Sea Ice Component Research Articles

Description and Evaluation of the CNRM‐Cerfacs Climate Prediction System (C3PS)

AbstractThe CNRM‐Cerfacs Climate Prediction System (C3PS) is a new research modeling tool for performing climate reanalyzes and seasonal‐to‐multiannual predictions for a wide array of Earth system variables. C3PS is based on the CNRM‐ESM2‐1 model including interactive aerosols and stratospheric chemistry schemes as well as terrestrial and marine biogeochemistry enabling a comprehensive representation of the global carbon cycle. C3PS operates through a seamless coupled initialization for the atmosphere, land, ocean, sea ice and biogeochemistry components that allows a continuum of predictions across seasonal to multiannual time‐scales. C3PS has also contributed to the Decadal Climate Prediction Project (DCPP‐A) as part of the sixth Coupled Model Intercomparison Project (CMIP6). Here we describe the main characteristics of this novel Earth system‐based prediction platform, including the methodological steps for obtaining initial states to produce forecasts. We evaluate the entire C3PS initialization procedure with the most up‐to‐date observations and reanalyzes over 1960–2021, and we discuss the overall performance of the system in the light of the lessons learned from previous and actual prediction platforms. Regarding the forecast skill, C3PS exhibits comparable seasonal predictive skill to other systems. At the multiannual scale, C3PS shows significant predictive skill in surface temperature during the first 2 years after initialization in several regions of the world. C3PS also exhibits potential predictive skill in Net primary production (NPP) and carbon fluxes several years in advance. This expands the possibility of applications of forecasting systems, such as the possibility of performing multiannual predictions of marine ecosystems and carbon cycle.

Antarctic Slope Undercurrent and onshore heat transport driven by ice shelf melting.

Elevated ice shelf melt rates in West Antarctica have been attributed to transport of warm Circumpolar Deep Water (CDW) onto the continental shelf via bathymetric troughs. These inflows are supplied by an eastward, subsurface slope current (referred to as the Antarctic Slope Undercurrent) that opposes the westward momentum input from local winds and tides. Despite its importance to basal melt, the mechanism via which the undercurrent forms, and thus what controls the shoreward heat transport, remains unclear. In this study, the dynamics of the undercurrent are investigated using high-resolution process-oriented simulations with coupled ocean, sea ice, and ice shelf components. It is shown that the bathymetric steering of the undercurrent toward the ice shelf is driven by upwelling of meltwater within the ice shelf cavity. Increased basal melt therefore strengthens the undercurrent and enhances onshore CDW transport, which indicates a positive feedback that may accelerate future melt of ice shelves, potentially further destabilizing the West Antarctic Ice Sheet.

Description of FIO-ESM version 2.1 and evaluation of its sea ice simulations

To improve Arctic sea ice simulations by the First Institute of Oceanography–Earth System Model (FIO–ESM), the model version has been updated from FIO–ESM v2.0 to FIO–ESM v2.1 by upgrading its sea ice component from Los Alamos Sea–Ice Model (CICE) version 4.0 (CICE4.0) to CICE6.0, and improving the ice–ocean heat exchange process from a two–equation boundary condition parameterization to a more realistic three–equation boundary condition parameterization. Numerical experiments show that the underestimation of Arctic summer sea ice extent (SIE) in FIO–ESM v2.0 is significantly improved by the model enhancements. The root mean square error of the simulated Arctic September SIE during 1979–2014 is reduced from 2.9 million to 0.7 million km2. Nevertheless, the biases of Antarctic SIE increase following the model version update. FIO–ESM v2.1 performs well for the simulations of surface air temperature, sea surface temperature, Atlantic Meridional Overturning Circulation, and Arctic SIE; however, it overestimates summer SIE in the Antarctic. Furthermore, future projections based on FIO–ESM v2.1 indicate that the first ice–free Arctic summer will occur in the 2050s and the 2040s under SSP2–4.5 and SSP5–8.5, respectively.

Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)

Abstract. This paper describes the global eddying ocean–sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model (GLOB16) is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16∘ and 98 vertical levels and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment. To assess the accuracy of simulated 3D ocean fields and highlight the relative benefits of resolving mesoscale processes, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean–sea ice component of the fully coupled CMCC climate model. The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed-layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling efforts, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions.

