Simulating North American Weather Types With Regional Climate Models

Regional climate models (RCMs) are able to simulate small-scale processes that are missing in their coarser resolution driving data and thereby provide valuable climate information for climate impact assessments. Less attention has been paid to the ability of RCMs to capture large-scale weather types (WTs). An inaccurate representation of WTs can result in biases and uncertainties in current and future climate simulations that cannot be easily detected by standard model evaluation metrics. Here we define 12 hydrologically important WTs in the contiguous United States (CONUS). We test if RCMs from the North American CORDEX (NA-CORDEX) and the Weather Research and Forecasting (WRF) model large physics ensembles (WRF36) can capture those WTs in the current climate and how they simulate changes in the future. Our results show that the NA-CORDEX RCMs are able to simulate WTs more accurately than members of the WRF36 ensemble. The much larger WRF36 domain in combination with not constraining large-scale conditions by spectral nudging results in lower WT skill. The selection of the driving global climate model (GCM) has a large effect on the skill of NA-CORDEX simulations but a smaller impact on the WRF36 runs. The formulation of the RCM is of minor importance except for capturing the variability within WTs. Changing the model physics or increasing the RCM horizontal grid spacing has little effect. These results highlight the importance of selecting GCMs with accurate synoptic-scale variability for downscaling and to find a balance between large domains that can result in biased WT representations and small domains that inhibit the realistic development of mesoscale processes. At the end of the century, monsoonal flow conditions increase systematically by up to 30% and a WT that is a significant source of moisture for the Northern Plains during the growing seasons decreases systematically up to –30%

Earth Virtualization Engines -- A Technical Perspective

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change

Intensification of mesoscale convective systems in the East Asian rainband over the past two decades

As one of the major producers of extreme precipitation, mesoscale convective systems (MCSs) have received much attention. Recently, MCSs over several hotpots, including the Sahel and US Great Plains, have been found to intensify under global warming. However, relevant studies on the East Asian rainband, another MCS hotpot, are scarce. Here, by using a novel rain-cell tracking algorithm on a high spatiotemporal resolution satellite precipitation product, we show that both the frequency and intensity of MCSs over the East Asian rainband have increased by 21.8% and 9.8% respectively over the past two decades (2000–2021). The more frequent and intense MCSs contribute nearly three quarters to the total precipitation increase. The changes in MCSs are caused by more frequent favorable large-scale water vapor-rich environments that are likely to increase under global warming. The increased frequency and intensity of MCSs have profound impacts on the hydroclimate of East Asia, including producing extreme events such as severe flooding

Earth Virtualization Engines: a technical perspective

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change

Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius–Clapeyron (CC) rate (6–7% K−1), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks

Atmospheric soundings derived from kilometer-scale climate simulations over North America to initialize idealized simulations of mesoscale convective systems

Inflow soundings of simulated mesoscale convective systems (MCSs) in the central United States under current and future (end of the century under a high-end emission scenario) climate conditions. These are not observed soundings but are derived from current and future kilometer-scale climate simulations. The simulations, from which the soundings are derived, are described in Liu et al. (2017; https://link.springer.com/article/10.1007/s00382-016-3327-9). More details about how the soundings are derived can be found in Prein et al. (2020; under review). These soundings compare well with observed pre-MCS soundings in the U.S. Southern Great Planes (Wang et al. 2020). The archived soundings are in a format that can directly be read by the Weather Research and Forecasting model (https://www.climatescience.org.au/sites/default/files/WRF_ideal_201711.pdf). Each datafile contains five columns representing different variables and the rows show the changes in the variables through an atmospheric column. The header row variables are from left to right: surface pressure [hPa], surface potential temperature [K], and surface vater vapor mixing ratio [g/kg]. The variables in the second to the last row are fro the left column to the right: height above surface [m], potential temperature [K], water vapor mixing ratio [g/kg], zonal wind speed [m/s], and meridional wind speed [m/s

Impacts of uncertainties in European gridded precipitation observations on regional climate analysis

Gridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio‐temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan‐European data sets and a set that combines eight very high‐resolution station‐based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post‐processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small‐scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate‐mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments

Daily gridded hail risk estimates on a global scale (1979 to 2015), link to netCDF files

This dataset contains daily gridded hail risk estimates on a global scale for the period 1979 to 2015. It is based on the presence of environmental conditions in which large hail (diameter >2.5 cm) has been observed. The data is provided on a global grid with 0.75°x075° grid spacing. The hail risk varies between zero and one where one means a high probability that the location will experience large hail on the corresponding day

Daily large hail probability on a global scale (1979 to 2015), Version 2, link to netCDF files

This is an updated version of a dataset that contains daily gridded hail hazard estimates on a global scale for the period 1979 to 2015. It is based on the presence of environmental conditions in which large hail (diameter >2.5 cm) has been observed. The data is provided on a global grid with 0.7°x07° grid spacing. The hail risk varies between zero and one where one means a high probability that the location will experience large hail on a corresponding day