ALBERT

Description: The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) was an infrared limb emission spectrometer on the Envisat platform. From 2002 to 2012, it performed pole-to-pole measurements during day and night, producing more than 1000 profiles per day. The European Space Agency (ESA) recently released the new version 7 of Level 1B MIPAS spectra, in which a new set of time-dependent correction coefficients for the nonlinearity in the detector response functions was implemented. This change is expected to reduce the long-term drift of the MIPAS Level 2 data. We evaluate the long-term stability of ozone Level 2 data retrieved from MIPAS v7 Level 1B spectra with the IMK/IAA scientific level 2 processor. For this, we compare MIPAS data with ozone measurements from the Microwave Limb Sounder (MLS) instrument on NASA's Aura satellite, ozonesondes and ground-based lidar instruments. The ozonesondes and lidars alone do not allow us to conclude with enough significance that the new version is more stable than the previous one, but a clear improvement in long-term stability is observed in the satellite-data-based drift analysis. The results of ozonesondes, lidars and satellite drift analysis are consistent: all indicate that the drifts of the new version are less negative/more positive nearly everywhere above 15 km. The 10-year MIPAS ozone trends calculated from the old and the new data versions are compared. The new trends are closer to old drift-corrected trends than the old uncorrected trends were. From this, we conclude that the nonlinearity correction performed on Level 1B data is an improvement. These results indicate that MIPAS data are now even more suited for trend studies, alone or as part of a merged data record.

Description: Trend estimates with different signs are reported in the literature for lower stratospheric water vapour considering the time period between the late 1980s and 2010. The NOAA (National Oceanic and Atmospheric Administration) frost point hygrometer (FPH) observations at Boulder (Colorado, 40.0° N, 105.2° W) indicate positive trends (about 0.1 to 0.45 ppmv decade−1). On the contrary, negative trends (approximately −0.2 to −0.1 ppmv decade−1) are derived from a merged zonal mean satellite data set for a latitude band around the Boulder latitude. Overall, the trend differences between the two data sets range from about 0.3 to 0.5 ppmv decade−1, depending on altitude. It has been proposed that a possible explanation for these discrepancies is a different temporal behaviour at Boulder and the zonal mean. In this work we investigate trend differences between Boulder and the zonal mean using primarily simulations from ECHAM/MESSy (European Centre for Medium-Range Weather Forecasts Hamburg/Modular Earth Submodel System) Atmospheric Chemistry (EMAC), WACCM (Whole Atmosphere Community Climate Model), CMAM (Canadian Middle Atmosphere Model) and CLaMS (Chemical Lagrangian Model of the Stratosphere). On shorter timescales we address this aspect also based on satellite observations from UARS/HALOE (Upper Atmosphere Research Satellite/Halogen Occultation Experiment), Envisat/MIPAS (Environmental Satellite/Michelson Interferometer for Passive Atmospheric Sounding) and Aura/MLS (Microwave Limb Sounder). Overall, both the simulations and observations exhibit trend differences between Boulder and the zonal mean. The differences are dependent on altitude and the time period considered. The model simulations indicate only small trend differences between Boulder and the zonal mean for the time period between the late 1980s and 2010. These are clearly not sufficient to explain the discrepancies between the trend estimates derived from the FPH observations and the merged zonal mean satellite data set. Unless the simulations underrepresent variability or the trend differences originate from smaller spatial and temporal scales than resolved by the model simulations, trends at Boulder for this time period should also be quite representative for the zonal mean and even other latitude bands. Trend differences for a decade of data are larger and need to be kept in mind when comparing results for Boulder and the zonal mean on this timescale. Beyond that, we find that the trend estimates for the time period between the late 1980s and 2010 also significantly differ among the simulations. They are larger than those derived from the merged satellite data set and smaller than the trend estimates derived from the FPH observations.

