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Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence

Climate models are crucial for predicting climate change in detail. This paper proposes a balanced approach to improving their accuracy by combining traditional process-based methods with modern artificial intelligence (AI) techniques while maximizing the resolution to allow for ensemble simulations. The authors propose using AI to learn from both observational and simulated data while incorporating existing physical knowledge to reduce data demands and improve climate prediction reliability.

600 years of wine must quality and April to August temperatures in western Europe 1420–2019

This bottle of Riesling from the traditional Bassermann Jordan winery in Deidesheim (Germany) is a relic of the premium wine harvested in 1811. It was named “Comet Wine” after the bright comet that year. The study shows that wine quality can be used to infer summer weather conditions over the past 600 years. After rainy summers with cold winds, wines turned sour, while long periods of high pressure led to excellent qualities. Since 1990, only good wines have been produced due to rapid warming.

Impact of Hurricane Irma on coral reef sediment redistribution at Looe Key Reef, Florida, USA

Global understanding of storm-driven sediment transport along coral reefs and its impact on species and habitats is limited. Measurement of seafloor elevation and volume change due to a category 4 hurricane showed rapid burial of coral reef habitats and migration of large seafloor features due to the storm. Post-storm erosion rates were 2 orders of magnitude greater than historical rates, indicating areas of seafloor instability that could be less suitable for restoration of benthic species.

USA">Read more

A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)

The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.

REPs)">Read more

Opinion: Strengthening research in the Global South – atmospheric science opportunities in South America and Africa

To tackle the current pressing atmospheric science issues, as well as those in the future, a robust scientific community is necessary in all regions across the globe. Unfortunately, this does not yet exist. There are many geographical areas that are still underrepresented in the atmospheric science community, many of which are in the Global South. There are also larger gaps in the understanding of atmospheric composition, processes, and impacts in these regions. In this opinion, we focus on two geographical areas in the Global South to discuss some common challenges and constraints, with a focus on our strengths in atmospheric science research. It is these strengths, we believe, that highlight the critical role of Global South researchers in the future of atmospheric science research.

Applying global warming levels of emergence to highlight the increasing population exposure to temperature and precipitation extremes

Using a special suite of climate simulations, we determine if and when climate change is detectable and translate this to the global warming prevalent in the corresponding year. Our results show that, at 1.5°C warming, >85 % of the global population (>95 % at 2.0° warming) is already exposed to nighttime temperatures altered by climate change beyond natural variability. Furthermore, even incremental changes in global warming levels result in increased human exposure to emerged climate signals.

Synergistic approach of frozen hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework

In cloudy situations, infrared and microwave observations are complementary, with infrared being sensitive to cloud tops and microwave sensitive to precipitation. However, infrared satellite observations are underused. This study aims to quantify if the inconsistencies in the modelling of clouds prevent the use of cloudy infrared observations in the process of weather forecasting. It shows that the synergistic use of infrared and microwave observations is beneficial, despite inconsistencies.

On the importance of middle-atmosphere observations on ionospheric dynamics using WACCM-X and SAMI3

This study shows how middle-atmospheric data (starting at 40 km) affect day-to-day ionospheric variability. We do this by using lower atmospheric measurements that include and exclude the middle atmosphere in a coupled ionosphere–thermosphere model. Comparing the two simulations reveals differences in two thermosphere–ionosphere coupling mechanisms. Additionally, comparison against observations showed that including the middle-atmospheric data improved the resulting ionosphere.

WACCM-X and SAMI3">Read more

The effect of temperature on photosystem II efficiency across plant functional types and climate

A first-of-its-kind global-scale model of temperature resilience and tolerance of photosystem II maximum quantum yield informs how plants maintain their efficiency of converting light energy to chemical energy for photosynthesis under temperature changes. Our finding explores this variation across plant functional types and habitat climatology, highlighting diverse temperature response strategies and a method to improve global-scale photosynthesis modeling under climate change.

Geomorphic risk maps for river migration using probabilistic modeling – a framework

In this paper, we propose a framework for generating risk maps that provide the probabilities of erosion due to river migration. This framework uses concepts from probability theory to learn the river migration model’s parameter values from satellite data while taking into account parameter uncertainty. Our analysis shows that such geomorphic risk estimation is more reliable than models that do not explicitly consider various sources of variability and uncertainty.

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