Climate processes

Abstract:

From 2017 to 2019, vast swathes of eastern Australia were affected by the severe and devastating Tinderbox Drought. Here, we present the first extended drought climatology for New England, spanning 1880 to 2022, and explore trends in drought characteristics over the past 142 years. We use newly recovered historical temperature and rainfall observations, the latest version of the Australian Bureau of Meteorology’s gridded rainfall dataset and a global gridded extreme dataset to assess changes in precipitation signatures and temperature events during droughts. Our analysis identifies 32 meteorological droughts from 1880 to 2022, lasting from 7 months to over 7 years. The climatology also reveals a change in the nature of drought, with a shift from events characterised by warm season rainfall deficiencies to events with greater rainfall reduction in the cool half of the year. Despite this shift, we also find a significant decrease in the number of cold extremes occurring during droughts, and an increase in hot extremes. Droughts in New England have been associated with a greater than average frequency of cold nights and frost days, but this relationship has weakened over recent decades. Conversely, they are generally associated with a greater than average frequency of hot days, a relationship that has increased over time. The Tinderbox Drought was the second-most extreme meteorological drought for New England in terms of rainfall deficit and drought severity, and was associated with the highest number of extreme warm temperature events. The new drought climatology for New England can now be used to provide regional drought information for decision makers and the community.

Abstract:

To study and monitor the Sun and its atmosphere, various space missions have been launched in the past decades. With rapid improvement in technology and different mission requirements, the data products are subject to constant change. However, for such long-term studies as solar variability or multi-instrument investigations, uniform data series are required. In this study, we built on and expanded the instrument-to-instrument translation (ITI) framework, which provides unpaired image translations. We applied the tool to data from the Extreme Ultraviolet Imager (EUI), specifically the Full Sun Imager (FSI) on Solar Orbiter and the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). This approach allowed us to create a homogeneous dataset that combines the two extreme ultraviolet (EUV) imagers in the 174/171 Å and 304 Å channels. We demonstrate that ITI is able to provide image calibration between Solar Orbiter and SDO EUV imagers, independent of the varying orbital position of Solar Orbiter. The comparison of the intercalibrated light curves derived from 174/171 Å and 304 Å filtergrams from EUI and AIA shows that ITI can provide uniform data series that outperform a standard baseline calibration. We evaluate the perceptual similarity in terms of the Fréchet inception distance, which demonstrates that ITI achieves a significant improvement of perceptual similarity between EUI and AIA. The study provides intercalibrated observations from Solar Orbiter/EUI/FSI with SDO/AIA, enabling a homogeneous dataset suitable for solar cycle studies and multi-viewpoint investigations.

Abstract:

Abstract. The Next Generation of Earth Modeling Systems (nextGEMS) project aimed to produce multidecadal climate simulations, for the first time, with resolved kilometer-scale (km-scale) processes in the ocean, land, and atmosphere. In only 3 years, nextGEMS achieved this milestone with the two km-scale Earth system models, ICOsahedral Non-hydrostatic model (ICON) and Integrated Forecasting System coupled to the Finite-volumE Sea ice-Ocean Model (IFS-FESOM). nextGEMS was based on three cornerstones: (1) developing km-scale Earth system models with small errors in the energy and water balance, (2) performing km-scale climate simulations with a throughput greater than 1 simulated year per day, and (3) facilitating new workflows for an efficient analysis of the large simulations with common data structures and output variables. These cornerstones shaped the timeline of nextGEMS, divided into four cycles. Each cycle marked the release of a new configuration of ICON and IFS-FESOM, which were evaluated at hackathons. The hackathon participants included experts from climate science, software engineering, and high-performance computing as well as users from the energy and agricultural sectors. The continuous efforts over the four cycles allowed us to produce 30-year simulations with ICON and IFS-FESOM, spanning the period 2020–2049 under the SSP3-7.0 scenario. The throughput was about 500 simulated days per day on the Levante supercomputer of the German Climate Computing Center (DKRZ). The simulations employed a horizontal grid of about 5 km resolution in the ocean and 10 km resolution in the atmosphere and land. Aside from this technical achievement, the simulations allowed us to gain new insights into the realism of ICON and IFS-FESOM. Beyond its time frame, nextGEMS builds the foundation of the Climate Change Adaptation Digital Twin developed in the Destination Earth initiative and paves the way for future European research on climate change.

Abstract:

We developed a cloud microphysics parameterization for the icosahedral nonhydrostatic modeling framework (ICON) model based on physics-informed machine learning (ML). By training our ML model on high-resolution simulation data, we enhance the representation of cloud microphysics in Earth system models (ESMs) compared to traditional parameterization schemes, in particular by considering the influence of high-resolution dynamics that are not resolved in coarse ESMs. We run a global, kilometer-scale ICON simulation with a one-moment cloud microphysics scheme, the complex graupel scheme, to generate 12 days of training data. Our ML approach combines a microphysics trigger classifier and a regression model. The microphysics trigger classifier identifies the grid cells where changes due to the cloud microphysical parameterization are expected. In those, the workflow continues by calling the regression model and additionally includes physical constraints for mass positivity and water mass conservation to ensure physical consistency. The microphysics trigger classifier achieves an F1 score of 0.93 on classifying unseen grid cells. The regression model reaches an score of 0.72 averaged over all seven microphysical tendencies on simulated days used for validation only. This results in a combined offline performance of 0.78. Using explainability techniques, we explored the correlations between input and output features, finding a strong alignment with the graupel scheme and, hence, physical understanding of cloud microphysical processes. This parameterization provides the foundation to advance the representation of cloud microphysical processes in climate models with ML, leading to more accurate climate projections and improved comprehension of the Earth’s climate system.

Abstract:

Earth system modeling is currently undergoing an exciting transformation, thanks to new technical capabilities that allow for significant spatial refinement. For the first time, these capabilities allow us to explicitly simulate extreme precipitation and its effects on climate-relevant timescales on a global scale. Thus, new Earth system data from high-resolution modeling approaches offer an exciting foundation for new analyses and research. In our study, we examine the distribution and changes in extreme precipitation from global simulations. We obtained this data from the ICON Earth system model simulations conducted within the nextGEMS project, which aims to create future projections up to the year 2050 with a grid spacing of approximately 5 km. Our analysis focuses on the portion of precipitation contributing to the top ten percent of globally accumulated precipitation. Using the open-source tool tobac we identify and track the resulting precipitation cells over time. Our analysis reveals that warming causes the most extreme precipitation cells to become more intense. At the same time, the data shows a significant decrease in the total number of cells, resulting in fewer, more intense extremes. Finally, we discuss these findings in relation to changes in the spatial distribution of the cells and changed environmental conditions.