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Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using Neural General…
Machine-learning (ML) models, such as the AIFS at the ECMWF, have revolutionised weather forecasting in recent years. We present an extension of the AIFS that jointly models the atmosphere and surface ocean, including ocean waves and sea…
Current satellite precipitation retrievals like GPROF assume that brightness temperature is sufficient to constrain rainfall. This information, however, often represents multiple rain states, resulting in rainfall estimate uncertainties.…
It is increasingly recognized that the multiple and systemic impacts of Earth system change threaten the prosperity of society through altered land carbon dynamics, freshwater variability, biodiversity loss, and climate extremes. For…
Improving the representation of precipitation in Earth system models (ESMs) is critical for assessing the impacts of climate change and especially of extreme events like floods and droughts. In existing ESMs, precipitation is not resolved…
Tidally locked terrestrial planets around low-mass stars are the prime targets of finding potentially habitable exoplanets. Several atmospheric general circulation models have been employed to simulate their possible climates, however,…
Rapid progress in the field of machine-learning for weather prediction has led to the emergence of algorithms whose forecasting skill can exceed that of traditional physically based models. This development represents an opportunity to…
Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in…
We determine the thermal evolution of the intergalactic medium (IGM) over $3\, \mathrm{Gyr}$ of cosmic time $1.8<z<5.4$ by comparing measurements of the Ly{\alpha} forest power spectrum to a suite of $\sim70$ hydrodynamical simulations. We…
This article describes the software engineering framework and computation performance of a global climate system model which helps the user to understand the step-by-step technical to DIY(do it yourself) a climate model by your own. The…
An unresolved problem of present generation coupled climate models is the realistic distribution of rainfall over Indian monsoon region, which is also related to the persistent dry bias over Indian land mass. Therefore, quantitative…
Global climate models (GCMs), typically run at ~100-km resolution, capture large-scale environmental conditions but cannot resolve convection and cloud processes at kilometer scales. Convection-permitting models offer higher-resolution…
Amid accelerated digitalization, not only is the scale of data processing and storage increasing, but so too is the associated infrastructure load on the climate. Current climate models and environmental protocols almost entirely overlook…
We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future. Drawing from our review, we identify three recommendations.…
Variations in zonal surface temperature gradients and zonally asymmetric tropical overturning circulations (Walker circulations) are examined over a wide range of climates simulated with an idealized atmospheric general circulation model…
Atmospheric ozone is a crucial absorber of solar radiation and an important greenhouse gas. However, most climate models participating in the Coupled Model Intercomparison Project (CMIP) still lack an interactive representation of ozone due…
This study introduces the second version of the Integrated Climate Model (ICM). ICM is developed by the Center for Monsoon System Research, Institute of Atmospheric Physics to improve the short-term climate prediction of the East…
We test the scaling performance of seven leading global climate models by using detrended fluctuation analysis. We analyse temperature records of six representative sites around the globe simulated by the models, for two different…
Global Climate Models are key tools for predicting the future response of the climate system to a variety of natural and anthropogenic forcings. Here we show how to use statistical mechanics to construct operators able to flexibly predict…
Future changes in the location of the intertropical convergence zone (ITCZ) due to climate change are of high interest since they could substantially alter precipitation patterns in the tropics and subtropics. Although models predict a…