Stochastic Modeling of Stratospheric Temperature

This study suggests a stochastic model for time series of daily zonal (circumpolar) mean stratospheric temperature at a given pressure level. It can be seen as an extension of previous studies which have developed stochastic models for surface temperatures. The proposed model is a combination of a deterministic seasonality function and a Lévy-driven multidimensional Ornstein–Uhlenbeck process, which is a mean-reverting stochastic process. More specifically, the deseasonalized temperature model is an order 4 continuous-time autoregressive model, meaning that the stratospheric temperature is modeled to be directly dependent on the temperature over four preceding days, while the model’s longer-range memory stems from its recursive nature. This study is based on temperature data from the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis model product. The residuals of the autoregressive model are well represented by normal inverse Gaussian-distributed random variables scaled with a time-dependent volatility function. A monthly variability in speed of mean reversion of stratospheric temperature is found, hence suggesting a generalization of the fourth-order continuous-time autoregressive model. A stochastic stratospheric temperature model, as proposed in this paper, can be used in geophysical analyses to improve the understanding of stratospheric dynamics. In particular, such characterizations of stratospheric temperature may be a step towards greater insight in modeling and prediction of large-scale middle atmospheric events, such as sudden stratospheric warming. Through stratosphere–troposphere coupling, the stratosphere is hence a source of extended tropospheric predictability at weekly to monthly timescales, which is of great importance in several societal and industry sectors.

Analyzing ozone variations and uncertainties at high latitudes during Sudden Stratospheric Warming events using MERRA-2

Stratospheric circulation is a critical part of the Arctic ozone cycle. Sudden stratospheric warming events (SSWs) manifest the strongest alteration of stratospheric dynamics. Changes in planetary wave propagation vigorously influence zonal mean zonal wind, temperature, and tracer concentrations in the stratosphere over the high latitudes. In this study, we examine six major SSWs from 2004 to 2020 using the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Using the unique density of observations around the Greenland sector at high latitudes, we perform comprehensive comparisons of high latitude observations with the MERRA-2 ozone dataset during the six major SSWs. Our results show that MERRA-2 captures the high variability of mid stratospheric ozone fluctuations during SSWs over high latitudes. However, larger uncertainties are observed in the lower stratosphere and troposphere. The zonally averaged stratospheric ozone shows a dramatic increase of 9–29 % in total column ozone (TCO) near the time of each SSW, which lasts up to two months. The SSWs exhibit a more significant impact on ozone over high northern latitudes when the polar vortex is mostly elongated as seen in 2009 and 2018 compared to the events in which the polar vortex is displaced towards Europe. The regional impact of SSWs over Greenland has a similar structure as the zonal average, however, exhibits more intense ozone anomalies which is reflected by 15–37 % increase in TCO. The influence of SSW on mid stratospheric ozone levels persists longer than their impact on temperature. This paper is focused on the increased (suppressed) wave activity before (after) the SSWs and their impact on ozone variability at high latitudes. This includes an investigation of the different terms of tracer continuity using MERRA-2 parameters, which emphasizes the key role of vertical advection on mid-stratospheric ozone during the SSWs.

Airborne measurements of oxygen concentration from the surface to the lower stratosphere and pole to pole

We have developed in situ and flask sampling systems for airborne measurements of variations in the O2/N2 ratio at the part per million level. We have deployed these instruments on a series of aircraft campaigns to measure the distribution of atmospheric O2 from 0–14 km and 87∘ N to 86∘ S throughout the seasonal cycle. The National Center for Atmospheric Research (NCAR) airborne oxygen instrument (AO2) uses a vacuum ultraviolet (VUV) absorption detector for O2 and also includes an infrared CO2 sensor. The VUV detector has a precision in 5 s of ±1.25 per meg (1σ) δ(O2/N2), but thermal fractionation and motion effects increase this to ±2.5–4.0 per meg when sampling ambient air in flight. The NCAR/Scripps airborne flask sampler (Medusa) collects 32 cryogenically dried air samples per flight under actively controlled flow and pressure conditions. For in situ or flask O2 measurements, fractionation and surface effects can be important at the required high levels of relative precision. We describe our sampling and measurement techniques and efforts to reduce potential biases. We also present a selection of observational results highlighting the individual and combined instrument performance. These include vertical profiles, O2:CO2 correlations, and latitudinal cross sections reflecting the distinct influences of terrestrial photosynthesis, air–sea gas exchange, burning of various fuels, and stratospheric dynamics. When present, we have corrected the flask δ(O2/N2) measurements for fractionation during sampling or analysis with the use of the concurrent δ(Ar/N2) measurements. We have also corrected the in situ δ(O2/N2) measurements for inlet fractionation and humidity effects by comparison to the corrected flask values. A comparison of Ar/N2-corrected Medusa flask δ(O2/N2) measurements to regional Scripps O2 Program station observations shows no systematic biases over 10 recent campaigns (+0.2±8.2 per meg, mean and standard deviation, n=86). For AO2, after resolving sample drying and inlet fractionation biases previously on the order of 10–100 per meg, independent AO2 δ(O2/N2) measurements over six more recent campaigns differ from coincident Medusa flask measurements by -0.3±7.2 per meg (mean and standard deviation, n=1361) with campaign-specific means ranging from −5 to +5 per meg.