Subseasonal-to-seasonal prediction of arctic sea ice Using a Fully Coupled dynamical ensemble forecast system

With the opening of Arctic passage for maritime transportation under global warming, accurate prediction of Arctic sea ice (ASI) on subseasonal-to-seasonal (S2S) timescales becomes increasingly crucial for the economy and society but challenging. This study examined the S2S hindcast skills of monthly ASI during 1992–2019 using Flexible Global Ocean-Atmosphere-Land System, Finite-Volume version 2 (FGOALS-f2), a global coupled model that includes an interactive dynamical sea ice component. Firstly, the prediction system can accurately capture the seasonal cycle of Arctic sea ice extent (SIE). Still, it slightly overestimates SIE in March–June and significantly underestimates SIE in August–December. Meanwhile, the declining trend of SIE is underestimated in predictions for all target seasons, particularly for spring. Secondly, relatively high skills with an average ACC of 0.44 are found in predicting monthly SIE anomalies (SIEA) after removing the long-term linear trend at a one-to-six-month lead. However, the one-to-six-month lead prediction skills in April drop significantly by 36% when the interdecadal variation is removed. We find that the variation of April SIEA after removing the linear trend is dominated by the Pacific Decadal Oscillation (PDO), which can be better predicted than interannual variability ahead of subseasonal timescales. Unlike the SIE error-based metrics considered in previous studies, the one-to-three-month lead prediction in FGOALS-f2 shows significantly better performance in predicting the spatial distribution of September ASI for extreme years compared to normal years. Finally, the skill for predicting minimum SIEA in FGOALS-f2 is considerably higher at a three-to-six-month lead than anomaly persistence, indicating the advantage of this dynamical prediction system at a longer lead time.

Wave-triggered breakup in the marginal ice zone generates lognormal floe size distributions: a simulation study

Abstract. Fragmentation of the sea ice cover by ocean waves is an important mechanism impacting ice evolution. Fractured ice is more sensitive to melt, leading to a local reduction in ice concentration, facilitating wave propagation. A positive feedback loop, accelerating sea ice retreat, is then introduced. Despite recent efforts to incorporate this process and the resulting floe size distribution (FSD) into the sea ice components of global climate models (GCMs), the physics governing ice breakup under wave action remains poorly understood and its parametrisation highly simplified. We propose a two-dimensional numerical model of wave-induced sea ice breakup to estimate the FSD resulting from repeated fracture events. This model, based on linear water wave theory and visco-elastic sea ice rheology, solves for the scattering of an incoming time-harmonic wave by the ice cover and derives the corresponding strain field. Fracture occurs when the strain exceeds an empirical threshold. The geometry is then updated for the next iteration of the breakup procedure. The resulting FSD is analysed for both monochromatic and polychromatic forcings. For the latter results, FSDs obtained for discrete frequencies are combined following a prescribed wave spectrum. We find that under realistic wave forcing, lognormal FSDs emerge consistently in a large variety of model configurations. Care is taken to evaluate the statistical significance of this finding. This result contrasts with the power law FSD behaviour often assumed by modellers. We discuss the properties of these modelled distributions with respect to the ice rheological properties and the forcing waves. The projected output can be used to improve empirical parametrisations used to couple sea ice and ocean wave GCM components.

Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks.

The marginal ice zone (MIZ) is the dynamic interface between the open ocean and sea ice-covered ocean. It is characterized by interactions between surface gravity waves and granular ice covers consisting of relatively small, thin chunks of sea ice known as floes. This structure gives the MIZ markedly different properties to the thicker, quasi-continuous ice cover of the inner pack that waves do not reach, strongly influencing various atmosphere–ocean fluxes, especially the heat flux. The MIZ is a significant component of contemporary sea ice covers in both the Antarctic, where the ice cover is surrounded by the Southern Ocean and its fierce storms, and the Arctic, where the MIZ now occupies vast expanses in areas that were perennial only a decade or two ago. The trend towards the MIZ is set to accelerate, as it reinforces positive feedbacks weakening the ice cover. Therefore, understanding the complex, multiple-scale dynamics of the MIZ is essential to understanding how sea ice is evolving and to predicting its future.This article is part of the theme issue ‘Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks’.

