The cryosphere and the processes that force its evolution have profound and permanent effects on the global climate. In particular, Arctic amplification leads to extreme mid-latitude weather and glacier and ice sheet retreat is increasing global mean sea level causing the ocean to encroach onto coastal communities. Despite potentially devastating impacts, accurate predictions of future dynamics and rigorous characterizations of the associated uncertainty remain elusive. Misunderstood physics and computational limitations require complex physical processes to be parameterized and calibrated using noisy data that is sparse in both space and time. However, collecting data in remote polar regions is difficult, dangerous, and expensive. Therefore, we must leverage remote sensing techniques and wisely allocate limited resources. Finally, predictive uncertainties must be quantified to give meaningful error bounds on quantities of interest, such as future mean sea level. This session discusses recent advancements trying to understand the dynamic processes governing the cryosphere given observations and/or models as well as techniques to obtain and analyze data.
10:30
Quantifying the Skill and Bias of Arctic Sea Ice Simulations
Andrew Roberts | Los Alamos National Laboratory | United States
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Andrew Roberts | Los Alamos National Laboratory | United States
This talk addresses a deficiency in the evaluation of sea ice thickness in earth system models (ESMs), and describes the principles for satellite emulators that compare sea ice freeboard from forward models with polar ocean retrievals by space-borne altimeters. To illustrate the method, we use late winter, springtime and autumnal measurements of surface topography of the rapidly-changing Arctic Ocean by the Geoscience Laser Altimeter System (GLAS) instrument aboard the Ice, Cloud, and land Elevation Satellite (ICESat) to evaluate three fully coupled models: The Regional Arctic System Model (RASM), the Energy Exascale Earth System Model (E3SM), and the Community Earth System Model (CESM). Sea ice freeboard is retrieved from model grid cells in spatiotemporal proximity to GLAS samples, and used to generate basin-wide skill and bias statistics of ESMs. The emulator is used to benchmark historical Coupled Model Inter-comparison Project (CMIP6) ensemble members from E3SM and CESM against RASM hindcasts, serving as a gauge of sea ice variability and fidelity in two current global models. Applicability of the methods to CryoSat-2 and ICESat-2 is discussed, along with the application of machine learning to the emulator to extend the temporal relevance of the data to pre-industrial simulations and provide a computationally-efficient and objective measure of the polar climate performance in Earth system models.
11:00
Forcing meshfree simulations of sea ice in the Nares Strait using uncertain wind current data
Brendan A. West | Cold Regions Research and Engineering Laboratory | United States
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Brendan A. West | Cold Regions Research and Engineering Laboratory | United States
Andrew D. Davis | Cold Regions Research and Engineering Laboratory | United States
Taylor S. Hodgdon | Cold Regions Research and Engineering Laboratory | United States
Devin O'Connor | Cold Regions Research and Engineering Laboratory | United States
Matthew Parno | Cold Regions Research and Engineering Laboratory | United States
Data-driven particle methods can provide detailed descriptions of sea ice dynamics that explicitly model fracture and ridging, which is not possible with typical continuum approaches. We use the ParticLS software library to develop meshfree simulations of sea ice in the Nares Strait near Greenland. We employ ParticLS’ discrete element method (DEM) capabilities and model sea ice as a collection of bonded particles. We investigate two methods for representing ice floes frozen together: (i) peridynamic integro-differential equations that can model deforming and fracturing bodies, (2) cohesive beams that model the interaction between particles with Euler–Bernoulli beam equations. This allows us to model the ice as a cohesive material while allowing it to fracture into smaller pieces, which then interact following DEM principles. The sea ice dynamics are therefore emergent properties given these small-scale interactions. In addition to modeling the dynamic sea ice patterns in the Nares Strait (arching, lead formation), our model provides a natural way to combine numerical simulations with observations. We generate realistic particle configurations by discretizing MODIS satellite imagery into polygonal floes, and then force the particles with realistic wind and ocean currents. We also investigate methods for quantitatively comparing model outputs to remote sensing measurements of the ice, which provides a rigorous approach for determining how well the model captures the dynamics.
11:30
Observe Greenland remotely from the subpolar North Atlantic? Uncertainty quantified in a large-scale oceanographic inverse problem
Nora Loose | Oden Institute for Computational Engineering and Sciences & Jackson School of Geosciences, The University of Texas at Austin | United States
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Nora Loose | Oden Institute for Computational Engineering and Sciences & Jackson School of Geosciences, The University of Texas at Austin | United States
Patrick Heimbach | Oden Institute for Computational Engineering and Sciences & Jackson School of Geosciences, The University of Texas at Austin | United States
Helen R. Pillar | Oden Institute for Computational Engineering and Sciences & Jackson School of Geosciences, The University of Texas at Austin | United States
Kerim H. Nisancioglu | Department of Earth Science, University of Bergen & Bjerknes Centre for Climate Research | Norway
The interaction of Greenland’s marine-terminating glaciers with warm ocean waters has been suggested as a dominant trigger for the glaciers’ recent retreat and acceleration. It is logistically challenging to directly measure oceanic heat transport to the ice margin. However, existing remote ocean observing systems, such as the recently deployed OSNAP (Overturning in the Subpolar North Atlantic Program) mooring array, may have high information potential because ocean dynamics can deliver warm waters to the ice front.
An ideal framework to assess ocean observing systems is an oceanographic inverse problem, where observations are connected to unobserved quantities through dynamical principles encapsulated in a numerical model. Here, we quantify the value of the OSNAP array for determining subsurface temperature at Greenland’s marine margins, by means of Hessian-based Uncertainty Quantification in the state and parameter estimation framework of the ECCO (Estimating the Circulation and Climate of the Ocean) project. We find that heat transport inferences from the OSNAP-West array, extending from Labrador to South Greenland, impose a stronger constraint on subsurface temperature at Greenland’s margins than heat transport inferences across the OSNAP-East array, extending from South Greenland to Scotland.
Quantifying uncertainties in a large-scale and state-of-the-art oceanographic inverse problem, as performed in this work, is a novelty in the field of ocean climate research.
12:00
High-resolution Remote Sensing Sea Ice Observations for Improved Prediction
Ellen Buckley | University of Maryland, Department of Atmospheric and Oceanic Science | United States
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Ellen Buckley | University of Maryland, Department of Atmospheric and Oceanic Science | United States
Sinead L. Farrell | University of Maryland, Department of Geographical Sciences | United States
Observations of Arctic sea ice reveal a negative and accelerating trend of end-of-summer extent, outpacing model projections, which suggests some sea ice processes are not well represented in models. In summer, snow atop the sea ice melts into ponds, decreasing surface albedo and contributing to the ice albedo feedback. Recent model sensitivity studies have shown that including melt pond (MP) parameters in sea ice forecasting end-of-summer ice predictions.
In summer, abundant moisture due to extensive open water areas results in the formation of low-lying clouds that can obscure surface observations. MPs appear radiometrically similar to open water and leads, impeding disambiguation of these features. Thus, our understanding of MP processes is lacking at an Arctic-wide level. Scientists rely on predictive models to supplement the limited summer observations. Here, we present new observational data from the ICESat-2 satellite that may be of interest to the modeling community. ICESat-2, launched by NASA in 2018, has demonstrated the ability to precisely (~2 cm) measure sea ice height with along-track sampling of 0.7m. We present examples of high-resolution spring sea ice topography and freeboard that may be used for model initialization, and new observations of MPs that may be used for validation of summer sea ice forecasting. Our ICESat-2 results are complemented by extensive, high-resolution airborne observations of MPs collected by Operation IceBridge in July 2016 and 2017.