The Montreal Protocol protects the terrestrial carbon sink.

The control of the production of ozone-depleting substances through the Montreal Protocol means that the stratospheric ozone layer is recovering1 and that consequent increases in harmful surface ultraviolet radiation are being avoided2,3. The Montreal Protocol has co-benefits for climate change mitigation, because ozone-depleting substances are potent greenhouse gases4-7. The avoided ultraviolet radiation and climate change also have co-benefits for plants and their capacity to store carbon through photosynthesis8, but this has not previously been investigated. Here, using a modelling framework that couples ozone depletion, climate change, damage to plants by ultraviolet radiation and the carbon cycle, we explore the benefits of avoided increases in ultraviolet radiation and changes in climate on the terrestrial biosphere and its capacity as a carbon sink. Considering a range of strengths for the effect of ultraviolet radiation on plant growth8-12, we estimate that there could have been 325-690 billion tonnes less carbon held in plants and soils by the end of this century (2080-2099) without the Montreal Protocol (as compared to climate projections with controls on ozone-depleting substances). This change could have resulted in an additional 115-235 parts per million of atmospheric carbon dioxide, which might have led to additional warming of global-mean surface temperature by 0.50-1.0 degrees. Our findings suggest that the Montreal Protocol may also be helping to mitigate climate change through avoided decreases in the land carbon sink.

Mapping hydroxyl variability throughout the global remote troposphere via synthesis of airborne and satellite formaldehyde observations.

The hydroxyl radical (OH) fuels tropospheric ozone production and governs the lifetime of methane and many other gases. Existing methods to quantify global OH are limited to annual and global-to-hemispheric averages. Finer resolution is essential for isolating model deficiencies and building process-level understanding. In situ observations from the Atmospheric Tomography (ATom) mission demonstrate that remote tropospheric OH is tightly coupled to the production and loss of formaldehyde (HCHO), a major hydrocarbon oxidation product. Synthesis of this relationship with satellite-based HCHO retrievals and model-derived HCHO loss frequencies yields a map of total-column OH abundance throughout the remote troposphere (up to 70% of tropospheric mass) over the first two ATom missions (August 2016 and February 2017). This dataset offers unique insights on near-global oxidizing capacity. OH exhibits significant seasonality within individual hemispheres, but the domain mean concentration is nearly identical for both seasons (1.03 ± 0.25 × 106 cm-3), and the biseasonal average North/South Hemisphere ratio is 0.89 ± 0.06, consistent with a balance of OH sources and sinks across the remote troposphere. Regional phenomena are also highlighted, such as a 10-fold OH depression in the Tropical West Pacific and enhancements in the East Pacific and South Atlantic. This method is complementary to budget-based global OH constraints and can help elucidate the spatial and temporal variability of OH production and methane loss.

Evaluation of NASA's high-resolution global composition simulations: Understanding a pollution event in the Chesapeake Bay during the 2017 OWLETS campaign.

