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The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO 2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature. [ABSTRACT FROM AUTHOR]

We review how the international modelling community, encompassing Integrated Assessment models, global and regional Earth system and climate models, and impact models, have worked together over the past few decades, to advance understanding of Earth system change and its impacts on society and the environment, and support international climate policy. We then recommend a number of priority research areas for the coming ~6 years (i.e. until ~2030), a timescale that matches a number of newly starting international modelling activities and encompasses the IPCC 7th Assessment Report (AR7) and the 2nd UNFCCC Global Stocktake. Progress in these areas will significantly advance our understanding of Earth system change and its impacts and increase the quality and utility of science support to climate policy. We emphasize the need for continued improvement in our understanding of, and ability to simulate, the coupled Earth system and the impacts of Earth system change. There is an urgent need to investigate plausible pathways and emission scenarios that realize the Paris Climate Targets, including pathways that overshoot the 1.5 °C and 2 °C targets, before later returning to them. Earth System models (ESMs) need to be capable of thoroughly assessing such warming overshoots, in particular, the efficacy of negative CO2 emission actions in reducing atmospheric CO2 and driving global cooling. An improved assessment of the long-term consequences of stabilizing climate at 1.5 °C or 2 °C above pre-industrial temperatures is also required. We recommend ESMs run overshoot scenarios in CO2-emission mode, to more fully represent coupled climate - carbon cycle feedbacks. Regional downscaling and impact models should also use forcing data from these simulations, so impact and regional climate projections are as realistic as possible. An accurate simulation of the observed record remains a key requirement of models, as does accurate simulation of key metrics, such as the Effective Climate Sensitivity. For adaptation, improved guidance on potential changes in climate extremes and the modes of variability these extremes develop in, is a key demand. Such improvements will most likely be realized through a combination of increased model resolution and improvement of key parameterizations. We propose a deeper collaboration across modelling efforts targeting increased process realism and coupling, enhanced model resolution, parameterization improvement, and data-driven Machine Learning methods. With respect to sampling future uncertainty, increased collaboration between approaches that emphasize large model ensembles and those focussed on statistical emulation is required. We recommend increased attention is paid to High Impact Low Likelihood (HILL) outcomes. In particular, the risk and consequences of exceeding critical tipping points during a warming overshoot. For a comprehensive assessment of the impacts of Earth system change, including impacts arising directly from specific mitigation actions, it is important detailed, disaggregated information from the Integrated Assessment Models (IAMs) used to generate future scenarios is available to impact models. Conversely, methods need to be developed to incorporate potential future societal responses to the impacts of Earth system change into scenario development. Finally, the new models, simulations, data, and scientific advances, proposed in this article will not be possible without long-term development and maintenance of a robust, globally connected infrastructure ecosystem. This system must be easily accessible and useable across all modelling communities and across the world, allowing the global research community to be fully engaged in developing and delivering new scientific knowledge to support international climate policy. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts to better understand past, present and future climate changes in a multi-model context. A total of 21 Model Intercomparison Projects (MIPs) were endorsed in its 6th phase (CMIP6), which included 190 different experiments that were used to simulate 40000 years and produced around 40 PB of data in total. This paper shows the main results obtained from the collection of performance metrics done for CMIP6 (CPMIP). The document provides the list of partners involved, the CPMIP metrics per institution/model, and the approach used for the collection and the coordination behind this process. Furthermore, a section has been included to analyze the results and prove the usefulness of the metrics for the community. Moreover, we describe the main difficulties faced during the collection and propose recommendations for future exercises. [ABSTRACT FROM AUTHOR]

