Within the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) there is a need for an assessment of the uncertainty in the integrated water vapour (IWV) in the atmosphere ...estimated from ground-based global navigation satellite system (GNSS) observations. All relevant error sources in GNSS-derived IWV are therefore essential to be investigated. We present two approaches, a statistical and a theoretical analysis, for the assessment of the uncertainty of the IWV. The method is valuable for all applications of GNSS IWV data in atmospheric research and weather forecast. It will be implemented to the GNSS IWV data stream for GRUAN in order to assign a specific uncertainty to each data point. In addition, specific recommendations are made to GRUAN on hardware, software, and data processing practices to minimise the IWV uncertainty. By combining the uncertainties associated with the input variables in the estimations of the IWV, we calculated the IWV uncertainties for several GRUAN sites with different weather conditions. The results show a similar relative importance of all uncertainty contributions where the uncertainties in the zenith total delay (ZTD) dominate the error budget of the IWV, contributing over 75 % of the total IWV uncertainty. The impact of the uncertainty associated with the conversion factor between the IWV and the zenith wet delay (ZWD) is proportional to the amount of water vapour and increases slightly for moist weather conditions. The GRUAN GNSS IWV uncertainty data will provide a quantified confidence to be used for the validation of other measurement techniques.
The abundance of chlorine in the Earth's atmosphere increased considerably during the 1970s to 1990s, following large emissions of anthropogenic long-lived chlorine-containing source gases, notably ...the chlorofluorocarbons. The chemical inertness of chlorofluorocarbons allows their transport and mixing throughout the troposphere on a global scale, before they reach the stratosphere where they release chlorine atoms that cause ozone depletion. The large ozone loss over Antarctica was the key observation that stimulated the definition and signing in 1987 of the Montreal Protocol, an international treaty establishing a schedule to reduce the production of the major chlorine- and bromine-containing halocarbons. Owing to its implementation, the near-surface total chlorine concentration showed a maximum in 1993, followed by a decrease of half a per cent to one per cent per year, in line with expectations. Remote-sensing data have revealed a peak in stratospheric chlorine after 1996, then a decrease of close to one per cent per year, in agreement with the surface observations of the chlorine source gases and model calculations. Here we present ground-based and satellite data that show a recent and significant increase, at the 2σ level, in hydrogen chloride (HCl), the main stratospheric chlorine reservoir, starting around 2007 in the lower stratosphere of the Northern Hemisphere, in contrast with the ongoing monotonic decrease of near-surface source gases. Using model simulations, we attribute this trend anomaly to a slowdown in the Northern Hemisphere atmospheric circulation, occurring over several consecutive years, transporting more aged air to the lower stratosphere, and characterized by a larger relative conversion of source gases to HCl. This short-term dynamical variability will also affect other stratospheric tracers and needs to be accounted for when studying the evolution of the stratospheric ozone layer.
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DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Ground-based Fourier transform infrared (FTIR) measurements of solar absorption spectra can provide ozone total columns with a precision of 2% but also independent partial column amounts in about ...four vertical layers, one in the troposphere and three in the stratosphere up to about 45km, with a precision of 5-6%. We use eight of the Network for the Detection of Atmospheric Composition Change (NDACC) stations having a long-term time series of FTIR ozone measurements to study the total and vertical ozone trends and variability, namely, Ny-Aalesund (79 degree N), Thule (77 degree N), Kiruna (68 degree N), Harestua (60 degree N), Jungfraujoch (47 degree N), Izana (28 degree N), Wollongong (34 degree S) and Lauder (45 degree S). The length of the FTIR time series varies by station but is typically from about 1995 to present. We applied to the monthly means of the ozone total and four partial columns a stepwise multiple regression model including the following proxies: solar cycle, quasi-biennial oscillation (QBO), El Nino-Southern Oscillation (ENSO), Arctic and Antarctic Oscillation (AO/AAO), tropopause pressure (TP), equivalent latitude (EL), Eliassen-Palm flux (EPF), and volume of polar stratospheric clouds (VPSC). At the Arctic stations, the trends are found mostly negative in the troposphere and lower stratosphere, very mixed in the middle stratosphere, positive in the upper stratosphere due to a large increase in the 1995-2003 period, and non-significant when considering the total columns. The trends for mid-latitude and subtropical stations are all non-significant, except at Lauder in the troposphere and upper stratosphere and at Wollongong for the total columns and the lower and middle stratospheric columns where they are found positive. At Jungfraujoch, the upper stratospheric trend is close to significance (+0.9 plus or minus 1.0% decade-1). Therefore, some signs of the onset of ozone mid-latitude recovery are observed only in the Southern Hemisphere, while a few more years seem to be needed to observe it at the northern mid-latitude station.