Recirculation of pollutants due to a bay breeze effect is a key meteorological mechanism impacting air quality near urban coastal areas, but regional and global chemical transport models have historically struggled to capture this phenomenon. We present a case study of a high ozone (O3) episode observed over the Chesapeake Bay during the NASA Ozone Water-Land Environmental Transition Study (OWLETS) in summer 2017. OWLETS included a complementary suite of ground-based and airborne observations, with which we characterize the meteorological and chemical context of this event and develop a framework to evaluate model performance. Two publicly-available NASA global high-resolution coupled chemistry-meteorology models (CCMMs) are investigated: GEOS-CF and MERRA2-GMI. The GEOS-CF R2 value for comparisons between the NASA Sherpa C-23 aircraft measurements to the GEOS-CF resulted in good agreement (R2: 0.67) on July 19th and fair agreement (R2: 0.55) for July 20th. Compared to surface observations, we find the GEOS-CF product with a 25 x 25 km2 grid box, at an hourly (R2: 0.62 to 0.87) and 15-minute (R2: 0.64 to 0.87) interval for six regional sites outperforms the hourly nominally 50 x 50 km2 gridded MERRA2-GMI (R2: 0.53 to 0.76) for four of the six sites, suggesting it is better capable of simulating complex chemical and meteorological features associated with ozone transport within the Chesapeake Bay airshed. When the GEOS-CF product was compared to the TOLNet LiDAR observations at both NASA Langley Research Center (LaRC) and the Chesapeake Bay Bridge Tunnel (CBBT), the median differences at LaRC were -6 to 8% and at CBBT were ± 7% between 400 to 2000 m ASL. This indicates that, for this case study, the GEOS-CF is able to simulate surface level ozone diurnal cycles and vertical ozone profiles at small scales between the surface level and 2000 m ASL. Evaluating global chemical model simulations at sub-regional scales will help air quality scientists understand the complex processes occurring at small spatial and temporal scales within complex surface terrain changes, simulating nighttime chemistry and deposition, and the potential to use global chemical transport simulations in support of regional and sub-regional field campaigns.

Recent decline in extratropical lower stratospheric ozone attributed to circulation changes.

1998-2016 ozone trends in the lower stratosphere (LS) are examined using the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) and related NASA products. After removing biases resulting from step-changes in the MERRA-2 ozone observations, a discernible negative trend of -1.67±0.54 Dobson units per decade (DU/decade) is found in the 10-km layer above the tropopause between 20°N and 60°N. A weaker but statistically significant trend of -1.17±0.33 DU/decade exists between 50°S and 20°S. In the Tropics, a positive trend is seen in a 5-km layer above the tropopause. Analysis of an idealized tracer in a model simulation constrained by MERRA-2 meteorological fields provides strong evidence that these trends are driven by enhanced isentropic transport between the tropical (20°S-20°N) and extratropical LS in the past two decades. This is the first time that a reanalysis dataset has been used to detect and attribute trends in lower stratospheric ozone.

Stratospheric variability contributed to and sustained the recent hiatus in Eurasian winter warming.

The recent hiatus in global-mean surface temperature warming was characterized by a Eurasian winter cooling trend, and the cause(s) for this cooling is unclear. Here we show that the observed hiatus in Eurasian warming was associated with a recent trend toward weakened stratospheric polar vortices. Specifically, by calculating the change in Eurasian surface air temperature associated with a given vortex weakening, we demonstrate that the recent trend toward weakened polar vortices reduced the anticipated Eurasian warming due to increasing greenhouse gas concentrations. Those model integrations whose stratospheric vortex evolution most closely matches that in reanalysis data also simulate a hiatus. While it is unclear whether the recent weakening of the midwinter stratospheric polar vortex was forced, a properly configured model can simulate substantial deviations of the polar vortex on decadal timescales and hence such hiatus events, implying that similar hiatus events may recur even as greenhouse gas concentrations rise.

The Effect of Representing Bromine from VSLS on the Simulation and Evolution of Antarctic Ozone.

We use the Goddard Earth Observing System Chemistry-Climate Model (GEOSCCM), a contributor to both the 2010 and 2014 WMO Ozone Assessment Reports, to show that inclusion of 5 parts per trillion (ppt) of stratospheric bromine (Bry) from very short-lived substances (VSLS) is responsible for about a decade delay in ozone hole recovery. These results partially explain the significantly later recovery of Antarctic ozone noted in the 2014 report, as bromine from VSLS was not included in the 2010 Assessment. We show multiple lines of evidence that simulations that account for VSLS Bry are in better agreement with both total column BrO and the seasonal evolution of Antarctic ozone reported by the Ozone Monitoring Instrument (OMI) on NASA's Aura satellite. In addition, the near zero ozone levels observed in the deep Antarctic lower stratospheric polar vortex are only reproduced in a simulation that includes this Bry source from VSLS.

