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 and temperature projections under the Representative Concentration Pathways.

We explore the implicit assumptions that underlie many published probabilistic estimates of the equilibrium climate sensitivity – that is, the amount the climate will warm under a doubling of the atmospheric CO₂ concentration. We demonstrate that many such estimates have made assumptions that would be difficult to justify and show how the calculations can be repeated in a more defensible manner. Our results show some significant differences from previous calculations.

We modeled how agricultural performance and demand will shift as a result of climate change and population growth, and how the resulting adaptations will affect aspects of the Earth system upon which humanity depends. We found that the impacts of land use and management can have stronger impacts than climate change on some such ecosystem services. The overall impacts are strongest in future scenarios with more severe climate change, high population growth, and/or resource-intensive lifestyles.

Regional Climate Models constitute a downscaling tool to provide high-resolution data for impact and adaptation studies. However, there is no unique definition of the added value of downscaling as it depends on many factors. We investigate the impact of spatial resolution and model formulation on downscaled rainfall in Africa. Our results show that improvements in downscaled rainfall compared to the driving reanalysis are often related to model formulation and not always to higher resolution.

We present an open-source software framework for developing so-called world–Earth models that link physical, chemical and biological processes with social, economic and cultural processes to study the Earth system's future trajectories in the Anthropocene. Due to its modular structure, the software allows interdisciplinary studies of global change and sustainable development that combine stylized model components from Earth system science, climatology, economics, ecology and sociology.

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 depends on the techniques used, geoengineering magnitude, and background circumstances. We separated the independent and dependent components of precipitation responses to temperature, which were then used to explain the precipitation changes in the studied climate model simulations.

Based on climate simulations, we suggested that historical warming increased chances of drought exceeding the severe 2015 event in equatorial Asia due to El Niño. The fire and fire emissions of CO₂/PM_2.5 will largely increase at 1.5 and 2 °C warming. If global warming reaches 3 °C, as is expected from the current mitigation policies, chances of fire and CO₂/PM_2.5 emissions exceeding the 2015 event become approximately 100 %. Future climate policy has to consider these climate change effects.

Under continued anthropogenic greenhouse gas emissions, it is likely that global mean surface temperature will continue to increase. Little is known about changes in climate variability. We analyze surface climate variability and compare it to mean change in colder- and warmer-than-present climate model simulations. In most locations, but not on subtropical land, simulated temperature variability up to decadal timescales decreases with mean temperature, and precipitation variability increases.

This study presents a large novel data set of climate model simulations for central Europe covering the years 1900–2028 at a 25 km resolution. The focus is on intensive areal precipitation values. The data set is validated against observations using different statistical approaches. The results reveal an adequate quality in a statistical sense as well as some long-term variability with phases of increased and decreased heavy precipitation. The predictions of the near future show continuity.

Projections of climate change are uncertain because climate models are imperfect, future greenhouse gases emissions are unknown and climate is to some extent chaotic. To partition and understand these sources of uncertainty and make the best use of climate projections, large ensembles with multiple climate models are needed. Such ensembles now exist in a public data archive. We provide several novel applications focused on global and regional temperature and precipitation projections.

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

The variations in oceanic and atmospheric modes play important roles in global and regional climate variability. The relationships between their variations and regional climate variability have been extensively examined, but the interconnections among these climate modes remain unclear. We show that the base periods and their harmonic oscillations that appear to be related to QBO, ENSO, and solar activities act as key connections among the climatic modes with synchronous behaviors.

Recently, multivariate bias correction (MBC) methods designed to adjust climate simulations have been proposed. However, they use different approaches, leading potentially to different results. Therefore, this study intends to intercompare four existing MBC methods to provide end users with aid in choosing such methods for their applications. To do so, a wide range of evaluation criteria have been used to assess the ability of MBC methods to correct statistical properties of climate models.

Levels of future temperature change are often used interchangeably with carbon budget allowances in climate policy, a relatively robust relationship on the timescale of this century. However, recent advances in understanding underline that continued warming after net-zero emissions have been achieved cannot be ruled out by observations of warming to date. We consider here how such behavior could be constrained and how policy can be framed in the context of these uncertainties.