ALBERT

Description: SWIFT-AI-DS is a benchmark dataset that consists of samples that have been derived from two simulation runs (each 2.5 years long) of the chemistry and transport model ATLAS (Wohltmann and Rex, 2009; Wohltmann et al., 2010). This data set of nearly 200 million samples meets the requirements of a labelled data set and is ideally suited for training and testing of a machine learning based surrogate model. Two time periods were considered in the simulation runs: first from November 1998 to March 2001 and the second from November 2004 to March 2007. The dataset covers the entire Earth geographically, but is vertically restricted to the altitudes of the lower to middle stratosphere, for which the SWIFT (Rex et al., 2014; Kreyling et. al, 2017; Wohltmann et al., 2017) approach of 24-hour ozone tendencies can be applied. Applicability was determined in terms of the chemical lifetime of stratospheric ozone, which is a function of solar irradiance and altitude. It can be described by a dynamic upper bound [Kreyling et. Al, 2017]. Within the range where the chemical lifetime is longer than 14 days, ozone is not in quasi-chemical equilibrium. Moreover, this data set focuses on the region of the lower to middle stratosphere because it is the region with the largest contribution to the total ozone column. State-of-the-art physical process models for stratospheric chemistry require enormous computational time. Our research is focused on developing much faster, yet accurate, surrogate models for computing the 24-hour tendencies of stratospheric ozone. Much faster models of stratospheric ozone provide a new application area such as for climate models. These surrogate models benefit greatly from the methodological and hardware improvements of the last decade. Each simulation run uses the full stratospheric chemistry model to solve a system of differential equations involving 47 chemical species and 171 chemical reactions at a very high (〈〈 seconds) and variable temporal resolution. The ATLAS model is driven by ECMWF reanalysis data (either ERA-I or ERA5). The air parcel state has been sampled at a 24-hour time step (00:00 UTC model time). During postprocessing some variables are stored as 24-hour averages, as 24-hour tendencies or as the state at the beginning of the 24-hour time step. The dataset is stored in 12 monthly netCDF-files.

Description: Arctic warming was more pronounced than warming in midlatitudes in the last decades making this region a hotspot of climate change. Associated with this, a rapid decline of sea-ice extent and a decrease of its thickness has been observed. Sea-ice retreat allows for an increased transport of heat and momentum from the ocean up to the tropo- and stratosphere by enhanced upward propagation of planetary-scale atmospheric waves. In the upper atmosphere, these waves deposit the momentum transported, disturbing the stratospheric polar vortex, which can lead to a breakdown of this circulation with the potential to also significantly impact the troposphere in mid- to late-winter and early spring. Therefore, an accurate representation of stratospheric processes in climate models is necessary to improve the understanding of the impact of retreating sea ice on the atmospheric circulation. By modeling the atmospheric response to a prescribed decline in Arctic sea ice, we show that including interactive stratospheric ozone chemistry in atmospheric model calculations leads to an improvement in tropo-stratospheric interactions compared to simulations without interactive chemistry. This suggests that stratospheric ozone chemistry is important for the understanding of sea ice related impacts on atmospheric dynamics.

Description: Chemical loss of Arctic ozone due to anthropogenic halogens is driven by temperature, with more loss occurring during cold winters favourable for formation of polar stratospheric clouds (PSCs). We show that a positive, statistically significant rise in the local maxima of PSC formation potential (PFP^LM) for cold winters is apparent in meteorological data collected over the past half century. Output from numerous General Circulation Models (GCMs) also exhibits positive trends in PFP^LM over 1950 to 2100, with highest values occurring at end of century, for simulations driven by a large rise in the radiative forcing of climate from greenhouse gases (GHGs). We combine projections of stratospheric halogen loading and humidity with GCM-based forecasts of temperature to suggest that conditions favourable for large, seasonal loss of Arctic column O3 could persist or even worsen until the end of this century, if future abundances of GHGs continue to steeply rise.