback show a six-fold increase in the variance in annual USGP precipitation compared with simulations in which the soil feedback is excluded. In addition to increasing variance, the interactions with the soil introduce year-to-year memory in the hydrological cycle that is consistent with a red noise process, in which the low frequencies in the deep soil are the result of integrating a net forcing (precipitation-evaporation-runoff) that is white noise on interannual time scales. As such, the role of low frequency SST variability is to introduce a bias to the net forcing on the soil moisture that drives the random process preferentially to either wet or dry conditions.

Schubert, Siegfried D.↗
DOE OSTI2022

CMIP6-based Multi-model Hydroclimate Projection over the Conterminous US

We present a suite of high-resolution downscaled hydro-climate projections over the conterminous United States (CONUS) based on a six-member General Climate Model (GCM) ensemble from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using two different downscaling approaches (statistical-based DBCCA & dynamical-based RegCM) based on two meteorological reference observations (Daymet & Livneh), and then fed to two calibrated hydrologic models (VIC & PRMS) to simulate projected future hydrologic responses. Each ensemble member covers 1980–2019 in the historic period and 2020–2059 in the near-term future period under the high-end (SSP585) emission scenario. Major variables such as daily maximum temperature (tmax), daily minimum temperature (tmin), daily total precipitation (prcp), daily average wind speed (wind), and daily total runoff (runoff) at 1/24° (~4 km) spatial resolution across the CONUS are provided through this data portal. This dataset is derived to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). Further details of this dataset can be referred to Kao et al. (2022) and Rastogi et al. (2022).

13 HYDRO ENERGY↗
DOE OSTI2023

Time-Resolved Line Shapes of Single Quantum Emitters via Machine Learned Photon Correlations

Solid-state single-photon emitters (SPEs) are quantum light sources that combine atomlike optical properties with solid-state integration and fabrication capabilities. SPEs are hindered by spectral diffusion, where the emitter’s surrounding environment induces random energy fluctuations. Timescales of spectral diffusion span nanoseconds to minutes and require probing single emitters to remove ensemble averaging. Photon correlation Fourier spectroscopy (PCFS) can be used to measure time-resolved single emitter line shapes, but is hindered by poor signal-to-noise ratio in the measured correlation functions at early times due to low photon counts. Here, we develop a framework to simulate PCFS correlation functions directly from diffusing spectra that match well with experimental data for single colloidal quantum dots. We use these simulated datasets to train a deep ensemble autoencoder machine learning model that outputs accurate, noiseless, and probabilistic reconstructions of the noisy correlations. Using this model, we obtain reconstructed time-resolved single dot emission line shapes at timescales as low as 10 ns, which are otherwise completely obscured by noise. This enables PCFS to extract optical coherence times on the same timescales as Hong-Ou-Mandel two-photon interference, but with the advantage of providing spectral information in addition to estimates of photon indistinguishability. Further, our machine learning approach is broadly applicable to different photon correlation spectroscopy techniques and SPE systems, offering an enhanced tool for probing single emitter line shapes on previously inaccessible timescales.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1996

Ensemble averaged dynamic modeling

The possibility of using the information from simultaneous equivalent Large Eddy Simulations (LAS) for improving the subgrid scale modeling is investigated. An ensemble average dynamic model is proposed as an alternative to the usual spatial average versions. It is shown to be suitable independently of the existence of any homogeneity directions, and its formulation is thus universal. The ensemble average dynamic model is shown to give very encouraging results for as few as 16 simultaneous LES's.

Carati, D.↗
NASA NTRS2003

Precipitation Processes Developed During ARM (1997), TOGA COARE (1992) GATE (1974), SCSMEX (1998), and KWAJEX (1999): Consistent 3D, Semi-3D and 3D Cloud Resolving Model Simulations

Real clouds and cloud systems are inherently three-dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D) have been used to study the response of clouds to large-scale forcing. IN these 3D simulators, the model domain was small, and the integration time was 6 hours. Only recently have 3D experiments been performed for multi-day periods for tropical clouds systems with large horizontal domains at the National Center of Atmospheric Research (NCAR) and at NASA Goddard Space Center. At Goddard, a 3D cumulus Ensemble (GCE) model was used to simulate periods during TOGA COARE, GATE, SCSMEX, ARM, and KWAJEX using a 512 by 512 km domain (with 2-km resolution). The result indicate that surface precipitation and latent heating profiles are very similar between the 2D and 3D GCE model simulation. The major objective of this paper are: (1) to assess the performance of the super-parametrization technique, (2) calculate and examine the surface energy (especially radiation) and water budget, and (3) identify the differences and similarities in the organization and entrainment rates of convection between simulated 2D and 3D cloud systems.

