scholarly journals A method for merging nadir-sounding climate records, with an application to the global-mean stratospheric temperature data sets from SSU and AMSU

2015 ◽  
Vol 15 (16) ◽  
pp. 9271-9284 ◽  
Author(s):  
C. McLandress ◽  
T. G. Shepherd ◽  
A. I. Jonsson ◽  
T. von Clarmann ◽  
B. Funke
Keyword(s):  
Michelson Interferometer ◽  
Data Sets ◽  
Climate Data ◽  
Data Set ◽  
Linear Temperature ◽  
Temperature Changes ◽  
Stratospheric Temperature ◽  
Temperature Trends ◽  
Microwave Sounding ◽  
Global Mean

Abstract. A method is proposed for merging different nadir-sounding climate data records using measurements from high-resolution limb sounders to provide a transfer function between the different nadir measurements. The two nadir-sounding records need not be overlapping so long as the limb-sounding record bridges between them. The method is applied to global-mean stratospheric temperatures from the NOAA Climate Data Records based on the Stratospheric Sounding Unit (SSU) and the Advanced Microwave Sounding Unit-A (AMSU), extending the SSU record forward in time to yield a continuous data set from 1979 to present, and providing a simple framework for extending the SSU record into the future using AMSU. SSU and AMSU are bridged using temperature measurements from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), which is of high enough vertical resolution to accurately represent the weighting functions of both SSU and AMSU. For this application, a purely statistical approach is not viable since the different nadir channels are not sufficiently linearly independent, statistically speaking. The near-global-mean linear temperature trends for extended SSU for 1980–2012 are −0.63 ± 0.13, −0.71 ± 0.15 and −0.80 ± 0.17 K decade−1 (95 % confidence) for channels 1, 2 and 3, respectively. The extended SSU temperature changes are in good agreement with those from the Microwave Limb Sounder (MLS) on the Aura satellite, with both exhibiting a cooling trend of ~ 0.6 ± 0.3 K decade−1 in the upper stratosphere from 2004 to 2012. The extended SSU record is found to be in agreement with high-top coupled atmosphere–ocean models over the 1980–2012 period, including the continued cooling over the first decade of the 21st century.

scholarly journals A method for merging nadir-sounding climate records, with an application to the global-mean stratospheric temperature data sets from SSU and AMSU

2015 ◽  
Vol 15 (7) ◽  
pp. 10085-10122 ◽  
Author(s):  
C. McLandress ◽  
T. G. Shepherd ◽  
A. I. Jonsson ◽  
T. von Clarmann ◽  
B. Funke
Keyword(s):  
Climate Model ◽  
Michelson Interferometer ◽  
Data Sets ◽  
Climate Data ◽  
Data Set ◽  
Stratospheric Temperature ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Climate Model Simulations ◽  
Global Mean

Abstract. A method is proposed for merging different nadir-sounding climate data records using measurements from high resolution limb sounders to provide a transfer function between the different nadir measurements. The nadir-sounding records need not be overlapping so long as the limb-sounding record bridges between them. The method is applied to global mean stratospheric temperatures from the NOAA Climate Data Records based on the Stratospheric Sounding Unit (SSU) and the Advanced Microwave Sounding Unit-A (AMSU), extending the SSU record forward in time to yield a continuous data set from 1979 to present. SSU and AMSU are bridged using temperature measurements from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), which is of high enough vertical resolution to accurately represent the weighting functions of both SSU and AMSU. For this application, a purely statistical approach is not viable since the different nadir channels are not sufficiently linearly independent, statistically speaking. The extended SSU global-mean data set is in good agreement with temperatures from the Microwave Limb Sounder (MLS) on the Aura satellite, with both exhibiting a cooling trend of ~ 0.6 ± 0.3 K decade−1 in the upper stratosphere from 2004–2012. The extended SSU data set also compares well with chemistry-climate model simulations over its entire record, including the contrast between the weak cooling seen over 1995–2004 compared with the large cooling seen in the period 1986–1995 of strong ozone depletion.

scholarly journals The Stratospheric Changes Inferred from 10 Years of AIRS and AMSU-A Radiances