Description: The primary instrument on the Greenhouse gases Observing SATellite (GOSAT) is the Thermal And Near infrared Sensor for carbon Observations (TANSO) Fourier transform spectrometer (FTS). TANSO-FTS uses three short-wave infrared (SWIR) bands to retrieve total columns of CO2 and CH4 along its optical line of sight and one thermal infrared (TIR) channel to retrieve vertical profiles of CO2 and CH4 volume mixing ratios (VMRs) in the troposphere. We examine version 1 of the TANSO-FTS TIR CH4 product by comparing co-located CH4 VMR vertical profiles from two other remote-sensing FTS systems: the Canadian Space Agency's Atmospheric Chemistry Experiment FTS (ACE-FTS) on SCISAT (version 3.5) and the European Space Agency's Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat (ESA ML2PP version 6 and IMK-IAA reduced-resolution version V5R_CH4_224/225), as well as 16 ground stations with the Network for the Detection of Atmospheric Composition Change (NDACC). This work follows an initial inter-comparison study over the Arctic, which incorporated a ground-based FTS at the Polar Environment Atmospheric Research Laboratory (PEARL) at Eureka, Canada, and focuses on tropospheric and lower-stratospheric measurements made at middle and tropical latitudes between 2009 and 2013 (mid-2012 for MIPAS). For comparison, vertical profiles from all instruments are interpolated onto a common pressure grid, and smoothing is applied to ACE-FTS, MIPAS, and NDACC vertical profiles. Smoothing is needed to account for differences between the vertical resolution of each instrument and differences in the dependence on a priori profiles. The smoothing operators use the TANSO-FTS a priori and averaging kernels in all cases. We present zonally averaged mean CH4 differences between each instrument and TANSO-FTS with and without smoothing, and we examine their information content, their sensitive altitude range, their correlation, their a priori dependence, and the variability within each data set. Partial columns are calculated from the VMR vertical profiles, and their correlations are examined. We find that the TANSO-FTS vertical profiles agree with the ACE-FTS and both MIPAS retrievals' vertical profiles within 4 % (± ∼  40 ppbv) below 15 km when smoothing is applied to the profiles from instruments with finer vertical resolution but that the relative differences can increase to on the order of 25 % when no smoothing is applied. Computed partial columns are tightly correlated for each pair of data sets. We investigate whether the difference between TANSO-FTS and other CH4 VMR data products varies with latitude. Our study reveals a small dependence of around 0.1 % per 10 degrees latitude, with smaller differences over the tropics and greater differences towards the poles.

Description: The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) was an IR limb emission spectrometer on the Envisat platform. From 2002 to 2012, it performed pole-to-pole measurements during day and night, producing more than 1000 profiles/day. The European Space Agency (ESA) has recently released the new version 7 of Level 1 MIPAS spectra, in which a new set of time-dependent correction coefficients for the non-linearity in the detectors’ response functions was implemeted. This change is expected to reduce the long-term drift of the MIPAS Level 2 data. We evaluate the long-term stability of ozone level 2 data retrieved from MIPAS V7 Level 1 spectra with the IMK/IAA Scientific Level 2 Processor. We compare it with ozone measurements from the Microwave Limb Sounder (MLS) instrument on NASA’s Aura satellite, ozonesondes and ground-based lidar instruments. The ozonesondes and lidars alone do not allow us to conclude with enough significance that the new version is more stable than the previous one, but a clear improvement in long-term stability is observed in the satellite-data based drift analysis. The results of ozonesondes, lidars and satellite drift analysis are consistent: all indicate that the drifts of the new version are less negative/more positive nearly everywhere above 15 km. These results indicate that MIPAS data are now even more suited for trend studies, alone or as part of a merged data record.