Tao, W.-K.↗
DOE OSTI2020

CoSim: A Simulator for Co-Scheduling of Batch and On-Demand Jobs in HPC Datacenters

The increasing scale and complexity of scientific applications are rapidly transforming the ecosystem of tools, methods, and workflows adopted by the high-performance computing (HPC) community. Big data analytics and deep learning are gaining traction as essential components in this ecosystem in a variety of scenarios, such as, steering of experimental instruments , acceleration of high-fidelity simulations through surrogate computations, and guided ensemble searches. In this context, the batch job model traditionally adopted by the supercomputing in-frastructures needs to be complemented with support to schedule opportunistic on-demand analytics jobs, leading to the problem of efficient preemption of batch jobs with minimum loss of progress. In this paper, we design and implement a simulator, CoSim, that enables on-the-fly analysis of the trade-offs arising between delaying the start of opportunistic on-demand jobs, which leads to longer analytics latency, and loss of progress due to preemption of batch jobs, which is necessary to make room for on-demand jobs. To this end, we propose an algorithm based on dynamic programming with predictable performance and scalability that enables supercomputing infrastructure schedulers to analyze the aforementioned trade-off and take decisions in near real-time. Compared with other state-of-art approaches using traces of the Theta pre-Exascale machine, our approach is capable of finding the optimal solution, while achieving high performance and scalability.

batch job preemption↗
NASA NTRS2003

Precipitation Processes Developed During ARM (1997), TOGA COARE (1992), GATE (1974), SCSMEX (1998), and KWAJEX (1999): Consistent 2D, Semi-3D and 3D Cloud Resolving Model Simulations

Real clouds and cloud systems are inherently three-dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D). A few 3D CRMs have been used to study the response of clouds to large-scale forcing. In these 3D simulations, the model domain was small, and the integration time was 6 hours. Only recently have 3D experiments been performed for multi-day periods for tropical cloud systems with large horizontal domains at the National Center for Atmospheric Research (NACAR) and at NASA Goddard Space Flight Center . At Goddard, a 3D Goddard Cumulus Ensemble (GCE) model was used to simulate periods during TOGA COARE, SCSMEX and KWAJEX using 512 by 512 km domain (with 2 km resolution). The results indicate that surface precipitation and latent heating profiles are very similar between the 2D and 3D GCE model simulations. The reason for the strong similarity between the 2D and 3D CRM simulations is that the same observed large-scale advective tendencies of potential temperature, water vapor mixing ratio, and horizontal momentum were used as the main focusing in both the 2D and 3D models. Interestingly, the 2D and 3D versions of the CRM used at CSU showed significant differences in the rainfall and cloud statistics for three ARM cases. The major objectives of this paper are: (1) to assess the performance of the super-parameterization technique, (2) calculate and examine the surface energy (especially radiation) and water budgets, and (3) identify the differences and similarities in the organization and entrainment rates of convection between simulated 2D and 3D cloud systems.

Tao, W-K.↗
NASA NTRS1994

The proton cyclotron instability and the anisotropy/B inverse correlation

Spacecraft observations in the strongly compressed subsolar magnetosheath show an inverse correlation between the proton temperature anistrophy (T(sub perpendicular to p)/T(sub parallel to p) greater than 1 where perpendicular to and parallel to denote directions perpendicular and parallel to the background magnetic field) and the parallel proton beta (beta(sub parallel to p). This manuscript uses one-dimensional hybrid simulations of the proton cyclotron anisotropy instability in homogeneous electron-proton plasmas to study this correlation which may represent a limited closure relation for fluid theories of anisotropic space plasmas. The emphasis is on driven simulation which increase the temperature anisotropy by periodically reducing the magnetic-field-aligned velocities of the protons. The late-time states from ensembles of both initial value and driven simulations yield very similar expressions for the proton anisotropy/beta(sub parallel to P) inverse correlation, and provide a basis for explaining differences between sheath observations from different spacecraft. The driven simulations also yield expressions for the maximum instability growth rate and the fluctuating field energy as functions of beta (sub parallel to p) and a parameter characterizing the anisotropy driver.