2017 ◽  
Vol 30 (15) ◽  
pp. 6005-6016 ◽  
Author(s):  
Fang Pan ◽  
Xianglei Huang ◽  
Stephen S. Leroy ◽  
Pu Lin ◽  
L. Larrabee Strow ◽  
...  
Keyword(s):  
Cooling Rate ◽  
Lower Stratosphere ◽  
Natural Variability ◽  
Atmospheric Infrared Sounder ◽  
Stratospheric Temperature ◽  
Temperature Trends ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Microwave Radiances ◽  
Global Mean

Global-mean radiances observed by the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit A (AMSU-A) are analyzed from 2003 to 2012. The focus of this study is on channels sensitive to emission and absorption in the stratosphere. Optimal fingerprinting is used to obtain estimates of changes of stratospheric temperature in five vertical layers due to external forcing in the presence of natural variability. Natural variability is estimated using synthetic radiances based on the 500-yr GFDL CM3 and 240-yr HadGEM2-CC control runs. The results show a cooling rate of 0.65 ± 0.11 (2 σ) K decade−1 in the upper stratosphere above 6 hPa, approximately 0.46 ± 0.24 K decade−1 in two midstratospheric layers between 6 and 30 hPa, and 0.39 ± 0.32 K decade−1 in the lower stratosphere (30–60 hPa). The cooling rate in the lowest part of the stratosphere (60–100 hPa) is −0.014 ± 0.22 K decade−1, which is smallest among all five layers and statistically insignificant. The synergistic use of well-calibrated passive infrared and microwave radiances permits disambiguation of trends of carbon dioxide and stratospheric temperature, increases vertical resolution of detected stratospheric temperature trends, and effectively reduces uncertainties of estimated temperature trends.

scholarly journals Seasonal stratospheric ozone trends over 2000–2018 derived from several merged data sets

2020 ◽  
Vol 20 (11) ◽  
pp. 7035-7047 ◽  
Author(s):  
Monika E. Szeląg ◽  
Viktoria F. Sofieva ◽  
Doug Degenstein ◽  
Chris Roth ◽  
Sean Davis ◽  
...  
Keyword(s):  
Stratospheric Ozone ◽  
Equatorial Region ◽  
Data Sets ◽  
Opposite Pattern ◽  
The North ◽  
Stratospheric Temperature ◽  
Temperature Trends ◽  
Ozone Trends ◽  
Seasonal Dependence

Abstract. In this work, we analyze the seasonal dependence of ozone trends in the stratosphere using four long-term merged data sets, SAGE-CCI-OMPS, SAGE-OSIRIS-OMPS, GOZCARDS, and SWOOSH, which provide more than 30 years of monthly zonal mean ozone profiles in the stratosphere. We focus here on trends between 2000 and 2018. All data sets show similar results, although some discrepancies are observed. In the upper stratosphere, the trends are positive throughout all seasons and the majority of latitudes. The largest upper-stratospheric ozone trends are observed during local winter (up to 6 % per decade) and equinox (up to 3 % per decade) at mid-latitudes. In the equatorial region, we find a very strong seasonal dependence of ozone trends at all altitudes: the trends vary from positive to negative, with the sign of transition depending on altitude and season. The trends are negative in the upper-stratospheric winter (−1 % per decade to −2 % per decade) and in the lower-stratospheric spring (−2 % per decade to −4 % per decade), but positive (2 % per decade to 3 % per decade) at 30–35 km in spring, while the opposite pattern is observed in summer. The tropical trends below 25 km are negative and maximize during summer (up to −2 % per decade) and spring (up to −3 % per decade). In the lower mid-latitude stratosphere, our analysis points to a hemispheric asymmetry: during local summers and equinoxes, positive trends are observed in the south (+1 % per decade to +2 % per decade), while negative trends are observed in the north (−1 % per decade to −2 % per decade). We compare the seasonal dependence of ozone trends with available analyses of the seasonal dependence of stratospheric temperature trends. We find that ozone and temperature trends show positive correlation in the dynamically controlled lower stratosphere and negative correlation above 30 km, where photochemistry dominates. Seasonal trend analysis gives information beyond that contained in annual mean trends, which can be helpful in order to better understand the role of dynamical variability in short-term trends and future ozone recovery predictions.

scholarly journals A reassessment of the discrepancies in the annual variation of <i>δ</i>D-H<sub>2</sub>O in the tropical lower stratosphere between the MIPAS and ACE-FTS satellite data sets