Description: The primary instrument on the Greenhouse gases Observing SATellite (GOSAT) is the Thermal And Near infrared Sensor for carbon Observations (TANSO) Fourier Transform Spectrometer (FTS). TANSO-FTS uses three short-wave infrared (SWIR) bands to retrieve total columns of CO2 and CH4 along its optical line-of-sight, and one thermal infrared (TIR) channel to retrieve vertical profiles of CO2 and CH4 volume mixing ratios (VMRs) in the troposphere. We examine version 1 of the TANSO-FTS TIR CH4 product by comparing co-located CH4 VMR vertical profiles from two other remote sensing FTS systems: the Canadian Space Agency's Atmospheric Chemistry Experiment-FTS (ACE-FTS) on SCISAT (version 3.5), and the European Space Agency's Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat (ESA ML2PP version 6 and IMK-IAA reduced-resolution version V5R_CH4_224/225), as well as 16 ground stations with the Network for the Detection of Atmospheric Composition Change (NDACC). This work follows an initial inter-comparison study over the Arctic, which incorporated a ground-based FTS at the Polar Environment Atmospheric Research Laboratory (PEARL) at Eureka, Canada, and focuses on tropospheric and lower-stratospheric measurements made at middle and tropical latitudes between 2009 to 2013 (mid 2012 for MIPAS). For comparison, vertical profiles from all instruments are interpolated onto a common pressure grid, and the ACE-FTS, MIPAS, and NDACC vertical profiles are smoothed using the TANSO-FTS averaging kernels. We present zonally-averaged mean CH4 differences between each instrument and TANSO-FTS with and without smoothing, examine their information content, sensitive altitude range, correlation, a priori dependence, and the variability within each data set. Partial columns are calculated from the VMR vertical profiles, and their correlations are examined. We find that the TANSO-FTS vertical profiles agree with the ACE-FTS and both MIPAS retrievals' vertical profiles within 4 % below 15 km when smoothing is applied to the profiles from instruments with finer vertical resolution, but that the relative differences can increase to on the order of 25 % when no smoothing is applied. Computed partial columns are tightly correlated for each pair of data sets. We investigated whether the difference between TANSO-FTS and other CH4 VMR data products varies with latitude. Our study reveals a small dependence of around 0.1 % per ten degrees latitude, with smaller differences over the equator, and greater differences towards the poles.

Description: Trend estimates with different signs are reported in the literature for lower stratospheric water vapour considering the time period between the late 1980s and 2010. The NOAA (National Oceanic and Atmospheric Administration) frost point hygrometer (FPH) observations at Boulder (Colorado, 40.0° N, 105.2° W) indicate positive trends (about 0.12 ppmv decade−1–0.45 ppmv decade−1). Contrary, negative trends (approximately −0.15 ppmv decade−1–−0.05  ppmv decade−1) are derived from a merged zonal mean satellite data set for a latitude band around the Boulder latitude. Overall, the trend differences between the two data sets range from about 0.25 ppmv decade−1 to 0.45 ppmv decade−1, depending on altitude. A possible explanation for these discrepancies is a different temporal behaviour at Boulder and the zonal mean, which simply indicates a sampling bias. In this work we investigate trend differences between Boulder and the zonal mean using primarily simulations from ECHAM/MESSy (European Centre for Medium-Range Weather Forecasts Hamburg/Modular Earth Submodel System) Atmospheric Chemistry (EMAC), WACCM (Whole Atmosphere Community Climate Model), CMAM (Canadian Middle Atmosphere Model) and CLaMS (Chemical Lagrangian Model of the Stratosphere). On shorter time scales we address this aspect also based on satellite observations from UARS/HALOE (Upper Atmosphere Research Satellite/Halogen Occultation Experiment), Envisat/MIPAS (Environmental Satellite/Michelson Interferometer for Passive Atmospheric Sounding) and Aura/MLS (Microwave Limb Sounder). Overall, both the simulations and observations exhibit trend differences between Boulder and the zonal mean. The differences are dependent on altitude and the time period considered. The model simulations indicate only small trend differences between Boulder and the zonal mean for the time period between the late 1980s and 2010. These are clearly not sufficient to explain the discrepancies between the trend estimates derived from the FPH observations and the merged zonal mean satellite data set. Unless the simulations underrepresent variability or the trend differences originate from smaller spatial and temporal scales than resolved by the model simulations, trends at Boulder for this time period should be quite representative also for the zonal mean and even other latitude bands. Trend differences for a decade of data are larger and need to be kept in mind when comparing results for Boulder and the zonal mean on this time scale. Beyond that, we find that the trend estimates for the time period between the late 1980s and 2010 also significantly differ among the simulations. They are larger than those derived from the merged satellite data set and smaller than the trend estimates derived from the FPH observations.

