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Wojciech W. Grabowski and Andreas F. Prein

Abstract

Climate change affects the dynamics and thermodynamics of moist convection. Changes in the dynamics concern, for instance, an increase of convection strength due to increases of convective available potential energy (CAPE). Thermodynamics involve increases in water vapor that the warmer atmosphere can hold and convection can work with. Small-scale simulations are conducted to separate these two components for daytime development of unorganized convection over land. The simulations apply a novel modeling technique referred to as the piggybacking approach and consider the global climate model (GCM)-predicted change of atmospheric temperature and moisture profiles in the Amazon region at the end of the century under a business-as-usual scenario. The simulations show that the dynamic impact dominates because changes in cloudiness and rainfall come from cloud dynamics considerations, such as the change in CAPE and convective inhibition (CIN) combined with the impact of environmental relative humidity (RH) on deep convection. The small RH reduction between the current and future climate significantly affects the mean surface rain accumulation as it changes from a small reduction to a small increase when the RH decrease is eliminated. The thermodynamic impact on cloudiness and precipitation is generally small, with the extreme rainfall intensifying much less than expected from an atmospheric moisture increase. These results are discussed in the context of previous studies concerning climate change–induced modifications of moist convection. Future research directions applying the piggybacking method are discussed.

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Andreas F. Prein, Roy Rasmussen, and Graeme Stephens
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Martin W. Jury, Andreas F. Prein, Heimo Truhetz, and Andreas Gobiet

Abstract

The quality of regional climate model (RCM) simulations is strongly dependent on the quality of data provided as lateral boundary conditions (LBCs). Frequently, the quality of near-surface variables of general circulation model (GCM) simulations like those from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is analyzed in the region of interest. However, such analysis does not necessarily lead to the selection of high-quality LBCs, as demonstrated in this study. The study region is the European domain of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX), where a model performance index (MPI) is used to evaluate the skill of CMIP5 GCMs to reproduce near-surface variables within the EURO-CORDEX domain and free atmosphere variables along its lateral boundaries as a proxy for LBCs used in regional climate modeling. The results suggest that a GCM’s skill in simulating near-surface variables is correlated with 0.62 (Spearman’s r) to its skill in simulating LBCs for regional climate simulations. However, there is hardly any correlation between the performances of different variables, implying that a GCM evaluation solely based on surface parameters or a few variables is inadequate to select suitable driving data for regional climate models. The selection should include the evaluation of all variables passed to the RCM as LBCs in the lateral boundary zone (LBZ) on at least one midtropospheric level.

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Maria J. Molina, John T. Allen, and Andreas F. Prein

Abstract

The tornado outbreak of 21–23 January 2017 caused 20 fatalities, more than 200 injuries, and over a billion dollars in damage in the Southeast United States. The event occurred concurrently with a record-breaking warm Gulf of Mexico (GoM) basin. This article explores the influence that warm GoM sea surface temperatures (SSTs) had on the tornado outbreak. Backward trajectory analysis, combined with a Lagrangian-based moisture-attribution algorithm, reveals that the tornado outbreak’s moisture predominantly originated from the southeast GoM and the northwest Caribbean Sea. We used the WRF Model to generate a control simulation of the event and explore the response to perturbed SSTs. With the aid of a tornadic storm proxy derived from updraft helicity, we show that the 21–23 January 2017 tornado outbreak exhibits sensitivity to upstream SSTs during the first day of the event. Warmer SSTs across remote moisture sources and adjacent waters increase tornado frequency, in contrast to cooler SSTs, which reduce tornado activity. Upstream SST sensitivity is reduced once convection is ongoing and modifying local moisture and instability availability. Our results highlight the importance of air–sea interactions before airmass advection toward the continental United States. The complex and nonlinear nature of the relationship between upstream SSTs and local precursor environments is also discussed.

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Gabriel T. Bromley, Tobias Gerken, Andreas F. Prein, and Paul C. Stoy

Abstract

We examined climate trends in the northern North American Great Plains (NNAGP) from 1970 to 2015, a period that aligns with widespread land-use changes in this globally important agricultural region. Trends were calculated from the Climatic Research Unit (CRU) and other climate datasets using a linear regression model that accounts for temporal autocorrelation. The NNAGP warmed on an annual basis, with the largest change occurring in winter (DJF) at 0.4°C decade−1. January in particular warmed at nearly 0.9°C decade−1. The NNAGP cooled by −0.18°C decade−1 during May and June, nearly the opposite of global warming trends during the study period. The atmospheric vapor pressure deficit (VPD), which can limit crop growth, decreased in excess of −0.4 hPa decade−1 during climatological summer in the southeastern part of the study domain. Precipitation P increased in the eastern portion of the NNAGP during all seasons except fall and increased during May and June in excess of 8 mm decade−1. Climate trends in the NNAGP largely followed global trends except during the early warm season (May and June) during which 2-m air temperature T air became cooler, VPD lower, and P greater across large parts of the study region. These changes are consistent with observed agricultural intensification during the study period, namely the reduction of summer fallow and expansion of agricultural land use. Global climate model simulations indicate that observed T air trends cannot be explained by natural climate variability. However, further climate attribution experiments are necessary to understand if observed changes are caused by increased agricultural intensity or other factors.