2020 ◽  
Vol 13 (1) ◽  
pp. 287-308
Author(s):  
Stefan Lossow ◽  
Charlotta Högberg ◽  
Farahnaz Khosrawi ◽  
Gabriele P. Stiller ◽  
Ralf Bauer ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Annual Variation ◽  
Lower Stratosphere ◽  
Michelson Interferometer ◽  
Tape Recorder ◽  
Data Sets ◽  
Altitude Effect ◽  
Data Set ◽  
Tropical Tropopause ◽  
Tropopause Region

Abstract. The annual variation of δD in the tropical lower stratosphere is a critical indicator for the relative importance of different processes contributing to the transport of water vapour through the cold tropical tropopause region into the stratosphere. Distinct observational discrepancies of the δD annual variation were visible in the works of Steinwagner et al. (2010) and Randel et al. (2012). Steinwagner et al. (2010) analysed MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) observations retrieved with the IMK/IAA (Institut für Meteorologie und Klimaforschung in Karlsruhe, Germany, in collaboration with the Instituto de Astrofísica de Andalucía in Granada, Spain) processor, while Randel et al. (2012) focused on ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. Here we reassess the discrepancies based on newer MIPAS (IMK/IAA) and ACE-FTS data sets, also showing for completeness results from SMR (Sub-Millimetre Radiometer) observations and a ECHAM/MESSy (European Centre for Medium-Range Weather Forecasts Hamburg and Modular Earth Submodel System) Atmospheric Chemistry (EMAC) simulation (Eichinger et al., 2015b). Similar to the old analyses, the MIPAS data set yields a pronounced annual variation (maximum about 75 ‰), while that derived from the ACE-FTS data set is rather weak (maximum about 25 ‰). While all data sets exhibit the phase progression typical for the tape recorder, the annual maximum in the ACE-FTS data set precedes that in the MIPAS data set by 2 to 3 months. We critically consider several possible reasons for the observed discrepancies, focusing primarily on the MIPAS data set. We show that the δD annual variation in the MIPAS data up to an altitude of 40 hPa is substantially impacted by a “start altitude effect”, i.e. dependency between the lowermost altitude where MIPAS retrievals are possible and retrieved data at higher altitudes. In itself this effect does not explain the differences with the ACE-FTS data. In addition, there is a mismatch in the vertical resolution of the MIPAS HDO and H2O data (being consistently better for HDO), which actually results in an artificial tape-recorder-like signal in δD. Considering these MIPAS characteristics largely removes any discrepancies between the MIPAS and ACE-FTS data sets and shows that the MIPAS data are consistent with a δD tape recorder signal with an amplitude of about 25 ‰ in the lowermost stratosphere.

scholarly journals The SPARC water vapour assessment II: Profile-to-profile and climatological comparisons of stratospheric <i>δ</i>D(H<sub>2</sub>O) observations from satellite

2018 ◽  
Author(s):  
Charlotta Högberg ◽  
Stefan Lossow ◽  
Ralf Bauer ◽  
Kaley A. Walker ◽  
Patrick Eriksson ◽  
...  
Keyword(s):  
Water Vapour ◽  
Atmospheric Chemistry ◽  
Lower Stratosphere ◽  
Isotopic Ratio ◽  
Michelson Interferometer ◽  
Data Sets ◽  
Data Set ◽  
Spectroscopic Database ◽  
Temporal And Spatial ◽  
Chemistry Experiment

