We produced one of the largest single model initial-condition ensembles thus far using the MIROC6 coupled atmosphere–ocean global climate model (MIROC6-LE). MIROC6-LE includes historical simulations, eight single forcing historical experiments, five future scenario experiments and three single forcing future experiments with 10- or 50-ensemble members. We describe the experimental design and show initial analyses. This dataset would be useful to a wide range of research communities.

Our study focuses on Chile, which is affected by the prevailing ocean currents, upwellings and El Niño; these generate the weather in Chile. That is why we conducted a study on the dynamics of this system using spectral and nonlinear analysis techniques. We obtained periodicities related to internal and external forcing; we find that the dynamics is not chaotic but nonlinear. Finally, the northern part presents a strong correlation between ONI and LLE due to regional characteristics.

The Arctic is warming several times faster than the rest of the planet. Here, we use climate model projections to quantify for the first time how this faster warming in the Arctic impacts the timing of crossing the 1.5 °C and 2 °C thresholds defined in the Paris Agreement. We show that under plausible emissions scenarios that fail to meet the Paris 1.5 °C target, a hypothetical world without faster warming in the Arctic would breach that 1.5 °C target around 5 years later.

The worsening effects of climate change and other environmental problems impact the ability of societies to tackle the causes of these problems. We term this "derailment risk": the risk that the path to re-stabilising the natural world is derailed by the worsening instability of societies. But, conversely, instability can also provide opportunities for positive change. Acting on derailment risk is needed to ensure positive change can win out and so is critical for avoiding catastrophic outcomes.

We present model-based topographic sensitivity experiments that provide valuable constraints for interpreting past proxies and records of climate and tectonic processes. The study uses a climate model to quantify the response of regional climate and oxygen isotopic composition of precipitation to diachronous surface uplift scenarios across the European Alps. The results suggest that isotopic signal changes can be measured in geologic archives using stable isotope paleoaltimetry.

Ecosystem resistance reflects their susceptibility during adverse conditions and can be changed by land management. We estimate ecosystem resistance to drought and temperature globally. We find a higher resistance to drought in forests compared to croplands and an evident loss of resistance to drought when primary forests are converted to secondary forests or they are harvested. Old-growth trees tend to be more resistant in some forests and crops benefit from irrigation during drought periods.

Vegetation largely controls land hydrology by transporting water from the subsurface to the atmosphere through roots and is highly variable in space and time. However, current land surface models have limitations in capturing this variability at a global scale, limiting accurate modeling of land hydrology. We found that satellite-based vegetation variability considerably improved modeled land hydrology and therefore has potential to improve climate predictions of, for example, droughts.

Ocean surface temperature is strongly affected by vertical mixing processes induced by waves. The correct estimation of a thermal state of the upper ocean layer is crucial for climate modelling and weather forecasting. The accuracy of predictions of exchange of heat between oceanic waters and the atmosphere depends on a reliable input from wave models admitting mixing processes. The study discusses the importance of higher-order approximation of vertical mixing due to high and steep waves.

The glacial–interglacial cycles of the Quaternary exhibit 41 kyr periodicity before the Mid-Pleistocene Transition (MPT) around 1.2–0.8 Myr ago and ~100 kyr periodicity after that. The mechanism generating these periodicities remains elusive. Through an analysis of an Earth system model of intermediate complexity, CLIMBER-2, we show that the dominant periodicities of glacial cycles can be explained from the viewpoint of synchronization theory.

We investigate the flux of carbon from the atmosphere into the land surface and ocean for multiple models and over a range of future scenarios. We did this by comparing simulations after the same change in the global-mean near-surface temperature. Using this method, we show that the choice of scenario can impact the carbon allocation to the land, ocean, and atmosphere. Scenarios with higher emissions reach the same warming levels sooner, but also with relatively more carbon in the atmosphere.

The Indonesia Throughflow is an important pathway connecting the Pacific and Indian oceans and is part of a wind-driven circulation that is expected to reduce under greenhouse gas forcing. Solar dimming and sulfate aerosol injection geoengineering may reverse this effect. But stratospheric sulfate aerosols affect winds more than simply ``shading the sun''; they cause a reduction in water transport similar to that we simulate for a scenario with unabated greenhouse gas emissions.

Climate models are powerful tools, but they have high computational costs, hindering their use in exploring future climate extremes. We demonstrate MESMER-X, the only existing emulator for spatial climate extremes (heatwaves, fires, droughts) that mimics all of their relevant properties. Thanks to its negligible computational cost, MESMER-X may greatly accelerate the exploration of future climate extremes or enable the integration of climate extremes in economic and financial models.

Downward longwave radiation (R_ld) is critical for the surface energy budget, but its climatological variation on a global scale is not yet well understood physically. We use a semi-empirical equation derived by Brutsaert (1975) to identify the controlling role that atmospheric heat storage plays in spatiotemporal variations of R_ld. Our work helps us to better understand aspects of climate variability, extreme events, and global warming by linking these to the mechanistic contributions of R_ld.