Quality control for community-based sea-ice model development Roberts, Andrew F.; Hunke, Elizabeth C.; Allard, Richard ...
Philosophical transactions - Royal Society. Mathematical, Physical and engineering sciences/Philosophical transactions - Royal Society. Mathematical, physical and engineering sciences,
09/2018, Letnik:
376, Številka:
2129
Journal Article
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A new collaborative organization for sea-ice model development, the CICE Consortium, has devised quality control procedures to maintain the integrity of its numerical codes' physical representations, ...enabling broad participation from the scientific community in the Consortium's open software development environment. Using output from five coupled and uncoupled configurations of the Los Alamos Sea Ice Model, CICE, we formulate quality control methods that exploit common statistical properties of sea-ice thickness, and test for significant changes in model results in a computationally efficient manner. New additions and changes to CICE are graded into four categories, ranging from bit-for-bit amendments to significant, answer-changing upgrades. These modifications are assessed using criteria that account for the high level of autocorrelation in sea-ice time series, along with a quadratic skill metric that searches for hemispheric changes in model answers across an array of different CICE configurations. These metrics also provide objective guidance for assessing new physical representations and code functionality.
This article is part of the theme issue 'Modelling of sea-ice phenomena'.
In the last decades, the Arctic climate has changed dramatically, with the loss of multiyear sea ice one of the clearest consequences. These changes have occurred on relatively rapid timescales, and ...both accurate short-term Arctic prediction (e.g., 10 days to three months) and climate projection of future Arctic scenarios present ongoing challenges. Here we describe a representation of the Arctic ocean and sea ice in a ultrahigh resolution simulation in which the horizontal grid mesh reduces from 8 km at the equator to 2 km at the poles (UH8to2) for the years 2017–2020. We find the simulation reproduces observed distributions of seasonal sea-ice thickness and concentration realistically, although concentration is biased low in the spring and summer and low biases in thickness are found in the central and eastern basins in the fall. Volume, fresh water, and heat transports through key passages are realistic, lying within observationally determined ranges. Climatological comparisons reveal that the UH8to2 Atlantic Water is shallower, warmer, and saltier than the World Ocean Atlas 2018 climatology for 2005–2017 in the eastern basin. Our analysis suggests that these biases, combined with a lack of stratification in the upper 100 m of the simulated ocean, contribute to the winter biases in modeled sea ice thickness. This relationship between biases in the sea ice and ocean points to a potential positive feedback within the model, illuminating challenges for long term model predictive power in a changing Arctic climate.
•8-to-2 km resolution global model reproduces major Arctic currents and water masses.•Biases in ocean heat and mixed layer depth lead to overly high ocean–ice heat fluxes.•Overly deep winter mixed layers entrain heat from the ocean, causing excess ice melt.
We present a new one‐dimensional parameterization of gravity drainage implemented in an all‐new thermodynamic component of the Los Alamos Sea Ice Model (CICE), based on mushy layer theory. We solve a ...set of coupled, nonlinear equations for sea‐ice temperature (enthalpy) and salinity using an implicit Jacobian‐free Newton‐Krylov method. Time resolved observations of gravity drainage show two modes of desalination during growth. Rapid drainage occurs in a thin region just above the ice/ocean interface, while slower drainage occurs throughout the ice. Parameterizations are designed to represent each of these modes and work simultaneously. Near the interface, desalination occurs primarily via the fast drainage, while slow drainage continues to desalinate ice above the interface. The rapid desalination is convectively driven and is parameterized based on a consideration of flow driven upward within the mush and downward in chimneys, modified by the Rayleigh number. The slow desalination is represented as a simple relaxation of bulk salinity to a value based on a critical porosity for sea‐ice permeability. It is shown that these parameterizations can adequately reproduce observational data from laboratory experiments and field measurements.
Key Points
We implement a 1D gravity drainage parameterization in the sea ice model CICEGravity drainage observations show fast and slow desalination modesThe parameterization is designed to represent each of these modes
► We have developed a new parameterization for melt ponds in the sea ice model CICE. ► Ponds are treated as tracers on the undeformed ice area of each thickness category. ► Melt pond processes are ...simulated using physically based descriptions. ► Level ice and pond area increase as sea ice thins, enhancing ice thinning. ► Shading by snow partially compensates for the effects of ponds on sea ice volume.
