Chihiro Kodama

Storm‐resolving models have the potential to outperform conventional models with a horizontal resolution of 150 km in simulating global atmospheric circulation by resolving deep convection and gravity waves. Recent studies have suggested that the long‐standing “too zonal and too equatorward” storm‐track bias in climate models can be mitigated by enhancing the horizontal resolution. This study investigates the horizontal structures of storm tracks using multiple global storm‐resolving models with a resolution of 5 km or finer under a realistic land‐sea configuration focusing on the winter of 2020. Storm‐resolving models substantially mitigate the long‐standing storm‐track biases in the lower‐tropospheric North Atlantic, but appear to introduce a northward‐displacement bias in the upper troposphere. In contrast, the long‐standing biases remain in the North Pacific, with only marginal improvements. These results highlight the importance of considering regional and vertical characteristics of storm tracks to reduce their biases in climate models.

Mesoscale convective systems (MCSs) are crucial for the hydrological cycle and frequently produce severe hazards over the central United States and eastern China. Understanding their future changes is therefore essential. This study investigates projected changes in summer MCSs using the global cloud‐system‐resolving model, the Nonhydrostatic ICosahedral Atmospheric Model (NICAM), by comparing MCSs tracked in the historical (1951−1960) and future (2041−2050, SSP5‐8.5) periods. The results show that the fractional contribution of MCSs to summer precipitation remains nearly unchanged over the central United States and eastern China. The total number of MCS initiations exhibited little overall change in both regions. However, the frequency of MCSs with weak mean rain rates decreases, whereas that of MCSs with heavy mean rain rates increases substantially. In particular, the number of MCSs exceeding the MCS historical 90th percentile mean rain rate rises by approximately 35% per degree of mean warming in both regions. MCS mean, heavy, and maximum rain rates intensify robustly by ∼5.7%, 3.5%, and 5.2% per degree of warming, respectively, whereas changes in MCS track duration and total rain area are small and statistically insignificant. Light rain areas contract markedly, whereas extreme heavy rain areas (≥30 mm/hr) expand by approximately 11.5% per degree of warming, resulting in insignificant changes in the MCS rain area.

Control simulation experiments (CSEs) aim to steer model predictions toward a desired target by intervening in the model inputs. This concept is mathematically analogous to data assimilation, in which model states are adjusted to align with observations. In recent machine-learning models, automatic differentiation is used to compute gradients of a loss function and to optimize model parameters to minimize the difference between predictions and targets (e.g., reanalysis data). By optimizing input variables (initial conditions) instead of model parameters, CSEs can be formulated for differentiable models. In this study, we conduct CSEs for Typhoon Jebi (2018) using NeuralGCM, a fully differentiable global climate model. Tropospheric temperature perturbations are optimized to minimize the mismatch of 500-hPa geopotential height (Z500) over 130°E–160°E and 10°N–40°N. The results demonstrate that the predicted fields converge toward the target, indicating that CSEs can be successfully implemented and executed in a differentiable GCM framework.

