Hans Joachim Schellnhuber’s research while affiliated with International Institute for Applied Systems Analysis and other places

The El Ni\~no Southern Oscillation (ENSO) is the strongest driver of interannual global climate variability and can lead to extreme weather events like droughts and flooding. Additionally, ENSO influences the mean global temperature with strong El Ni\~no events often leading, in a warming climate, to new record highs. Recently, we have developed two approaches for the early forecasting of El Ni\~no. The climate network-based approach allows forecasting the onset of an El Ni\~no event about 1 year ahead. The complexity-based approach allows additionally to forecast the magnitude of an upcoming El Ni\~no event in the calendar year before. These methods successfully forecasted the onset of an Eastern Pacific El Ni\~no for 2023/24 and the subsequent record-breaking warming of 2024. Here, we apply these methods to forecast the ENSO state in 2025. Both methods forecast the absence of an El Ni\~no in 2025, with 91.2% and 91.7% probability, respectively. Combining these forecasts with a logistic regression based on the Oceanic Ni\~no Index (ONI) leads to a 69.6% probability that 2025/26 will be a neutral ENSO event. We estimate the probability of a La Ni\~na at 21.8%. This makes it likely that the mean global temperature in 2025 will decrease somewhat compared to the 2024 level.

Multiple recent record-shattering weather events raise questions about the adequacy of climate models to effectively predict and prepare for unprecedented climate impacts on human life, infrastructure, and ecosystems. Here, we show that extreme heat in several regions globally is increasing significantly and faster in magnitude than what state-of-the-art climate models have predicted under present warming even after accounting for their regional summer background warming. Across all global land area, models underestimate positive trends exceeding 0.5 °C per decade in widening of the upper tail of extreme surface temperature distributions by a factor of four compared to reanalysis data and exhibit a lower fraction of significantly increasing trends overall. To a lesser degree, models also underestimate observed strong trends of contraction of the upper tails in some areas, while moderate trends are well reproduced in a global perspective. Our results highlight the need to better understand and model the drivers of extreme heat and to rapidly mitigate greenhouse gas emissions to avoid further harm from unexpected weather events.

Multiple recent record shattering weather events raise questions about the adequacy of climate models to effectively predict and prepare for unprecedented climate impacts on human life, infrastructure, and ecosystems. Here we show that extreme heat in several regions globally is increasing significantly and faster in magnitude than what state-of-the-art climate models have predicted under present warming even after accounting for their regional summer background warming. Across all global land area, models underestimate positive trends exceeding 0.5 °C per decade in widening of the upper tail width of extreme surface temperature distributions by a factor of 4.1 compared to reanalysis data, and exhibit a lower fraction of significantly-increasing trends overall. This highlights the need to better understand and model the drivers of extreme heat and to rapidly mitigate greenhouse gas emissions to avoid further harm from unexpected weather events.

In an ever warming world the need for action on the mitigation of climate change and climate restoration is overdue. To stay within the Paris Agreement guardrail of 2 °C above pre-industrial level of atmospheric CO2, the global economy and transport system must be decarbonized by 2050 at the latest. With the climate system more and more out of balance the need for additional carbon removal from the atmosphere will be inevitable. But how can this be done? One scalable solution is a transition of the building sector towards a sustainable bio-based construction economy. Based on the transdisciplinary idea of the Bauhaus movement in the early 20ies of the last century, the Bauhaus Earth was founded to bring their vision and ideals to live today with the climate crisis at its core. Key are climate-friendly materials, integrated planning and nature-based design. Sustainable forestry provides an extraordinary building material, which combined with today’s technology and innovation will transform cities into wooden human-made carbon sinks in order to restore the global climate, tenth by tenth of a degree Celsius.

El Niño episodes are part of the El Niño-Southern Oscillation (ENSO), which is the strongest driver of interannual climate variability, and can trigger extreme weather events and disasters in various parts of the globe. Previously we have described a network approach that allows to forecast El Niño events about 1 year ahead. Here we evaluate the real-time forecasts of this approach between 2011 and 2022. We find that the approach correctly predicted (in 2013 and 2017) the onset of both El Niño periods (2014-2016 and 2018-2019) and generated only 1 false alarm in 2019. In June 2022, the approach correctly forecasted the onset of an El Niño event in 2023. For determining the p-value of the 12 real-time forecasts, we consider 2 null hypotheses: (a) random guessing where we assume that El Niño onsets occur randomly, and (b) correlated guessing where we assume that in the year an El Niño ends, no new El Niño will start. We find pa≅0.005$p_a\cong 0.005$ and pb≅0.015$p_b\cong 0.015$, this way rejecting both the null hypotheses that the same quality of the forecast can be obtained by chance. We also discuss how the network algorithm can be further improved by systematically reducing the number of false alarms. For 2024, the method indicates the absence of a new El Niño event.

El Niño events represent anomalous episodic warmings, which can peak in the equatorial Central Pacific (CP events) or Eastern Pacific (EP events). The type of an El Niño (CP or EP) has a major influence on its impact and can even lead to either dry or wet conditions in the same areas on the globe. Here we show that the difference of the sea surface temperature anomalies between the equatorial western and central Pacific in December enables an early forecast of the type of an upcoming El Niño ( p -value < 10 ⁻³ ). Combined with a previously introduced climate network-based approach that allows to forecast the onset of an El Niño event, both the onset and type of an upcoming El Niño can be efficiently forecasted. The lead time is about 1 year and should allow early mitigation measures. In December 2022, the combined approach forecasted the onset of an EP event in 2023.