Carbonyl sulfide (OCS) is a non‐hygroscopic trace species in the free troposphere and a large sulfur reservoir maintained by both direct oceanic, geologic, biogenic, and anthropogenic emissions and the oxidation of other sulfur‐containing source species. It is the largest source of sulfur transported to the stratosphere during volcanically quiescent periods. Data from 22 ground‐based globally dispersed stations are used to derive trends in total and partial column OCS. Middle infrared spectral data are recorded by solar‐viewing Fourier transform interferometers that are operated as part of the Network for the Detection of Atmospheric Composition Change between 1986 and 2020. Vertical information in the retrieved profiles provides analysis of discreet altitudinal regions. Trends are found to have well‐defined inflection points. In two linear trend time periods ∼2002 to 2008 and ∼2008 to 2016 tropospheric trends range from ∼0.0 to (1.55 ± 0.30%/yr) in contrast to the prior period where all tropospheric trends are negative. Regression analyses show strongest correlation in the free troposphere with anthropogenic emissions. Stratospheric trends in the period ∼2008 to 2016 are positive up to (1.93 ± 0.26%/yr) except notably low latitude stations that have negative stratospheric trends. Since ∼2016, all stations show a free tropospheric decrease to 2020. Stratospheric OCS is regressed with simultaneously measured N₂O to derive a trend accounting for dynamical variability. Stratospheric lifetimes are derived and range from (54.1 ± 9.7)yr in the sub‐tropics to (103.4 ± 18.3)yr in Antarctica. These unique long‐term measurements provide new and critical constraints on the global OCS budget.

Stratospheric circulation is a critical part of the Arctic ozone cycle. Sudden stratospheric warming events (SSWs) manifest the strongest alteration of stratospheric dynamics. During SSWs, 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 persistent 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 2 months. This study shows that the average shape of the Arctic polar vortex before SSWs influences the geographical extent, timing, and magnitude of ozone changes. The SSWs exhibit a more significant impact on ozone over high northern latitudes when the average polar vortex is mostly elongated as seen in 2009 and 2018 compared to the events in which the polar vortex is displaced towards Europe. Strong correlation (R²=90 %) is observed between the magnitude of change in average equivalent potential vorticity before and after SSWs and the associated averaged total column ozone changes over high latitudes. This paper investigates the different terms of the ozone continuity equation using MERRA-2 circulation, which emphasizes the key role of vertical advection in mid-stratospheric ozone during the SSWs and the magnified vertical advection in elongated vortex shape as seen in 2009 and 2018.

Understanding variations in atmospheric ozone in the Arctic is difficult because there are only a few long-term records of vertical ozone profiles in this region. We present 12 years of ozone profiles from February 2005 to February 2017 at four sites: Summit Station, Greenland; Ny-Ålesund, Svalbard, Norway; and Alert and Eureka, Nunavut, Canada. These profiles are created by combining ozonesonde measurements with ozone profile retrievals using data from the Microwave Limb Sounder (MLS). This combination creates a high-quality dataset with low uncertainty values by relying on in situ measurements of the maximum altitude of the ozonesondes (∼30 km) and satellite retrievals in the upper atmosphere (up to 60 km). For each station, the total column ozone (TCO) and the partial column ozone (PCO) in four atmospheric layers (troposphere to upper stratosphere) are analyzed. Overall, the seasonal cycles are similar at these sites. However, the TCO over Ny-Ålesund starts to decline 2 months later than at the other sites. In summer, the PCO in the upper stratosphere over Summit Station is slightly higher than at the other sites and exhibits a higher standard deviation. The decrease in PCO in the middle and upper stratosphere during fall is also lower over Summit Station. The maximum value of the lower- and middle-stratospheric PCO is reached earlier in the year over Eureka. Trend analysis over the 12-year period shows significant trends in most of the layers over Summit and Ny-Ålesund during summer and fall. To understand deseasonalized ozone variations, we identify the most important dynamical drivers of Arctic ozone at each level. These drivers are chosen based on mutual selected proxies at the four sites using stepwise multiple regression (SMR) analysis of various dynamical parameters with deseasonalized data. The final regression model is able to explain more than 80 % of the TCO and more than 70 % of the PCO in almost all of the layers. The regression model provides the greatest explanatory value in the middle stratosphere. The important proxies of the deseasonalized ozone time series at the four sites are tropopause pressure (TP) and equivalent latitude (EQL) at 370 K in the troposphere, the quasi-biennial oscillation (QBO) in the troposphere and lower stratosphere, the equivalent latitude at 550 K in the middle and upper stratosphere, and the eddy heat flux (EHF) and volume of polar stratospheric clouds throughout the stratosphere.