Abstract. Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), we have evaluated five data sets of δD(H2O) obtained from observations of Odin/SMR (Sub-Millimetre Radiometer), Envisat/MIPAS (Environmental Satellite/Michelson Interferometer for Passive Atmospheric Sounding) and SCISAT/ACE-FTS (Science Satellite/Atmospheric Chemistry Experiment-Fourier Transform Spectrometer) using profile-to-profile and climatological comparisons. Our focus is on stratospheric altitudes, but results from the upper troposphere to the lower mesosphere are provided. There are clear quantitative differences in the measurements of the isotopic ratio, which primarily concerns the comparisons to the SMR data set. In the lower stratosphere, this data set shows a higher depletion than the MIPAS and ACE-FTS data sets. The differences maximise close to 50 hPa and exceed 200 per mille. With increasing altitude, the biases typically decrease. Above 4 hPa, the SMR data set shows a lower depletion than the MIPAS data sets, on occasion exceeding 100 per mille. Overall, the δD biases of the SMR data set are driven by HDO biases in the lower stratosphere and by H2O biases in the upper stratosphere and lower mesosphere. In between, in the middle stratosphere, the biases in δD are a combination of deviations in both HDO and H2O. These biases are attributed to issues with the calibration, in particular in terms of the sideband filtering for H2O, and uncertainties in spectroscopic parameters. The MIPAS and ACE-FTS data sets agree rather well between about 100 hPa and 10 hPa. The MIPAS data sets show less depletion below about 15 hPa (up to about 30 per mille), due to differences in both HDO and H2O. Higher up the picture is reversed, and towards the upper stratosphere the biases typically increase. This is driven by increasing biases in H2O and on occasion the differences in δD exceed 80 per mille. Below 100 hPa, the differences between the MIPAS and ACE-FTS data sets are even larger. In the climatological comparisons, the MIPAS data sets continue to show less depletion than the ACE-FTS data sets below 15 hPa during all seasons, with some variations in magnitude. The differences between the MIPAS and ACE-FTS data come from different aspects, such as differences in the temporal and spatial sampling (except for the profile-to-profile comparisons), cloud influence, vertical resolution, and the microwindows and spectroscopic database chosen. Differences between data sets from the same instrument are typically small in the stratosphere.

scholarly journals Robustness of Tropospheric Temperature Trends from MSU Channels 2 and 4

2006 ◽  
Vol 19 (17) ◽  
pp. 4234-4242 ◽  
Author(s):  
Celeste M. Johanson ◽  
Qiang Fu
Keyword(s):  
Training Dataset ◽  
Retrieval Algorithm ◽  
Tropospheric Temperature ◽  
Temperature Trends ◽  
Combining Data ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
The Mean ◽  
Stratospheric Cooling ◽  
Global Mean

Abstract Tropospheric temperature trends based on Microwave Sounding Unit (MSU) channel 2 data are susceptible to contamination from strong stratospheric cooling. Recently, Fu et al. devised a method of removing the stratospheric contamination by linearly combining data from MSU channels 2 and 4. In this study the sensitivity of the weights of the two channels in the retrieval algorithm for the tropospheric temperatures to the choice of period of record used in the analysis and to the choice of training dataset is examined. The weights derived using monthly temperature anomalies are within about 10% of those obtained by Fu et al. irrespective of the choice of analysis period or training dataset. The trend errors in the retrieved global-mean tropospheric temperatures tested using two independent radiosonde datasets are less than about 0.01 K decade−1 for all time periods of 25 yr or longer with different starting and ending years during 1958–2004. It is found that the retrievals are more robust if they are interpreted in terms of the layer-mean temperature for the entire troposphere, rather than the mean of the 850–300-hPa layer. Because large spurious jumps remain in the reanalyses, especially prior to 1979, one should be cautious when using them as training datasets and in testing the trend errors.

scholarly journals How Robust Are Trends in the Brewer–Dobson Circulation Derived from Observed Stratospheric Temperatures?

2015 ◽  
Vol 28 (8) ◽  
pp. 3024-3040 ◽  
Author(s):  
Albert Ossó ◽  
Yolanda Sola ◽  
Karen Rosenlof ◽  
Birgit Hassler ◽  
Joan Bech ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
First Century ◽  
Global Circulation ◽  
Global Circulation Models ◽  
Global Mean Temperature ◽  
Stratospheric Temperature ◽  
Spatially Inhomogeneous ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Global Mean

Abstract Most global circulation models and climate–chemistry models forced with increasing greenhouse gases predict a strengthening of the Brewer–Dobson circulation (BDC) in the twenty-first century, and some of them claim that such strengthening has already begun at the end of the twentieth century. However, observational evidence for such a trend remains inconclusive. The goal of this paper is to examine the evidence for observed trends in the stratospheric overturning circulation using a suite of currently available observational stratospheric temperature data. Trends are examined as “departures” from the global mean temperature, since such trends reflect the effects of dynamics and spatially inhomogeneous radiative forcing and are to first order independent of the direct radiative effects of increasing well-mixed greenhouse gas concentrations. The primary conclusion of the study is that temperature observations do not reveal statistically significant trends in the Brewer–Dobson circulation over the period from 1979 to the present, as covered by Microwave Sounding Unit and Stratospheric Sounding Unit temperatures. The estimated trends in the BDC are weak in all datasets and not statistically significant at the 95% confidence level. In many cases, different data products yield very different results, particularly when the trends are stratified by season. Implications for the interpretation of recent stratospheric climate change are discussed. The results illustrate the essential need to better constrain the accuracy of future stratospheric temperature datasets.

