ALBERT

Description: Observing systems consisting of a finite number of in situ monitoring stations can provide high-quality measurements with the ability to quality assure both the instruments and the data but offer limited information over larger geographic areas. This paper quantifies the spatial coverage represented by a finite set of monitoring stations by using global data—data that are possibly of lower resolution and quality. For illustration purposes, merged satellite temperature data from Microwave Sounding Units are used to estimate the representativeness of the Global Climate Observing System Reference Upper-Air Network (GRUAN). While many metrics exist for evaluating the representativeness of a site, the ability to have highly accurate monthly averaged data is essential for both trend detection and climatology evaluation. The calculated correlations of the monthly averaged upper-troposphere satellite-derived temperatures over the GRUAN stations with all other pixels around the globe show that the current 9 certified GRUAN stations have moderate correlations (r ≥ 0.7) for approximately 10% of the earth, but an expanded network incorporating another 15 stations would result in moderate correlations for just over 60% of the earth. This analysis indicates that the value of additional stations can be quantified by using historical, satellite, or model data and can be used to reveal critical gaps in current monitoring capabilities. Evaluating the value of potential additional stations and prioritizing their initiation can optimize networks. The expansion of networks can be evaluated in a manner that allows for optimal benefit on the basis of optimization theory and economic analyses.

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: Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), profile-to-profile comparisons of stratospheric and lower mesospheric water vapour were performed by considering 33 data sets derived from satellite observations of 15 different instruments. These comparisons aimed to provide a picture of the typical biases and drifts in the observational database and to identify data-set-specific problems. The observational database typically exhibits the largest biases below 70 hPa, both in absolute and relative terms. The smallest biases are often found between 50 and 5 hPa. Typically, they range from 0.25 to 0.5 ppmv (5 % to 10 %) in this altitude region, based on the 50 % percentile over the different comparison results. Higher up, the biases increase with altitude overall but this general behaviour is accompanied by considerable variations. Characteristic values vary between 0.3 and 1 ppmv (4 % to 20 %). Obvious data-set-specific bias issues are found for a number of data sets. In our work we performed a drift analysis for data sets overlapping for a period of at least 36 months. This assessment shows a wide range of drifts among the different data sets that are statistically significant at the 2σ uncertainty level. In general, the smallest drifts are found in the altitude range between about 30 and 10 hPa. Histograms considering results from all altitudes indicate the largest occurrence for drifts between 0.05 and 0.3 ppmv decade−1. Comparisons of our drift estimates to those derived from comparisons of zonal mean time series only exhibit statistically significant differences in slightly more than 3 % of the comparisons. Hence, drift estimates from profile-to-profile and zonal mean time series comparisons are largely interchangeable. As for the biases, a number of data sets exhibit prominent drift issues. In our analyses we found that the large number of MIPAS data sets included in the assessment affects our general results as well as the bias summaries we provide for the individual data sets. This is because these data sets exhibit a relative similarity with respect to the remaining data sets, despite the fact that they are based on different measurement modes and different processors implementing different retrieval choices. Because of that, we have by default considered an aggregation of the comparison results obtained from MIPAS data sets. Results without this aggregation are provided on multiple occasions to characterise the effects due to the numerous MIPAS data sets. Among other effects, they cause a reduction of the typical biases in the observational database.