Planetary Wave Spectrum in the Stratosphere–Mesosphere during Sudden Stratospheric Warming 2018

The planetary wave activity in the stratosphere–mesosphere during the Arctic major Sudden Stratospheric Warming (SSW) in February 2018 is discussed on the basis of microwave radiometer (MWR) measurements of carbon monoxide (CO) above Kharkiv, Ukraine (50.0° N, 36.3° E) and the Aura Microwave Limb Sounder (MLS) measurements of CO, temperature and geopotential heights. From the MLS data, eastward and westward migrations of wave 1/wave 2 spectral components were differentiated, to which less attention was paid in previous studies. Abrupt changes in zonal wave spectra occurred with the zonal wind reversal near 10 February 2018. Eastward wave 1 and wave 2 were observed before the SSW onset and disappeared during the SSW event, when westward wave 1 became dominant. Wavelet power spectra of mesospheric CO variations showed statistically significant periods of 20–30 days using both MWR and MLS data. Although westward wave 1 in the mesosphere dominated with the onset of the SSW 2018, it developed independently of stratospheric dynamics. Since the propagation of upward planetary waves was limited in the easterly zonal flow in the stratosphere during SSW, forced planetary waves in the mid-latitude mesosphere may exist due to the instability of the zonal flow.

Stochastic modelling of stratospheric temperature

<p>A stochastic model for daily-spatial mean stratospheric temperature over a given area is suggested. The model is a sum of a deterministic seasonality function and a Lévy driven vectorial Ornstein-Uhlenbeck process, which is a mean-reverting stochastic process. More specifically, the model is an order 4 continuous-time autoregressive (CAR(4)) process, derived from data analysis suggesting an order 4 autoregressive (AR(4)) process to model the deseasonalized stochastic temperature data empirically. In this analysis, temperature data as represented in ECMWF re-analysis model products are considered. The residuals of the AR(4) process turn out to be normal inverse Gaussian distributed random variables scaled with a time dependent volatility function. In general, it is possible to show that the discrete time AR(p) process is closely related to CAR(p) processes, its continuous counterpart. An equivalent effort is made in deriving a dual stochastic model for stratospheric temperature, in the sense that the year is divided into summer and winter seasons. However, this seems to further complicate the modelling, rather than obtaining a simplified analytic framework. A stochastic characterization of the stratospheric temperature representation in model products, such as the model proposed in this paper, can be used in geophysical analyses to improve our understanding of stratospheric dynamics. In particular, such characterizations of stratospheric temperature may be a step towards greater insight in modelling and prediction of large-scale middle atmospheric events like sudden stratospheric warmings. Through stratosphere-troposphere coupling, this is important in the work towards an extended predictability of long-term tropospheric weather forecasting.</p>

INVESTIGATION OF WINTER ARCTIC STRATOSPHERIC DYNAMICS IN PRESENT AND FUTURE CLIMATE

. Using reanalysis data sets variability of temperature, zonal mean, amplitude-planetary waves, as well as the influence of the Arctic stratospheric polar vortex changes on the circulation of troposphere from 2016 to 2021 are studied. The results of calculations of the climate model of the INM RAS CM5 for the current and future climate are used to analyze changes in the volume of air masses inside the stratospheric polar vortex with temperatures sufficient for the formation of polar stratospheric clouds necessary for the destruction of the ozone layer.

