The emergence of snow droughts as drivers of negative extremes in plant productivity over the past decades, Gampe, D, Sharon, E, Mehdi-Schulz, B, Varela, M, O’Sullivan, M, Buermann, W, Fader, M, Koch, F

The authors theoretically delineate the maximal increases in tree growth that can be expected from increases in plant intrinsic water-use efficiency, which increases with rising CO2. They highlight en...

Species ecological parameters are crucial for understanding the functional responses to environmental perturbations, aiding in informing conservation …

Anthropogenic global warming is set to exceed 1.5 °C by 2028 and hence improved quantification of the Paris climate goals is proposed, based on 20-year means of a benchmark timeseries from 1850 to 203...

Abstract. The first version of the Detection and Attribution Model Intercomparison Project (DAMIP v1.0) coordinated key simulations exploring the role of individual forcings in past, current and futur...

Dr. Christoph Jörges is collaborating with Prof. Matthias Forkel from TU Dresden for his postdoctoral studies.

In this series, members of our research group introduce themselves through three questions. Next up: Dr. David Gampe.

Abstract. Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe th...

A curated collection of all things relating to the Blue Sky social media platform.

Abstract. The swift and ongoing rise of global temperatures over the past decades led to an increasing number of climate variables showing statistically significant changes compared to their pre-industrial state. Determining when these climate signals emerge from the noise of internal climate variability (i.e., estimating the time of emergence, ToE) is crucial for climate risk assessments and adaptation planning. However, robustly disentangling the climate signal from internal variability represents a challenging task. While climate projections are communicated increasingly frequently through global warming levels (GWLs), the ToE is usually still expressed in terms of time horizons. Here, we present a framework to robustly derive global warming levels of emergence (GWLoE) using five single-model initial-condition large ensembles (SMILEs) and apply it to four selected temperature and precipitation indices. We show that the concept of GWLoE is particularly promising to constrain temperature projections and that it proves a viable tool to communicate scientific results. We find that > 85 % of the global population is exposed to emerged signals of nighttime temperatures at a GWL of 1.5 °C, increasing to > 95 % at 2.0 °C. Daily maximum temperature follows a similar yet less pronounced path. Emerged signals for mean and extreme precipitation start appearing at current GWLs and increase steadily with further warming (∼ 10 % population exposed at 2.0 °C). Related probability ratios for the occurrence of extremes indicate a strong increase with widespread saturation of temperature extremes (extremes relative to historical conditions occur every year) reached below 2.5 °C warming particularly in (sub)tropical regions. These results indicate that we are in a critical period for climate action as every fraction of additional warming substantially increases the adverse effects on human wellbeing.

Yeo et al. use digital data on student behaviour (Wi-Fi connections and Learning Management System logins) to examine associations between sleep and academic outcomes.