Improved teleconnection between Arctic sea ice and the North Atlantic Oscillation through stochastic process representation

Abstract. The extent to which interannual variability in Arctic sea ice influences the mid-latitude circulation has been extensively debated. While observational data support the existence of a teleconnection between November sea ice in the Barents–Kara region and the subsequent winter North Atlantic Oscillation, climate models do not consistently reproduce such a link, with only very weak inter-model consensus. We show, using the EC-Earth3 climate model, that while an ensemble of coupled EC-Earth3 simulations shows no evidence of such a teleconnection, the inclusion of stochastic parameterizations to the ocean and sea ice component results in the emergence of a robust teleconnection comparable in magnitude to that observed. While the exact mechanisms causing this remain unclear, we argue that it can be accounted for by an improved ice–ocean–atmosphere coupling due to the stochastic perturbations, which aim to represent the effect of unresolved ice and ocean variability. In particular, the weak inter-model consensus may to a large extent be due to model biases in surface coupling, with stochastic parameterizations being one possible remedy.

Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model

AbstractFor decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.

MPAS-Seaice (v1.0.0): sea-ice dynamics on unstructured Voronoi meshes

Abstract. We present MPAS-Seaice, a sea-ice model which uses the Model for Prediction Across Scales (MPAS) framework and spherical centroidal Voronoi tessellation (SCVT) unstructured meshes. As well as SCVT meshes, MPAS-Seaice can run on the traditional quadrilateral grids used by sea-ice models such as CICE. The MPAS-Seaice velocity solver uses the elastic–viscous–plastic (EVP) rheology and the variational discretization of the internal stress divergence operator used by CICE, but adapted for the polygonal cells of MPAS meshes, or alternatively an integral (“finite-volume”) formulation of the stress divergence operator. An incremental remapping advection scheme is used for mass and tracer transport. We validate these formulations with idealized test cases, both planar and on the sphere. The variational scheme displays lower errors than the finite-volume formulation for the strain rate operator but higher errors for the stress divergence operator. The variational stress divergence operator displays increased errors around the pentagonal cells of a quasi-uniform mesh, which is ameliorated with an alternate formulation for the operator. MPAS-Seaice shares the sophisticated column physics and biogeochemistry of CICE and when used with quadrilateral meshes can reproduce the results of CICE. We have used global simulations with realistic forcing to validate MPAS-Seaice against similar simulations with CICE and against observations. We find very similar results compared to CICE, with differences explained by minor differences in implementation such as with interpolation between the primary and dual meshes at coastlines. We have assessed the computational performance of the model, which, because it is unstructured, runs with 70 % of the throughput of CICE for a comparison quadrilateral simulation. The SCVT meshes used by MPAS-Seaice allow removal of equatorial model cells and flexibility in domain decomposition, improving model performance. MPAS-Seaice is the current sea-ice component of the Energy Exascale Earth System Model (E3SM).

An evaluation of the E3SMv1 Arctic ocean and sea-ice regionally refined model

Abstract. The Energy Exascale Earth System Model (E3SM) is a state-of-the-science Earth system model (ESM) with the ability to focus horizontal resolution of its multiple components in specific areas. Regionally refined global ESMs are motivated by the need to explicitly resolve, rather than parameterize, relevant physics within the regions of refined resolution, while offering significant computational cost savings relative to the respective cost of configurations with high-resolution (HR) everywhere on the globe. In this paper, we document results from the first Arctic regionally refined E3SM configuration for the ocean and sea-ice components (E3SM-Arctic-OSI), while employing data-based atmosphere, land, and hydrology components. Our aim is an improved representation of the Arctic coupled ocean and sea-ice state, its variability and trends, and the exchanges of mass and property fluxes between the Arctic and the sub-Arctic. We find that E3SM-Arctic-OSI increases the realism of simulated Arctic ocean and sea-ice conditions compared to a similar low-resolution E3SM simulation without the Arctic regional refinement in ocean and sea-ice components (E3SM-LR-OSI). In particular, exchanges through the main Arctic gateways are greatly improved with respect to E3SM-LR-OSI. Other aspects, such as the Arctic freshwater content variability and sea-ice trends, are also satisfactorily simulated. Yet, other features, such as the upper-ocean stratification and the sea-ice thickness distribution, need further improvements, involving either more advanced parameterizations, model tuning, or additional grid refinements. Overall, E3SM-Arctic-OSI offers an improved representation of the Arctic system relative to E3SM-LR-OSI, at a fraction (15 %) of the computational cost of comparable global high-resolution configurations, while permitting exchanges with the lower-latitude oceans that cannot be directly accounted for in Arctic regional models.