Open access
Sagar K. Tamang, Ardeshir M. Ebtehaj, Andreas F. Prein, and Andrew J. Heymsfield

Abstract

Snowfall is one of the primary drivers of the global cryosphere and is declining in many regions of the world with widespread hydrological and ecological consequences. Previous studies have shown that the probability of snowfall occurrence is well described by wet-bulb temperatures below 1°C (1.1°C) over land (ocean). Using this relationship, wet-bulb temperatures from three reanalysis products as well as multisatellite and reanalysis precipitation data are analyzed from 1979 to 2017 to study changes in potential snowfall areas, snowfall-to-rainfall transition latitude, snowfall amount, and snowfall-to-precipitation ratio (SPR). Results are presented at hemispheric scales, as well as for three Köppen–Geiger climate classes and four major mountainous regions including the Alps, the western United States, High Mountain Asia (HMA), and the Andes. In all reanalysis products, while changes in the wet-bulb temperature over the Southern Hemisphere are mostly insignificant, significant positive trends are observed over the Northern Hemisphere (NH). Significant reductions are observed in annual-mean potential snowfall areas over NH land (ocean) by 0.52 (0.34) million km2 decade−1 due to an increase of 0.34°C (0.35°C) decade−1 in wet-bulb temperature. The fastest retreat in NH transition latitudes is observed over Europe and central Asia at 0.7° and 0.45° decade−1. Among mountainous regions, the largest decline in potential snowfall areas is observed over the Alps at 3.64% decade−1 followed by the western United States at 2.81% and HMA at 1.85% decade−1. This maximum decrease over the Alps is associated with significant reductions in annual snowfall of 20 mm decade−1 and SPR of 2% decade−1.

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Puxi Li, Christopher Moseley, Andreas F. Prein, Haoming Chen, Jian Li, Kalli Furtado, and Tianjun Zhou

Abstract

Mesoscale convective systems (MCSs) play an important role in modulating the global water cycle and energy balance and frequently generate high-impact weather events. The majority of existing literature studying MCS activity over East Asia is based on specific case studies and more climatological investigations revealing the precipitation characteristics of MCSs over eastern China are keenly needed. In this study, we use an iterative rain cell tracking method to identify and track MCS precipitation during 2008–16 to investigate regional differences and seasonal variations of MCS precipitation characteristics. Our results show that the middle-to-lower reaches of the Yangtze River basin (YRB-ML) receive the largest amount and exhibit the most pronounced seasonal cycle of MCS precipitation in eastern China. MCS precipitation over YRB-ML can exceed 2.6 mm day−1 in June, contributing over 30.0% of April–July total rainfall. Particularly long-lived MCSs occur over the eastern periphery of the Tibetan Plateau (ETP), with 25% of MCSs over the ETP persisting for more than 18 h in spring. In addition, spring MCSs feature larger rainfall areas, longer durations, and faster propagation speeds. Summer MCSs have a higher precipitation intensity and a more pronounced diurnal cycle except for southeastern China, where MCSs have similar precipitation intensity in spring and summer. There is less MCS precipitation in autumn, but an MCS precipitation center over the ETP still persists. MCSs reach peak hourly rainfall intensities during the time of maximum growth (a few hours after genesis), reach their maximum size around 5 h after genesis, and start decaying thereafter.

Open access
Gabriel T. Bromley, Tobias Gerken, Andreas F. Prein, and Paul C. Stoy
Open access
Andreas F. Prein, Gregory J. Holland, Roy M. Rasmussen, James Done, Kyoko Ikeda, Martyn P. Clark, and Changhai H. Liu

Abstract

Summer and winter daily heavy precipitation events (events above the 97.5th percentile) are analyzed in regional climate simulations with 36-, 12-, and 4-km horizontal grid spacing over the headwaters of the Colorado River. Multiscale evaluations are useful to understand differences across horizontal scales and to evaluate the effects of upscaling finescale processes to coarser-scale features associated with precipitating systems.

Only the 4-km model is able to correctly simulate precipitation totals of heavy summertime events. For winter events, results from the 4- and 12-km grid models are similar and outperform the 36-km simulation. The main advantages of the 4-km simulation are the improved spatial mesoscale patterns of heavy precipitation (below ~100 km). However, the 4-km simulation also slightly improves larger-scale patterns of heavy precipitation.

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Tomáš Púčik, Pieter Groenemeijer, Anja T. Rädler, Lars Tijssen, Grigory Nikulin, Andreas F. Prein, Erik van Meijgaard, Rowan Fealy, Daniela Jacob, and Claas Teichmann

Abstract

The occurrence of environmental conditions favorable for severe convective storms was assessed in an ensemble of 14 regional climate models covering Europe and the Mediterranean with a horizontal grid spacing of 0.44°. These conditions included the collocated presence of latent instability and strong deep-layer (surface to 500 hPa) wind shear, which is conducive to the severe and well-organized convective storms. The occurrence of precipitation in the models was used as a proxy for convective initiation. Two climate scenarios (RCP4.5 and RCP8.5) were investigated by comparing two future periods (2021–50 and 2071–2100) to a historical period (1971–2000) for each of these scenarios. The ensemble simulates a robust increase (change larger than twice the ensemble sample standard deviation) in the frequency of occurrence of unstable environments (lifted index ≤ −2) across central and south-central Europe in the RCP8.5 scenario in the late twenty-first century. This increase coincides with the increase in lower-tropospheric moisture. Smaller, less robust changes were found until midcentury in the RCP8.5 scenario and in the RCP4.5 scenario. Changes in the frequency of situations with strong (≥15 m s−1) deep-layer shear were found to be small and not robust, except across far northern Europe, where a decrease in shear is projected. By the end of the century, the simultaneous occurrence of latent instability, strong deep-layer shear, and model precipitation is simulated to increase by up to 100% across central and eastern Europe in the RCP8.5 and by 30%–50% in the RCP4.5 scenario. Until midcentury, increases in the 10%–25% range are forecast for most regions. A large intermodel variability is present in the ensemble and is primarily due to the uncertainties in the frequency of the occurrence of unstable environments.

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