Gary, S. Peter↗
NASA NTRS2003

Radar Reflectivity Simulated by a 2-D Spectra Bin Model: Sensitivity of Cloud-aerosol Interaction

The Goddard Cumulus Ensemble (GCE) model with bin spectra microphysics is used to simulate mesoscale convective systems.The model uses explicit bins to represent size spectra of cloud nuclei, water drops, ice crystals, snow and graupel. Each hydrometeorite category is described by 33 mass bins. The simulations provide a unique data set of simulated raindrop size distribution in a realistic dynamic frame. Calculations of radar parameters using simulated drop size distribution serve as an evaluation of numerical model performance. In addition, the GCE bin spectra modes is a very useful tool to study uncertainties related to radar observations; all the environmental parameters are precisely known. In this presentation, we concentrate on the discussion of Z-R (ZDR-R) relation in the simulated systems. Due to computational limitations, the spectra bin model has been run in two dimensions with 31 stretched vertical layers and 1026 horizontal grid points (1 km resolution). Two different cases, one in midlatitude continent, the other in tropical ocean, have been simulated. The continental case is a strong convection which lasted for two hours. The oceanic case is a persistent system with more than 10 hours' life span. It is shown that the simulated Z-R (ZDR-R) relations generally agree with observations using radar and rain gauge data. The spatial and temporal variations of Z-R relation in different locations are also analyzed. Impact of aerosols on cloud formation and raindrop size distribution was studied. Both clean (low CCN) and dirty (high CCN) cases are simulated. The Z-R relation is shown to vary considerable in the initial CCN concentrations.

Li, Kiaowen↗
DOE OSTI2024

Concurrent Inter-Model Spread of Boreal Winter Westerly Jet Meridional Positions Between the Northern and Southern Hemispheres in CMIP6 Models

Here, this study investigates the inter-model spread of climatological extratropical westerly jets in boreal winter, using the historical simulation of 52 Coupled Model Intercomparison Project phase 6 (CMIP6) models from 1851 to 2014. The results show that there is a substantial spread in the latitude of the upper-tropospheric westerly jet across models, characterised by large inter-model standard deviations to both the poleward and equatorward sides of the jet axis, although the multi-model ensemble mean (MME) performs well in simulating meridional position of westerly jets. Furthermore, we detect the consistency of inter-model jet position spread between the Northern and Southern Hemispheres, based on the inter-model empirical orthogonal function (EOF) decomposition and correlation of regional-averaged zonal winds. Specifically, the models that simulate the westerly jets poleward/equatorward relative to the MME position in one hemisphere also tend to simulate the jets poleward/equatorward in the other hemisphere. Accordingly, we define a global jet spread index to depict the concurrence of jet shift in the two hemispheres. The results of inter-model regression analyses based on this index indicate that the models positioning the jets poleward than the MME tend to simulate a wider Hadley Cell, a poleward-shifted Ferrel Cell in the Southern Hemisphere, enhanced precipitation in the subtropics and suppressed precipitation in the tropics, and warmer sea surface temperatures in the subtropics and mid-latitudes. The present results suggest that improving the simulation of jet positions in climate models requires a comprehensive consideration of thermal states in the tropics and subtropics/mid latitudes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Simulation of pitch angle diffusion of charged particles in a disordered magnetic field

Results are reported for computer simulation experiments in which a statistical ensemble of random magnetic field realizations is generated, orbits of charged particles in the random fields are followed, and a pitch-angle diffusion coefficient is derived from the temporal evolution of the orbits. Diffusion coefficients predicted by three nonlinear theories are compared with the derived coefficients for the standard quasilinear theory of velocity diffusion, and the goals of future simulations are outlined.

Kaiser, T. B.↗
NASA NTRS2008

Application of the CloudSat and NEXRAD Radars Toward Improvements in High Resolution Operational Forecasts

As computational power increases, operational forecast models are performing simulations with higher spatial resolution allowing for the transition from sub-grid scale cloud parameterizations to an explicit forecast of cloud characteristics and precipitation through the use of single- or multi-moment bulk water microphysics schemes. investments in space-borne and terrestrial remote sensing have developed the NASA CloudSat Cloud Profiling Radar and the NOAA National Weather Service NEXRAD system, each providing observations related to the bulk properties of clouds and precipitation through measurements of reflectivity. CloudSat and NEXRAD system radars observed light to moderate snowfall in association with a cold-season, midlatitude cyclone traversing the Central United States in February 2007. These systems are responsible for widespread cloud cover and various types of precipitation, are of economic consequence, and pose a challenge to operational forecasters. This event is simulated with the Weather Research and Forecast (WRF) Model, utilizing the NASA Goddard Cumulus Ensemble microphysics scheme. Comparisons are made between WRF-simulated and observed reflectivity available from the CloudSat and NEXRAD systems. The application of CloudSat reflectivity is made possible through the QuickBeam radiative transfer model, with cautious application applied in light of single scattering characteristics and spherical target assumptions. Significant differences are noted within modeled and observed cloud profiles, based upon simulated reflectivity, and modifications to the single-moment scheme are tested through a supplemental WRF forecast that incorporates a temperature dependent snow crystal size distribution.