A new meltpond parameterization has been developed for the CICE sea ice model, taking advantage of the level ice tracer available in the model. The ponds evolve according to physically based process descriptions, assuming a depth-area ratio for changes in pond volume. A novel aspect of the new scheme is that the ponds are carried as tracers on the level ice area of each thickness category, thus limiting their spatial extent based on the simulated sea ice topography. This limiting is meant to approximate the horizontal drainage of melt water into depressions in ice floes. Simulated melt pond processes include collection of liquid melt water and rain into ponds, drainage through permeable sea ice or over the edges of floes, infiltration of snow by pond water, and refreezing of ponds. Furthermore, snow that falls on top of ponds whose top surface has refrozen blocks radiation from penetrating into the ponds and sea ice below.
Along with a control simulation, we present a range of sensitivity tests to parameters related to each subprocess described by the parameterization. With the exception of one parameter that alters the albedo of snow-covered pond ice, results are not highly sensitive to these parameters unless an entire process is removed. The snow simulation itself is critical, because the volume of snow deposition and rate of snow melt largely determine the timing and extent of the simulated melt ponds. Nevertheless, compensating effects moderate the model’s sensitivity to precipitation changes. For instance, infiltration of the snow by melt water postpones the appearance of ponds and the subsequent acceleration of melting through albedo feedback, while snow on top of refrozen pond ice also reduces the ponds’ effect on the radiation budget.
By construction, the model simulation of level and ridged ice is also important for this parameterization. We find that as sea ice thins, either through time or when comparing sensitivity tests, the area of level ice increases. This leads to an enhanced thinning feedback in the model, because a greater ice area may be exposed to ponding and further thinning due to lowered albedo.
Behavior of the elastic–viscous–plastic (EVP) model for sea ice dynamics is explored, with particular attention to a necessary numerical linearization of the internal ice stress term in the momentum ...equation. Improvements to both the mathematical and numerical formulations of the model have moderated the impact of linearizing the stress term; simulations with the original EVP formulation and the improved version are used to explain the consequences of using different numerical approaches. In particular, we discuss the model behavior in two regimes, low ice concentration such as occurs in the marginal ice zone, and very high ice concentration, where the ice is nearly rigid. Most of these results are highly relevant to the viscous–plastic (VP) ice dynamics model on which the EVP model is based. We provide examples of certain pathologies that the VP model and its numerical formulations exhibit at steady state.
A moderately fine‐resolution (0.4°, 40 vertical levels), global, coupled ice‐ocean model was configured and run for 24 years (1979–2002), forced with high‐frequency National Center for Environmental ...Prediction/National Center for Atmospheric Research (NCEP/NCAR) atmospheric fluxes. The model consists of the Los Alamos National Laboratory Parallel Ocean Program (POP) and sea ice model (CICE). The fidelity of the simulated mean climatological state and variability of key variables such as ice concentration, total ice area, ice thickness and drift were compared to observational data sets from satellite and ice drift buoy measurements. Basin‐scale changes in the lower atmosphere/surface ocean/sea‐ice in the simulated Arctic and Nordic Seas before and after the North Atlantic Oscillation (NAO) phase switch in 1995 were examined using winter composite analyses over the period 1990–1999. Ice cover changes between the two NAO phases were consistent with observations in that reduced concentrations were found in the Nordic and Barents Seas and increased values occurred in the Labrador Sea. Next we regionally evaluated the relative importance of winter anomalies of upper‐ocean mixed layer net heat fluxes and of ocean temperature advection on marginal ice zone variability in the Irminger, Greenland, and Barents Seas for this ten‐year period. We found that the net heat flux winter anomaly was at least four times more important than the winter anomaly of ocean temperature advection in the Greenland and Barents Seas, while it was twice as important in the Irminger Sea. The Ekman ocean temperature advection component generally dominated the geostrophic component in all three regions.