scholarly journals Uncertainty of the stratospheric/tropospheric temperature trends in 1979–2008: Multiple Satellite MSU, radiosonde, and reanalysis datasets

2011 ◽  
Vol 11 (6) ◽  
pp. 16639-16654
Author(s):  
J. Xu ◽  
Keyword(s):  
Weighting Function ◽  
Satellite Observations ◽  
Tropospheric Temperature ◽  
Stratospheric Temperature ◽  
Temperature Trends ◽  
Microwave Sounding

Abstract. The trends and spreads of tropospheric and stratospheric temperature are discussed in terms of three groups of datasets in 1979–2008. These datasets include (a) three satellite observations of Microwave Sounding Units (MSU) measurements, (b) five radiosonde observations and (c) five reanalysis products. The equivalent tropospheric and stratospheric temperature from radiosonde and reanalyses are calculated based on the vertical weighting function of the MSU channel 2 (CH2) and channel 4 (CH4) measurements, respectively. The results show that both cooling in the stratosphere and warming in troposphere significantly depends on the datasets and latitudes.

scholarly journals The SPARC water vapour assessment II: profile-to-profile and climatological comparisons of stratospheric <i>δ</i>D(H<sub>2</sub>O) observations from satellite

2019 ◽  
Vol 19 (4) ◽  
pp. 2497-2526 ◽  
Author(s):  
Charlotta Högberg ◽  
Stefan Lossow ◽  
Farahnaz Khosrawi ◽  
Ralf Bauer ◽  
Kaley A. Walker ◽  
...  
Keyword(s):  
Water Vapour ◽  
Atmospheric Chemistry ◽  
Lower Stratosphere ◽  
Isotopic Ratio ◽  
Michelson Interferometer ◽  
Data Sets ◽  
Comprehensive Overview ◽  
Data Set ◽  
Upper Troposphere ◽  
Modelling Studies

Abstract. Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), we evaluated five data sets of δD(H2O) obtained from observations by Odin/SMR (Sub-Millimetre Radiometer), Envisat/MIPAS (Environmental Satellite/Michelson Interferometer for Passive Atmospheric Sounding), and SCISAT/ACE-FTS (Science Satellite/Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) using profile-to-profile and climatological comparisons. These comparisons aimed to provide a comprehensive overview of typical uncertainties in the observational database that could be considered in the future in observational and modelling studies. Our primary focus is on stratospheric altitudes, but results for the upper troposphere and lower mesosphere are also shown. There are clear quantitative differences in the measurements of the isotopic ratio, mainly with regard to comparisons between the SMR data set and both the MIPAS and ACE-FTS data sets. In the lower stratosphere, the SMR data set shows a higher depletion in δD than the MIPAS and ACE-FTS data sets. The differences maximise close to 50 hPa and exceed 200 ‰. With increasing altitude, the biases decrease. Above 4 hPa, the SMR data set shows a lower δD depletion than the MIPAS data sets, occasionally exceeding 100 ‰. Overall, the δD biases of the SMR data set are driven by HDO biases in the lower stratosphere and by H2O biases in the upper stratosphere and lower mesosphere. In between, in the middle stratosphere, the biases in δD are the result of deviations in both HDO and H2O. These biases are attributed to issues with the calibration, in particular in terms of the sideband filtering, and uncertainties in spectroscopic parameters. The MIPAS and ACE-FTS data sets agree rather well between about 100 and 10 hPa. The MIPAS data sets show less depletion below approximately 15 hPa (up to about 30 ‰), due to differences in both HDO and H2O. Higher up this behaviour is reversed, and towards the upper stratosphere the biases increase. This is driven by increasing biases in H2O, and on occasion the differences in δD exceed 80 ‰. Below 100 hPa, the differences between the MIPAS and ACE-FTS data sets are even larger. In the climatological comparisons, the MIPAS data sets continue to show less depletion in δD than the ACE-FTS data sets below 15 hPa during all seasons, with some variations in magnitude. The differences between the MIPAS and ACE-FTS data have multiple causes, such as differences in the temporal and spatial sampling (except for the profile-to-profile comparisons), cloud influence, vertical resolution, and the microwindows and spectroscopic database chosen. Differences between data sets from the same instrument are typically small in the stratosphere. Overall, if the data sets are considered together, the differences in δD among them in key areas of scientific interest (e.g. tropical and polar lower stratosphere, lower mesosphere, and upper troposphere) are too large to draw robust conclusions on atmospheric processes affecting the water vapour budget and distribution, e.g. the relative importance of different mechanisms transporting water vapour into the stratosphere.