Aircraft observations since the 1990s reveal increases of tropospheric ozone at multiple locations across the Northern Hemisphere.

Tropospheric ozone is an important greenhouse gas, is detrimental to human health and crop and ecosystem productivity, and controls the oxidizing capacity of the troposphere. Because of its high spatial and temporal variability and limited observations, quantifying net tropospheric ozone changes across the Northern Hemisphere on time scales of two decades had not been possible. Here, we show, using newly available observations from an extensive commercial aircraft monitoring network, that tropospheric ozone has increased above 11 regions of the Northern Hemisphere since the mid-1990s, consistent with the OMI/MLS satellite product. The net result of shifting anthropogenic ozone precursor emissions has led to an increase of ozone and its radiative forcing above all 11 study regions of the Northern Hemisphere, despite NO x emission reductions at midlatitudes.

Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke.

INTRODUCTION: Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use. METHODS: Using ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008-2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model. RESULTS: Ensemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 µg/m3 (R2: 0.94) and test RMSE of 2.29 µg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 µg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers. CONCLUSION: Our method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies.

The salience of nonlinearities in the boreal winter response to ENSO: Arctic stratosphere and Europe.

The Arctic stratospheric response to El Niño (EN) and La Niña (LN) is evaluated in a 41 member ensemble of the period 1980 to 2009 in the Goddard Earth Observing System Chemistry-Climate Model. We consider whether the responses to EN and LN are equal in magnitude and opposite in sign, whether the responses to moderate and extreme events are proportionate, and if the response depends on whether sea surface temperature anomalies (SSTs) peak in the Eastern Pacific (EP) or Central Pacific (CP). There is no indication of any nonlinearities between EN and LN, though in ~ 15% of the ensemble members the stratospheric sudden warming (SSW) frequencies for EN and LN are similar, suggesting that a similar SSW frequency for EN and LN, as has occurred over the past ~ 60 years, can occur by chance. The response to extreme EN events is not proportionate to the amplitude of the underlying SST anomalies in spring. EP EN events preferentially increase zonal wavenumber 1 and decrease zonal wavenumber 2 as compared to CP EN events, however the zonal-mean Arctic stratospheric and subpolar surface response is generally little different between EP EN and CP EN once one accounts for the relative weakness of CP events. These differences between EP and CP events and between moderate and extreme EN events only emerge if at least 25 events are composited, however, due to the small signal-to-noise ratio, and hence these differences may be of little practical benefit.

The Downward Influence of Sudden Stratospheric Warmings: Association with Tropospheric Precursors.

Tropospheric features preceding sudden stratospheric warming events (SSWs) are identified using a large compendium of events obtained from a chemistry-climate model. In agreement with recent observational studies, it is found that approximately one-third of SSWs are preceded by extreme episodes of wave activity in the lower troposphere. The relationship becomes stronger in the lower stratosphere, where ~60% of SSWs are preceded by extreme wave activity at 100 hPa. Additional analysis characterizes events that do or do not appear to subsequently impact the troposphere, referred to as downward and non-downward propagating SSWs, respectively. On average, tropospheric wave activity is larger preceding downward-propagating SSWs compared to non-downward propagating events, and associated in particular with a doubly strengthened Siberian high. Of the SSWs that were preceded by extreme lower-tropospheric wave activity, ~2/3 propagated down to the troposphere, and hence the presence of extreme lower-tropospheric wave activity can only be used probabilistically to predict a slight increase or decrease at the onset, of the likelihood of tropospheric impacts to follow. However, a large number of downward and non-downward propagating SSWs must be considered (>35), before the difference becomes statistically significant. The precursors are also robust upon comparison with composites consisting of randomly selected tropospheric northern annular mode (NAM) events. The downward influence and precursors to split and displacement events are also examined. It is found that anomalous upward wave-1 fluxes precede both cases. Splits exhibit a near instantaneous, barotropic response in the stratosphere and troposphere, while displacements have a stronger long-term influence.