Description: Improved versions of CH4 and N2O profiles derived at the Institute of Meteorology and Climate Research and Instituto de Astrofísica de Andalucía (CSIC) from spectra measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) have become available. For the MIPAS full-resolution period (2002–2004) these are V5H_CH4_21 and V5H_N2O_21 and for the reduced-resolution period (2005–2012) these are V5R_CH4_224, V5R_CH4_225, V5R_N2O_224 and V5R_N2O_225. Here, we compare CH4 profiles to those measured by the Fourier Transform Spectrometer on board of the Atmospheric Chemistry Experiment (ACE-FTS), the HALogen Occultation Experiment (HALOE) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY), to the Global Cooperative Air Sampling Network (GCASN) surface data. We find the MIPAS CH4 profiles below 25 km to be typically higher of the order of 0.1 ppmv for both measurement periods. N2O profiles are compared to those measured by ACE-FTS, the Microwave Limb Sounder on board of the Aura satellite (Aura-MLS) and the Sub-millimetre Radiometer on board of the Odin satellite (Odin-SMR) as well as to the Halocarbons and other Atmospheric Trace Species Group (HATS) surface data. The mixing ratios of the satellite instruments agree well with each other for the full-resolution period. For the reduced-resolution period, MIPAS produces similar values as Odin-SMR, but higher values than ACE-FTS and HATS. Below 27 km, the MIPAS profiles show higher mixing ratios than Aura-MLS, and lower values between 27 and 41 km. Cross-comparisons between the two MIPAS measurement periods show that they generally agree quite well, but, especially for CH4, the reduced-resolution period seems to produce slightly higher mixing ratios than the full-resolution data.

Description: Stratospheric profiles of methane (CH4) and carbon dioxide (CO2) have been derived from solar occultation measurements of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The retrieval is performed using a method called onion peeling DOAS (ONPD), which combines an onion peeling approach with a weighting function DOAS (differential optical absorption spectroscopy) fit in the spectral region between 1559 and 1671 nm. By use of updated pointing information and optimisation of the data selection as well as of the retrieval approach, the altitude range for reasonable CH4 could be broadened from 20 to 40 km to about 17 to 45 km. Furthermore, the quality of the derived CO2 has been assessed such that now the first stratospheric profiles (17–45 km) of CO2 from SCIAMACHY are available. Comparisons with independent data sets yield an estimated accuracy of the new SCIAMACHY stratospheric profiles of about 5–10 % for CH4 and 2–3 % for CO2. The accuracy of the products is currently mainly restricted by the appearance of unexpected vertical oscillations in the derived profiles which need further investigation. Using the improved ONPD retrieval, CH4 and CO2 stratospheric data sets covering the whole SCIAMACHY time series (August 2002–April 2012) and the latitudinal range between about 50 and 70° N have been derived. Based on these time series, CH4 and CO2 trends have been estimated. CH4 trends above about 20 km are not significantly different from zero and the trend at 17 km is about 3 ppbv year−1. The derived CO2 trends show a general decrease with altitude with values of about 1.9 ppmv year−1 at 21 km and about 1.3 ppmv year−1 at 39 km. These results are in reasonable agreement with total column trends for these gases. This shows that the new SCIAMACHY data sets can provide valuable information about the stratosphere.

