We present a comprehensive analysis of the trends of stratospheric ozone in the midlatitudes and subtropics. The analysis is performed using ground‐based and space‐based measurements over the light ...detection and ranging stations for the period 1985–2012. Also, trends are estimated for the zonal mean data made from a merged satellite data set, Global OZone Chemistry And Related trace gas Data records for the Stratosphere, over 1979–2012. The linear trends in stratospheric ozone are estimated using piecewise linear trend (PWLT) functions. The ozone trends during the increasing phase of halogens (before 1997) range from −0.2 ± 0.08 to −1 ± 0.07% yr−1 in the midlatitudes and −0.2 ± 0.06 to −0.7 ± 0.05 % yr−1 in the subtropics at 15–45 km, depending on altitude. In 1997–2012, the PWLT analyses show a positive trend, significantly different from zero at the 95% confidence intervals, toward ozone recovery in the middle‐ and low‐latitude upper stratosphere (35–45 km), and the trends are about +0.5 ± 0.07% yr−1 at midlatitudes and about +0.3 ± 0.05% yr−1 at subtropical latitudes. However, negative and insignificant trends are estimated in the lower stratosphere (15–20 km) over 1997–2012 in the midlatitudes, mainly due to the dynamics, as demonstrated by the large (50–60%) contributions from the quasi‐biennial oscillation, El Niño–Southern Oscillation, and planetary wave activity to recent ozone changes. This suggests that the ozone changes are governed by the interannual variations in meteorology and dynamics of the regions; these factors will influence the recovery detection time and the behavior of the recovery path to pre‐1980 levels.
Key Points
Presents the midlatitude and subtropical ozone trends
Shows clear recovery signal in the upper stratosphere
Lower stratospheric recovery is now masked by the dynamics
Traditional validation of atmospheric profiles is based on the intercomparison of two or more data sets in predefined ranges or classes of a given observational characteristic such as latitude or ...solar zenith angle. In this study we trained a self-organising map (SOM) with a full time series of relative difference profiles of SCIAMACHY limb v5.02 and lidar ozone profiles from seven observation sites. Each individual observation characteristic was then mapped to the obtained SOM to investigate to which degree variation in this characteristic is explanatory for the variation seen in the SOM map. For the studied data sets, altitude-dependent relations for the global data set were found between the difference profiles and studied variables. From the lowest altitude studied (18 km) ascending, the most influencing factors were found to be longitude, followed by solar zenith angle and latitude, sensor age and again solar zenith angle together with the day of the year at the highest altitudes studied here (up to 45 km). After accounting for both latitude and longitude, residual partial correlations with a reduced magnitude are seen for various factors. However, (partial) correlations cannot point out which (combination) of the factors drives the observed differences between the ground-based and satellite ozone profiles as most of the factors are inter-related. Clustering into three classes showed that there are also some local dependencies, with for instance one cluster having a much stronger correlation with the sensor age (days since launch) between 36 and 42 km. The proposed SOM-based approach provides a powerful tool for the exploration of differences between data sets without being limited to a priori defined data subsets.
In this paper we evaluate the potential of ENVISAT-Medium Resolution Imaging Spectrometer (MERIS) fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes. A ...series of MERIS fused images (15 spectral bands; 25 m pixel size) is created using the linear mixing model and a Landsat Thematic Mapper (TM) image acquired over the Netherlands. First, the fused images are classified to produce a map of the eight main land-cover types of the Netherlands. Subsequently, the maps are validated using the Dutch land-cover/land-use database as a reference. Then, the fused image with the highest overall classification accuracy is selected as the best fused image. Finally, the best fused image is used to compute three vegetation indices: the normalized difference vegetation index (NDVI) and two indices specifically designed to monitor vegetation status using MERIS data: the MERIS terrestrial chlorophyll index (MTCI) and the MERIS global vegetation index (MGVI).
Results indicate that the selected data fusion approach is able to downscale MERIS data to a Landsat-like spatial resolution. The spectral information in the fused images originates fully from MERIS and is not influenced by the TM data. Classification results for the TM and for the best fused image are similar and, when comparing spectrally similar images (i.e. TM with no short-wave infrared bands), the results of the fused image outperform those of TM. With respect to the vegetation indices, a good correlation was found between the NDVI computed from TM and from the best fused image (in spite of the spectral differences between these two sensors). In addition, results show the potential of using MERIS vegetation indices computed from fused images to monitor individual fields. This is not possible using the original MERIS full resolution image. Therefore, we conclude that MERIS-TM fused images are very useful to map heterogeneous landscapes.