Ultraviolet radiation modelling using output from the Chemistry Climate Model Initiative.

We have derived values of the Ultraviolet Index (UVI) at solar noon using the Tropospheric Ultraviolet Model (TUV) driven by ozone, temperature and aerosol fields from climate simulations of the first phase of the Chemistry-Climate Model Initiative (CCMI-1). Since clouds remain one of the largest uncertainties in climate projections, we simulated only the clear-sky UVI. We compared the modelled UVI climatologies against present-day climatological values of UVI derived from both satellite data (the OMI-Aura OMUVBd product) and ground-based measurements (from the NDACC network). Depending on the region, relative differences between the UVI obtained from CCMI/TUV calculations and the ground-based measurements ranged between -5.9% and 10.6%. We then calculated the UVI evolution throughout the 21st century for the four Representative Concentration Pathways (RCPs 2.6, 4.5, 6.0 and 8.5). Compared to 1960s values, we found an average increase in the UVI in 2100 (of 2-4%) in the tropical belt (30°N-30°S). For the mid-latitudes, we observed a 1.8 to 3.4 % increase in the Southern Hemisphere for RCP 2.6, 4.5 and 6.0, and found a 2.3% decrease in RCP 8.5. Higher increases in UVI are projected in the Northern Hemisphere except for RCP 8.5. At high latitudes, ozone recovery is well identified and induces a complete return of mean UVI levels to 1960 values for RCP 8.5 in the Southern Hemisphere. In the Northern Hemisphere, UVI levels in 2100 are higher by 0.5 to 5.5% for RCP 2.6, 4.5 and 6.0 and they are lower by 7.9% for RCP 8.5. We analysed the impacts of greenhouse gases (GHGs) and ozone-depleting substances (ODSs) on UVI from 1960 by comparing CCMI sensitivity simulations (1960-2100) with fixed GHGs or ODSs at their respective 1960 levels. As expected with ODS fixed at their 1960 levels, there is no large decrease in ozone levels and consequently no sudden increase in UVI levels. With fixed GHG, we observed a delayed return of ozone to 1960 values, with a corresponding pattern of change observed on UVI, and looking at the UVI difference between 2090s values and 1960s values, we found an 8 % increase in the tropical belt during the summer of each hemisphere. Finally we show that, while in the Southern Hemisphere the UVI is mainly driven by total ozone column, in the Northern Hemisphere both total ozone column and aerosol optical depth drive UVI levels, with aerosol optical depth having twice as much influence on the UVI as total ozone column does.

Nonlinear response of tropical lower stratospheric temperature and water vapor to ENSO.

A series of simulations using the NASA Goddard Earth Observing System Chemistry-Climate Model are analyzed in order to assess interannual and sub-decadal variability in the tropical lower stratosphere over the past 35 years. The impact of El Niño-Southern Oscillation on temperature and water vapor in this region is nonlinear in boreal spring. While moderate El Niño events lead to cooling in this region, strong El Niño events lead to warming, even as the response of the large scale Brewer Dobson Circulation appears to scale nearly linearly with El Niño. This nonlinearity is shown to arise from the response in the Indo-West Pacific to El Niño: strong El Niño events lead to tropospheric warming extending into the tropical tropopause layer and up to the cold point in this region, where it allows for more water vapor to enter the stratosphere. The net effect is that both strong La Niña and strong El Niño events lead to enhanced entry water vapor and stratospheric moistening in boreal spring and early summer. These results lead to the following interpretation of the contribution of sea surface temperatures to the decline in water vapor from the late 1990s to the early 2000s: the very strong El Niño event in 1997/1998, followed by more than two consecutive years of La Niña, led to enhanced lower stratospheric water vapor. As this period ended in early 2001, entry water vapor concentrations declined. This effect accounts for approximately one-quarter of the observed drop.

Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry-climate model.

