Current CO₂ emission rates are incompatible with the 2 °C target for global warming. Negative emission technologies are therefore an important basis for climate policy scenarios. We show that photoelectrochemical CO₂ reduction might be a viable, high-efficiency alternative to biomass-based approaches, which reduce competition for arable land. To develop them, chemical reactions have to be optimized for CO₂ removal, which deviates from energetic efficiency optimization in solar fuel applications.

Climate regime shift during the 1980s identified by abrupt change in temperature, precipitation, etc. had a substantial impact on the ecosystem at different scales. Our paper identifies the spatial and temporal characteristics of the effects of climate variability, global warming, and eCO₂ on ecosystem trends before and after the shift. We found about 15 % (20 %) of the global land area had enhanced positive trend (trend sign reversed) during the 1980s due to climate regime shift.

Many climate models have biases in different variables throughout the world. Adjusting these biases is necessary for estimating climate impacts. Here we demonstrate that widely used univariate bias adjustment methods do not work well for multivariate impacts. We illustrate this problem using fire risk and heat stress as impact indicators. Using an approach that adjusts not only biases in the individual climate variables but also biases in the correlation between them can resolve these problems.

This paper shows that the absorption of carbon dioxide by the ocean is predictable several years in advance. This is important because fossil-fuel-derived carbon dioxide is largely responsible for anthropogenic global warming and because carbon dioxide emission management and global carbon cycle budgeting exercises can benefit from foreknowledge of ocean carbon absorption. The promising results from this new forecast system justify the need for additional oceanic observations.

The identification of moisture sources for a region is of prominent importance regarding the characterization of precipitation. In this work, the moisture sources for the western North American monsoon (WNAM) region are identified; these sources are the Gulf of California, the WNAM itself, eastern Mexico and the Caribbean Sea. We find that rainfall intensity over the WNAM region is related to the amount of moisture transported from the Caribbean Sea and eastern Mexico during the preceding days.

The paper revisits the critical issue of precipitation characteristics in response to global warming through a new analysis of global and regional climate projections and a summary of previous work. Robust responses are identified and the underlying processes investigated. Examples of applications are given, such as the evaluation of risks associated with extremes. The paper, solicited by the EGU executive office, is based on the 2018 EGU Alexander von Humboldt medal lecture by Filippo Giorgi.

Best estimates of future climate projections typically rely on a range of climate models from different international research institutions. However, it is unclear how independent these different estimates are, and, for example, the degree to which their agreement implies robustness. This work presents a review of the varied and disparate attempts to quantify and address model dependence within multi-model climate projection ensembles.

We analyse moisture transport triggered by the Great Plains low-level jet (GPLLJ), a maximum in wind speed fields located within the first kilometre of the US Great Plain's troposphere, through the innovative Eulerian Weather Research and Forecasting Model tracer tool. Much moisture associated with this low-level jet has been found in northern regions located in a vast extension of the continent, highlighting the key role played by the GPLLJ in North America's advective transport of moisture.

Ice melting at the scale of inter-annual fluctuations against the trend is favoured by an increase in moisture transport in summer, autumn, and winter and a decrease in spring. On a daily basis extreme humidity transport increases the formation of ice in winter and decreases it in spring, summer, and autumn; in these three seasons it thus contributes to Arctic sea ice melting. These patterns differ sharply from that linked to decline, especially in summer when the opposite trend applies.

We find that for mitigation scenarios, prescribing atmosphere–ocean general circulation models' (AOGCMs') respective equilibrium climate sensitivities (ECSs) and transient climate responses (TCRs) to the one-box model results in too high global mean temperature projections due to the information loss resulting from the reduction of complexity. The one-box model offers a good emulator of these AOGCMs, provided the AOGCM's ECS and TCR values are mapped onto effective one-box counterparts.

We develop a method to keep the amount of water in the soil at the present-day level, using only local water sources in a global climate model. This leads to less drying over many land areas, but also decreases river runoff. We find that temperature extremes in the 21st century decrease substantially using our method. This provides a new perspective on how land water can influence regional climate and introduces land water management as potential tool for local mitigation of climate change.

In this study, the regional MiKlip decadal prediction system is evaluated. This system has been established to deliver highly resolved forecasts for the timescale of 1 to 10 years for Europe. Evidence of the general potential for regional decadal predictability for the variables temperature, precipitation, and wind speed is provided, but the performance of the prediction system depends on region, variable, and system generation.

Based on a simple statistical model we show that the September sea ice extent has a high predictive skill, up to 4 months ahead, based on previous months' oceanic and atmospheric conditions. Our statistical model skillfully captures the interannual variability of the September sea ice extent and could provide a valuable tool for identifying relevant regions and oceanic and atmospheric parameters that are important for the sea ice development in the Arctic.