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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility. It consists of (1) an easy-to-install, well-documented Python package providing the core functionalities (ESMValCore) that performs common preprocessing operations and (2) a diagnostic part that includes tailored diagnostics and performance metrics for specific scientific applications. Here we describe large-scale diagnostics of the second major release of the tool that supports the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). ESMValTool v2.0 includes a large collection of diagnostics and performance metrics for atmospheric, oceanic, and terrestrial variables for the mean state, trends, and variability. ESMValTool v2.0 also successfully reproduces figures from the evaluation and projections chapters of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and incorporates updates from targeted analysis packages, such as the NCAR Climate Variability Diagnostics Package for the evaluation of modes of variability, the Thermodynamic Diagnostic Tool (TheDiaTo) to evaluate the energetics of the climate system, as well as parts of AutoAssess that contains a mix of top–down performance metrics. The tool has been fully integrated into the Earth System Grid Federation (ESGF) infrastructure at the Deutsches Klimarechenzentrum (DKRZ) to provide evaluation results from CMIP6 model simulations shortly after the output is published to the CMIP archive. A result browser has been implemented that enables advanced monitoring of the evaluation results by a broad user community at much faster timescales than what was possible in CMIP5., Geoscientific Model Development, 13 (7), ISSN:1991-9603, ISSN:1991-959X

The fully coupled climate model AWI-CM is run on two different ocean grids to investigate the impact of horizontal resolution on the simulated Arctic Ocean circulation, and Arctic-Atlantic as well as Arctic Pacific linkages. The ocean-sea ice component runs on an unstructured grid, the low-resolution version has a grid size of about 24 km in the Arctic Ocean, while the high-resolution grid has a grid size of about 4.5 km resolution in the Arctic Ocean. The high-resolution grid has already been tested in uncoupled runs with CORE-II forcing, where is showed improved representation of Atlantic Water circulation and associated heat content in the Arctic. Now, the impact of the higher resolution will be assessed in the coupled set-up and on longer time scales (150 years).

The climate in Mexico and Central America is influenced by the Pacific and the Atlantic oceanic basins and atmospheric conditions over continental North and South America. These factors and important ocean–atmosphere coupled processes make the region’s climate a great challenge for global and regional climate modeling. We explore the benefits that coupled regional climate models may introduce in the representation of the regional climate with a set of coupled and uncoupled simulations forced by reanalysis and global model data. Uncoupled simulations tend to stay close to the large-scale patterns of the driving fields, particularly over the ocean, while over land they are modified by the regional atmospheric model physics and the improved orography representation. The regional coupled model adds to the reanalysis forcing the air–sea interaction, which is also better resolved than in the global model. Simulated fields are modified over the ocean, improving the representation of the key regional structures such as the Intertropical Convergence Zone and the Caribbean Low Level Jet. Higher resolution leads to improvements over land and in regions of intense air–sea interaction, e.g., off the coast of California. The coupled downscaling improves the representation of the Mid Summer Drought and the meridional rainfall distribution in southernmost Central America. Over the regions of humid climate, the coupling corrects the wet bias of the uncoupled runs and alleviates the dry bias of the driving model, yielding a rainfall seasonal cycle similar to that in the reanalysis-driven experiments.

Substantial changes have occurred in the Arctic Ocean in the last decades. Not only sea ice has retreated significantly, but also the ocean at middepth showed a warming tendency. By using simulations we identified a mechanism that intensifies the upward trend in ocean heat supply to the Arctic Ocean through Fram Strait. The reduction in sea ice export through Fram Strait induced by Arctic sea ice decline increases the salinity in the Greenland Sea, which lowers the sea surface height and strengthens the cyclonic gyre circulation in the Nordic Seas. The Atlantic Water volume transport to the Nordic Seas and Arctic Ocean is consequently strengthened. This enhances the warming trend of the Arctic Atlantic Water layer, potentially contributing to the Arctic “Atlantification.” Our study suggests that the Nordic Seas can play the role of a switchyard to influence the heat budget of the Arctic Ocean.

We examine the historical evolution and projected changes in the hydrography of the deep basin of the Arctic Ocean in 23 climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The comparison between historical simulations and observational climatology shows that the simulated Atlantic Water (AW) layer is too deep and thick in the majority of models, including the multi‐model mean (MMM). Moreover, the halocline is too fresh in the MMM. Overall our findings indicate that there is no obvious improvement in the representation of the Arctic hydrography in CMIP6 compared to CMIP5. The climate change projections reveal that the sub‐Arctic seas are outstanding warming hotspots, causing a strong warming trend in the Arctic AW layer. The MMM temperature increase averaged over the upper 700 m at the end of the 21st century is about 40% and 60% higher in the Arctic Ocean than the global mean in the SSP245 and SSP585 scenarios, respectively. Salinity in the upper few hundred meters is projected to decrease in the Arctic deep basin in the MMM. However, the spread in projected salinity changes is large and the tendency toward stronger halocline in the MMM is not simulated by all the models. The identified biases and projection uncertainties call for a concerted effort for major improvements of coupled climate models., Plain Language Summary: Coupled climate models are crucial tools for understanding and projecting climate change, especially for the Arctic where the climate is changing at unprecedented rates. A cold fresh layer of water (aka halocline) has been protecting sea‐ice at the surface from the warm layer of water (aka Atlantic Water layer) which flows underneath and could potentially accelerate sea ice melting from below. Climate change disturbs this vertical structure by changing the temperature and salinity of the Arctic Ocean (in a process known as Atlantification and Pacification) which may lead to additional sea ice basal melting and accelerate sea ice decline. We examined the simulated temperature and salinity in the Arctic Ocean deep basin in state‐of‐the‐art climate model simulations which provided the basis for the IPCC Assessment Report. We found that although there are persistent inaccuracies in the representation of Arctic temperature and salinity, the Arctic Ocean below 100 m is subject to much stronger warming than the average global ocean. On the other hand, the upper Arctic Ocean salinity is projected to decrease, which on average may strengthen the isolation of sea ice from Atlantic Water heat in the Arctic deep basin area., Key Points: A too deep and thick Arctic Atlantic Water layer continues to be a major issue in contemporary climate models contributing to the CMIP6. The Arctic Ocean below the halocline is subject to much stronger warming than the global mean during the 21st century. The multi‐model mean upper ocean salinity is projected to decrease in the future but with high uncertainty., European union's Horizon 2020 research and innovation programme, German Helmholtz climate initiative REKLIM, Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659, https://esgf-data.dkrz.de/projects/esgf-dkrz/, http://psc.apl.washington.edu/nonwp_projects/PHC/Data3.html

