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This study evaluates five bias correction methods (four univariate and one multivariate) for correcting multivariate heat-stress indices. We show that the multivariate method can benefit the indirect correction that first adjusts individual components before indices calculation, and its advantage is more evident for index relying equally on multiple drivers. Meanwhile, the direct correction of heat-stress indices by univariate Quantile Delta Mapping approach also has a comparable performance.

Understanding whether the El Niño–Southern Oscillation (ENSO) is likely to change in the future is important due to its widespread impacts. By using large ensembles, we can robustly isolate the time evolving response of ENSO variability in 14 climate models. We find that ENSO variability evolves in a non-linear fashion in many models and that there are large differences between models. These non-linear changes imply that ENSO impacts may vary dramatically throughout the 21st century.

Ocean surface temperature is strongly affected by vertical mixing processes induced by waves. The correct estimation of a thermal state of the ocean upper layer is crucial for climate modeling 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.

Climate change is caused by the accumulated heat in the Earth system, with the land storing the second largest amount of this extra heat. Here, new estimates of continental heat storage are obtained, including changes in inland water heat storage and permafrost heat storage in addition to changes in ground heat storage. We also argue that heat gains in all three components should be monitored independently of their magnitude due to heat-dependent processes affecting society and ecosystems.

We examine how geoengineering using aerosols in the atmosphere might impact urban climate in the greater Beijing region containing over 50 million people. Climate models have too coarse resolutions to resolve regional variations well, so we compare two work arounds for this – an expensive physical model, and a cheaper statistical method. The statistical method generally gives a reasonable representation of climate, has limited resolution, and a different seasonality from the physical model.

One limit of planetary habitability is defined by the threshold of global glaciation: If Earth cools, growing ice cover makes it brighter, leading to further cooling since more sunlight is reflected, eventually leading to global ice cover ("Snowball Earth"). We study how much carbon dioxide is needed to prevent global glaciation in Earth's history given the slow increase in the Sun's brightness. We find an unexpected change in the characteristics of climate states close to the Snowball limit.

We carry out an assessment of an ensemble of general climate models (CMIP6), based on the ability of the models to represent the key physical process that are important for representing the European climate. Filtering the models with the assessment leads to more of the models with less global warming being removed, and this shifts the lower part of the projected temperature range towards greater warming. This is in contrast to the affect of weighting the ensemble using global temperature trends.

Understanding the variable global carbon budget (GCB) is essential for tracing the fate of carbon. For the first time, our emission-driven simulations allow the reconstruction and prediction of variations in prognostic atmospheric CO₂.The evolution of GCB is well reconstructed by MPI-ESM assimilation within a closed Earth system. Retrospective predictions show high confidence to predict the next year GCB and hence support the Global Carbon Project and inform climate relevant policy.

This paper presents a new classification method for synoptic circulation patterns and its application on ERA-Interim reanalysis data. The output fields of the CMIP6 models are assigned to the reanalysis-derived classes and a new quality index, built on the statistics between each model and the reference, is introduced to quantify the “quality” of the respective model. CMIP6 models are ranked according to the new quality score.

The early 19^th century was the coldest period over the past 500 years, when strong tropical volcanic events and a solar minimum coincided. This study quantifies potential surface cooling from the solar and volcanic forcing in the early 19^th century with large ensemble simulations and identifies the regions that their impacts cannot be simply additive. The cooling perspective of Arctic Amplification exists in both solar and post-volcano period with the albedo feedback as the main contribution.

Abrupt seasonal onset of monsoon as well as abrupt changes in paleoclimatic records have sometimes been ascribed to bifurcations and characterized as tipping points. We describe a mechanism by which nonlinear lower-order monsoon models may not manifest bifurcations latent in their mathematical form. This contributes to resolving a long-standing contradiction between data/higher order models and lower-order monsoon models, regarding bifurcations explaining abrupt changes seen in monsoon.

Solar geoengineering has been proposed as a way of counteracting the warming effects of increasing greenhouse gases by reflecting solar radiation. This work shows the solar geoengineering can slow down the northern high-latitude permafrost degradation, but can not preserve the permafrost ecosystem as that under a climate of the same warming level without solar geoengineering.

Studies analysing the impact of the future climate on ecosystems employ climate projections simulated by global circulation models. These climate projections display biases which translate into significant uncertainty in projections of the future carbon cycle. Here, we test different methods to constrain the uncertainty in simulations of the carbon cycle over Australia. We find that all methods reduce the bias in the steady-state carbon variables but temporal properties do not improve.

Precipitation changes are an important consequence of climate change. We use a method based on models and observations to detect changes in mean and extreme precipitation caused by external influence. We detect forced change in three observational datasets for global mean and extreme precipitation, but different observational datasets show different magnitudes of forced change. It is thus uncertain how large the change is, which is important for prediction of future changes and adaptation.

We present a simple system to forecast the MJO. We are using atmospheric circulation as input to our system. We found a good skill forecast of the MJO amplitude within 40 days using this methodology. Comparing our results with ECMWF and machine learning forecasts confirmed the good skill of our system.

In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.