As climate models advance toward higher resolutions, they become increasingly capable of resolving key Earth system processes, which in turn raises the need for robust and quantitative evaluation methods. In response to this challenge, we present pyhanami, an open-source Python package developed within the HANAMI project to assess the replicability and scientific skill of Earth System Models (ESMs) using statistical testing and objective, scalar-based metrics. Besides, to facilitate the practical application of these evaluations, pyhanami features a structured data interface that efficiently loads and inspects compatible model outputs.An ESM is considered replicable if the same experiment run on different computing environments or with different compilers produces identical results, i.e., representing the same climate. This ensures that differences between simulations reflect only the intended scientific changes in the model setup. Because bit-for-bit replicability is often unattainable across environments due to the chaotic nature of climate models, our practical goal is to achieve statistical indistinguishability. Building on existing methodologies, pyhanami provides an ensemble-based replicability test that combines multiple statistical tests and metrics to determine whether two simulated ensembles are statistically indistinguishable, as described in (K.Keller et al., 2025; doi.org/10.5194/gmd-18-10221-2025). To the best of our knowledge, automated and standardized replicability assessment is not currently supported in model evaluation tools, despite its importance for climate model development, validation, intercomparison, and porting. Complementing replicability, the scientific skill of an ESM describes its ability to accurately reproduce observed features of the climate system, from regional patterns to large-scale teleconnections. Many existing tools to evaluate this skill rely on visualization-based diagnostics, which often require expert knowledge and can be biased by subjective interpretation. In contrast, scalar metrics and scores provide quantitative and comparable measures of scientific skill, which are essential for interpreting climate projections, guiding model development, and model intercomparison. However, diagnostics for physical processes that require km-scale, high-resolution global climate models to be properly resolved remain underrepresented in state-of-the-art diagnostic suites. Although several metrics have been proposed for such small-scale processes, many lack standardized and widely available implementations. As high-resolution climate simulations become more common, the demand for objective diagnostics to support model tuning and improvement is increasing. pyhanami addresses this need by providing a growing set of scalar scientific skill metrics that enable quantitative and easily interpretable evaluation of phenomena such as Tropical Cyclones and the Tropical Intraseasonal Oscillation (ISO), including the Madden-Julian Oscillation and the Boreal Summer ISO modes. By integrating replicability testing, scientific skill metrics, and visualization tools into a single, self-contained package with a generic data interface, pyhanami streamlines evaluation workflows and supports the development of reliable climate projections, advancing the quality and reproducibility of geosciences research.

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. Consequently, the role of enhanced horizontal resolution in improved process representation in all components of the climate system continues to be of great interest. Recent simulations suggest the possibility of significant changes in both large-scale aspects of the ocean and atmospheric circulations and in the regional responses to climate change, as well as improvements in representations of small-scale processes and extremes, when resolution is enhanced. The first phase of the High-Resolution Model Intercomparison Project (HighResMIP1) was successful at producing a baseline multi-model assessment of global simulations with model grid spacings of 25–50 km in the atmosphere and 10–25 km in the ocean, a significant increase when compared to models with standard resolutions on the order of 1° that are typically used as part of the Coupled Model Intercomparison Project (CMIP) experiments. In addition to over 250 peer-reviewed manuscripts using the published HighResMIP1 datasets, the results were widely cited in the Intergovernmental Panel on Climate Change report and were the basis of a variety of derived datasets, including tracked cyclones (both tropical and extratropical), river discharge, storm surge, and impact studies. There were also suggestions from the few ocean eddy-rich coupled simulations that aspects of climate variability and change might be significantly influenced by improved process representation in such models. The compromises that HighResMIP1 made should now be revisited, given the recent major advances in modelling and computing resources. Aspects that will be reconsidered include experimental design and simulation length, complexity, and resolution. In addition, larger ensemble sizes and a wider range of future scenarios would enhance the applicability of HighResMIP. Therefore, we propose the High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) to improve and extend the previous work, to address new science questions, and to further advance our understanding of the role of horizontal resolution (and hence process representation) in state-of-the-art climate simulations. With further increases in high-performance computing resources and modelling advances, along with the ability to take full advantage of these computational resources, an enhanced investigation of the drivers and consequences of variability and change in both large- and synoptic-scale weather and climate is now possible. With the arrival of global cloud-resolving models (currently run for relatively short timescales), there is also an opportunity to improve links between such models and more traditional CMIP models, with HighResMIP providing a bridge to link understanding between these domains. HighResMIP also aims to link to other CMIP projects and international efforts such as the World Climate Research Program lighthouse activities and various digital twin initiatives. It also has the potential to be used as training and validation data for the fast-evolving machine learning climate models.