Key Points
Bottom ice melt rates closely related to the ocean advection
Bottom ice melt rates dominate the top ice melt rates in MIZ
Ageostrophic ocean advection dominates the geostrophic during winter and NAO+
Complex surface topography is a characteristic especially of older sea ice, and an observed loss of older or multi-year sea ice in the Arctic indicates an imminent transition of the Arctic ice cover ...from perennial to seasonal. Prediction of this transition in the Arctic system is one of today's “big science” questions. The objective of this paper is to compare model output and data analysis, toward addressing a key problem in sea-ice modeling, the correct representation of ridges and other spatial features that result from deformation. Morphologically complex, ridged ice exists in Fram Strait. High-resolution airborne remote sensing data, including image data and altimeter data showing ridging, collected from unmanned aircraft over Fram Strait during the Characterization of Arctic Sea Ice Experiment (CASIE) in 2009, are analyzed using geostatistical classification. This approach results in parameters that capture deformation characteristics and facilitates comparison to model results. Ridging and other forms of deformation are implemented in the Los Alamos sea-ice model CICE. The main parameters that are compared are freeboard as a proxy of ice thickness and percentages of level versus ridged ice from modeling and laser altimeter data analysis. Results from freeboard analysis indicate that except for the elevation class 0.1m–0.2m, models and observations match very well. For concentration of deformed ice, results from CICE using the standard parameter configuration are within 20% of deformed ice area concentration compared to results from altimeter data analysis. Variation of several physical parameters indicates the sensitivity of model results to ridging parameters and provides results that are within 7% of data analysis results in each grid cell, with the parameter that yields the best match depending on geographic location and morphologic province. In general our approach demonstrates an avenue for parameterization on both the data analysis side and the modeling side that allows a direct comparison of results from sea-ice models and data analysis and hence an evaluation of numerical sea-ice models.
•We address a key problem in sea-ice modeling, the correct representation of ridges and other deformation features.•We derive a novel approach for sea-ice model-data comparison and hence for evaluation of numerical models.•We perform a comparison of results from a sea-ice model, CICE, and analysis of remote sensing data.•The approach utilizes parameterization on both the data analysis and the modeling side.•Results indicate that deformed ice concentration is in the same range for model and data (within 7%) in Fram Strait.
Improved parallel performance of the CICE model in CESM1 Craig, Anthony P; Mickelson, Sheri A; Hunke, Elizabeth C ...
The international journal of high performance computing applications,
05/2015, Letnik:
29, Številka:
2
Journal Article
Recenzirano
The Los Alamos sea ice model, CICE, is a sophisticated finite difference grid point model. It has been a part of the National Science Foundation and Department of Energy community climate models ...(Community Climate System Model (CCSM) and Community Earth System Model (CESM)) for over a decade. It includes various physical and dynamical processes and is parallelized to run on large-scale computer systems. The CICE model was assessed in the CESM at different resolutions and target processor counts to better understand and optimize the performance. Several new decompositions and a new feature to reduce the halo cost were added to the model. The new decompositions better leverage land block elimination and take advantage of scaling opportunities in different computational kernels. As a result of these new features, the CICE model performance has been improved by up to 45% and has more flexibility to be run efficiently at arbitrary Message Passing Interface (MPI) task counts.
Recent observations of Arctic sea ice show that the decrease in summer ice cover over the last few decades has occurred in conjunction with a significant loss of multiyear ice. The transition to an ...Arctic that is populated by thinner, first-year sea ice has important implications for future trends in area and volume. Here, a reduced model for Arctic sea ice is developed. This model is used to investigate how the survivability of first-year and multiyear ice controls the mean state, variability, and trends in ice area and volume. A hindcast with a global dynamic–thermodynamic sea ice model that traces first-year and multiyear ice is used to estimate the survivability of each ice type. These estimates of survivability, in concert with the reduced model, yield persistence time scales of September area and volume anomalies and the characteristics of the sensitivity of sea ice to climate forcing that compare well with a fully coupled climate model. The September area is found to be nearly in equilibrium with climate forcing at all times, and therefore the observed decline in summer sea ice cover is a clear indication of a changing climate. Keeping an account of first-year and multiyear ice area within global climate models offers a powerful way to evaluate those models with observations, and could help to constrain projections of sea ice decline in a warming climate.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The mechanisms forcing variability in Southern Ocean sea ice and sea surface temperature from 600 years of a control climate coupled model integration are discussed. As in the observations, the ...leading mode of simulated variability exhibits a dipole pattern with positive anomalies in the Pacific sector associated with negative anomalies in the Atlantic. It is found that in the Pacific ocean circulation changes associated with variable wind forcing modify the ocean heat flux convergence and sea ice transport, resulting in sea surface temperature and sea ice anomalies. The Pacific ice and ocean anomalies persist over a number of years due to reductions in ocean shortwave absorption reinforcing the initial anomalies. In the Atlantic sector, no single process dominates in forcing the anomalies. Instead there are contributions from changing ocean and sea ice circulation and surface heat fluxes. While the absorbed solar radiation in the Atlantic is modified by the changing surface albedo, the anomalies are much shorter-lived than in the Pacific because the ocean circulation transports them northward, removing them from ice formation regions. Sea ice and ocean anomalies associated with the El Niño–Southern Oscillation and the Southern Annular Mode both exhibit a dipole pattern and contribute to the leading mode of ice and ocean variability.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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