scholarly journals Investigations into the development of a satellite-based aerosol climate data record using ATSR-2, AATSR and AVHRR data over north-eastern China from 1987 to 2012

2019 ◽  
Vol 12 (7) ◽  
pp. 4091-4112 ◽  
Author(s):  
Yahui Che ◽  
Jie Guang ◽  
Gerrit de Leeuw ◽  
Yong Xue ◽  
Ling Sun ◽  
...  
Keyword(s):  
Eastern China ◽  
Data Sets ◽  
Climate Data ◽  
Data Set ◽  
North Eastern ◽  
Data Record ◽  
Single Sensor ◽  
Sun Photometer ◽  
Climate Data Record ◽  
Better Than

Abstract. Satellites provide information on the temporal and spatial distributions of aerosols on regional and global scales. With the same method applied to a single sensor all over the world, a consistent data set is to be expected. However, the application of different retrieval algorithms to the same sensor and the use of a series of different sensors may lead to substantial differences, and no single sensor or algorithm is better than any other everywhere and at all times. For the production of long-term climate data records, the use of multiple sensors cannot be avoided. The Along Track Scanning Radiometer (ATSR-2) and the Advanced ATSR (AATSR) aerosol optical depth (AOD) data sets have been used to provide a global AOD data record over land and ocean of 17 years (1995–2012), which is planned to be extended with AOD retrieved from a similar sensor. To investigate the possibility of extending the ATSR data record to earlier years, the use of an AOD data set from the Advanced Very High Resolution Radiometer (AVHRR) is investigated. AOD data sets used in this study were retrieved from the ATSR sensors using the ATSR Dual View algorithm ADV version 2.31, developed by Finnish Meteorological Institute (FMI), and from the AVHRR sensors using the aerosol optical depth over land (ADL) algorithm developed by RADI/CAS. Together, these data sets cover a multi-decadal period (1987–2012). The study area includes two contrasting areas, both in regards to aerosol content and composition and surface properties, i.e. a region over north-eastern China, encompassing a highly populated urban/industrialized area (Beijing–Tianjin–Hebei) and a sparsely populated mountainous area. Ground-based AOD observations available from ground-based sun photometer AOD data in AERONET and CARSNET are used as a reference, together with broadband extinction method (BEM) data at Beijing to cover the time before sun photometer observations became available in the early 2000s. In addition, MODIS-Terra C6.1 AOD data are used as a reference data set over the wide area where no ground-based data are available. All satellite data over the study area were validated against the reference data, showing the qualification of MODIS for comparison with ATSR and AVHRR. The comparison with MODIS shows that AVHRR performs better than ATSR in the north of the study area (40∘ N), whereas further south ATSR provides better results. The validation against sun photometer AOD shows that both AVHRR and ATSR underestimate the AOD, with ATSR failing to provide reliable results in the wintertime. This is likely due to the highly reflecting surface in the dry season, when AVHRR-retrieved AOD traces both MODIS and reference AOD data well. However, AVHRR does not provide AOD larger than about 0.6 and hence is not reliable when high AOD values have been observed over the last decade. In these cases, ATSR performs much better for AOD up to about 1.3. AVHRR-retrieved AOD compares favourably with BEM AOD, except for AOD higher than about 0.6. These comparisons lead to the conclusion that AVHRR and ATSR AOD data records each have their strengths and weaknesses that need to be accounted for when combining them in a single multi-decadal climate data record.

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