Projections of stratospheric ozone from a suite of chemistry-climate models (CCMs) have been analyzed. In addition to a reference simulation where anthropogenic halogenated ozone depleting substances ...(ODSs) and greenhouse gases (GHGs) vary with time, sensitivity simulations with either ODS or GHG concentrations fixed at 1960 levels were performed to disaggregate the drivers of projected ozone changes. These simulations were also used to assess the two distinct milestones of ozone returning to historical values (ozone return dates) and ozone no longer being influenced by ODSs (full ozone recovery). The date of ozone returning to historical values does not indicate complete recovery from ODSs in most cases, because GHG-induced changes accelerate or decelerate ozone changes in many regions. In the upper stratosphere where CO2-induced stratospheric cooling increases ozone, full ozone recovery is projected to not likely have occurred by 2100 even though ozone returns to its 1980 or even 1960 levels well before (~2025 and 2040, respectively). In contrast, in the tropical lower stratosphere ozone decreases continuously from 1960 to 2100 due to projected increases in tropical upwelling, while by around 2040 it is already very likely that full recovery from the effects of ODSs has occurred, although ODS concentrations are still elevated by this date. In the midlatitude lower stratosphere the evolution differs from that in the tropics, and rather than a steady decrease in ozone, first a decrease in ozone is simulated from 1960 to 2000, which is then followed by a steady increase through the 21st century. Ozone in the midlatitude lower stratosphere returns to 1980 levels by ~2045 in the Northern Hemisphere (NH) and by ~2055 in the Southern Hemisphere (SH), and full ozone recovery is likely reached by 2100 in both hemispheres. Overall, in all regions except the tropical lower stratosphere, full ozone recovery from ODSs occurs significantly later than the return of total column ozone to its 1980 level. The latest return of total column ozone is projected to occur over Antarctica (~2045–2060) whereas it is not likely that full ozone recovery is reached by the end of the 21st century in this region. Arctic total column ozone is projected to return to 1980 levels well before polar stratospheric halogen loading does so (~2025–2030 for total column ozone, cf. 2050–2070 for Cly+60×Bry) and it is likely that full recovery of total column ozone from the effects of ODSs has occurred by ~2035. In contrast to the Antarctic, by 2100 Arctic total column ozone is projected to be above 1960 levels, but not in the fixed GHG simulation, indicating that climate change plays a significant role.
The performance of 18 coupled Chemistry Climate Models (CCMs) in the Tropical Tropopause Layer (TTL) is evaluated using qualitative and quantitative diagnostics. Trends in tropopause quantities in ...the tropics and the extratropical Upper Troposphere and Lower Stratosphere (UTLS) are analyzed. A quantitative grading methodology for evaluating CCMs is extended to include variability and used to develop four different grades for tropical tropopause temperature and pressure, water vapor and ozone. Four of the 18 models and the multi‐model mean meet quantitative and qualitative standards for reproducing key processes in the TTL. Several diagnostics are performed on a subset of the models analyzing the Tropopause Inversion Layer (TIL), Lagrangian cold point and TTL transit time. Historical decreases in tropical tropopause pressure and decreases in water vapor are simulated, lending confidence to future projections. The models simulate continued decreases in tropopause pressure in the 21st century, along with ∼1K increases per century in cold point tropopause temperature and 0.5–1 ppmv per century increases in water vapor above the tropical tropopause. TTL water vapor increases below the cold point. In two models, these trends are associated with 35% increases in TTL cloud fraction. These changes indicate significant perturbations to TTL processes, specifically to deep convective heating and humidity transport. Ozone in the extratropical lowermost stratosphere has significant and hemispheric asymmetric trends. O3 is projected to increase by nearly 30% due to ozone recovery in the Southern Hemisphere (SH) and due to enhancements in the stratospheric circulation. These UTLS ozone trends may have significant effects in the TTL and the troposphere.
The impact of stratospheric ozone on the tropospheric general circulation of the Southern Hemisphere (SH) is examined with a set of chemistry‐climate models participating in the Stratospheric ...Processes and their Role in Climate (SPARC)/Chemistry‐Climate Model Validation project phase 2 (CCMVal‐2). Model integrations of both the past and future climates reveal the crucial role of stratospheric ozone in driving SH circulation change: stronger ozone depletion in late spring generally leads to greater poleward displacement and intensification of the tropospheric midlatitude jet, and greater expansion of the SH Hadley cell in the summer. These circulation changes are systematic as poleward displacement of the jet is typically accompanied by intensification of the jet and expansion of the Hadley cell. Overall results are compared with coupled models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4), and possible mechanisms are discussed. While the tropospheric circulation response appears quasi‐linearly related to stratospheric ozone changes, the quantitative response to a given forcing varies considerably from one model to another. This scatter partly results from differences in model climatology. It is shown that poleward intensification of the westerly jet is generally stronger in models whose climatological jet is biased toward lower latitudes. This result is discussed in the context of quasi‐geostrophic zonal mean dynamics.