IS-ENES is the distributed e-infrastructure of the European Network for Earth System Modelling (ENES).A complex and heterogeneous infrastructure, IS-ENES encompasses models and tools development, documentation, deployment and sharing (including HPC aspects), data storage, processing and distribution as well as the interface with climate impact science and climate services, therefore serving climate science and the users of climate data. This direct impact has a path to many socio-economic sectors (energy, agriculture, transport, water management, health, safety...), as well as national and international policy makers. IS-ENES' third phase (H2020 funded I3 European project H2020 2019-20122) IS-ENES3 starts as the European climate modelling community faces the challenges of contributing to the next IPCC assessment report through the 6th phase of the Coupled Model Intercomparison Project (CMIP). IS-ENES3 addresses these challenges by developing services to deal with unprecedented data volumes and model complexity. The infrastructure delivers the European part of the Earth System Grid Federation and a central point of entry to services providing access to new data, software, models and tools.IS-ENES3 will further stimulate collaboration, disseminate software and data, and integrate and expand Europe wide its user base. Moreover it will find innovative ways of working with the Copernicus programme, already relying on IS-ENES ESGF services, and with other parts of the European data infrastructure, and with the high performance computing and data analytics industries.We propose to employ a blend of indicators to show the advancement of key aspects/activities toward the achievement of the project's manifold short and long-term impacts. Besides relying on indicators used so far to measure software uptake and use and data processing and download, including the reliability of help and documentation services, we plan in IS-ENES3 to add ways to measure contributions to joint development activities (e.g. for new model modules), map applications for Trans National Access and the landscape of national and international science projects relying on the infrastructure. Targeting infrastructure openness and outreach toward adjacent communities, we deem important to map participation in interoperability initiatives, as well as involvement per community and career stage in IS-ENES outreach and training initiatives. We also aim at discussing ways on how to measure tangible outcomes resulting from engagement with Copernicus, EOSC, other European infrastructures, as well as with the industry. We offer a retrospective analysis of the methodology used in the previous phases of IS-ENES to measure impacts and an outlook of what IS-ENES3 proposes to increase and optimize them. In the crucial moment of preparing sustainability of a mature, heterogeneous, acknowledged yet expanding infrastructure, estimates of the extent of impacts (including financial ones when applicable) and return of target communities might ultimately feed back into infrastructure architecture decisions. [ABSTRACT FROM AUTHOR]

We describe the development of a resilient, distributed data infrastructure created to serve global climate projections data to the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). The CDS provides a single public point of access for freely available climate-related observations, re-analyses and model data. This is achieved using a distributed architecture, with individual participating data providers hosting and serving datathrough to the CDS via an agreed set of web service interfaces. The CP4CDS project was established to provide access to a quality controlled subset of CMIP5 model data and is led by CEDA working together with partners DKRZ and IPSL.From the outset, an architecture was proposed leveraging the existing Earth System Grid Federation (ESGF), a globally distributed software infrastructure operated to provide access to CMIP data and other earth sciences data. However, the project requirements specified a high level of resilience for the data services (98% uptime) which no single site could meet as part of its standard SLA. A novel architecture was developed to meet this requirement based on a distributed deployment model and taking advantage of public cloud capabilities. Each of the three partner site provides an identical mirror of the data services. These are combined using load-balancing to deliver a single point of access thus providing an aggregate uptime which meets requirements. Amazon Web Services' Route 53 was selected in order to provide DNS-based load balancing. Cloud computing technologies have also had an important role in the hosting of the services themselves with CEDA's services hosted on-premise on the JASMIN data analysis facility's community cloud. In addition, container and container orchestration technologies have have many attractive features for building a highly available system. Over the last year, the ESGF collaboration have worked together to port the ESGF software stack to Docker and Kubernetes. This has facilitated a deployment of part of the system to Google Cloud.Public cloud hosting provides the means to meet the requirements for availability and uptime with a single deployment and potentially dispenses with the need for a distributed architecture. However, this must be weighed up against the high relative cost for the hosting of large volumes of model data (100s of TBs) when compared with on-premise hosting. In the short to medium term, a likely scenario in the wider ESGF community is the continued use of on-premise hosting for large datasets together with increased adoption of public cloud for the hosting of lighter weight services such a data search where there are much smaller storage requirements.The project has successfully delivered an operational system then, based on a hybrid model of a distributed data infrastructure together with public cloud. The same approach will be applied for a project to deliver regional model data CORDEX4CDS led by IPSL and will also influence the evolution of the architecture for ESGF. [ABSTRACT FROM AUTHOR]