Evaluation of Whole Atmosphere Community Climate Model simulations of ozone during Arctic winter 2004–2005

Abstract

[1] The work presented here evaluates polar stratospheric ozone simulations from the Whole Atmosphere Community Climate Model (WACCM) for the Arctic winter of [2004][2005]. We use the Specified Dynamics version of WACCM (SD-WACCM), in which temperatures and winds are nudged to meteorological assimilation analysis results. Model simulations of ozone and related constituents generally compare well to observations from the Earth Observing System Microwave Limb Sounder (MLS). At most times, modeled ozone agrees with MLS data to within~10%. However, a systematic high bias in ozone in the model of~18% is found in the lowermost stratosphere in March. We attribute most of this ozone bias to too little heterogeneous processing of halogens late in the winter. We suggest that the model under-predicts ClONO 2 early in the winter, which leads to less heterogeneous processing and too little activated chlorine. Model HCl could also be overestimated due to an underestimation of HCl uptake into supercooled ternary solution (STS) particles. In late winter, the model overestimates gas-phase HNO 3 , and thus NO y , which leads to an over-prediction of ClONO 2 (under-prediction of activated chlorine). A sensitivity study, in which temperatures for heterogeneous chemistry reactions were reduced by 1.5 K, shows significant improvement of modeled ozone. Chemical ozone loss is inferred from the MLS observations using the pseudo-passive subtraction approach. The inferred ozone loss using this method is in agreement with or less than previous independent results for the Arctic winter of 2004-2005, reaching 1.0 ppmv on average and up to 1.6 ppmv locally in the polar vortex.

A Match-based approach to the estimation of polar stratospheric ozone loss using Aura Microwave Limb Sounder observations

“…Chemical ozone loss commenced around 12 January (Fig. 3d), consistent with findings from previous studies (e.g., Manney et al, 2006;Brakebusch et al, 2013), and hourly loss rates peaked around 30 January at 500 K (∼ 20 km) and 20 February at 450 K (∼ 18 km). When scaled by the hours of sunlight available, the 450 K losses in late February are more significant than those at 500 K in the late January period (Fig.…”

“…This method was first developed by Manney et al (1995b, a) to quantify ozone loss during the 1992/93 Arctic winter, using a trajectory-based passive ozone estimate, and has been subsequently applied in similar form to other seasons (e.g., Manney et al, 1996aManney et al, , b, 1997Manney et al, , 2003Schoeberl et al, 2002). Where a full chemistry transport model is employed (e.g., Deniel et al, 1998;Goutail et al, 1999;Singleton et al, , 2007; L. Grooß and Müller, 2007;Rösevall et al, 2008;Jackson and Orsolini, 2008;Kuttippurath et al, , 2012Feng et al, 2011;Brakebusch et al, 2013), ozone loss can be estimated by comparing the modeled passive ozone to both the ozone simulated by the same model and to observed ozone, with comparisons between the latter two fields typically used to quantify the overall accuracy of the model calculations (both from the dynamical and chemical perspective). The passive subtraction approach can be taken a stage further by considering a "pseudo passive" ozone tracer , subject to both dynamical and gas-phase chemistry influences, but not the losses due to chlorine activated through heterogeneous processes.…”

Comparison of ECHAM5/MESSy Atmospheric Chemistry (EMAC) simulations of the Arctic winter 2009/2010 and 2010/2011 with Envisat/MIPAS and Aura/MLS observations

“…Sensitivity simulations or adjustments based on observations concerning, for instance, the partitioning between STS and NAT and/or the limit for the NAT number density as done by Brakebusch et al (2013) and Wegner et al (2013) would help to find the best model set-up for simulating Arctic PSCs. Further, the results derived here and upcoming sensitivity simulations will serve as a benchmark for the development of the PSC parameterization in other atmospheric models, such as ICON-ART (ICOsahedral Nonhydrostatic Model -Aerosols and Reactive Trace gases), and will help to improve the performance of EMAC in future model intercomparison studies.…”

Comparisons of polar processing diagnostics from 34 years of the ERA-Interim and MERRA reanalyses

“…The lower stratospheric processes that lead to chemical ozone destruction include the development of polar stratospheric clouds (PSCs), denitrification via sedimentation of PSCs, and conversion of inert chlorine reservoirs to ozone-destroying forms by reactions on the surfaces of PSCs (e.g., Solomon, 1999). Because these phenomena depend critically on temperatures and winds throughout the lower stratosphere (e.g., WMO, 2011WMO, , 2015Brakebusch et al, 2013;Manney et al, 2011;Sinnhuber et al, 2011;and references therein), diagnostics related to ozone loss require fields (e.g., winds) and data coverage (e.g., vertically resolved, hemispheric, multiannual) that cannot be obtained from individual measurement systems such as satellites and radiosonde networks. As a result, the global analyses of meteorological fields provided by data assimilation systems (DAS) that combine many of these measurements are invaluable for polar processing and ozone loss studies.…”

“…Sinnhuber et al (2011) found that reducing the temperatures from the ECMWF operational analyses by 1 K in CTM runs for the 2010/2011 Arctic winter resulted in a substantial increase in ozone loss. Brakebusch et al (2013) reduced GEOS-5 (Goddard Earth Observing System model, version 5) temperatures by 1.5 K in a Whole Atmosphere Community Climate Model simulation of ozone for the 2004/2005 Arctic winter; applying this temperature bias improved the agreement of simulated ozone with measurements from the Aura Microwave Limb Sounder satellite instrument. In other cases, some DAS analyses have been shown to have significant shortcomings for use in polar processing and ozone loss research.…”