The Atlantic meridional overturning circulation (AMOC) represents the zonally integrated stream function of meridional volume transport in the Atlantic Basin. The AMOC plays an important role in transporting heat meridionally in the climate system. Observations suggest a heat transport by the AMOC of 1.3 PW at 26°N-a latitude which is close to where the Atlantic northward heat transport is thought to reach its maximum. This shapes the climate of the North Atlantic region as we know it today. In recent years there has been significant progress both in our ability to observe the AMOC in nature and to simulate it in numerical models. Most previous modeling investigations of the AMOC and its impact on climate have relied on models with horizontal resolution that does not resolve ocean mesoscale eddies and the dynamics of the Gulf Stream/North Atlantic Current system. As a result of recent increases in computing power, models are now being run that are able to represent mesoscale ocean dynamics and the circulation features that rely on them. The aim of this review is to describe new insights into the AMOC provided by high-resolution models. Furthermore, we will describe how high-resolution model simulations can help resolve outstanding challenges in our understanding of the AMOC. Key Points:• Observations and high-resolution models have changed view on the AMOC pathways • High-resolution models suggest the presence of previously unknown high-frequency AMOC variability • High-resolution models allow to estimate the intrinsic/chaotic component of the AMOCCorrespondence to:

Broad, long-living, ice-free areas in midwinter northeast of Svalbard between 2011 and 2014 are investigated. The formation of these persistent and reemerging anomalies is linked, hypothetically, with the increased seasonality of Arctic sea ice cover, enabling an enhanced influence of oceanic heat on sea ice and, in particular, heat transported by Atlantic Water. The ''memory'' of ice-depleted conditions in summer is transferred to the fall season through excess heat content in the upper mixed layer, which in turn transfers to midwinter via thinner and younger ice. This thinner ice is more fragile and mobile, thus facilitating the formation of polynyas and leads. When openings in ice cover form along the Atlantic Water pathway, weak density stratification at the mixed layer base supports the development of thermohaline convection, which further entrains warm and salty water from deeper layers. Convection-induced upward heat flux from the Atlantic layer retards ice formation, either keeping ice thickness low or blocking ice formation entirely. Certain stages of this chain of events have been examined in a region north of Svalbard and Franz Joseph Land, between 808 and 838N and 158 and 608E, where the top hundred meters of Atlantic inflow through the Fram Strait cools and freshens rapidly. Complementary research methods, including statistical analyses of observations and numerical modeling, are used to support the basic concept that the recently observed retreat of sea ice northeast of Svalbard in winter may be explained by a positive feedback between summer ice decay and the growing influence of oceanic heat on a seasonal time scale.

Abstract. We present a new framework for global ocean–sea-ice model simulations based on phase 2 of the Ocean Model Intercomparison Project (OMIP-2), making use of the surface dataset based on the Japanese 55-year atmospheric reanalysis for driving ocean–sea-ice models (JRA55-do). We motivate the use of OMIP-2 over the framework for the first phase of OMIP (OMIP-1), previously referred to as the Coordinated Ocean–ice Reference Experiments (COREs), via the evaluation of OMIP-1 and OMIP-2 simulations from 11 state-of-the-science global ocean–sea-ice models. In the present evaluation, multi-model ensemble means and spreads are calculated separately for the OMIP-1 and OMIP-2 simulations and overall performance is assessed considering metrics commonly used by ocean modelers. Both OMIP-1 and OMIP-2 multi-model ensemble ranges capture observations in more than 80 % of the time and region for most metrics, with the multi-model ensemble spread greatly exceeding the difference between the means of the two datasets. Many features, including some climatologically relevant ocean circulation indices, are very similar between OMIP-1 and OMIP-2 simulations, and yet we could also identify key qualitative improvements in transitioning from OMIP-1 to OMIP-2. For example, the sea surface temperatures of the OMIP-2 simulations reproduce the observed global warming during the 1980s and 1990s, as well as the warming slowdown in the 2000s and the more recent accelerated warming, which were absent in OMIP-1, noting that the last feature is part of the design of OMIP-2 because OMIP-1 forcing stopped in 2009. A negative bias in the sea-ice concentration in summer of both hemispheres in OMIP-1 is significantly reduced in OMIP-2. The overall reproducibility of both seasonal and interannual variations in sea surface temperature and sea surface height (dynamic sea level) is improved in OMIP-2. These improvements represent a new capability of the OMIP-2 framework for evaluating process-level responses using simulation results. Regarding the sensitivity of individual models to the change in forcing, the models show well-ordered responses for the metrics that are directly forced, while they show less organized responses for those that require complex model adjustments. Many of the remaining common model biases may be attributed either to errors in representing important processes in ocean–sea-ice models, some of which are expected to be reduced by using finer horizontal and/or vertical resolutions, or to shared biases and limitations in the atmospheric forcing. In particular, further efforts are warranted to resolve remaining issues in OMIP-2 such as the warm bias in the upper layer, the mismatch between the observed and simulated variability of heat content and thermosteric sea level before 1990s, and the erroneous representation of deep and bottom water formations and circulations. We suggest that such problems can be resolved through collaboration between those developing models (including parameterizations) and forcing datasets. Overall, the present assessment justifies our recommendation that future model development and analysis studies use the OMIP-2 framework.