We present a retrieval method for ammonia (NH3) total columns from ground-based Fourier transform infrared (FTIR) observations. Observations from Bremen (53.10° N, 8.85° E), Lauder (45.04° S, 169.68° ...E), Réunion (20.9° S, 55.50° E) and Jungfraujoch (46.55° N, 7.98° E) were used to illustrate the capabilities of the method. NH3 mean total columns ranging 3 orders of magnitude were obtained, with higher values at Bremen (mean of 13.47 × 1015 molecules cm−2) and lower values at Jungfraujoch (mean of 0.18 × 1015 molecules cm−2). In conditions with high surface concentrations of ammonia, as in Bremen, it is possible to retrieve information on the vertical gradient, as two layers can be distinguished. The retrieval there is most sensitive to ammonia in the planetary boundary layer, where the trace gas concentration is highest. For conditions with low concentrations, only the total column can be retrieved. Combining the systematic and random errors we have a mean total error of 26 % for all spectra measured at Bremen (number of spectra (N) = 554), 30 % for all spectra from Lauder (N = 2412), 25 % for spectra from Réunion (N = 1262) and 34 % for spectra measured at Jungfraujoch (N = 2702). The error is dominated by the systematic uncertainties in the spectroscopy parameters. Station-specific seasonal cycles were found to be consistent with known seasonal cycles of the dominant ammonia sources in the station surroundings. The developed retrieval methodology from FTIR instruments provides a new way of obtaining highly time-resolved measurements of ammonia burdens. FTIR-NH3 observations will be useful for understanding the dynamics of ammonia concentrations in the atmosphere and for satellite and model validation. It will also provide additional information to constrain the global ammonia budget.
TROPOMI (the TROPOspheric Monitoring Instrument), on board the Sentinel-5 Precursor (S5P) satellite, has been monitoring the Earth's atmosphere since October 2017 with an unprecedented horizontal ...resolution (initially 7 km2×3.5 km2, upgraded to 5.5 km2×3.5 km2 in August 2019). Monitoring air quality is one of the main objectives of TROPOMI; it obtains measurements of important pollutants such as nitrogen dioxide, carbon monoxide, and formaldehyde (HCHO). In this paper we assess the quality of the latest HCHO TROPOMI products versions 1.1.(5-7), using ground-based solar-absorption FTIR (Fourier-transform infrared) measurements of HCHO from 25 stations around the world, including high-, mid-, and low-latitude sites.
Most of these stations are part of the Network for the Detection of Atmospheric Composition Change (NDACC), and they provide a wide range of observation conditions, from very clean remote sites to those with high HCHO levels from anthropogenic or biogenic emissions. The ground-based HCHO retrieval settings have been optimized and harmonized at all the stations, ensuring a consistent validation among the sites. In this validation work, we first assess the accuracy of TROPOMI HCHO tropospheric columns using the median of the relative differences between TROPOMI and FTIR ground-based data (BIAS). The pre-launch accuracy requirements of TROPOMI HCHO are 40 %–80 %. We observe that these requirements are well reached, with the BIAS found below 80 % at all the sites and below 40 % at 20 of the 25 sites. The provided TROPOMI systematic uncertainties are well in agreement with the observed biases at most of the stations except for the highest-HCHO-level site, where it is found to be underestimated. We find that while the BIAS has no latitudinal dependence, it is dependent on the HCHO concentration levels: an overestimation (+26±5 %) of TROPOMI is observed for very low HCHO levels (<2.5×1015 molec. cm−2), while an underestimation (-30.8%±1.4 %) is found for high HCHO levels (>8.0×1015 molec. cm−2). This demonstrates the great value of such a harmonized network covering a wide range of concentration levels, the sites with high HCHO concentrations being crucial for the determination of the satellite bias in the regions of emissions and the clean sites allowing a small TROPOMI offset to be determined. The wide range of sampled HCHO levels within the network allows the robust determination of the significant constant and proportional TROPOMI HCHO biases (TROPOMI =+1.10±0.05 ×1015+0.64±0.03 × FTIR; in molecules per square centimetre). Second, the precision of TROPOMI HCHO data is estimated by the median absolute deviation (MAD) of the relative differences between TROPOMI and FTIR ground-based data. The clean sites are especially useful for minimizing a possible additional collocation error. The precision requirement of 1.2×1016 molec. cm−2 for a single pixel is reached at most of the clean sites, where it is found that the TROPOMI precision can even be 2 times better (0.5–0.8×1015 molec. cm−2 for a single pixel). However, we find that the provided TROPOMI random uncertainties may be underestimated by a factor of 1.6 (for clean sites) to 2.3 (for high HCHO levels). The correlation is very good between TROPOMI and FTIR data (R=0.88 for 3 h mean coincidences; R=0.91 for monthly means coincidences). Using about 17 months of data (from May 2018 to September 2019), we show that the TROPOMI seasonal variability is in very good agreement at all of the FTIR sites. The FTIR network demonstrates the very good quality of the TROPOMI HCHO products, which is well within the pre-launch requirements for both accuracy and precision. This paper makes suggestions for the refinement of the TROPOMI random uncertainty budget and TROPOMI quality assurance values for a better filtering of the remaining outliers.