Upper stratospheric ozone anomalies from the satellite-borne Solar Backscatter Ultra-Violet (SBUV), Stratospheric Aerosol and Gas Experiment II (SAGE II), Halogen Occultation Experiment (HALOE), ...Global Ozone Monitoring by Occultation of Stars (GOMOS), and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instruments agree within 5% or better with ground-based data from lidars and microwave radiometers at five stations of the Network for the Detection of Atmospheric Composition Change (NDACC), from 45°S to 48°N. From 1979 until the late 1990s, all available data show a clear decline of ozone near 40 km, by 10%-15%. This decline has not continued in the last 10 years. At some sites, ozone at 40 km appears to have increased since 2000, consistent with the beginning decline of stratospheric chlorine. The phaseout of chlorofluorocarbons after the International Montreal Protocol in 1987 has been successful, and is now showing positive effects on ozone in the upper stratosphere. Temperature anomalies near 40 km altitude from European Centre for Medium Range Weather Forecast reanalyses (ERA-40), from National Centers for Environmental Prediction (NCEP) operational analyses, and from HALOE and lidar measurements show good consistency at the five stations, within about 3 K. Since about 1985, upper stratospheric temperatures have been fluctuating around a constant level at all five NDACC stations. This non-decline of upper stratospheric temperatures is a significant change from the more or less linear cooling of the upper stratosphere up until the mid-1990s, reported in previous trend assessments. It is also at odds with the almost linear 1 K per decade cooling simulated over the entire 1979-2010 period by chemistry-climate models (CCMs). The same CCM simulations, however, track the historical ozone anomalies quite well, including the change of ozone tendency in the late 1990s.
The long-term evolution of stratospheric ozone at different stations in the low and mid-latitudes is investigated. The analysis is performed by comparing the collocated profiles of ozone lidars, at ...the northern mid-latitudes (Meteorological Observatory Hohenpeißenberg, Haute-Provence Observatory, Tsukuba and Table Mountain Facility), tropics (Mauna Loa Observatory) and southern mid-latitudes (Lauder), with ozonesondes and space-borne sensors (SBUV(/2), SAGE II, HALOE, UARS MLS and Aura MLS), extracted around the stations. Relative differences are calculated to find biases and temporal drifts in the measurements. All measurement techniques show their best agreement with respect to the lidar at 20–40 km, where the differences and drifts are generally within ±5% and ±0.5% yr−1, respectively, at most stations. In addition, the stability of the long-term ozone observations (lidar, SBUV(/2), SAGE II and HALOE) is evaluated by the cross-comparison of each data set. In general, all lidars and SBUV(/2) exhibit near-zero drifts and the comparison between SAGE II and HALOE shows larger, but insignificant drifts. The RMS of the drifts of lidar and SBUV(/2) is 0.22 and 0.27% yr−1, respectively at 20–40 km. The average drifts of the long-term data sets, derived from various comparisons, are less than ±0.3% yr−1 in the 20–40 km altitude at all stations. A combined time series of the relative differences between SAGE II, HALOE and Aura MLS with respect to lidar data at six sites is constructed, to obtain long-term data sets lasting up to 27 years. The relative drifts derived from these combined data are very small, within ±0.2% yr−1.
Consistent satellite image time series are increasingly accessible to geoscientists, allowing an effective monitoring of environmental phenomena. Specifically, the use of vegetation index time series ...has pushed forward the monitoring of large-scale vegetation phenology. Most of these studies derive key phenological metrics from the Normalized Difference Vegetation Index (NDVI) time series on a per-pixel basis. This paper demonstrates an approach to analyze synoptic spatiotemporal phenological patterns over large areas, rather than per pixel. The selected approach involves data mining using a self-organizing map (SOM) and Sammon's projection. To illustrate our approach, we trained a SOM using 13 years of ten-day NDVI composites from the Système Pour l'Observation de la Terre-VEGETATION over the Kruger National Park, South Africa. This resulted in a topologically ordered set of phenological synoptic states. The Sammon's projection was then used to create a simplified representation of the trained SOM that reflects the similarities among the synoptic states. Subsequently, we depicted phenological trajectories for each vegetation season to show how phenological development changes between years. This time series data mining approach provides a holistic characterization of the main regional phenological dynamics and effectively summarizes the information present in the time series, thus facilitating further interpretation.