Description: Retrievals of vertical profiles of key atmospheric gases provide a critical long-term record from ground-based Fourier transform infrared (FTIR) solar absorption measurements. However, the characterization of the retrieved vertical profile structure can be difficult to validate, especially for gases with large vertical gradients and spatial–temporal variability such as water vapor. In this work, we evaluate the accuracy of the most common water vapor isotope (H216O, hereafter WV) FTIR retrievals in the lower and upper troposphere–lower stratosphere. Coincident high-quality vertically resolved WV profile measurements obtained from 2010 to 2016 with balloon-borne NOAA frost point hygrometers (FPHs) are used as reference to evaluate the performance of the retrieved profiles at two sites: Boulder (BLD), Colorado, and at the mountaintop observatory of Mauna Loa (MLO), Hawaii. For a meaningful comparison, the spatial–temporal variability has been investigated. We present results of comparisons among FTIR retrievals with unsmoothed and smoothed FPH profiles to assess WV vertical gradients. Additionally, we evaluate the quantitative impact of different a priori profiles in the retrieval of WV. An orthogonal linear regression analysis shows the best correlation among tropospheric layers using ERA-Interim (ERA-I) a priori profiles and biases are lower for unsmoothed comparisons. In Boulder, we found a negative bias of 0.02±1.9 % (r=0.95) for the 1.5–3 km layer. A larger negative bias of 11.1±3.5 % (r=0.97) was found in the lower free troposphere layer of 3–5 km attributed to rapid vertical change of WV, which is not always captured by the retrievals. The bias improves in the 5–7.5 km layer (1.0±5.3 %, r=0.94). The bias remains at about 13 % for layers above 7.5 km but below 13.5 km. At MLO the spatial mismatch is significantly larger due to the launch of the sonde being farther from the FTIR location. Nevertheless, we estimate a negative bias of 5.9±4.6 % (r=0.93) for the 3.5–5.5 km layer and 9.9±3.7 % (r=0.93) for the 5.5–7.5 km layer, and we measure positive biases of 6.2±3.6 % (r=0.95) for the 7.5–10 km layer and 12.6 % and greater values above 10 km. The agreement for the first layer is significantly better at BLD because the air masses are similar for both FTIR and FPH. Furthermore, for the first time we study the influence of different WV a priori profiles in the retrieval of selected gas profiles. Using NDACC standard retrievals we present results for hydrogen cyanide (HCN), carbon monoxide (CO), and ethane (C2H6) by taking NOAA FPH profiles as the ground truth and evaluating the impact of other WV profiles. We show that the effect is minor for C2H6 (bias

Description: In this paper, we describe the construction of the Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database, which includes vertically resolved ozone and water vapor data from limb profiling satellite instruments operating since the 1980’s. SWOOSH includes both individual satellite source data as well as a merged data product. A key aspect of the merged product is that the source records are homogenized to account for inter-satellite biases and to minimize artificial jumps in the record. We describe the SWOOSH homogenization process, which involves adjusting the satellite data records to a “reference” satellite using coincident observations during time periods of instrument overlap. The reference satellite is chosen based on the best agreement with independent balloon-based sounding measurements, with the goal of producing a long-term data record that is both homogeneous and accurate. This paper details the choice of reference measurements, homogenization, and gridding process involved in the construction of the combined SWOOSH product, and also presents the ancillary information stored in SWOOSH that can be used in future studies of water vapor and ozone variability. Furthermore, a discussion of uncertainties in the combined SWOOSH record is presented, and examples of the SWOOSH record are provided to illustrate its use for studies of ozone and water vapor variability on interannual to decadal time scales. The version 2.5 SWOOSH data are publicly available at https://data.noaa.gov/dataset/stratospheric-water-and-ozone-satellite-homogenized-swoosh-data-set.

Description: In this paper, we describe the construction of the Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database, which includes vertically resolved ozone and water vapor data from a subset of the limb profiling satellite instruments operating since the 1980s. The primary SWOOSH products are zonal-mean monthly-mean time series of water vapor and ozone mixing ratio on pressure levels (12 levels per decade from 316 to 1 hPa). The SWOOSH pressure level products are provided on several independent zonal-mean grids (2.5, 5, and 10°), and additional products include two coarse 3-D griddings (30° long  ×  10° lat, 20°  ×  5°) as well as a zonal-mean isentropic product. SWOOSH includes both individual satellite source data as well as a merged data product. A key aspect of the merged product is that the source records are homogenized to account for inter-satellite biases and to minimize artificial jumps in the record. We describe the SWOOSH homogenization process, which involves adjusting the satellite data records to a “reference” satellite using coincident observations during time periods of instrument overlap. The reference satellite is chosen based on the best agreement with independent balloon-based sounding measurements, with the goal of producing a long-term data record that is both homogeneous (i.e., with minimal artificial jumps in time) and accurate (i.e., unbiased). This paper details the choice of reference measurements, homogenization, and gridding process involved in the construction of the combined SWOOSH product and also presents the ancillary information stored in SWOOSH that can be used in future studies of water vapor and ozone variability. Furthermore, a discussion of uncertainties in the combined SWOOSH record is presented, and examples of the SWOOSH record are provided to illustrate its use for studies of ozone and water vapor variability on interannual to decadal timescales. The version 2.5 SWOOSH data are publicly available at doi:10.7289/V5TD9VBX.