Earth system model parameter adjustment using a Green's functions approach

Abstract. We demonstrate the practicality and effectiveness of using a Green's functions estimation approach for adjusting uncertain parameters in an Earth system model (ESM). This estimation approach has previously been applied to an intermediate-complexity climate model and to individual ESM components, e.g., ocean, sea ice, or carbon cycle components. Here, the Green's functions approach is applied to a state-of-the-art ESM that comprises a global atmosphere/land configuration of the Goddard Earth Observing System (GEOS) coupled to an ocean and sea ice configuration of the Massachusetts Institute of Technology general circulation model (MITgcm). Horizontal grid spacing is approximately 110 km for GEOS and 37–110 km for MITgcm. In addition to the reference GEOS-MITgcm simulation, we carried out a series of model sensitivity experiments, in which 20 uncertain parameters are perturbed. These “control” parameters can be used to adjust sea ice, microphysics, turbulence, radiation, and surface schemes in the coupled simulation. We defined eight observational targets: sea ice fraction, net surface shortwave radiation, downward longwave radiation, near-surface temperature, sea surface temperature, sea surface salinity, and ocean temperature and salinity at 300 m. We applied the Green's functions approach to optimize the values of the 20 control parameters so as to minimize a weighted least-squares distance between the model and the eight observational targets. The new experiment with the optimized parameters resulted in a total cost reduction of 9 % relative to a simulation that had already been adjusted using other methods. The optimized experiment attained a balanced cost reduction over most of the observational targets. We also report on results from a set of sensitivity experiments that are not used in the final optimized simulation but helped explore options and guided the optimization process. These experiments include an assessment of sensitivity to the number of control parameters and to the selection of observational targets and weights in the cost function. Based on these sensitivity experiments, we selected a specific definition for the cost function. The sensitivity experiments also revealed a decreasing overall cost as the number of control variables was increased. In summary, we recommend using the Green's functions estimation approach as an additional fine-tuning step in the model development process. The method is not a replacement for modelers' experience in choosing and adjusting sensitive model parameters. Instead, it is an additional practical and effective tool for carrying out final adjustments of uncertain ESM parameters.

Has substantial sea ice loss along the Siberian coast contributed to the 2020/2021 winter cold wave in China?

In winter 2020/2021, several episodes of cold air outbreaks have wreaked havoc across much of China, resulting in exceptionally low temperatures over northern-to-southeastern China. Our analysis shows that the anomalous circulation leading to the cold wave in China was characterized as: (a) a strengthening of the Siberian high, (b) a deepening of the East Asian trough, and (c) a weakening of the stratospheric polar vortex. Prior to the winter, in summer and autumn, the largest sea ice loss along the Siberian coast was observed. We therefore address the question whether the substantial sea ice loss has contributed to the cold wave. First, applying singular value decomposition (SVD) analysis, we find strong coherence between the autumn Arctic sea ice coverage and temperatures over the central-to-east Asian region with the sea ice component (SVD1_SIC) reaching a record high in 2020 and displaying a decadal shift around 2005. Associated with both the interannual and decadal variations of the SVD1_SIC, there are similar circulation anomalies as described in (a), (b) and (c) above. Second, applying self-organizing maps, wintertime daily air temperatures are clustered into nine nodes with Node-1 mostly resembling “warm Arctic, cold Eurasia” pattern. The frequency of Node-1 shows a similar decadal shift as observed in SVD_SIC1 above, with a correlation of about 0.56. Associated with Node-1, the circulation anomalies are consistent with features highlighted in (a), (b) and (c) above. Thus, taking all the evidence into account, we conclude that the substantial sea ice loss along the Siberian coast contributed significantly to the 2020/2021 winter cold wave in China. The mechanism behind the contribution in this study highlights that the Arctic sea ice loss is favourable for certain circulation patterns on climate timescale, reinforcing disruptive chain of reactions on weather timescale, and leading to severe cold events.

Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2‐CISM2 Coupled Climate–Ice Sheet Model

AbstractDuring the Last Interglacial, approximately 129 to 116 ka (thousand years ago), the Arctic summer climate was warmer than the present, and the Greenland Ice Sheet retreated to a smaller extent than its current state. Previous model‐derived and geological reconstruction estimates of the sea‐level contribution of the Greenland Ice Sheet during the Last Interglacial vary widely. Here, we conduct a transient climate simulation from 127 to 119 ka using the Community Earth System Model (CESM2), which includes a dynamic ice sheet component (the Community Ice Sheet Model, CISM2) that is interactively coupled to the atmosphere, land, ocean, and sea ice components. Vegetation distribution is updated every 500 years based on biomes simulated using a monthly climatology to force the BIOME4 equilibrium vegetation model. Results show a substantial retreat of the Greenland Ice Sheet, reaching a minimum extent at 121.9 ka, equivalent to a 3.0 m rise in sea level relative to the present day, followed by gradual regrowth. In contrast, a companion simulation employing static vegetation based on pre‐industrial conditions shows a much smaller ice‐sheet retreat, highlighting the importance of the changes in high‐latitude vegetation distribution for amplifying the ice‐sheet response.