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. Consequently, the role of enhanced horizontal resolution in improved process representation in all components of the climate system continues to be of great interest. Recent simulations suggest both the possibility of significant changes in large-scale aspects of the ocean and atmospheric circulations and the regional responses to climate change, as well as improvements in representations of small-scale processes and extremes, when resolution is enhanced. The first phase of HighResMIP (HighResMIP1) was successful in producing a baseline multi-model assessment of global simulations with model grid spacings of 25–50 km in the atmosphere and 10–25 km in the ocean, a significant increase when compared to models with standard resolutions of order 1-degree typically used as part of the Coupled Model Intercomparison Project (CMIP) experiments. In addition to over 250 peer-reviewed manuscripts using the published HighResMIP1 datasets, the results were widely cited in the Intergovernmental Panel on Climate Change report and were the basis for a variety of derived datasets, including tracked cyclones (both tropical and extratropical), river discharge, storm surge, and others that were used for impact studies. There were also suggestions from the few ocean eddy-rich coupled simulations that aspects of climate variability and change might be significantly influenced by improved process representation in such models. The compromises that HighResMIP1 made should now be revisited, given the recent major advances in modelling and computing resources. Aspects that will be reconsidered include experimental design and simulation length, complexity, and resolution. In addition, larger ensemble sizes and a wider range of future scenarios would enhance the applicability of HighResMIP. Therefore, we propose an updated HighResMIP2 to improve and extend the previous work, to address new science questions, and to further advance our understanding of the role of horizontal resolution (and hence process representation) in state-of-the-art climate simulations. With further increases in high-performance computing resources and modelling advances along with the ability to take full advantage of these computational resources, an enhanced investigation of the drivers and consequences of variability and change in both large- and synoptic-scale weather and climate is now made possible. With the arrival of global cloud-resolving models (currently run for relatively short timescales), there is also an opportunity to improve links between such models and more traditional CMIP models, with HighResMIP providing a bridge to link understanding between these domains. HighResMIP also aims to link to other CMIP projects and international efforts such as the World Climate Research Program lighthouse activities and various Digital Twin initiatives, as well as having the potential to be used as training and validation data for the fast evolving Machine Learning climate models.

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. Consequently, the role of enhanced horizontal resolution in improved process representation in all components of the climate system continues to be of great interest. Recent simulations suggest both the possibility of significant changes in large-scale aspects of the ocean and atmospheric circulations and the regional responses to climate change, as well as improvements in representations of small-scale processes and extremes, when resolution is enhanced. The first phase of HighResMIP (HighResMIP1) was successful in producing a baseline multi-model assessment of global simulations with model grid spacings of 25–50 km in the atmosphere and 10–25 km in the ocean, a significant increase when compared to models with standard resolutions of order 1-degree typically used as part of the Coupled Model Intercomparison Project (CMIP) experiments. In addition to over 250 peer-reviewed manuscripts using the published HighResMIP1 datasets, the results were widely cited in the Intergovernmental Panel on Climate Change report and were the basis for a variety of derived datasets, including tracked cyclones (both tropical and extratropical), river discharge, storm surge, and others that were used for impact studies. There were also suggestions from the few ocean eddy-rich coupled simulations that aspects of climate variability and change might be significantly influenced by improved process representation in such models. The compromises that HighResMIP1 made should now be revisited, given the recent major advances in modelling and computing resources. Aspects that will be reconsidered include experimental design and simulation length, complexity, and resolution. In addition, larger ensemble sizes and a wider range of future scenarios would enhance the applicability of HighResMIP. Therefore, we propose an updated HighResMIP2 to improve and extend the previous work, to address new science questions, and to further advance our understanding of the role of horizontal resolution (and hence process representation) in state-of-the-art climate simulations. With further increases in high-performance computing resources and modelling advances along with the ability to take full advantage of these computational resources, an enhanced investigation of the drivers and consequences of variability and change in both large- and synoptic-scale weather and climate is now made possible. With the arrival of global cloud-resolving models (currently run for relatively short timescales), there is also an opportunity to improve links between such models and more traditional CMIP models, with HighResMIP providing a bridge to link understanding between these domains. HighResMIP also aims to link to other CMIP projects and international efforts such as the World Climate Research Program lighthouse activities and various Digital Twin initiatives, as well as having the potential to be used as training and validation data for the fast evolving Machine Learning climate models.

