Seasonality, intensity, and duration of rainfall extremes change in a warmer climate

Precipitation extremes are expected to intensify under climate change with consequent impacts in flooding and ecosystem functioning. Here we use station data and high‐resolution simulations from the WRF convection permitting climate model (∼4 km, 1 h) over the US to assess future changes in hourly precipitation extremes. It is demonstrated that hourly precipitation extremes and storm depths are expected to intensify under climate change and what is now a 20‐year rainfall will become a 7‐year rainfall on average for ∼ 75% of gridpoints over the US. This intensification is mostly expressed as an increase in rainfall tail heaviness. Statistically significant changes in the seasonality and duration of rainfall extremes are also exhibited over ∼ 95% of the domain. Our results suggest more non‐linear future precipitation extremes with shorter spell duration that are distributed more uniformly throughout the year

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

Blocking representation in the ERA-Interim driven EURO-CORDEX RCMs

While Regional Climate Models (RCMs) have been shown to yield improved simulations compared to General Circulation Model (GCM), their representation of large-scale phenomena like atmospheric blocking has been hardly addressed. Here, we evaluate the ability of RCMs to simulate blocking situations present in their reanalysis driving data and analyse the associated impacts on anomalies and biases of European 2-m air temperature (TAS) and precipitation rate (PR). Five RCM runs stem from the EURO-CORDEX ensemble while three RCMs are WRF models with different nudging realizations, all of them driven by ERA-Interim for the period 1981?2010. The detected blocking systems are allocated to three sectors of the Euro-Atlantic region, allowing for a characterization of distinctive blocking-related TAS and PR anomalies. Our results indicate some misrepresentation of atmospheric blocking over the EURO-CORDEX domain, as compared to the driving reanalysis. Most of the RCMs showed fewer blocks than the driving data, while the blocking misdetection was negligible for RCMs strongly conditioned to the driving data. A higher resolution of the RCMs did not improve the representation of atmospheric blocking. However, all RCMs are able to reproduce the basic anomaly structure of TAS and PR connected to blocking. Moreover, the associated anomalies do not change substantially after correcting for the misrepresentation of blocking in RCMs. The overall model bias is mainly determined by pattern biases in the representations of surface parameters during non-blocking situations. Biases in blocking detections tend to have a secondary influence in the overall bias due to compensatory effects of missed blockings and non-blockings. However, they can lead to measurable effects in the presence of a strong blocking underestimation.This work was funded by the Austrian Science Fund (FWF) under the project: Understanding Contrasts in high Mountain hydrology in Asia (UNCOMUN: I 1295-N29). This research was supported by the Faculty of Environmental, Regional and Educational Sciences (URBI), University of Graz, as well as the Federal Ministry of Science, Research and Economy (BMWFW) by funding the OeAD Grant Marietta Blau. This work was partially supported (JMG and SH) by the project MULTI-SDM (CGL2015-66583- R, MINECO/FEDER). DB was supported by the PALEOSTRAT (CGL2015-69699-R) project funded by the Spanish Ministry of Economy and Competitiveness (MINECO)