Ice Algae Model Intercomparison Project phase 2 (IAMIP2)

Abstract. Ice algae play a fundamental role in shaping sea-ice-associated ecosystems and biogeochemistry. This role can be investigated by field observations; however the influence of ice algae at the regional and global scales remains unclear due to limited spatial and temporal coverage of observations and because ice algae are typically not included in current Earth system models. To address this knowledge gap, we introduce a new model intercomparison project (MIP), referred to here as the Ice Algae Model Intercomparison Project phase 2 (IAMIP2). IAMIP2 is built upon the experience from its previous phase and expands its scope to global coverage (both Arctic and Antarctic) and centennial timescales (spanning the mid-20th century to the end of the 21st century). Participating models are three-dimensional regional and global coupled sea-ice–ocean models that incorporate sea-ice ecosystem components. These models are driven by the same initial conditions and atmospheric forcing datasets by incorporating and expanding the protocols of the Ocean Model Intercomparison Project, an endorsed MIP of the Coupled Model Intercomparison Project phase 6 (CMIP6). Doing so provides more robust estimates of model bias and uncertainty and consequently advances the science of polar marine ecosystems and biogeochemistry. A diagnostic protocol is designed to enhance the reusability of the model data products of IAMIP2. Lastly, the limitations and strengths of IAMIP2 are discussed in the context of prospective research outcomes.

Snowball Earth Bifurcations in a Fully-Implicit Earth System Model

There is now much geological evidence that the Earth was fully glaciated during several periods in the geological past (about 700[Formula: see text]Myr ago) and attained a so-called Snowball Earth (SBE) state. Additional support for this idea has come from climate models of varying complexity that show transitions to SBE states and undergo hysteresis under changes in solar radiation. In this paper, we apply large-scale bifurcation analyses to a novel, fully-implicit Earth System Model of Intermediate Complexity (I-EMIC) to study SBE transitions. The I-EMIC contains a primitive equation ocean model, a model for atmospheric heat and moisture transport, a sea ice component and formulations for the adjustment of albedo over snow and ice. With the I-EMIC, high-dimensional branches of the SBE bifurcation diagram are obtained through parameter continuation. We are able to identify stable and unstable equilibria and uncover an intricate bifurcation structure associated with the ice-albedo feedback. Moreover, large-scale linear stability analyses are performed near major bifurcations, revealing the spatial nature of destabilizing perturbations.

An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models

Abstract. We compare the mass budget of the Arctic sea ice for 15 models submitted to the latest Coupled Model Intercomparison Project (CMIP6), using new diagnostics that have not been available for previous model inter-comparisons. These diagnostics allow us to look beyond the standard metrics of ice cover and thickness to compare the processes of sea ice growth and loss in climate models in a more detailed way than has previously been possible. For the 1960–1989 multi-model mean, the dominant processes causing annual ice growth are basal growth and frazil ice formation, which both occur during the winter. The main processes by which ice is lost are basal melting, top melting and advection of ice out of the Arctic. The first two processes occur in summer, while the latter process is present all year. The sea ice budgets for individual models are strikingly similar overall in terms of the major processes causing ice growth and loss and in terms of the time of year during which each process is important. However, there are also some key differences between the models, and we have found a number of relationships between model formulation and components of the ice budget that hold for all or most of the CMIP6 models considered here. The relative amounts of frazil and basal ice formation vary between the models, and the amount of frazil ice formation is strongly dependent on the value chosen for the minimum frazil ice thickness. There are also differences in the relative amounts of top and basal melting, potentially dependent on how much shortwave radiation can penetrate through the sea ice into the ocean. For models with prognostic melt ponds, the choice of scheme may affect the amount of basal growth, basal melt and top melt, and the choice of thermodynamic scheme is important in determining the amount of basal growth and top melt. As the ice cover and mass decline during the 21st century, we see a shift in the timing of the top and basal melting in the multi-model mean, with more melt occurring earlier in the year and less melt later in the summer. The amount of basal growth reduces in the autumn, but it increases in the winter due to thinner sea ice over the course of the 21st century. Overall, extra ice loss in May–June and reduced ice growth in October–November are partially offset by reduced ice melt in August and increased ice growth in January–February. For the individual models, changes in the budget components vary considerably in terms of magnitude and timing of change. However, when the evolving budget terms are considered as a function of the changing ice state itself, behaviours common to all the models emerge, suggesting that the sea ice components of the models are fundamentally responding in a broadly consistent way to the warming climate. It is possible that this similarity in the model budgets may represent a lack of diversity in the model physics of the CMIP6 models considered here. The development of new observational datasets for validating the budget terms would help to clarify this.