Tropopause-level planetary wave source and its role in two-way troposphere–stratosphere coupling

Atmospheric planetary waves play a fundamental role in driving stratospheric dynamics, including sudden stratospheric warming (SSW) events. It is well established that the bulk of the planetary wave activity originates near the surface. However, recent studies have pointed to a planetary wave source near the tropopause that may play an important role in the development of SSWs. Here we analyze the dynamical origin of this wave source and its impact on stratosphere–troposphere coupling, using an idealized model and a quasi-reanalysis. It is shown that the tropopause-level planetary wave source is associated with nonlinear wave–wave interactions, but it can also manifest as an apparent wave source due to transient wave decay. The resulting planetary waves may then propagate deep into the stratosphere, where they dissipate and may help to force SSWs. Our results indicate that SSWs preceded by both the tropopause and the surface wave-source events tend to be followed by a weakened tropospheric zonal flow several weeks later. However, while in the case of a preceding surface wave-source event this downward impact is found mainly poleward of 60∘ N, it appears to be the strongest between 40 and 60∘ N for SSWs preceded by tropopause wave-source events. This suggests that tropopause wave-source events could potentially serve as an additional predictor of not only SSWs but also their downward impact as well.

Airborne measurements of oxygen concentration from the surface to the lower stratosphere and pole to pole

We have developed in situ and flask sampling systems for airborne measurements of variations in the O2/N2 ratio at the part per million level. We have deployed these instruments on a series of aircraft campaigns to measure the distribution of atmospheric O2 from 0–14 km and 87° N to 85° S throughout the seasonal cycle. The NCAR airborne oxygen instrument (AO2) uses a vacuum ultraviolet (VUV) absorption detector for O2 and also includes an infrared CO2 sensor. The VUV detector has a precision in 5 seconds of ±1.25 per meg (1σ) δ(O2/N2), but thermal fractionation and motion effects increase this to ±2.5–4.0 per meg when sampling ambient air in flight. The NCAR/Scripps airborne flask sampler (Medusa) collects 32 cryogenically dried air samples per flight under actively controlled flow and pressure conditions. For in situ or flask O2 measurements, fractionation and surface effects can be important at the required high levels of relative precision. We describe our sampling and measurement techniques, and efforts to reduce potential biases. We also present a selection of observational results highlighting the individual and combined instrument performance. These include vertical profiles, O2 : CO2 correlations, and latitudinal cross sections reflecting the distinct influences of terrestrial photosynthesis, air-sea gas exchange, burning of various fuels, and stratospheric dynamics. When present, we have corrected the flask δ(O2/N2) measurements for fractionation during sampling or analysis, with the use of the concurrent δ(Ar/N2) measurements. We have also corrected the in situ δ(O2/N2) measurements for inlet fractionation and humidity effects by comparison to the corrected flask values. A comparison of Ar/N2-corrected Medusa flask δ(O2/N2) measurements to regional Scripps O2 Network station observations shows no systematic biases over 10 recent campaigns (+0.2 ± 8.2 per meg, mean and standard deviation, n = 86). For AO2, after resolving sample drying and inlet fractionation biases previously on the order of 10–100 per meg, independent AO2 δ(O2/N2) measurements over 6 more recent campaigns differ from coincident Medusa flask measurements by −0.3 ± 7.2 per meg (mean and standard deviation, n = 1361), with campaign-specific means ranging from −5 to +5 per meg.

Dynamics of Anomalous Stratospheric Eddy Heat Flux Events in an Idealized Model

Extreme stratospheric eddy and sudden stratospheric warming (SSW) events both involve anomalous stratospheric eddy heat flux. The cause of the anomaly has been hypothesized to be due to tropospheric or stratospheric dynamics. Here, ensemble spectral nudging experiments in a dry dynamical-core model are used to quantify the role of the troposphere versus the stratosphere. The experiments focus on the wavenumber-1 heat flux since it dominates the anomalous stratospheric eddy heat flux during both events. Nudging the stratospheric zonal-mean flow does not account for the anomalous stratospheric wave-1 heat flux. Nudging either tropospheric wave-1 or higher-order wavenumbers (k ≥ 2) accounts for a large fraction of the anomalous stratospheric wave-1 heat flux. Mechanism denial experiments, whereby tropospheric eddies (wave 1 or k ≥ 2) are nudged and the zonal-mean flow is fixed to climatology, suggest the climatological stratospheric zonal-mean flow is sufficient to account for the anomalous stratospheric wave-1 heat flux and wave–wave interaction plays a role in generating the anomalous tropospheric wave-1 source. Taken together, the experiments suggest the troposphere dominates the anomalous stratospheric eddy heat flux during extreme stratospheric eddy and SSW events while the stratospheric zonal-mean flow plays secondary role.