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. Consequently, the role of enhanced horizontal resolution in improved process representation in all components of the climate system continues to be of great interest. Recent simulations suggest both the possibility of significant changes in large-scale aspects of the ocean and atmospheric circulations and the regional responses to climate change, as well as improvements in representations of small-scale processes and extremes, when resolution is enhanced. The first phase of HighResMIP (HighResMIP1) was successful in producing a baseline multi-model assessment of global simulations with model grid spacings of 25–50 km in the atmosphere and 10–25 km in the ocean, a significant increase when compared to models with standard resolutions of order 1-degree typically used as part of the Coupled Model Intercomparison Project (CMIP) experiments. In addition to over 250 peer-reviewed manuscripts using the published HighResMIP1 datasets, the results were widely cited in the Intergovernmental Panel on Climate Change report and were the basis for a variety of derived datasets, including tracked cyclones (both tropical and extratropical), river discharge, storm surge, and others that were used for impact studies. There were also suggestions from the few ocean eddy-rich coupled simulations that aspects of climate variability and change might be significantly influenced by improved process representation in such models. The compromises that HighResMIP1 made should now be revisited, given the recent major advances in modelling and computing resources. Aspects that will be reconsidered include experimental design and simulation length, complexity, and resolution. In addition, larger ensemble sizes and a wider range of future scenarios would enhance the applicability of HighResMIP. Therefore, we propose an updated HighResMIP2 to improve and extend the previous work, to address new science questions, and to further advance our understanding of the role of horizontal resolution (and hence process representation) in state-of-the-art climate simulations. With further increases in high-performance computing resources and modelling advances along with the ability to take full advantage of these computational resources, an enhanced investigation of the drivers and consequences of variability and change in both large- and synoptic-scale weather and climate is now made possible. With the arrival of global cloud-resolving models (currently run for relatively short timescales), there is also an opportunity to improve links between such models and more traditional CMIP models, with HighResMIP providing a bridge to link understanding between these domains. HighResMIP also aims to link to other CMIP projects and international efforts such as the World Climate Research Program lighthouse activities and various Digital Twin initiatives, as well as having the potential to be used as training and validation data for the fast evolving Machine Learning climate models.

We propose a protocol to evaluate and analyze year-long simulations of global storm-resolving models (GSRMs). The proposed protocol complements an earlier 40-day simulation protocol under the DYAMOND (DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains) project to allow the analysis of the seasonal cycle and associated climatic relevant phenomena. This intercomparison aims to reveal how GSRMs, which can simulate meso-scale convective systems (MCSs) in the global domain, reproduce atmospheric large-scale structures related to convection beyond month-long simulations. The intercomparison for one-year simulations is conducted either by atmosphere-only models or atmosphere-ocean coupled models with atmospheric horizontal mesh sizes less than 5 km. We recommend the continuous four seasons from March 2020 to February 2021 as a target period for the intercomparison but with options for many groups to join more flexibly. The output variables are collected at 0.25° resolution, and archives of a small set of native grid variables are encouraged to analyze tropical cyclones and MCSs. Through the proposed global storm-resolving simulation, we will evaluate the climatological distributions of the atmospheric large-scale circulations, such as Intertropical Convergence Zone (ITCZ), monsoon, and mid-latitude jets, and their time evolution. We present sample analyses from a one-year simulation using the 3.5-km mesh Nonhydrostatic Icosahedral Atmospheric Model (NICAM), revealing the realistic zonal contrast of tropical precipitation, no double ITCZ structure, the reasonable mid-latitude jet position and intensity but a weak bias of storm track activities, and a warm bias over the Eurasia during boreal winter. The proposed one-year simulation protocol is not unique or definite for evaluating GSRMs; we prospect a hierarchical set of experiments from short-term to multi-year simulations as GSRM intercomparisons.

To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.