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Reanalyses and a High-Resolution Model Fail to Capture the ‘High Tail’ of CAPE Distributions

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Reanalyses and a High-Resolution Model Fail to Capture the ‘High Tail’ of CAPE Distributions 1 Reanalyses and a high-resolution model fail to capture the ‘high tail’ of CAPE distributions Ziwei Wang,1 James Franke,1 Zhenqi Luo,2 and Elisabeth Moyer1 December 2020 arXiv:2012.13383v1 [physics.ao-ph] 24 Dec 2020 Center for Robust Decision-making on Climate and Energy Policy University of Chicago Affiliations: 1University of Chicago, Department of Geophysical Sciences, Chicago, Illinois 2College of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China; This work has been submitted to Journal of Climate. Copyright in this work may be transferred without further notice. About RDCEP The Center brings together experts in economics, physical sciences, energy technologies, law, computational mathematics, statistics, and computer science to undertake a series of tightly connected research programs aimed at improving the computational models needed to evaluate climate and energy policies, and to make robust decisions based on outcomes. For more information please contact us at [email protected] or visit our website: www.rdcep.org RDCEP is funded by a grant from the National Science Foundation (NSF) #SES-1463644 through the Decision Making Under Uncertainty (DMUU) program. Center for Robust Decision-making on Climate and Energy Policy University of Chicago 5734 South Ellis Ave. Chicago, IL 60637 Email: [email protected] Contact: [email protected] Ziwei Wang: [email protected] Elisabeth Moyer: [email protected] This paper is available as a preprint. Please cite as: Ziwei Wang, James A. Franke, Zhenqi Luo, and Elisabeth J. Moyer. “Reanalyses and a high-resolution model fail to capture the ‘high tail’ of CAPE distributions.” RDCEP Working Paper Series, 2020. Submitted to Journal of Climate. ©2020 Ziwei Wang, James A. Franke, Zhenqi Luo, and Elisabeth J. Moyer. All rights reserved. Disclaimer: The views and opinions expressed herein are solely those of the authors and do not necessarily reflect the policy or position of the United States Government nor any agency thereof, Argonne National Laboratory, the University of Chicago or RDCEP. 2 3 Reanalyses and a high-resolution model fail to capture the ‘high tail’ of CAPE distributions Submitted to Journal of Climate, under review ZIWEI WANG Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois JAMES A. FRANKE Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois ZHENQI LUO College of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China ELISABETH J. MOYER ∗ Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois ABSTRACT Convective available potential energy (CAPE) is of strong interest in climate modeling because of its role in both severe weather and in model construction. Extreme levels of CAPE (¡ 2000 J/kg) are associated with high-impact weather events, and CAPE is widely used in convective parametrizations to help determine the strength and timing of convection. However, to date no study has systematically evaluated CAPE biases in models in a climatological context, in an assessment large enough to characterize the high tail of the CAPE distribution. This work compares CAPE distributions in over 200,000 summertime proximity soundings from four sources: the observational radiosonde network (IGRA), 0.125 degree reanalysis (ERA-Interim and ERA5), and a 4 km convection-permitting regional WRF simulation driven by ERA-Interim. Both reanalyses and model consistently show too-narrow distributions of CAPE, with the high tail (¡ 95th percentile) systematically biased low by up to 10% in surface-based CAPE and 20% at the most unstable layer. This “missing tail” corresponds to the most impacts-relevant conditions. CAPE bias in all datasets is driven by bias in surface temperature and humidity: reanalyses and model undersample observed cases of extreme heat and moisture. These results suggest that reducing inaccuracies in land surface and boundary layer models is critical for accurately reproducing CAPE. 1. Introduction and many others. In models, Paquin et al. (2014), for example, show that the number of extreme precipitation Convective Available Potential Energy (CAPE) is an in- events in general circulation models (GCMs) grows with tegral quantity of buoyancy in the convective layer (Mon- the covariate between CAPE and wind shear. crieff and Miller 1976), and is considered as a key pa- CAPE is also used as a key parameter in convective rameter in convection initiation and development. Closely schemes in GCMs to determine convective mass flux linked to updraft strength and storm intensity, CAPE pro- (Zhang and McFarlane 1995; Yano et al. 2013; Baba 2019). vides a way to understand the potential threat of some In CAPE–closure (or CR–closure) schemes, modelers rely high-impact weather events such as thunderstorms, hail, on