The transient climate response (TCR) is 20% higher in the Alfred Wegener Institute Climate Model (AWI‐CM) compared to the Max Planck Institute Earth System Model (MPI‐ESM) whereas the equilibrium climate sensitivity (ECS) is by up to 10% higher in AWI‐CM. These results are largely independent of the two considered model resolutions for each model. The two coupled CMIP6 models share the same atmosphere‐land component ECHAM6.3 developed at the Max Planck Institute for Meteorology (MPI‐M). However, ECHAM6.3 is coupled to two different ocean models, namely the MPIOM sea ice‐ocean model developed at MPI‐M and the FESOM sea ice‐ocean model developed at the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI). A reason for the different TCR is related to ocean heat uptake in response to greenhouse gas forcing. Specifically, AWI‐CM simulations show stronger surface heating than MPI‐ESM simulations while the latter accumulate more heat in the deeper ocean. The vertically integrated ocean heat content is increasing slower in AWI‐CM model configurations compared to MPI‐ESM model configurations in the high latitudes. Weaker vertical mixing in AWI‐CM model configurations compared to MPI‐ESM model configurations seems to be key for these differences. The strongest difference in vertical ocean mixing occurs inside the Weddell and Ross Gyres and the northern North Atlantic. Over the North Atlantic, these differences materialize in a lack of a warming hole in AWI‐CM model configurations and the presence of a warming hole in MPI‐ESM model configurations. All these differences occur largely independent of the considered model resolutions., Plain Language Summary: The transient climate response (TCR) describes how strongly near‐surface temperatures warm in response to gradually increasing greenhouse‐gas levels. Here we investigate the role of the ocean which takes up heat and thereby delays the surface warming. Two models of the Coupled Model Intercomparison Project Phase 6 (CMIP6), the Alfred Wegener Institute Climate Model (AWI‐CM) and the Max Planck Institute Earth System Model (MPI‐ESM), which use the same atmosphere model but different ocean models are selected for this study. In AWI‐CM the upper ocean layers heat faster than in MPI‐ESM, while the opposite is true for the deep ocean. As a consequence, the TCR is 20% stronger in AWI‐CM compared to MPI‐ESM. We find that weaker vertical ocean mixing in AWI‐CM compared to MPI‐ESM, especially over the northern North Atlantic and the Weddell and Ross Gyres, is key for these differences. Our findings corroborate the importance of realistic ocean mixing in climate models when it comes to getting the strength and timing of climate change right., Key Points: The transient climate response in two coupled models with the same atmosphere but different ocean components differs by 20%. The upper (deeper) ocean heats faster (slower) in AWI‐CM compared to MPI‐ESM, independent of model resolution. Vertical mixing in the northern North Atlantic and the Weddell and Ross Gyres appears to be key for these differences., Bundesministerium für Bildung und Forschung (BMBF) http://dx.doi.org/10.13039/501100002347, German Climate Computing Centre (DKRZ), Federal Ministry of Education and Research of Germany, Helmholtz Association http://dx.doi.org/10.13039/501100009318, https://esgf-data.dkrz.de/projects/cmip6-dkrz/

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project., This work has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955558. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Germany, France, Italy, Poland, Switzerland and Norway. In Spain, it has received complementary funding from MCIN/AEI/10.13039/501100011033, Spain and the European Union NextGenerationEU/PRTR (contracts PCI2021-121957, PCI2021-121931, PCI2021-121944, and PCI2021-121927). In Germany, it has received complementary funding from the German Federal Ministry of Education and Research (contracts 16HPC016K, 6GPC016K, 16HPC017 and 16HPC018). In France, it has received financial support from Caisse des dépôts et consignations (CDC) under the action PIA ADEIP (project Calculateurs). In Italy, it has been preliminary approved for complimentary funding by Ministero dello Sviluppo Economico (MiSE) (ref. project prop. 2659). In Norway, it has received complementary funding from the Norwegian Research Council, Norway under project number 323825. In Switzerland, it has been preliminary approved for complimentary funding by the State Secretariat for Education, Research, and Innovation (SERI), Norway. In Poland, it is partially supported by the National Centre for Research and Development under decision DWM/EuroHPCJU/4/2021. The authors also acknowledge financial support by MCIN/AEI /10.13039/501100011033, Spain through the “Severo Ochoa Programme for Centres of Excellence in R&D” under Grant CEX2018-000797-S, the Spanish Government, Spain (contract PID2019-107255 GB) and by Generalitat de Catalunya, Spain (contract 2017-SGR-01414). Anna Queralt is a Serra Húnter Fellow., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2018-000797-S).