Molthan, A. L.↗
DOE OSTI2025

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗
NASA NTRS2001

Risk analysis simulation of rover operations for Mars surface exploration

Risk management advocates have long sought to directly influence the early stages of the systems engineering process through a more effective role in system design trade studies. The principal obstacle to this has been the lack of credible ways to represent and quantify mission risk—that is, a combination of the probability of mission success (“system safety”) and science value—for the project manager and the rest of the design team. If it were possible to quantify mission risk, then the effects of proposed mission and system design changes could be calculated, and along with life-cycle costs, could be used to explore the design space more extensively and select better designs. JPL has been working to build the capability to quantify the probability of mission success using a federation of diverse simulations and models, each of which contributes some vital piece of the puzzle. The initial institutional focus has been on Mars surface operations. This ensemble computing framework enables the diverse models and simulations to work together seamlessly. Recent work at JPL has demonstrated the capability to exercise this ensemble from end-to-end using an Oracle-based database to automatically move results from one model/simulation to the next stage in the analysis.

Shishko, Robert↗
NASA NTRS2004

The Impact of Model Configuration and Large-Scale, Upper-Level Forcing on CRM- Simulated Convective Systems

Real clouds and cloud systems are inherently three-dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D, see a brief review by Tao 2003). Only recently have 3D experiments been performed for multi-day periods for tropical cloud systems with large horizontal domains at the National Center for Atmospheric Research, at NOAA GFDL, at the U. K. Met. Office, at Colorado State University and at NASA Goddard Space Flight Center (Tao 2003). At Goddard, a 3D Goddard Cumulus Ensemble (GCE) model was used to simulate periods during TOGA COARE (December 19-27, 1992), GATE (September 1-7, 1974), SCSMEX (June 2-11, 1998), ARM (June 26-30, 1997) and KWAJEX (August 7-13, August 18-21, and August 29-September 12, 1999) using a 512 by 512 km domain (with 2-km resolution). The results indicate that surface precipitation and latent heating profiles are similar between the 2D and 3D GCE model simulations. However, there are difference in radiation, surface fluxes and precipitation characteristics. The 2D GCE model was used to perform a long-term integration on ARM/GCSS case 4 (22 days at the ARM Southern Great Plains site in March 2000). Preliminary results showed a large temperature bias in the upper troposphere that had not been seen in previous tropical cases. The major objectives of this paper are: (1) to determine the sensitivities to model configuration (i.e., 2D in west-east, south-north or 3D), (2) to identify the differences and similarities in the organization and entrainment rates of convection between 2D- and 3D-simulated ARM cloud systems, and (3) assess the impact of upper tropospheric forcing on tropical and ARM case 4 cases.

Tao, W.-K.↗
NASA NTRS2004

The Impact of Model Configuration and Large-Scale, Upper-Level Forcing on CRM-Simulated Convective Systems

Real clouds and cloud systems are inherently three-dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D, see a brief review by Tao 2003). Only recently have 3D experiments been performed for multi-day periods for tropical cloud systems with large horizontal domains at the National Center for Atmospheric Research, at NOAA GFDL, at the U. K. Met. Office, at Colorado State University and at NASA Goddard Space Flight Center (Tao 2003). At Goddard, a 3D Goddard Cumulus Ensemble (GCE) model was used to simulate periods during TOGA COARE (December 19-27, 1992), GATE (September 1-7, 1974), SCSMEX (June 2-11, 1998), ARM (June 26-30, 1997) and KWAJEX (August 7-13, August 18-21, and August 29-September 12, 1999) using a 512 km domain (with 2-kilometer resolution). The results indicate that surface precipitation and latent heating profiles are similar between the 2D and 3D GCE model simulations. However, there are difference in radiation, surface fluxes and precipitation characteristics. The 2D GCE model was used to perform a long-term integration on ARM/GCSS case 4 (22 days at the ARM southern Great Plains site in March 2000). Preliminary results showed a large temperature bias in the upper troposphere that had not been seen in previous tropical cases. The major objectives of this paper are: (1) to determine the sensitivities to model configuration (ie., 2D in west-east, south-north or 3D), (2) to identify the differences and similarities in the organization and entrainment rates of convection between 2D- and 3D-simulated ARM cloud systems, and (3) assess the impact of upper tropospheric forcing on tropical and ARM case 4 cases.

