Andreas F. Prein

Last Name

Prein

First Name

Andreas F.

ORCID

0000-0001-6250-179X

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09844 - Prein, Andreas Franz / Prein, Andreas Franz

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Publications 1 - 10 of 109

Precipitation- and Temperature-Driven Future Changes to Flooding in Alaska
Michalek, Alexander T.; Villarini, Gabriele; Prein, Andreas F.; et al. (2025)
Geophysical Research Letters
Examining large-scale projected changes in streamflow and flood extent (e.g., inundation) for Alaska is essential for raising awareness of flood hazards under a changing climate and supporting broad-scale adaptation planning. Therefore, we examine projected changes in peak streamflow timing and magnitude using a physically based hydrologic model. For model inputs, we utilize climate simulations conducted at 4-km horizontal grid spacing over Alaska from 2005 to 2016, providing a historical and future pseudo-global warming scenario. Analysis of hydrographs reveals the peak timing shifts slightly earlier in the year for most of Alaska's streams. The change in peak magnitude is more heterogeneous across the state, with the northernmost region showing the highest projected increases. The changes in timing are driven by temperature, while precipitation and temperature drive the changes in magnitude. These changes are then transformed into inundation maps, showing a similar albeit more muted pattern compared to the changes in magnitude.
Convective-Permitting Modeling for Retrospective Subseasonal-to-Seasonal (S2S) Forecasting Using the Framework of the Coordinated Regional Ensemble Downscaling Experiment (CORDEX)
Castro, Christopher L.; Chang, Hsin-I; Prein, Andreas F.; et al. (2020)
EGUsphere
Convective-permitting modeling (CPM) yields step improvements in the representation of precipitation, as has been demonstrated in applications of numerical weather prediction and climate modeling. While CPM has been used in the context of historical climate simulations and climate change projections, its application to the sub-seasonal to seasonal (S2S) forecast timescale (weeks to months) is comparatively underexplored. New, long-term S2S reforecast products have recently been generated from operational global forecast models, for example as part of the S2S Project and North American Multimodel Ensemble (NMME). These are analogous to CMIP models used for climate change projection. It is now technically possible to dynamically downscale these reforecast data to CPM scale, to asess potential improvement in S2S forecast skill and create new S2S forecast metrics for extreme events. The Coordinated Regional Ensemble Downscaling Experiment (CORDEX) provides an existing robust community framework that can be leveraged to dynamically downscale S2S reforecast data, in a globally unified way. This overview presentation will highlight outcomes from a community discussion on this topic that took place at the 2019 Latsis Symposium "High-Resolution Climate Modeling: Perspectives and Challenges" at ETH Zurich, including a summary of the current state of the science, collective identification of research priorities, and proposed action items proceeding forward.
Dynamic and thermodynamic impacts of climate change on organized convection in Alaska
Poujol, Basile; Prein, Andreas F.; Molina, Maria J.; et al. (2021)
Climate Dynamics
Convective storms can cause economic damage and harm to humans by producing flash floods, lightning and severe weather. While organized convection is well studied in the tropics and mid-latitudes, few studies have focused on the physics and climate change impacts of pan-Arctic convective systems. Using a convection-permitting model we showed in a predecessor study that organized convective storm frequency might triple by the end of the century in Alaska assuming a high emission scenario. The present study assesses the reasons for this rapid increase in organized convection by investigating dynamic and thermodynamic changes within future storms and their environments, in light of canonical existing theories for mid-latitude and tropical deep convection. In a future climate, more moisture originates from Arctic marine basins increasing relative humidity over Alaska due to the loss of sea ice, which is in sharp contrast to lower-latitude land regions that are expected to become drier. This increase in relative humidity favors the onset of organized convection through more unstable thermodynamic environments, increased low-level buoyancy, and weaker downdrafts. Our confidence in these results is increased by showing that these changes can be analytically derived from basic physical laws. This suggests that organized thunderstorms might become more frequent in other pan-Arctic continental regions highlighting the uniqueness and vulnerability of these regions to climate change.
Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND Global km-Scale Simulations
Feng, Zhe; Prein, Andreas F.; Kukulies, Julia; et al. (2025)
Journal of Geophysical Research: Atmospheres
Global kilometer-scale models represent the future of Earth system modeling, enabling explicit simulation of organized convective storms and their associated extreme weather. Here, we comprehensively evaluate tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non-hydrostatic domains) simulations for both summer and winter phases. Using 10 different feature trackers applied to simulations and satellite observations, we assess MCS frequency, precipitation, and other key characteristics. Substantial differences (a factor of 2–3) arise among trackers in observed MCS frequency and their precipitation contribution, but model-observation differences in MCS statistics are more consistent across trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi-model mean biases ranging from −2%–8% over land and −8%–8% over ocean (summer vs. winter). However, most DYAMOND models underestimate MCS precipitation amount (23%) and their contribution to total precipitation (17%). Biases in precipitation contributions are generally smaller over land (13%) than over ocean (21%), with moderate inter-model variability. While models better simulate MCS diurnal cycles and cloud shield characteristics, they overestimate MCS precipitation intensity and underestimate stratiform rain contributions (up to a factor of 2), particularly over land, albeit observational uncertainties exist. Additionally, models exhibit a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, with many models showing exaggerated sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed here provide process-oriented diagnostics to guide future model development.