CAPE to trigger convection and to determine the total and tornadoes. Brooks et al. (2003) proposed a combina- vertical mass flux. While the timing of convection onset tion of CAPE and bulk wind shear as a metric for severe in most schemes does not depend on exact CAPE values, weather in reanalyses, with a 2000 J/kg as a threshold value and is driven instead by a range of conditions including for extreme events, and multiple subsequent studies con- dynamics and thermodynamics fields (Yang et al. 2018), firm this relationship in models and observations. Studies the magnitude of vertical mass flux is directly affected relating high CAPE values to extreme precipitation or in- tense storms in observations include Groenemeijer and van by an inaccurate representation of CAPE (Lee et al. 2008; Delden (2007), Lepore et al. (2015), Dong et al. (2019), Cortés-Hernández et al. 2016). In some recently developed new schemes intended to more realistically reproduce the diurnal cycle, convective triggering is directly dependent ∗Corresponding author: Elisabeth J. Moyer, [email protected] 4 MANUSCRIPT SUBMITTED TO JOURNAL OF CLIMATE on CAPE generation rate (dCAPE) (Xie and Zhang 2000; strength. Such cloud resolving models, with their improve- Wang et al. 2015). These schemes have been shown to ment in convective dynamics, have been assumed to help improve model performance for precipitation diurnal peak improve the representation of CAPE. time (Song and Zhang 2017; Xie et al. 2019), but introduce Given the extent of scientific use of reanalyses and model additional sensitivity to CAPE biases. simulations, it is valuable to ask how well these products CAPE is derived from vertical profiles of temperature, reproduce realistic CAPE values. Several studies assess pressure, and humidity, which are measured in-situ only CAPE bias in reanalysis or forecast models versus ra- from a sparse network of specialized weather stations. diosonde observations, but all use restricted samples of Radiosondes measure atmospheric profiles from weather soundings near severe weather events, and study results balloons released twice a day from ∼ 1000 stations glob- are inconsistent. Thompson et al. (2003) evaluate surface- ally (77 in the contiguous U.S. still in service). Because based CAPE (SBCAPE) from the Rapid Update Cycle radiosonde measurements are both spatially and tempo- (RUC-2) weather prediction system 0-hour analysis against rally sparse, researchers linking measured CAPE to severe radiosondes sampled near supercells (149 soundings from weather events have used “proximity soundings”, estimat- 1999–2001, in the U.S. Central and Southern Plains) and ing the severity of extreme weather events based on sound- find a low bias of ∼16% (mean bias of about -400 J/kg in ings taken within a range of ∼200 km (e.g. Brooks et al. mean conditions of ∼2500 J/kg). Coniglio (2012) com- 1994; Rasmussen and Blanchard 1998; Brooks and Craven pare SBCAPE in the RUC 0-hour analysis with a different 2002). More recent studies of CAPE and severe weather sample of soundings near supercell thunderstorms (582 use not soundings but reanalyses that assimilate in-situ and soundings during the VORTEX2 campaign in 2009–2010, remote observations in global models to provide informa- also in the Central and Southern Plains) and find a small tion at higher resolution (Brooks et al. 2003; Lepore et al. high bias (∼150 J/kg) with large spread. Allen and Karoly 2015; Dong et al. 2019). Global gridded reanalyses also (2014) compare mixed-layer CAPE (MLCAPE) in the re- allow ready construction of climatologies: for example, analysis product ERA-Interim (ERAI) and in the Australian Riemann-Campe et al. (2009) use the ERA40 reanalysis MesoLAPS (Mesoscale Limited Area Prediction System) to construct a 40-year climatology of CAPE, showing that weather model with radiosonde soundings near thunder- largest values and variability are found over tropical land storm events (3697 and 4988 soundings, respectively, from (mean ∼2000 J/kg), with a stronger dependence on specific 2003–2010, from 16 stations in Australia) and find slight humidity than temperature. high biases of 6 and 74 J/kg in conditions of 234 and 255 To diagnose potential changes in CAPE under future J/kg mean non-zero MLCAPE. higher CO conditions, studies must rely on numerical sim- 2 Many authors attribute errors in CAPE to incorrect tem- ulations. To get a basic sense of model performance under perature and humidity at the surface or boundary layer. current climate, Chen et al. (2020) validates the ability of GCM (CCSM4) to realistically capture spatial pattern of Several studies have explicitly tested this attribution by re- CAPE and CIN in reanalysis, but also identifies discrep- placing surface values in models and data products with ob- ancy even for the mean values up to 500 J/kg. With the served ones and noting the improved match to radiosonde growth of computational resources, the horizontal resolu- SBCAPE.
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