We construct a conceptual model to understand the interplay between the atmosphere and the ocean biosphere in a climate change framework, including couplings between extraction of carbon dioxide by phytoplankton and climate change, temperature and carrying capacity of phytoplankton, wind energy and phytoplankton production. We find that sufficiently strong mixing can result in decaying gloal phytoplankton content.

There is a paradox between systems that maximize the production of work or entropy (e.g. those with many degrees of freedom like the climate) and those that minimize it (constrained ones, like a heated rod). How free is free enough? Desert landforms provide an instructive analogy – sand dunes are simultaneously built and destroyed, and thus are free and maximize sand transport or work. Yardangs form only by erosion, are more constrained, and emerge with streamlined shape, minimizing work.

This paper introduces new geoengineering model experiments as part of a larger model intercomparison effort, using reflective particles to block some of the incoming solar radiation to reach surface temperature targets. Outcomes of these applications are contrasted that are based on a high greenhouse gas emission pathway and a pathway with strong mitigation and negative emissions after 2040. We compare quantities that matter for societal and ecosystem impacts between the different scenarios.

Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool to quantify the forced response and internal variability in various components of the climate system, but there is no established method to determine the required ensemble size for a given problem. We propose a new framework that can be used to estimate the required ensemble size from a model's control run.

The tropical Pacific Ocean maintains a self-regulating heat storage and release system that distributes heat to the top of the atmosphere and poles, acting like a heat engine with gears. It is normally in steady state but when overloaded by increasing heat, it shifts gear. The oceans, then the atmosphere warm in a series of regionally and globally synchronous step changes that shift climate. This results in infrequent but rapid warming episodes that rapidly change the risk of climate extremes.

Justifiable uncertainty estimates of future change in Northern European winter and Mediterranean summer temperature can be obtained by weighting a multi-model ensemble comprised of projections from different climate models and multiple projections (large ensembles) from the same climate model. Weights received are scaled based on a member's similarity to other members which allows large ensembles to be included without the undue influence of redundant information on uncertainty estimates.

We compare the inter-annual variability of three Single Model Initial condition Large Ensembles (SMILEs), downscaled with three regional climate models over Europe for seasonal temperature and precipitation, number of heatwaves and maximum length of dry periods. They all show good consistency with observational data. The magnitude of variability and the future development is similar in many cases. In general, variability increases for summer indicators and decreases for winter indicators.

Predicting the climate of the upcoming season is of big societal benefit. However, finding out which component of the climate system can act as predictor is difficult. In this study, we focus on Eurasian snow cover as such component and show that knowing the snow cover in November is really helpful in predicting the state of winter over Europe. This mechanism was questioned in previous studies. Using snow data that goes back 150 years into the past, we are now very confident in this relationship

This paper presents efficient Bayesian methods for linear response models of global mean surface temperature that take into account long-range dependence. We apply the methods to the instrumental temperature record, and historical model runs in the CMIP5 ensemble, to provide estimates of the Transient Climate Response, as well as temperature projections under the Representative Concentration Pathways.

What the Technocene idea makes clear is that as modern human societies exhibit an enormous coupling with technology and for the first time in human history that technology has the potential to modify the very core processes that drive Earth System dynamics, then Technology most be considered as a new dimension of analysis in the study of Earth system in its co-evolution with life and particularly human beings.

The variable-to-variable reconstruction in climate system is demonstrated variable- and direction-dependent, such as in reconstructing temperature series. We study how to better apply machine learning to reconstruct climate series under different coupling dynamics. Then the CCM causality coefficient is proposed to select explanatory variable, which is more effective than the Pearson correlation. It might offter some understanding for the application of machine learning to climate dynamics.

We have confirmed that there is a Supertidal cycle associated with the Supercontinent cycle. As continents drift due to plate tectonics, oceans also change size, controlling the strength of the tides and causing periods of Supertides. In this work, we used a coupled tectonic-tidal model of Earth's future to test four different scenarios that undergo different styles of ocean closure and periods of Supertides. This has implications for the Earth system and for other planets with liquid oceans.

Using the central theorem of the dimensional analysis, the π-theorem, we show that the relationship between the amplitude and duration of glacial cycles is governed by a property of scale-invariance that does not depend on the physical nature of the underlying positive and negative feedbacks incorporated by the system.

Regional Earth system model is a useful tool for climate change research. However, its added value of using high resolution can differ in contexts. In this study, we investigated the role of increasing model resolution in simulating precipitation over Africa by using three models and four spatial resolutions, and identified potential added values for specific climate variables over different geographical locations, which could be helpful when providing climate services to local stakeholders.

Geoengineering techniques have been proposed to prevent climate warming in the event of insufficient greenhouse gas emission reductions. Simultaneously these techniques have an impact on precipitation which is depending on the used techniques, magnitude of geoengineering and background circumstances. Here we separated the precipitation responses to temperature independent and dependent components which were then used to explain the precipitation changes in the studied climate model simulations.