When retrieving geophysical parameters, it is advantageous to have an estimate of prior information that is based on observations with associated uncertainties, but this is often not possible. Long-term ground-based remote sensing measurements and the ozonesonde program at Summit Station, Greenland, provide an opportunity to create a unique framework to retrieve atmospheric ozone using observationally based prior information in the Arctic. This study investigates the potential of using the ground-based polar atmospheric emitted radiance interferometer (P-AERI) to estimate ozone below 10 km. Downlooking or limb-viewing sensors, such as those from satellites, have limited sensitivity to the lower atmosphere; however, uplooking, ground-based instruments provide complementary information to satellite observations to improve trace gas estimates at lower atmospheric levels. Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) is a reanalysis product that integrates satellite information but also inherits their higher uncertainties at lower atmospheric levels. An observation-based climatology of the uncertainty in the MERRA-2 ozone dataset is estimated using ozonesondes launched at Summit Station. MERRA-2 shows high accuracy in the middle stratosphere but larger uncertainties below 10 km. Retrievals that use spectral radiance measurements from the P-AERI improve the estimates of ozone concentrations in the troposphere and lower stratosphere by using prior information from MERRA-2 and our climatology of MERRA-2 uncertainties as the covariance of the prior. Using ozonesonde observations from 2012 to 2017 at Summit Station, Greenland, the quality of the retrieved results is assessed. Comparisons show that retrieved partial columns reduce the bias of MERRA-2 ozone estimation below 10 km, and the average tropospheric ozone concentration is improved significantly.

The intensity of wildfires has increased dramatically in recent decades; thus, better understanding the impact of land-management efforts in biodiversity conservation on post-wildfire recovery could highlight the value of these interventions. Field assessments are often costly; therefore, monitoring the effectiveness of applied conservation practices using remote sensing tools is critical. The main goal of this study is to develop and apply a remote sensing framework to assess the impact of conservation practices on post-fire recovery. We focused on a study area in northern California and southern Oregon, a region with diverse conservation practices and increased wildfire activity in the past decade. The proposed framework uses the MODIS dataset to identify fire burn events and Landsat to analyze the time series of an area-aggregated vegetation index, the Normalized Burn Ratio (NBR). Using the remote sensing framework, we confirmed our hypothesis that in areas lacking conservation protection practices, post-fire recovery is slower and more lingering. The median 5-year dNBR recovery for unprotected burn events was around 27%, compared to 37% across all other burn areas. Along with our primary goal of recovery analysis, we also examined fire severity across different conservation practices to identify moderate-to-severe fire events and to capture differences in fire characteristics for the areas under different conversation practices. This analysis revealed that unprotected areas experienced more severe fire events. We also investigated the impact of conservation practices across three dominant forest types in our study area: Dry-Mesic Conifer, Mesic Conifer, and Evergreen Forests. The disparity in post-wildfire recovery between protected and non-protected areas was most pronounced in burn areas dominated by Evergreen Forests. Using the proposed aggregated remote sensing framework, this study highlights the importance of conservation practices in wildfire recovery. This approach could provide a cost-efficient tool for assessing the effectiveness of land-management practices on wildfire recovery across the globe.

This study focuses on the period from June 26 to 29, 2023, when record‐breaking Canadian wildfires severely impacted air quality in the Midwest United States. Using the Weather Research and Forecasting Model with Chemistry (WRF‐Chem) and four biomass‐burning data sets (Fire Inventory from NCAR version 1, Fire Inventory from NCAR version 2.5, Quick Fire Emissions Data set [QFED], and Regional ABI‐VIIRS Emission), we analyzed aerosol transport from Canada to the US and assessed the model's accuracy in predicting , , and aerosol weather feedback. Model simulations were compared with ground‐based and remote sensing observations as well as field measurements from the Community Research on Climate and Urban Science (CROCUS) project. Our findings show that the movement of a low‐pressure system from the Great Lakes to the Atlantic, combined with the high‐pressure system over the Atlantic, caused the transport of aerosols from Canadian wildfires to the US. Results show WRF‐Chem significantly underestimated key atmospheric components: aerosol optical depth (AOD) by over 50%, by 65%–90% and peak concentrations by 50%–55% across four biomass burning data sets. Additionally, CO and concentrations were underpredicted. The substantial underestimation of led to an overestimation of temperature by up to 3.6C primarily due to excessive downward shortwave radiation, which resulted from the underestimation of direct aerosol effects and an increase in sensible heat flux. Among the biomass‐burning data sets, QFED produced the most accurate AOD and predictions due to improved wildfire emission estimates, leading to a 1.0 to 1.5C reduction in temperature overestimation during the daytime. These findings underscore the need for improving wildfire emission estimates for trace gases and aerosols to enhance air quality and weather feedback predictions.