Tao, W.-K.↗
NASA NTRS2013

Simulations of the Mid-Pliocene Warm Period Using Two Versions of the NASA-GISS ModelE2-R Coupled Model

The mid-Pliocene Warm Period (mPWP) bears many similarities to aspects of future global warming as projected by the Intergovernmental Panel on Climate Change (IPCC, 2007). Both marine and terrestrial data point to high-latitude temperature amplification, including large decreases in sea ice and land ice, as well as expansion of warmer climate biomes into higher latitudes. Here we present our most recent simulations of the mid-Pliocene climate using the CMIP5 version of the NASAGISS Earth System Model (ModelE2-R). We describe the substantial impact associated with a recent correction made in the implementation of the Gent-McWilliams ocean mixing scheme (GM), which has a large effect on the simulation of ocean surface temperatures, particularly in the North Atlantic Ocean. The effect of this correction on the Pliocene climate results would not have been easily determined from examining its impact on the preindustrial runs alone, a useful demonstration of how the consequences of code improvements as seen in modern climate control runs do not necessarily portend the impacts in extreme climates.Both the GM-corrected and GM-uncorrected simulations were contributed to the Pliocene Model Intercomparison Project (PlioMIP) Experiment 2. Many findings presented here corroborate results from other PlioMIP multi-model ensemble papers, but we also emphasize features in the ModelE2-R simulations that are unlike the ensemble means. The corrected version yields results that more closely resemble the ocean core data as well as the PRISM3D reconstructions of the mid-Pliocene, especially the dramatic warming in the North Atlantic and Greenland-Iceland-Norwegian Sea, which in the new simulation appears to be far more realistic than previously found with older versions of the GISS model. Our belief is that continued development of key physical routines in the atmospheric model, along with higher resolution and recent corrections to mixing parameterisations in the ocean model, have led to an Earth System Model that will produce more accurate projections of future climate.

climate change↗
DOE OSTI2022

The contribution of Humboldt Glacier, northern Greenland, to sea-level rise through 2100 constrained by recent observations of speedup and retreat

Abstract. Humboldt Glacier, northern Greenland, has retreated and accelerated through the 21st century, raising concerns that it could be a significant contributor to future sea-level rise. We use a data-constrained ensemble of three-dimensional higher-order ice sheet model simulations to estimate the likely range of sea-level rise from the continued retreat of Humboldt Glacier. We first solve for basal traction using observed ice thickness, bed topography, and ice surface velocity from the year 2007 in a PDE-constrained (partial differential equation) optimization. Next, we impose calving rates to match mean observed retreat rates from winter 2007–2008 to winter 2017–2018 in a transient calibration of the exponent in the power-law basal friction relationship. We find that power-law exponents in the range of 1/7–1/5 – rather than the commonly used 1/3–1 – are necessary to reproduce the observed speedup over this period. We then tune an iceberg calving parameterization based on the von Mises stress yield criterion in another transient-calibration step to approximate both observed ice velocities and terminus position in 2017–2018. Finally, we use the range of basal friction relationship exponents and calving parameter values to generate the ensemble of model simulations from 2007–2100 under three climate forcing scenarios from CMIP5 (two RCP8.5 forcings, Representative Concentration Pathway) and CMIP6 (one SSP5-8.5 forcing, Shared Socioeconomic Pathway). Our simulations predict 5.2–8.7 mm of sea-level rise from Humboldt Glacier, significantly higher than a previous estimate (∼ 3.5 mm) and equivalent to a substantial fraction of the 40–140 mm predicted by ISMIP6 from the whole Greenland Ice Sheet. Our larger future sea-level rise prediction results from the transient calibration of our basal friction law to match the observed speedup, which requires a semi-plastic bed rheology. In many simulations, our model predicts the growth of a sizable ice shelf in the middle of the 21st century. Thus, atmospheric warming could lead to more retreat than predicted here if increased surface melt promotes hydrofracture of the ice shelf. Our data-constrained simulations of Humboldt Glacier underscore the sensitivity of model predictions of Greenland outlet glacier response to warming to choices of basal shear stress and iceberg calving parameterizations. Further, transient calibration of these parameterizations, which has not typically been performed, is necessary to reproduce observed behavior. Current estimates of future sea-level rise from the Greenland Ice Sheet could, therefore, contain significant biases.

54 ENVIRONMENTAL SCIENCES↗