Kilometer-scale multi-physics simulations of heavy precipitation events in Northeast China
Yu, Hongyong; Prein, Andreas F.; Qi, Dan; et al. (2024)
Climate Dynamics
Despite the fatal impact of heavy precipitation on people’s lives and the social economy, its accurate estimating remains challenging. In this study, we show how to address this issue by kilometer-scale simulations and how to reduce computational costs in Northeast China with the complex terrain and distribution of land and sea. Three typical heavy precipitation events are simulated at 3 km horizontal resolution, and each event is simulated with 24 combinations of schemes (with or without a scale-aware cumulus scheme, three microphysics schemes, and four planetary boundary layer schemes), which are evaluated against gauge observations. Compared to gauge observations, the ensemble mean of simulations of hourly maximum precipitation and average accumulated precipitation outperforms three widely accepted satellite products in the cold vortex and the snowstorm case, and is of comparable accuracy in the typhoon case. Overall, the microphysics scheme significantly impacts the maximum hourly precipitation, whereas the planetary boundary layer scheme has a strong control over the accumulated precipitation. The similarity among different simulations is linked to the level of convective instability's impact on heavy precipitation in each case, which also indicates that conducting 24 simulations can be not necessary. This study uses an ensemble performance estimation technique assuming the impact of different schemes is additive and finds that performing 13 rather than 24 simulations allows finding the best-performing combination of parameterization schemes, which allows for saving almost 50% of computational costs.
Resolving Mesoscale Convective Systems: Grid Spacing Sensitivity in the Tropics and Midlatitudes
Prein, Andreas F.; Wang, Dié; Ge, Ming; et al. (2025)
Journal of Geophysical Research: Atmospheres
Mesoscale convective systems (MCSs) are a critical global water cycle component and drive extreme precipitation events in tropical and midlatitude regions. However, simulating deep convection remains challenging for modern numerical weather and climate models due to the complex interactions of processes from microscales to synoptic scales. Recent models with kilometer-scale horizontal grid spacings ($\Delta$x) offer notable improvements in simulating deep convection compared to coarser-resolution models. Still, deficiencies in representing key physical processes, such as entrainment, lead to systematic biases. Additionally, evaluating model outputs using process-oriented observational data remain difficult. This study presents an ensemble of MCS simulations with $\Delta$x spanning the deep convective gray zone ($\Delta$x from 12 km to 125 m) in the Southern Great Plains of the U.S. and the Amazon Basin. Comparing these simulations with Atmospheric Radiation Measurement (ARM) wind profiler observations, we find greater $\Delta$x sensitivity in the Amazon Basin compared to the Great Plains. Convective drafts converge structurally at sub-kilometer scales, but some deficiencies remain. In both regions, simulated up and downdrafts are too deep and extreme downdrafts are not strong enough. Furthermore, Amazonian updrafts are too strong. Overall, we observe higher $\Delta$x sensitivity in the tropics, including an artificial buildup in vertical kinetic energy at scales of 5$\Delta$x, suggesting a need for $\Delta$x ≤ 250 m in this region. Nevertheless, bulk convergence - agreement of storm - average statistics-is achievable with kilometer-scale simulations within a$\pm$10% error margin with $\Delta$x = 1 km providing a good balance between accuracy and computational cost.
Impacts of Northerly Low-Level Jets on Mesoscale Convective Systems East of the Andes
Mu, Ye; Jones, Charles; Carvalho, Leila M.V.; et al. (2025)
Journal of Geophysical Research: Atmospheres
Low-level jets (LLJs) play a critical role in moisture transport, vertical motion enhancement, and orographic lifting, frequently leading to deep, organized convection formation in South America. This study examines the impacts of three types of LLJs (Central, Northern, and Andes) on Mesoscale Convective Systems (MCSs), using a 4 km horizontal grid spacing Weather Research and Forecasting (WRF) model simulation and satellite-based data. The Central and Andes LLJ types facilitate significant moisture flux convergence over the La Plata Basin (LPB), contributing to intense MCS activity and heavy precipitation. In contrast, the Northern LLJ type, operating over the eastern slopes of the northern Andes, exerts a weaker impact on MCS development over the Amazon Basin, leading to more scattered convection. Stronger LLJs support larger, longer-lived MCSs with higher mean precipitation in jet exit regions. El Niño Southern Oscillation modulates these relationships, with El Niño increasing MCS size and duration in the Central LLJ region, while La Niña enhances MCS frequency in the Andes and Northern LLJ regions. The WRF model captures many of these dynamics but produces higher extreme MCS mean precipitation than IMERG. These findings highlight the importance of LLJ variability in modulating MCSs and suggest that future LLJ changes could alter the hydroclimate and extreme weather patterns in the region. This study underscores the utility of km-scale models in representing the complex interactions of LLJs and MCSs, making them a promising tool to improve our understanding of these interactions and to assess the potential impacts of climate change on water resources and extreme weather in South America.