The validation of ozone profiles retrieved by satellite instruments through comparison with data from ground-based instruments is important to monitor the evolution of the satellite instrument, to ...assist algorithm development and to allow multi-mission trend analyses. In this study we compare ozone profiles derived from GOMOS night-time observations with measurements from lidar, microwave radiometer and balloon sonde. Collocated pairs are analysed for dependence on several geophysical and instrument observational parameters. Validation results are presented for the operational ESA level 2 data (GOMOS version 5.00) obtained during nearly seven years of observations and a comparison using a smaller dataset from the previous processor (version 4.02) is also included. The profiles obtained from dark limb measurements (solar zenith angle >107°) when the provided processing flag is properly considered match the ground-based measurements within ±2 percent over the altitude range 20 to 40 km. Outside this range, the pairs start to deviate more and there is a latitudinal dependence: in the polar region where there is a higher amount of straylight contamination, differences start to occur lower in the mesosphere than in the tropics, whereas for the lower part of the stratosphere the opposite happens: the profiles in the tropics reach less far down as the signal reduces faster because of the higher altitude at which the maximum ozone concentration is found compared to the mid and polar latitudes. Also the bias is shifting from mostly negative in the polar region to more positive in the tropics Profiles measured under "twilight" conditions are often matching the ground-based measurements very well, but care has to be taken in all cases when dealing with "straylight" contaminated profiles. For the selection criteria applied here (data within 800 km, 3 degrees in equivalent latitude, 20 h (5 h above 50 km) and a relative ozone error in the GOMOS data of 20% or less), no dependence was found on stellar magnitude, star temperature, nor the azimuth angle of the line of sight. No evidence of a temporal trend was seen either in the bias or frequency of outliers, but a comparison applying less strict data selection criteria might show differently.
The GOMOS ozone profiles have been analysed to evaluate the GOMOS ability to capture the long-term ozone evolution at mid-latitudes during the expected recovery phase of the ozone layer. Version 5 of ...the operational GOMOS ozone data has been compared with data from two of the longest ground-based instruments based on different techniques and already involved with many other previous space instrument validations. Comparisons between ground-based and GOMOS data confirm the occurrence of spurious retrievals mainly occurring since 2006. Using a selected set of data it is shown that some bad retrievals are induced by the increasing dark charge of the detectors combined with an inadequate method for its correction. This effect does not only induce a continuous bias, but is rather exhibiting a bimodal distribution including the correct profiles and the bad retrievals. For long-term analyses it is recommended filtering the data according to background light conditions and star temperature (spectrum shape). The new method of the dark charge estimate proposed to be implemented in the version 6 of the ESA algorithm seems to significantly reduce the occurrence of such effects and should allow to monitor stratospheric ozone using GOMOS data with greater confidence.
The ozone profile records of a large number of limb and occultation satellite instruments are widely used to address several key questions in ozone research. Further progress in some domains depends ...on a more detailed understanding of these data sets, especially of their long-term stability and their mutual consistency. To this end, we made a systematic assessment of fourteen limb and occultation sounders that, together, provide more than three decades of global ozone profile measurements. In particular, we considered the latest operational Level-2 records by SAGE II, SAGE III, HALOE, UARS MLS, Aura MLS, POAM II, POAM III, OSIRIS, SMR, GOMOS, MIPAS, SCIAMACHY, ACE-FTS and MAESTRO. Central to our work is a consistent and robust analysis of the comparisons against the ground-based ozonesonde and stratospheric ozone lidar networks. It allowed us to investigate, from the troposphere up to the stratopause, the following main aspects of satellite data quality: long-term stability, overall bias, and short-term variability, together with their dependence on geophysical parameters and profile representation. In addition, it permitted us to quantify the overall consistency between the ozone profilers. Generally, we found that between 20-40 km the satellite ozone measurement biases are smaller than ±5 %, the short-term variabilities are less than 5-12% and the drifts are at most ±5% decade
(or even ±3 % decade
for a few records). The agreement with ground-based data degrades somewhat towards the stratopause and especially towards the tropopause where natural variability and low ozone abundances impede a more precise analysis. In part of the stratosphere a few records deviate from the preceding general conclusions; we identified biases of 10% and more (POAM II and SCIAMACHY), markedly higher single-profile variability (SMR and SCIAMACHY), and significant long-term drifts (SCIAMACHY, OSIRIS, HALOE, and possibly GOMOS and SMR as well). Furthermore, we reflected on the repercussions of our findings for the construction, analysis and interpretation of merged data records. Most notably, the discrepancies between several recent ozone profile trend assessments can be mostly explained by instrumental drift. This clearly demonstrates the need for systematic comprehensive multi-instrument comparison analyses.
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