Abstract. This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958–2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (∼ 1∘) to eddy-resolving (∼ 0.1∘) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar, and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and the Antarctic Circumpolar Current – are significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve” most of these features, the improvements in temperature and salinity are inconsistent among the different model families, and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.

We discuss the performance of the Finite Element Ocean Model (FESOM) on locally eddy‐resolving global unstructured meshes. In particular, the utility of the mesh design approach whereby mesh horizontal resolution is varied as half the Rossby radius in most of the model domain is explored. Model simulations on such a mesh (FESOM‐XR) are compared with FESOM simulations on a smaller‐size mesh, where refinement depends only on the pattern of observed variability (FESOM‐HR). We also compare FESOM results to a simulation of the ocean model of the Max Planck Institute for Meteorology (MPIOM) on a tripolar regular grid with refinement toward the poles, which uses a number of degrees of freedom similar to FESOM‐XR. The mesh design strategy, which relies on the Rossby radius and/or the observed variability pattern, tends to coarsen the resolution in tropical and partly subtropical latitudes compared to the regular MPIOM grid. Excessive variations of mesh resolution are found to affect the performance in other nearby areas, presumably through dissipation that increases if resolution is coarsened. The largest improvement shown by FESOM‐XR is a reduction of the surface temperature bias in the so‐called North‐West corner of the North Atlantic Ocean where horizontal resolution was increased dramatically. However, other biases in FESOM‐XR remain largely unchanged compared to FESOM‐HR. We conclude that resolving the Rossby radius alone (with two points per Rossby radius) is insufficient, and that careful use of a priori information on eddy dynamics is required to exploit the full potential of ocean models on unstructured meshes.

Abstract. This paper describes the second major release of the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). Compared to version 1.0, released in 2016, ESMValTool version 2.0 (v2.0) features a brand new design, with an improved interface and a revised preprocessor. It also features a significantly enhanced diagnostic part that is described in three companion papers. The new version of ESMValTool has been specifically developed to target the increased data volume of CMIP Phase 6 (CMIP6) and the related challenges posed by the analysis and the evaluation of output from multiple high-resolution or complex ESMs. The new version takes advantage of state-of-the-art computational libraries and methods to deploy an efficient and user-friendly data processing. Common operations on the input data (such as regridding or computation of multi-model statistics) are centralized in a highly optimized preprocessor, which allows applying a series of preprocessing functions before diagnostics scripts are applied for in-depth scientific analysis of the model output. Performance tests conducted on a set of standard diagnostics show that the new version is faster than its predecessor by about a factor of 3. The performance can be further improved, up to a factor of more than 30, when the newly introduced task-based parallelization options are used, which enable the efficient exploitation of much larger computing infrastructures. ESMValTool v2.0 also includes a revised and simplified installation procedure, the setting of user-configurable options based on modern language formats, and high code quality standards following the best practices for software development.

We focus on the Arctic Ocean between Svalbard and Franz Joseph Land in order to elucidate the possible role of Atlantic water (AW) inflow in shaping ice conditions. Ice conditions substantially affect the temperature regime of the Spitsbergen archipelago, particularly in winter. We test the hypothesis that intensive vertical mixing at the upper AW boundary releases substantial heat upwards that eventually reaches the under-ice water layer, thinning the ice cover. We examine spatial and temporal variation of ice concentration against time series of wind, air temperature, and AW temperature. Analysis of 1979–2011 ice properties revealed a general tendency of decreasing ice concentration that commenced after the mid-1990s. AW temperature time series in Fram Strait feature a monotonic increase after the mid-1990s, consistent with shrinking ice cover. Ice thins due to increased sensible heat flux from AW; ice erosion from below allows wind and local currents to more effectively break ice. The winter spatial pattern of sea ice concentration is collocated with patterns of surface heat flux anomalies. Winter minimum sea ice thickness occurs in the ice pack interior above the AW path, clearly indicating AW influence on ice thickness. Our study indicates that in the AW inflow region heat flux from the ocean reduces the ice thickness.

The freshwater stored in the Arctic Ocean is an important component of the global climate system. Currently the Arctic liquid freshwater content (FWC) has reached a record high since the beginning of the last century. In this study we use numerical simulations to investigate the impact of sea ice decline on the Arctic liquid FWC and its spatial distribution. The global unstructured-mesh ocean general circulation model Finite Element Sea Ice–Ocean Model (FESOM) with 4.5-km horizontal resolution in the Arctic region is applied. The simulations show that sea ice decline increases the FWC by freshening the ocean through sea ice meltwater and modifies upper ocean circulation at the same time. The two effects together significantly increase the freshwater stored in the Amerasian basin and reduce its amount in the Eurasian basin. The salinification of the upper Eurasian basin is mainly caused by the reduction in the proportion of Pacific Water and the increase in that of Atlantic Water (AW). Consequently, the sea ice decline did not significantly contribute to the observed rapid increase in the Arctic total liquid FWC. However, the changes in the Arctic freshwater spatial distribution indicate that the influence of sea ice decline on the ocean environment is remarkable. Sea ice decline increases the amount of Barents Sea branch AW in the upper Arctic Ocean, thus reducing its supply to the deeper Arctic layers. This study suggests that all the dynamical processes sensitive to sea ice decline should be taken into account when understanding and predicting Arctic changes.