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

Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations

Daily precipitation statistics as simulated by the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble are evaluated over two distinct regions of the European continent, namely the European Alps and Spain. The potential added value of the high-resolution 12 km experiments with respect to their 50 km resolution counterparts is investigated. The statistics considered consist of wet-day intensity and precipitation frequency as a measure of mean precipitation, and three precipitation-derived indicators (90th percentile on wet days?90pWET, contribution of the very wet days to total precipitation?R95pTOT and number of consecutive dry days?CDD). As reference for model evaluation high resolution gridded observational data over continental Spain (Spain011/044) and the Alpine region (EURO4M-APGD) are used. The assessment and comparison of the two resolutions is accomplished not only on their original horizontal grids (approximately 12 and 50 km), but the high-resolution RCMs are additionally regridded onto the coarse 50 km grid by grid cell aggregation for the direct comparison with the low resolution simulations. The direct application of RCMs e.g. in many impact modelling studies is hampered by model biases. Therefore bias correction (BC) techniques are needed at both resolutions to ensure a better agreement between models and observations. In this work, the added value of the high resolution (before and after the bias correction) is assessed and the suitability of these BC methods is also discussed. Three basic BC methods are applied to isolate the effect of biases in mean precipitation, wet-day intensity and wet-day frequency on the derived indicators. Daily precipitation percentiles are strongly affected by biases in the wet-day intensity, whereas the dry spells are better represented when the simulated precipitation frequency is adjusted to the observed one. This confirms that there is no single optimal way to correct for RCM biases, since correcting some distributional features typically leads to an improvement of some aspects but to a deterioration of others. Regarding mean seasonal biases before the BC, we find only limited evidence for an added value of the higher resolution in the precipitation intensity and frequency or in the derived indicators. Thereby, evaluation results considerably depend on the RCM, season and indicator considered. High resolution simulations better reproduce the indicators? spatial patterns, especially in terms of spatial correlation. However, this improvement is not statistically significant after applying specific BC methods.The authors are grateful to Prof. C. Schär for his helpful comments and E. van Meijgaard for making available the RACMO model data. We acknowledge the observational data providers. Calculations for WRF311F were made using the TGCC super computers under the GENCI time allocation GEN6877. The WRF331A from CRP-GL (now LIST) was funded by the Luxembourg National Research Fund (FNR) through grant FNR C09/SR/16 (CLIMPACT). The KNMI-RACMO2 simulations were supported by the Dutch Ministry of Infrastructure and the Environment. The CCLM and REMO simulations were supported by the Federal Ministry of Education and Research (BMBF) and performed under the Konsortial share at the German Climate Computing Centre (DKRZ). The CCLM simulations were furthermore supported by the Swiss National Supercomputing Centre (CSCS) under project ID s78. Part of the SMHI contribution was carried out in the Swedish Mistra-SWECIA programme founded by Mistra (the Foundation for Strategic Environmental Research). This work is supported by CORWES (CGL2010-22158-C02) and EXTREMBLES (CGL2010-21869) projects funded by the Spanish R&D programme and the European COST ACTION VALUE (ES1102). A. C. thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI programme (BES-2011-047612 and EEBB-I-13-06354). We also thank two anonymous referees for their useful comments that helped to improve the original manuscript

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

Proportions of Convective and Stratiform Precipitation Revealed in Water Isotope Ratios

Tropical and midlatitude precipitation is fundamentally of two types, spatially-limited and high-intensity convective or widespread and lower-intensity stratiform, owing to differences in vertical air motions and microphysical processes governing rain formation. These processes are difficult to observe or model and precipitation partitioning into rain types is critical for understanding how the water cycle responds to climate changes. Here, we combine two independent data sets – convective and stratiform precipitation fractions, derived from the Tropical Rainfall Measuring Mission satellite or synoptic cloud observations, and stable isotope and tritium compositions of surface precipitation, derived from a global network – to show that isotope ratios reflect rain type proportions and are negatively correlated with stratiform fractions. Condensation and riming associated with boundary layer moisture produces higher isotope ratios in convective rain, along with higher tritium when riming in deep convection occurs with entrained air at higher altitudes. Based on our data, stable isotope ratios can be used to monitor changes in the character of precipitation in response to periodic variability or changes in climate. Our results also provide observational constraints for an improved simulation of convection in climate models and a better understanding of isotope variations in proxy archives, such as speleothems and tropical ice

Action anticipation based on an agent's epistemic state in toddlers and adults

Do toddlers and adults engage in spontaneous Theory of Mind (ToM)? Evidence from anticipatory looking (AL) studies suggests that they do. But a growing body of failed replication studies raised questions about the paradigm’s suitability. In this multi-lab collaboration, we test the robustness of spontaneous ToM measures. We examine whether 18- to 27-month-olds’ and adults’ anticipatory looks distinguish between two basic forms of an agent’s epistemic states: knowledge and ignorance. In toddlers [ANTICIPATED n = 520 50% FEMALE] and adults [ANTICIPATED n = 408, 50% FEMALE] from diverse ethnic backgrounds, we found [SUPPORT/NO SUPPORT] for epistemic state-based action anticipation. Future research can probe whether this conclusion extends to more complex kinds of epistemic states, such as true and false beliefs