Long-term climatology of air mass transport through the Tropical Tropopause Layer (TTL) during NH winter

A long-term climatology of air mass transport through the tropical tropopause layer (TTL) is presented, covering the period from 1962–2005. The transport through the TTL is calculated with a Lagrangian approach using radiative heating rates as vertical velocities in an isentropic trajectory model. We demonstrate the improved performance of such an approach compared to previous studies using vertical winds from meteorological analyses. Within the upper part of the TTL, the averaged diabatic ascent is 0.5 K/day during Northern Hemisphere (NH) winters 1992–2001. Climatological maps show a cooling and strengthening of this part of the residual circulation during the 1990s and early 2000s compared to the long-term mean. Lagrangian cold point (LCP) fields show systematic differences for varying time periods and natural forcing components. The interannual variability of LCP temperature and density fields is found to be influenced by volcanic eruptions, El Niño Southern Oscillation (ENSO), Quasi-Biennial Oscillation (QBO) and the solar cycle. The coldest and driest TTL is reached during QBO easterly phase and La Niña over the western Pacific, whereas during volcanic eruptions, El Niño and QBO westerly phase it is warmer and less dry

A quantitative analysis of the reactions involved in stratospheric ozone depletion in the polar vortex core

We present a quantitative analysis of the chemical reactions involved in polar ozone depletion in the stratosphere and of the relevant reaction pathways and cycles. While the reactions involved in polar ozone depletion are well known, quantitative estimates of the importance of individual reactions or reaction cycles are rare. In particular, there is no comprehensive and quantitative study of the reaction rates and cycles averaged over the polar vortex under conditions of heterogeneous chemistry so far. We show time series of reaction rates averaged over the core of the polar vortex in winter and spring for all relevant reactions and indicate which reaction pathways and cycles are responsible for the vortex-averaged net change of the key species involved in ozone depletion, i.e., ozone, chlorine species (ClOx, HCl, ClONO2), bromine species, nitrogen species (HNO3, NOx) and hydrogen species (HOx). For clarity, we focus on one Arctic winter (2004–2005) and one Antarctic winter (2006) in a layer in the lower stratosphere around 54  hPa and show results for additional pressure levels and winters in the Supplement. Mixing ratios and reaction rates are obtained from runs of the ATLAS Lagrangian chemistry and transport model (CTM) driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis data. An emphasis is put on the partitioning of the relevant chemical families (nitrogen, hydrogen, chlorine, bromine and odd oxygen) and activation and deactivation of chlorine

Update of the Polar SWIFT model for polar stratospheric ozone loss (Polar SWIFT version 2)

The Polar SWIFT model is a fast scheme for calculating the chemistry of stratospheric ozone depletion in polar winter. It is intended for use in global climate models (GCMs) and Earth system models (ESMs) to enable the simulation of mutual interactions between the ozone layer and climate. To date, climate models often use prescribed ozone fields, since a full stratospheric chemistry scheme is computationally very expensive. Polar SWIFT is based on a set of coupled differential equations, which simulate the polar vortex-averaged mixing ratios of the key species involved in polar ozone depletion on a given vertical level. These species are O3, chemically active chlorine (ClOx), HCl, ClONO2 and HNO3. The only external input parameters that drive the model are the fraction of the polar vortex in sunlight and the fraction of the polar vortex below the temperatures necessary for the formation of polar stratospheric clouds. Here, we present an update of the Polar SWIFT model introducing several improvements over the original model formulation. In particular, the model is now trained on vortex-averaged reaction rates of the ATLAS Chemistry and Transport Model, which enables a detailed look at individual processes and an independent validation of the different parameterizations contained in the differential equations. The training of the original Polar SWIFT model was based on fitting complete model runs to satellite observations and did not allow for this. A revised formulation of the system of differential equations is developed, which closely fits vortex-averaged reaction rates from ATLAS that represent the main chemical processes influencing ozone. In addition, a parameterization for the HNO3 change by denitrification is included. The rates of change of the concentrations of the chemical species of the Polar SWIFT model are purely chemical rates of change in the new version, whereas in the original Polar SWIFT model, they included a transport effect caused by the original training on satellite data. Hence, the new version allows for an implementation into climate models in combination with an existing stratospheric transport scheme. Finally, the model is now formulated on several vertical levels encompassing the vertical range in which polar ozone depletion is observed. The results of the Polar SWIFT model are validated with independent Microwave Limb Sounder (MLS) satellite observations and output from the original detailed chemistry model of ATLAS