Advanced parallel implementation of the coupled ocean–ice model FEMAO (version 2.0) with load balancing

Abstract. In this paper, we present a parallel version of the finite-element model of the Arctic Ocean (FEMAO) configured for the White Sea and based on MPI technology. This model consists of two main parts: an ocean dynamics model and a surface ice dynamics model. These parts are very different in terms of the number of computations because the complexity of the ocean part depends on the bottom depth, while that of the sea-ice component does not. In the first step, we decided to locate both submodels on the same CPU cores with a common horizontal partition of the computational domain. The model domain is divided into small blocks, which are distributed over the CPU cores using Hilbert-curve balancing. Partitioning of the model domain is static (i.e., computed during the initialization stage). There are three baseline options: a single block per core, balancing of 2D computations, and balancing of 3D computations. After showing parallel acceleration for particular ocean and ice procedures, we construct the common partition, which minimizes joint imbalance in both submodels. Our novelty is using arrays shared by all blocks that belong to a CPU core instead of allocating separate arrays for each block, as is usually done. Computations on a CPU core are restricted by the masks of non-land grid nodes and block–core correspondence. This approach allows us to implement parallel computations into the model that are as simple as when the usual decomposition to squares is used, though with advances in load balancing. We provide parallel acceleration of up to 996 cores for the model with a resolution of 500×500×39 in the ocean component and 43 sea-ice scalars, and we carry out a detailed analysis of different partitions on the model runtime.

Multi-Aspect Assessment of CMIP6 Models for Arctic Sea Ice Simulation

AbstractThis paper evaluates the ability of 35 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to simulate Arctic sea ice by comparing simulated results with observation from the aspects of spatial patterns and temporal variation. The simulation ability of each model is also quantified by the Taylor score and e score from these two aspects. Results show that biases between observed and simulated Arctic sea ice concentration (SIC) are mainly located in the East Greenland, Barents, and Bering Seas and the Sea of Okhotsk. The largest difference between the observed and simulated SIC spatial patterns occurs in September. Since the beginning of the twenty-first century, the ability of most models to simulate summer SIC spatial patterns has decreased. We also find that models with the Sea Ice Simulator (SIS) sea ice component in CMIP6 show a consistent larger positive simulation biases of SIC in the East Greenland and Barents Seas. In addition, for most models, the higher the model resolution is, the better the match between the simulated and observed spatial patterns of winter Arctic SIC is. Furthermore, this paper makes a detailed assessment for temporal variation of Arctic sea ice extent (SIE) with regard to climatological average, seasonal SIE, multiyear linear trend, and detrended standard deviation of SIE. The sensitivity of September Arctic SIE to a given change of Arctic surface air temperature over 1979–2014 in each model has also been investigated. Most models simulate a smaller loss of September Arctic SIE per degree of warming than is observed (1.37 × 106 km2 K−1).

Evolution of the East Asian winter land temperature trends during 1961–2018: role of internal variability and external forcing

Detecting the contributions of internal variability and external forcing to the evolution of surface air temperature (SAT) trend at regional scales is a challenge. Based on the observations and large-ensemble simulations of climate models, we estimate the contribution of the internal and forced components to the evolution of East Asian winter land SAT (EAWT) during 1961–2018. Although the external forcing induced EAWT trends show a slow increase, both the total and internally generated EAWT trends exhibit a decrease with the extension of the time period, suggesting a critical role of internal variability in the evolution of the EAWT trends. The internal variability contributes to about 70% of total EAWT trends during 1961–1995. With the extension of the time period, the contribution of internal variability decreases, whereas the contribution of external forcing gradually grows to dominate the EAWT trends. Based on the dynamical adjustment method, we identify that the internal dynamics and forced thermodynamics account for a majority of internal and forced EAWT variations, respectively. We further identify that the multidecadal fluctuation of internal component of autumn Arctic sea ice is a critical precursor of the internal variability, especially the internal dynamically induced EAWT variations, through triggering a meridional stationary Rossby wave response in the following boreal winter. Our findings provide an insight into the understanding of the present and future climate change over East Asia.