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Earth System Models (ESMs) are intricate models employed for simulating the Earth's climate, typically constructed from distinct independent components dedicated to simulate specific natural phenomena (such as atmosphere and ocean dynamics, atmospheric chemistry, land and ocean biosphere, etc.). In order to capture the interactions between these processes, ESMs utilize coupling libraries, which oversee the synchronization and field exchanges among independent developed codes typically operating in parallel as a Multi-Program, Multi-Data (MPMD) application. The performance achieved depends on the coupling approach, as well as on the number of parallel resources and scalability properties of each component. Determining the appropriate number of resources to use for each component in coupled ESMs is crucial for efficient utilization of the High Performance Computing (HPC) infrastructures used in climate modelling. However, this task traditionally involves manual testing of multiple process allocations by trial and error, requiring significant time investment from researchers. Thus, making the process more error-prone, and often resulting in a loss in application performance due to the complexity of the task. This paper introduces the automatic load-balance tool (auto-lb), a methodology and tool for determining the resource allocation to each component within coupled ESMs, aimed at improving the application's performance. Notably, this methodology is automatic and does not require expertise in HPC to improve the performance achieved by coupled ESMs. This is accomplished by minimizing the load-imbalance: reducing each constituent's execution cost (core-hours), as well as minimizing the core-hours wasted resulting from the synchronizations between them, without penalizing the execution speed of the entire model. This optimization is achieved regardless of the scalability properties of each constituent and the complexity of their dependencies during the coupling. To achieve this, we designed a new performance metric called 'Fittingness' to assess the performance of coupled execution evaluating the trade-off between the parallel efficiency and application throughput. This metric is intended for scenarios where optimality can depend on various criteria and constraints. Aiming for maximum speed might not be desirable if it leads to a decrease in parallel efficiency and, therefore, increasing the computational costs of simulation. The methodology was tested across multiple experiments using the widely recognized European ESM, EC-Earth3. The results were compared with real operational configurations, such as those used for the Coupled Model Intercomparison Project Phase 6 (CMIP6) and for the European Climate Prediction Project (EUCP), and validated on different HPC platforms. All of them suggest that the current approaches lead to performance loss, and that auto-lb can achieve better results in both, execution speed and reduction of the core-hours needed. When comparing to the EC-Earth standard-resolution CPMIP6 runs, we achieved a configuration 4.7 % faster while also reducing the core-hours required by 1.3 %. Likewise, when compared to the EC-Earth high-resolution EUCP runs, the method presented showed an improvement of 34 % in the speed, with a 6.7 % reduction in the core-hours consumed. [ABSTRACT FROM AUTHOR]

We here present the very-high resolution version of the EC-Earth global climate model, EC-Earth3P-VHR, developed for HighResMIP. The model features an atmospheric resolution of ~16 km and an oceanic resolution of 1/12° (~8 km), which makes it one of the finest combined resolutions ever used to complete historical and scenario-like CMIP6 simulations. To evaluate the influence of numerical resolution on the simulated climate, EC-Earth3P-VHR is compared with two configurations of the same model at lower resolution: the ~100-km-grid EC-Earth3P-LR, and the ~25-km-grid EC-Earth3P-HR. The models' biases are evaluated against observations over the period 1980–2014. Compared to LR and HR, VHR shows a reduced equatorial Pacific cold tongue bias, an improved Gulf Stream representation with a reduced coastal warm bias and a reduced subpolar North Atlantic cold bias, and more realistic orographic precipitation over mountain ranges. By contrast, VHR shows a larger warm bias and overly low sea ice extent over the Southern Ocean. Such biases in surface temperature have an impact on the atmospheric circulation aloft, with improved stormtrack over the North Atlantic, yet worsened stormtrack over the Southern Ocean compared to the lower resolution model versions. Other biases persist with increased resolution from LR to VHR, such as the warm bias over the tropical upwelling region and the associated cloud cover underestimation, and the precipitation excess over the tropical South Atlantic and North Pacific. VHR shows improved air–sea coupling over the tropical region, although it tends to overestimate the oceanic influence on the atmospheric variability at mid-latitudes compared to observations and LR and HR. Together, these results highlight the potential for improved simulated climate in key regions, such as the Gulf Stream and the Equator, when the atmospheric and oceanic resolutions are finer than 25 km in both the ocean and atmosphere. Thanks to its unprecedented resolution, EC-Earth3P-VHR offers a new opportunity to study climate variability and change of such areas on regional/local spatial scales, in line with regional climate models. [ABSTRACT FROM AUTHOR]

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]

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]