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Love number computation within the Ice-sheet and Sea-level System Model (ISSM v4.24)

Presented here is a new model of the solid-Earth response to tides and mass changes in ice sheets, oceans, and groundwater, in of terms of gravity change and bedrock motion. The model is capable simulating mantle deformation including elasticity, transient and steady-state viscous flow. We detail our approach to numerical optimization, and report the accuracy of results with respect to community benchmarks. The resulting coupled system features kilometer-scale resolution and fast computation.

The Destination Earth digital twin for climate change adaptation

The Climate Change Adaptation Digital Twin (Climate DT) pioneers the operationalisation of global climate projections. It produces global simulations with local granularity for adaptation decision-making. Applications are embedded to generate tailored indicators. A unified workflow orchestrates all components in several supercomputers. Data management ensures consistency and streaming enables real-time use. It is a complementary innovation to initiatives like CMIP, CORDEX, and climate services.

The Scenario Model Intercomparison Project for CMIP7 (ScenarioMIP-CMIP7)

We propose a set of seven plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.

NorESM2–DIAM: a coupled model for investigating global and regional climate-economy interactions

We introduce NorESM2-DIAM (Norwegian Earth System Model version 2-Disaggregated Integrated Assessment Model), a first-of-its-kind tool linking a climate model with a high-resolution economic model to study how climate change, internal variability, and economic activity interact across the world. The model reveals strong regional differences and large annual swings in economic impacts, offers insights for climate policy discussions, and provides a strong foundation for future model development.

Operational numerical weather prediction with ICON on GPUs (version 2024.10)

The ICON climate and numerical weather prediction model was fully ported to Graphical Processing Units (GPUs) using OpenACC compiler directives, covering all components required for operational weather prediction. The GPU port together with several performance optimizations led to a speed-up of 5.6× when comparing to traditional Central Processing Units (CPUs) . Thanks to this adaptation effort, MeteoSwiss became the first national weather service to run the ICON model operationally on GPUs.

Review of climate simulation by Simple Climate Models

Simple Climate Models (SCMs) are widely used tools to explore how Earth's climate may change in the future. In recent decades, the number and types of SCMs have increased significantly, hindering efforts to understand cross-model differences. In this study, we provide an overview of the main principles guiding climate simulation by SCMs, as well as a description of most high-profile SCMs. This work offers a clear reference to support the informed use of these important tools.

Feedback-based sea level rise impact modelling for integrated assessment models with FRISIAv1.0

We present FRISIA version 1.0, a model for emulating sea level rise (SLR) and representing SLR impacts and adaptation in integrated assessment models (IAMs). FRISIA includes previously uncaptured coastal socio-economic feedback and a diverse set of impact strains, thereby improving the represenation of SLR impacts in IAMs. Here we describe the baseline behaviour of FRISIA, explore the effects of the additional feedback and showcase the coupling of FRISIA to an IAM.

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.

nextGEMS: entering the era of kilometer-scale Earth system modeling

The Next Generation of Earth Modeling Systems project (nextGEMS) developed two Earth system models that use horizontal grid spacing of 10 km and finer, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS simulated the Earth System climate over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.

A dilatant visco-elasto-viscoplasticity model with globally continuous tensile cap: stable two-field mixed formulation

We present a simple plasticity model that can be used for robust modeling of strain localization in both shear and tensile failure regimes. The new model overcomes the difficulty related to combining these regimes and enables for particularly simple and reliable numerical implementation, which delivers regularized solutions that are insensitive to mesh resolution. We describe algorithmic details and demonstrate the applications to a number of relevant strain localization problems.

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