Quantitative evaluation of ozone and selected climate parameters in a set of EMAC simulations

Abstract

Abstract. Four simulations with the ECHAM/MESSy Atmospheric Chemistry (EMAC) model have been evaluated with the Earth System Model Validation Tool (ESMValTool) to identify differences in simulated ozone and selected climate parameters that resulted from (i) different setups of the EMAC model (nudged vs. free-running) and (ii) different boundary conditions (emissions, sea surface temperatures (SSTs) and sea ice concentrations (SICs)). To assess the relative performance of the simulations, quantitative performance metrics are calculated consistently for the climate parameters and ozone. This is important for the interpretation of the evaluation results since biases in climate can impact on biases in chemistry and vice versa. The observational data sets used for the evaluation include ozonesonde and aircraft data, meteorological reanalyses and satellite measurements. The results from a previous EMAC evaluation of a model simulation with nudging towards realistic meteorology in the troposphere have been compared to new simulations with different model setups and updated emission data sets in free-running time slice and nudged quasi chemistrytransport model (QCTM) mode. The latter two configurations are particularly important for chemistry-climate projections and for the quantification of individual sources (e.g., the transport sector) that lead to small chemical perturbations of the climate system, respectively. With the exception of some specific features which are detailed in this study, no large differences that could be related to the different setups (nudged vs. free-running) of the EMAC simulations were found, which offers the possibility to evaluate and improve the overall model with the help of shorter nudged simulations. The main differences between the two setups is a better representation of the tropospheric and stratospheric temperature in the nudged simulations, which also better reproduce stratospheric water vapor concentrations, due to the improved simulation of the temperature in the tropical tropopause layer. Ozone and ozone precursor concentrations, on the other hand, are very similar in the different model setups, if similar boundary conditions are used. Different boundary conditions however lead to relevant differences in the four simulations. Biases which are common to all simulations are the underestimation of the ozone hole and the overestimation of tropospheric column ozone, the latter being significantly reduced when lower lightning emissions of nitrogen oxides are used. To further investigate possible other reasons for such bias, two sensitivity simulations with an updated scavenging routine and the addition of a newly proposed HNO 3 -forming channel of the HO 2 + NO reaction were performed. The update in the scavenging routine resulted in a slightly better representation of ozone compared to the reference simulation. The introduction of the new HNO 3 -forming channel significantly reduces the overestimation of tropospheric ozone. Therefore, including the new reaction rate could potentially...

Earth System Chemistry integrated Modelling (ESCiMo) with the Modular Earth Submodel System (MESSy) version 2.51

“…30. It is positive in all regions and simulations (see also Righi et al, 2015). Taking into account the corrected road traffic emissions (RC1SD-base-10a), the tropospheric partial column ozone increases by up to 3 % in the tropics and thus also the bias is increased (not shown).…”

“…The simulations in the third category have a cold bias (of up to 4.5 K) around the tropopause and show a warm bias (of up to 4 K) in the Southern Hemisphere (SH) above 100 hPa, as also analysed from previous EMAC simulations by Righi et al (2015).…”

“…This analysis has been adapted from Righi et al (2015), also sharing some routines with the ESMValTool . Simulations were sampled at the ozone sonde locations, binned according to latitude ranges, and averaged with equal weights.…”

“…To test the statistical significance we applied the paired t test, based on observation-simulation pairs of values from each step of the respective time axes (diagnostic-specific). The portrait diagrams for overall mean bias metrics were adapted from the corresponding analyses of Righi et al (2015), and also share some routines with the ESMValTool . Figure 26 provides an overview of the TCO bias in the different simulations with respect to the BSTCO database, globally and for different latitude bands.…”

Effects of mixing on resolved and unresolved scales on stratospheric age of air

“…tion and leads to a too weak polar vortex (Righi et al, 2015;Jöckel et al, 2016;R. Deckert and D. Cai, personal communication, 2016).…”