Malcolm John Roberts

Tropical Cyclones (TCs) affecting southeastern Africa are poorly understood, due to limited focused research and challenges in modeling TCs. Higher resolution climate models have potential to provide more reliable projections. In this paper, we analyze the latest global high‐resolution simulations (“HighResMIP”), to evaluate what these models tell us about current and future TCs in the southwest Indian Ocean. TC tracks are identified by two tracking algorithms in 10 Global Climate Models (GCMs) and we compare to observations and reanalysis. GCMs capture TCs in the same region as observations during the austral summer cyclone season. However, TCs are too weak, not reaching equivalent low pressures to observed storms, but are more intense in high‐resolution GCMs (28–55 km) than lower resolution GCMs (≥78 km). We find little improvement in the representation of TCs with increasing resolution, beyond TC intensity. There is no one “best” model for simulating TCs, with notable variations between models in TC climatologies. In experiments of near future climate change (to 2050), there are some consistent signals between models, with nearly all models showing fewer TCs in future. Many models indicate a south‐westward shift in TCs. Only the highest resolution model indicates the Lifetime Maximum Intensity of TCs become more intense. All models indicate more favorable oceanic and atmospheric conditions for stronger and more impactful TCs in the Mozambique Channel, including an increase in the Potential Intensity. This study provides an essential benchmark for future research using forthcoming km‐scale simulations.

Recent advances in numerical modeling have enabled km‐scale climate simulations, improving global climate representation and local‐scale projections, critical to climate adaptation strategies. In this context, the present study assesses the performance of such models over coastal shelf seas—key climate‐sensitive regions—in their ability to represent the sea surface temperature (SST) and air temperature. Compared to satellite and reanalysis data, the models exhibit systematic warm biases (3°C in SST, 1.5°C in air temperature) in summer across several shelf seas: the European shelf, the Gulf of Maine, the Yellow sea, the Arctic and Patagonian shelves. These biases strongly correlate with tidal mixing fronts, driven by the dissipation of the barotropic tide and identified by the Simpson‐Hunter parameter. These findings suggest that missing tidal mixing is a significant error source on coastal shelves, highlighting the need for improved ocean mixing representations to enhance model accuracy.

Recent advances in numerical modeling enable km-scale climate simulations, improving global climate representation and local-scale projections, critical to climate adaptation strategies. In this context, the present study assesses the performance of such models over coastal shelf seas –key climate-sensitive regions– in their ability to represent the sea surface temperature (SST) and air temperature. Compared to satellite and reanalysis data, the models exhibit systematic warm biases (~3°C in SST, ~1.5°C in air temperature) in summer across several shelf seas: the European shelf, the Gulf of Maine, the Yellow sea, the Arctic and Patagonian shelves. These biases strongly correlate with tidal mixing fronts, driven by barotropic tide dissipation and identified by the Simpson-Hunter parameter. These findings suggest that missing tidal mixing is a significant error source on coastal shelves, highlighting the need for improved ocean mixing representations to enhance model accuracy.

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.

Regarded as the Asian Water Tower, the Tibetan Plateau (TP) collects atmospheric precipitation from a vast area of land and feeds into major rivers that sustain the livelihood of billions of people in East, South and Central Asia. It is critical to reasonably simulate the hydrological cycle over the TP in order to assess future climate risks to agriculture, water resources and ecosystem services. To address the chronic wet biases over the TP in state‐of‐the‐art climate models, we have compared 12 high‐resolution (HR) climate models (25–50 km) to their corresponding low‐resolution versions (100–200 km) with respect to the 1979–2014 climatology. It is found that the HR models consistently reduce about half of the wet biases over the TP, mainly from better resolved orography. The wet biases are reduced by 41% over the northern and western TP, mainly contributed by decreased frequency of light precipitation (0.1–10 mm day ⁻¹ ), which is attributed to reduced evaporation because of weakened surface wind by raised orography. The most significant reduction of biases (53%) rising from decreased frequency of mid‐heavy precipitation (10–50 mm day ⁻¹ ), appears over the southern and eastern TP, on the leeside of elevated orography where steeper orography enhances rain shadow effect by stronger downward motion along the sharper slope, while partly compensated by air column convergence due to vertical stretching of the downward flow for potential vorticity conservation. This study highlights the importance of surface processes and resolving complex orography in simulating precipitation and large‐scale hydrology around the TP which potentially benefits the future hydrological projection.

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.