Using Long Records of HCl to Understand Dynamical Processes Affecting Lower-Stratospheric Ozone Trends

Ground-based measurements show decreases in atmospheric concentrations of ozone depleting substances (ODSs) due to the Montreal Protocol (MP) and its amendments. These decreases prompt effort to detect and quantify consequent stratospheric ozone increases. The present rate of ODS decrease is smaller than the rate of ODS increase prior to the MP. Detection of lower stratospheric ozone increase due to ODS decrease is challenging due to the long ozone lifetime, inter-annual variability due to dynamics, and the need to combine data sets from various satellite sensors in order to obtain a global data set of sufficient length. Concurrent ozone changes are also probable due to changes stratospheric climate, circulation and mixing. Attempts to identify and quantify the dynamic contributions to ozone trends, i.e., the changes in lower stratospheric transport and mixing that masquerade as changes in photo-chemical loss due to composition change, began in the late 1990s and early 2000s. We are investigating how the dynamical processes affect detection of expected ozone recovery by quantifying how multi-year satellite and ground-based records of other constituents (e.g., nitrous oxide, hydrogen chloride) are impacted by inter-annual variability. We will present results from parallel analyses of observations and simulations. Observations include 25+ year records of column HCl from the Network for Detection of Stratospheric Composition Change (NDACC) and 15-year records of N₂O and HCl from Aura MLS. We also analyze a simulation using the Global Modeling Initiative chemistry and transport model driven by meteorological fields from MERRA-2 (1980-2019). We find that current approaches to account for variability and thereby detect small decadal scale trends are inadequate when applied either to observations (e.g., HCl, N₂O) or to simulated timeseries. In the northern hemisphere, the residual time series after application of Multiple Linear Regression (MLR) consistently exhibits the same autocorrelation that persists from the end of winter through to the seasonal transition in the fall as the original time series. We will also consider how these schemes interact with the annual cycle, noting that for constituents like nitrous oxide the annual cycle is dynamically driven whereas for ozone both dynamics and photochemistry play a role.