Description: The distribution of methane (CH4) in the stratosphere can be a major driver of spatial variability in the dry-air column-averaged CH4 mixing ratio (XCH4), which is being measured increasingly for the assessment of CH4 surface emissions. Chemistry-transport models (CTMs) therefore need to simulate the tropospheric and stratospheric fractional columns of XCH4 accurately for estimating surface emissions from XCH4. Simulations from three CTMs are tested against XCH4 observations from the Total Carbon Column Network (TCCON). We analyze how the model–TCCON agreement in XCH4 depends on the model representation of stratospheric CH4 distributions. Model equivalents of TCCON XCH4 are computed with stratospheric CH4 fields from both the model simulations and from satellite-based CH4 distributions from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) and MIPAS CH4 fields adjusted to ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. Using MIPAS-based stratospheric CH4 fields in place of model simulations improves the model–TCCON XCH4 agreement for all models. For the Atmospheric Chemistry Transport Model (ACTM) the average XCH4 bias is significantly reduced from 38.1 to 13.7 ppb, whereas small improvements are found for the models TM5 (Transport Model, version 5; from 8.7 to 4.3 ppb) and LMDz (Laboratoire de Météorologie Dynamique model with zooming capability; from 6.8 to 4.3 ppb). Replacing model simulations with MIPAS stratospheric CH4 fields adjusted to ACE-FTS reduces the average XCH4 bias for ACTM (3.3 ppb), but increases the average XCH4 bias for TM5 (10.8 ppb) and LMDz (20.0 ppb). These findings imply that model errors in simulating stratospheric CH4 contribute to model biases. Current satellite instruments cannot definitively measure stratospheric CH4 to sufficient accuracy to eliminate these biases. Applying transport diagnostics to the models indicates that model-to-model differences in the simulation of stratospheric transport, notably the age of stratospheric air, can largely explain the inter-model spread in stratospheric CH4 and, hence, its contribution to XCH4. Therefore, it would be worthwhile to analyze how individual model components (e.g., physical parameterization, meteorological data sets, model horizontal/vertical resolution) impact the simulation of stratospheric CH4 and XCH4.

Description: The distribution of methane (CH4) in the stratosphere can be a major driver of spatial variability in the dry-air column-averaged CH4 mixing ratio (XCH4), which is being measured increasingly for the assessment of CH4 surface emissions. Chemistry-transport models (CTMs) therefore need to simulate the tropospheric and stratospheric fractional columns of XCH4 accurately for estimating surface emissions from XCH4. Simulations from three CTMs are tested against XCH4 observations from the Total Carbon Column Network (TCCON). We analyze how the model-TCCON agreement in XCH4 depends on the model representation of stratospheric CH4 distributions. Model equivalents of TCCON XCH4 are computed with stratospheric CH4 fields from both the model simulations and from satellite-based CH4 distributions from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) and MIPAS CH4 fields adjusted to ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. In comparison to simulated model fields we find an improved model-TCCON XCH4 agreement for all models with MIPAS-based stratospheric CH4 fields. For the Atmospheric Chemistry Transport Model (ACTM) the average XCH4 bias is significantly reduced from 38.1 ppb to 13.7 ppb, whereas small improvements are found for the models TM5 (Transport Model, version 5; from 8.7 ppb to 4.3 ppb), and LMDz (Laboratoire de Météorologie Dynamique model with Zooming capability; from 6.8 ppb to 4.3 ppb), respectively. MIPAS stratospheric CH4 fields adjusted to ACE-FTS reduce the average XCH4 bias for ACTM (3.3 ppb), but increase the average XCH4 bias for TM5 (10.8 ppb) and LMDz (20.0 ppb). These findings imply that the range of satellite-based stratospheric CH4 is insufficient to resolve a possible stratospheric contribution to differences in total column CH4 between TCCON and TM5 or LMDz. Applying transport diagnostics to the models indicates that model-to-model differences in the simulation of stratospheric transport, notably the age of stratospheric air, can largely explain the inter-model spread in stratospheric CH4 and, hence, its contribution to XCH4. This implies that there is a need to better understand the impact of individual model transport components (e.g., physical parameterization, meteorological data sets, model horizontal/vertical resolution) on modeled stratospheric CH4.