We investigate the impact of biogenic emissions on carbon monoxide (CO) and formaldehyde (HCHO) in the Southern Hemisphere (SH), with simulations using two different biogenic emission inventories for ...isoprene and monoterpenes. Results from four atmospheric chemistry models are compared to continuous long-term ground-based CO and HCHO column measurements at the SH Network for the Detection of Atmospheric Composition Change (NDACC) sites, the satellite measurement of tropospheric CO columns from the Measurement of Pollution in the Troposphere (MOPITT), and in situ surface CO measurements from across the SH, representing a subset of the National Oceanic and Atmospheric Administration's Global Monitoring Division (NOAA GMD) network. Simulated mean model CO using the Model of Emissions of Gases and Aerosols from Nature (v2.1) computed in the frame work of the Land Community Model (CLM-MEGANv2.1) inventory is in better agreement with both column and surface observations than simulations adopting the emission inventory generated from the LPJ-GUESS dynamical vegetation model framework, which markedly underestimate measured column and surface CO at most sites. Differences in biogenic emissions cause large differences in CO in the source regions which propagate to the remote SH. Significant inter-model differences exist in modelled column and surface CO, and secondary production of CO dominates these inter-model differences, due mainly to differences in the models' oxidation schemes for volatile organic compounds, predominantly isoprene oxidation. While biogenic emissions are a significant factor in modelling SH CO, inter-model differences pose an additional challenge to constrain these emissions. Corresponding comparisons of HCHO columns at two SH mid-latitude sites reveal that all models significantly underestimate the observed values by approximately a factor of 2. There is a much smaller impact on HCHO of the significantly different biogenic emissions in remote regions, compared to the source regions. Decreased biogenic emissions cause decreased CO export to remote regions, which leads to increased OH; this in turn results in increased HCHO production through methane oxidation. In agreement with earlier studies, we corroborate that significant HCHO sources are likely missing in the models in the remote SH.
The Measurements of Pollution in the Troposphere (MOPITT) satellite instrument provides the longest continuous dataset of carbon monoxide (CO) from space. We perform the first validation of MOPITT ...version 6 retrievals using total column CO measurements from ground-based remote-sensing Fourier transform infrared spectrometers (FTSs). Validation uses data recorded at 14 stations, that span a wide range of latitudes (80° N to 78° S), in the Network for the Detection of Atmospheric Composition Change (NDACC). MOPITT measurements are spatially co-located with each station, and different vertical sensitivities between instruments are accounted for by using MOPITT averaging kernels (AKs). All three MOPITT retrieval types are analyzed: thermal infrared (TIR-only), joint thermal and near infrared (TIR–NIR), and near infrared (NIR-only). Generally, MOPITT measurements overestimate CO relative to FTS measurements, but the bias is typically less than 10 %. Mean bias is 2.4 % for TIR-only, 5.1 % for TIR–NIR, and 6.5 % for NIR-only. The TIR–NIR and NIR-only products consistently produce a larger bias and lower correlation than the TIR-only. Validation performance of MOPITT for TIR-only and TIR–NIR retrievals over land or water scenes is equivalent. The four MOPITT detector element pixels are validated separately to account for their different uncertainty characteristics. Pixel 1 produces the highest standard deviation and lowest correlation for all three MOPITT products. However, for TIR-only and TIR–NIR, the error-weighted average that includes all four pixels often provides the best correlation, indicating compensating pixel biases and well-captured error characteristics. We find that MOPITT bias does not depend on latitude but rather is influenced by the proximity to rapidly changing atmospheric CO. MOPITT bias drift has been bound geographically to within ±0.5 % yr−1 or lower at almost all locations.
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