Previous multi-model intercomparisons have shown that chemistry-climate models exhibit significant biases in tropospheric ozone compared with observations. We investigate annual-mean tropospheric column ozone in 15 models participating in the SPARC/IGAC (Stratosphere-troposphere Processes and their Role in Climate/International Global Atmospheric Chemistry) Chemistry-Climate Model Initiative (CCMI). These models exhibit a positive bias, on average, of up to 40-50% in the Northern Hemisphere compared with observations derived from the Ozone Monitoring Instrument and Microwave Limb Sounder (OMI/MLS), and a negative bias of up to ~30% in the Southern Hemisphere. SOCOLv3.0 (version 3 of the Solar-Climate Ozone Links CCM), which participated in CCMI, simulates global-mean tropospheric ozone columns of 40.2 DU - approximately 33% larger than the CCMI multi-model mean. Here we introduce an updated version of SOCOLv3.0, "SOCOLv3.1", which includes an improved treatment of ozone sink processes, and results in a reduction in the tropospheric column ozone bias of up to 8 DU, mostly due to the inclusion of N2O5 hydrolysis on tropospheric aerosols. As a result of these developments, tropospheric column ozone amounts simulated by SOCOLv3.1 are comparable with several other CCMI models. We apply Gaussian process emulation and sensitivity analysis to understand the remaining ozone bias in SOCOLv3.1. This shows that ozone precursors (nitrogen oxides (NOx), carbon monoxide, methane and other volatile organic compounds) are responsible for more than 90% of the variance in tropospheric ozone. However, it may not be the emissions inventories themselves that result in the bias, but how the emissions are handled in SOCOLv3.1, and we discuss this in the wider context of the other CCMI models. Given that the emissions data set to be used for phase 6 of the Coupled Model Intercomparison Project includes approximately 20% more NOx than the data set used for CCMI, further work is urgently needed to address the challenges of simulating sub-grid processes of importance to tropospheric ozone in the current generation of chemistry-climate models.

No Robust Evidence of Future Changes in Major Stratospheric Sudden Warmings: A Multi-model Assessment from CCMI.

Major stratospheric sudden warmings (SSWs) are the largest instance of wintertime variability in the Arctic stratosphere. Due to their relevance for the troposphere-stratosphere system, several previous studies have focused on their potential response to anthropogenic forcings. However, a wide range of results have been reported, from a future increase in the frequency of SSWs to a decrease. Several factors might explain these contradictory results, notably the use of different metrics for the identification of SSWs, and the impact of large climatological biases in single-model studies. Here we revisit the question of future SSWs changes, using an identical set of metrics applied consistently across 12 different models participating in the Chemistry Climate Model Initiative. From analyzing future integrations we find no statistically significant change in the frequency of SSWs over the 21st century, irrespective of the metric used for the identification of SSWs. Changes in other SSWs characteristics, such as their duration and the tropospheric forcing, are also assessed: again, we find no evidence of future changes over the 21st century.

A Cloud-Ozone Data Product from Aura OMI and MLS Satellite Measurements.

Ozone within deep convective clouds is controlled by several factors involving photochemical reactions and transport. Gas-phase photochemical reactions and heterogeneous surface chemical reactions involving ice, water particles, and aerosols inside the clouds all contribute to the distribution and net production and loss of ozone. Ozone in clouds is also dependent on convective transport that carries low troposphere/boundary layer ozone and ozone precursors upward into the clouds. Characterizing ozone in thick clouds is an important step for quantifying relationships of ozone with tropospheric H2O, OH production, and cloud microphysics/transport properties. Although measuring ozone in deep convective clouds from either aircraft or balloon ozonesondes is largely impossible due to extreme meteorological conditions associated with these clouds, it is possible to estimate ozone in thick clouds using backscattered solar UV radiation measured by satellite instruments. Our study combines Aura Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) satellite measurements to generate a new research product of monthly-mean ozone concentrations in deep convective clouds between 30°S to 30°N for October 2004 - April 2016. These measurements represent mean ozone concentration primarily in the upper levels of thick clouds and reveal key features of cloud ozone including: persistent low ozone concentrations in the tropical Pacific of ~10 ppbv or less; concentrations of up to 60 pphv or greater over landmass regions of South America, southern Africa, Australia, and India/east Asia; connections with tropical ENSO events; and intra-seasonal/Madden-Julian Oscillation variability. Analysis of OMI aerosol measurements suggests a cause and effect relation between boundary layer pollution and elevated ozone inside thick clouds over land-mass regions including southern Africa and India/east Asia.