Climate change reduces the wind chill hazard across Alaska
Kim, Taereem; Villarini, Gabriele; Prein, Andreas F.; et al. (2025)
Communications Earth & Environment
Low wind chill temperatures can have negative impacts on human health and the capability of performing outdoor activities. An open question is how climate change is projected to impact this hazard in high latitude land regions. Here we focus on changes in the magnitude and timing of extreme wind chill days (i.e., days with wind chill temperatures below -34.4 degrees C) in response to future changes in large-scale mean-state climate conditions in Alaska. We find a future reduction in extreme wind chill days, especially in northern Alaska and at lower elevations where most of the population resides. Moreover, the extreme wind chill days' mean date shifts by up to two weeks later in the future, with a narrower seasonal distribution compared to the historical period. These changes are primarily attributed to increased temperatures rather than changes in wind speed. Our finding highlights how this hazard decreases under future large-scale mean-state climate conditions, with likely positive impacts for human health and an increased capability to perform outdoor activities.
A Benchmark to Test Generalization Capabilities of Deep Learning Methods to Classify Severe Convective Storms in a Changing Climate
Molina, Maria J.; Gagne, David John; Prein, Andreas F. (2021)
Earth and Space Science
This is a test case study assessing the ability of deep learning methods to generalize to a future climate (end of 21st century) when trained to classify thunderstorms in model output representative of the present-day climate. A convolutional neural network (CNN) was trained to classify strongly rotating thunderstorms from a current climate created using the Weather Research and Forecasting model at high-resolution, then evaluated against thunderstorms from a future climate and found to perform with skill and comparatively in both climates. Despite training with labels derived from a threshold value of a severe thunderstorm diagnostic (updraft helicity), which was not used as an input attribute, the CNN learned physical characteristics of organized convection and environments that are not captured by the diagnostic heuristic. Physical features were not prescribed but rather learned from the data, such as the importance of dry air at mid-levels for intense thunderstorm development when low-level moisture is present (i.e., convective available potential energy). Explanation techniques also revealed that thunderstorms classified as strongly rotating are associated with learned rotation signatures. Results show that the creation of synthetic data with ground truth is a viable alternative to human-labeled data and that a CNN is able to generalize a target using learned features that would be difficult to encode due to spatial complexity. Most importantly, results from this study show that deep learning is capable of generalizing to future climate extremes and can exhibit out-of-sample robustness with hyperparameter tuning in certain applications.
Investigating the sensitivity to resolving aerosol interactions in downscaling regional model experiments with WRFv3.8.1 over Europe
Pavlidis, Vasileios; Katragkou, Eleni; Prein, Andreas F.; et al. (2020)
Geoscientific Model Development
In this work we present downscaling experiments with the Weather Research and Forecasting model (WRF) to test the sensitivity to resolving aerosol–radiation and aerosol–cloud interactions on simulated regional climate for the EURO-CORDEX domain. The sensitivities mainly focus on the aerosol–radiation interactions (direct and semi-direct effects) with four different aerosol optical depth datasets (Tegen, MAC-v1, MACC, GOCART) being used and changes to the aerosol absorptivity (single scattering albedo) being examined. Moreover, part of the sensitivities also investigates aerosol–cloud interactions (indirect effect). Simulations have a resolution of 0.44∘ and are forced by the ERA-Interim reanalysis. A basic evaluation is performed in the context of seasonal-mean comparisons to ground-based (E-OBS) and satellite-based (CM SAF SARAH, CLARA) benchmark observational datasets. The impact of aerosols is calculated by comparing it against a simulation that has no aerosol effects. The implementation of aerosol–radiation interactions reduces the direct component of the incoming surface solar radiation by 20 %–30 % in all seasons, due to enhanced aerosol scattering and absorption. Moreover the aerosol–radiation interactions increase the diffuse component of surface solar radiation in both summer (30 %–40 %) and winter (5 %–8 %), whereas the overall downward solar radiation at the surface is attenuated by 3 %–8 %. The resulting aerosol radiative effect is negative and is comprised of the net effect from the combination of the highly negative direct aerosol effect (−17 to −5 W m−2) and the small positive changes in the cloud radiative effect (+5 W m−2), attributed to the semi-direct effect. The aerosol radiative effect is also stronger in summer (−12 W m−2) than in winter (−2 W m−2). We also show that modelling aerosol–radiation and aerosol–cloud interactions can lead to small changes in cloudiness, mainly regarding low-level clouds, and circulation anomalies in the lower and mid-troposphere, which in some cases, mainly close to the Black Sea in autumn, can be of statistical significance. Precipitation is not affected in a consistent pattern throughout the year by the aerosol implementation, and changes do not exceed ±5 % except for the case of unrealistically absorbing aerosol. Temperature, on the other hand, systematically decreases by −0.1 to −0.5 ∘C due to aerosol–radiation interactions with regional changes that can be up to −1.5 ∘C.

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Andreas F. Prein

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