SWIFT: Semi-empirical and numerically efficient stratospheric ozone chemistry for global climate models

The SWIFT model is a fast yet accurate chemistry scheme for calculating the chemistry of stratospheric ozone. It is mainly intended for use in Global Climate Models (GCMs), Chemistry Climate Models (CCMs) and Earth System Models (ESMs). For computing time reasons these models often do not employ full stratospheric chem- istry modules, but use prescribed ozone instead. This can lead to insufficient representation between stratosphere and troposphere. The SWIFT stratospheric ozone chemistry model, focuses on the major reaction mechanisms of ozone production and loss in order to reduce the computational costs. SWIFT consists of two sub-models. 1) Inside the polar vortex, the model calculates polar vortex averaged ozone loss by solving a set of coupled differential equations for the key species in polar ozone chemistry. 2) The extrapolar regime, which this poster is going to focus on. Outside the polar vortex, the complex system of differential equations of a full stratospheric chemistry model is replaced by an explicit algebraic polynomial, which can be solved in a fraction of the time needed by the full scale model. The approach, which is used to construct the polynomial, is also referred to as repro-modeling and has been successfully applied to chemical models (Turanyi (1993), Lowe & Tomlin (2000)). The procedure uses data from the Lagrangian stratospheric chemistry and transport model ATLAS and yields one high-order polynomial for global ozone loss and production rates over 24h per month. The stratospheric ozone change rates can be sufficiently described by 9 variables. Latitude, altitude, temperature, the overhead ozone abundance, 4 mixing ratios of ozone depleting chemical families (chlorine, bromine, nitrogen-oxides and hydrogen-oxides) and the ozone concentrations itself. The ozone change rates in the lower stratosphere as a function of these 9 variables yield a sufficiently compact 9-D hyper-surface, which we can approximate with a polynomial. In the upper stratosphere (roughly above 30km) the ozone chemical lifetime becomes shorter than the transport time scales, thus the ozone concentrations are determined by the local atmospheric conditions. We therefore introduce an additional regime in the upper stratosphere, where the ozone concentrations, instead of the 24h change rates, are fitted. The fitted polynomial for upper stratospheric ozone is dependent on the same variables, except the ozone concentration, naturally. This poster shows results of simulations employing the polynomial scheme and discusses constraints on the method

Estimating the Rate of Change of Stratospheric Ozone using Deep Neural Networks

Due to the intensive ozone research in recent decades, the processes that influence stratospheric ozone are well understood. The chemistry and transport model ATLAS was developed to simulate the chemistry and transport of stratospheric ozone globally. The chemical rate of change of ozone is calculated at each model point and time step of the model by solving a system of differential equations that requires 55 input parameters (chemical species, temperatures, ...). But the computational e!ort to solve this complex system of differential equations is very high, and with respect to the overall limited computation time, this prevents the inclusion of ozone chemistry into ESMs. This project proposes a data-driven machine learning approach to predict the rate of change of stratospheric ozone. To derive a data set from modelled data, ATLAS was run for several short model runs. The rate of change of ozone and 55 parameters were stored at each model point and time step. By observing the co-variances of the high-dimensional feature-space, a large data set with reduced dimensionality has been created. A supervised learning algorithm used this data set of input and output pairs to train a deep feed- forward neural network (NN). This involved the identification and optimisation of several hyperparameters and to find a well- functioning combination of depth (number of layers) and width (number of neurons per layer). In this way, the NN model capacity is optimised with respect to the data itself. To evaluate this approach, the results were compared with another data-driven approach called SWIFT. The SWIFT model employs a repro-modelling approach that uses polynomials to approximate the rate of change of ozone. The resulting NN model is not only capable of learning the context of an eleven-dimensional hyperplane, but also improves the RMSE by about one order of magnitude compared to SWIFT’s previous polynomial approach. In addition, the deviations of the predictions at the boundaries (altitude and latitude) are significantly lower, which is a challenge for the polynomial approach. Only fully coupled ozone climate set ups are able to consider the complex interactions of the stratospheric ozone layer and climate. This is a step towards a computationally very fast but accurate application of an interactive ozone scheme in climate models