Chemical Mechanisms and Their Applications in the Goddard Earth Observing System (GEOS) Earth System Model.

NASA's Goddard Earth Observing System (GEOS) Earth System Model (ESM) is a modular, general circulation model (GCM), and data assimilation system (DAS) that is used to simulate and study the coupled dynamics, physics, chemistry, and biology of our planet. GEOS is developed by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. It generates near-real-time analyzed data products, reanalyses, and weather and seasonal forecasts to support research targeted to understanding interactions among Earth System processes. For chemistry, our efforts are focused on ozone and its influence on the state of the atmosphere and oceans, and on trace gas data assimilation and global forecasting at mesoscale discretization. Several chemistry and aerosol modules are coupled to the GCM, which enables GEOS to address topics pertinent to NASA's Earth Science Mission. This paper describes the atmospheric chemistry components of GEOS and provides an overview of its Earth System Modeling Framework (ESMF)-based software infrastructure, which promotes a rich spectrum of feedbacks that influence circulation and climate, and impact human and ecosystem health. We detail how GEOS allows model users to select chemical mechanisms and emission scenarios at run time, establish the extent to which the aerosol and chemical components communicate, and decide whether either or both influence the radiative transfer calculations. A variety of resolutions facilitates research on spatial and temporal scales relevant to problems ranging from hourly changes in air quality to trace gas trends in a changing climate. Samples of recent GEOS chemistry applications are provided.

Formaldehyde in the Tropical Western Pacific: Chemical sources and sinks, convective transport, and representation in CAM-Chem and the CCMI models.

Formaldehyde (HCHO) directly affects the atmospheric oxidative capacity through its effects on HOx. In remote marine environments, such as the Tropical Western Pacific (TWP), it is particularly important to understand the processes controlling the abundance of HCHO because model output from these regions is used to correct satellite retrievals of HCHO. Here, we have used observations from the CONTRAST field campaign, conducted during January and February 2014, to evaluate our understanding of the processes controlling the distribution of HCHO in the TWP as well as its representation in chemical transport/climate models. Observed HCHO mixing ratios varied from ~500 pptv near the surface to ~75 pptv in the upper troposphere. Recent convective transport of near surface HCHO and its precursors, acetaldehyde and possibly methyl hydroperoxide, increased upper tropospheric HCHO mixing ratios by ~33% (22 pptv); this air contained roughly 60% less NO than more aged air. Output from the CAM-Chem chemistry transport model (2014 meteorology) as well as nine chemistry climate models from the Chemistry-Climate Model Initiative (free-running meteorology) are found to uniformly underestimate HCHO columns derived from in situ observations by between 4 and 50%. This underestimate of HCHO likely results from a near factor of two underestimate of NO in most models, which strongly suggests errors in NOx emissions inventories and/or in the model chemical mechanisms. Likewise, the lack of oceanic acetaldehyde emissions and potential errors in the model acetaldehyde chemistry lead to additional underestimates in modeled HCHO of up to 75 pptv (~15%) in the lower troposphere.

Measuring and modeling the lifetime of nitrous oxide including its variability.

Nitrous oxide lifetime is computed empirically from MLS satellite dataEmpirical N2O lifetimes compared with models including interannual variabilityResults improve values for present anthropogenic and preindustrial emissions.