Description: Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), profile-to-profile comparisons of stratospheric and lower mesospheric water vapour were performed considering 33 data sets derived from satellite observations of 15 different instruments. These comparisons aimed to provide a picture of the typical biases and drifts in the observational database and to identify data set specific problems. The observational database typically exhibits the largest biases below 70 hPa, both in absolute and relative terms. The smallest biases are often found between 50 hPa and 5 hPa. Typically, they range from 0.25 ppmv to 0.5 ppmv (5 % to 10 %) in this altitude region, based on the 50 % percentile over the different comparison results. Higher up, the biases are overall increasing with altitude but this general behaviour is accompanied by considerable variations. Characteristic values vary between 0.3 ppmv and 1 ppmv (4 % to 20 %). Obvious data set specific bias issues are found for a number of data sets. In our work we performed a drift analysis for data sets overlapping for a period of at least 36 months. This assessment shows a wide range of drifts among the different data sets that are statistically significant at the 2σ uncertainty level. In general, the smallest drifts are found in the altitude range between about 30 hPa to 10 hPa. Histograms considering results from all altitudes indicate the largest occurrence for drifts between 0.05 ppmv decade−1 and 0.3 ppmv decade−1. Comparisons of our drift estimates to those derived from comparisons of zonal mean time series only exhibit statistically significant differences in slightly more than 3 % of the comparisons. Hence, drift estimates from profile-to-profile and zonal mean time series comparisons are largely interchangeable. Like for the biases, a number of data sets exhibit prominent drift issues. In our analyses we found that the large number of MIPAS data sets included in the assessment affects our general results as well as the bias summaries we provide for the individual data sets. This is because these data sets exhibit a relative similarity with respect to the remaining data sets, despite that they are based on different measurement modes and different processors implementing different retrieval choices. Because of that, we have by default considered an aggregation of the comparison results obtained from MIPAS data sets. Results without this aggregation are provided on multiple occasions to characterise the effects due to the numerous MIPAS data sets. Among other effects, they cause a reduction of the typical biases in the observational database.

Description: Retrievals of vertical profiles of key atmospheric gases provide a critical long-term data record from ground-based Fourier Transform InfraRed (FTIR) solar absorption measurements. However, the characterization of the retrieved vertical profile structure can be difficult to validate, especially for gases with large vertical gradients and spatial-temporal variability such as water vapor. In this work, we evaluate the accuracy of the most common water vapor isotope (H216O, hereafter WV) FTIR retrievals in the lower and upper troposphere – lower stratosphere. Coincident high-quality vertically resolved WV profile measurements obtained from 2010 to 2016 with balloon-borne NOAA Frost Point Hygrometers (FPH) are used as reference to evaluate the performance of the retrieved profiles at two sites: Boulder, Colorado and in the mountain top observatory of Mauna Loa, Hawaii. For a meaningful comparison, the spatial-temporal variability has been investigated. Additionally, we evaluate the quantitative impact of different a priori profiles in the retrieval of WV vertical profiles using un-smoothed comparisons. An orthogonal linear regression analysis shows the best correlation among all layers using ERA-Interim (ERA-I) a priori profiles. In Boulder, we found a negative bias of 0.02±1.9% and precision of 3.7% (r=0.95) for the 1.5–3km layer. A larger negative bias of 11.1±3.5% and precision of 7.0% was found in the lower free troposphere layer of 3–5km (r=0.97) attributed to rapid vertical change of WV, which is not always captured by the retrievals. The bias improves in the 5–7.5km layer (1.0±5.3%) and the precision worsens to about 10%. The bias remains at about 13% and the precision remains to about 10% for layers above 7.5km but below 13.5km. At MLO the spatial mismatch is significantly larger due to the launch of the sonde being farther from the FTIR location. Nevertheless, we estimate a negative biases of 5.9±4.6% for the 3.5–5.5km layer (r=0.93) and 9.9±3.7% for the 5.5–7.5km layer (r=0.93), and positive biases of 6.2±3.6% for the 7.5–10km layer (r=0.95), and 12.6% and greater values above 10km. The agreement for the first layer is significantly better at BLD likely that the air masses are similar for both FTIR and FPH. Furthermore, for the first time we study the influence of different sources of WV profiles in the retrieval of selected gas profiles. Using NDACC standard retrievals we present results for hydrogen cyanide (HCN), carbon monoxide (CO), and ethane (C2H6) by taking NOAA FPH profiles as the ground-truth and evaluate the impact of other WV profile sources. We show that the effect is minor for C2H6 (bias

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.