ALBERT

Description: The representation of upper tropospheric–lower stratospheric (UTLS) jet and tropopause characteristics is compared in five modern high-resolution reanalyses for 1980 through 2014. Climatologies of upper tropospheric jet, subvortex jet (the lowermost part of the stratospheric vortex), and multiple tropopause frequency distributions in MERRA (Modern-Era Retrospective analysis for Research and Applications), ERA-I (ERA-Interim; the European Centre for Medium-Range Weather Forecasts, ECMWF, interim reanalysis), JRA-55 (the Japanese 55-year Reanalysis), and CFSR (the Climate Forecast System Reanalysis) are compared with those in MERRA-2. Differences between alternate products from individual reanalysis systems are assessed; in particular, a comparison of CFSR data on model and pressure levels highlights the importance of vertical grid spacing. Most of the differences in distributions of UTLS jets and multiple tropopauses are consistent with the differences in assimilation model grids and resolution – for example, ERA-I (with coarsest native horizontal resolution) typically shows a significant low bias in upper tropospheric jets with respect to MERRA-2, and JRA-55 (the Japanese 55-year Reanalysis) a more modest one, while CFSR (with finest native horizontal resolution) shows a high bias with respect to MERRA-2 in both upper tropospheric jets and multiple tropopauses. Vertical temperature structure and grid spacing are especially important for multiple tropopause characterizations. Substantial differences between MERRA and MERRA-2 are seen in mid- to high-latitude Southern Hemisphere (SH) winter upper tropospheric jets and multiple tropopauses as well as in the upper tropospheric jets associated with tropical circulations during the solstice seasons; some of the largest differences from the other reanalyses are seen in the same times and places. Very good qualitative agreement among the reanalyses is seen between the large-scale climatological features in UTLS jet and multiple tropopause distributions. Quantitative differences may, however, have important consequences for transport and variability studies. Our results highlight the importance of considering reanalyses differences in UTLS studies, especially in relation to resolution and model grids; this is particularly critical when using high-resolution reanalyses as an observational reference for evaluating global chemistry–climate models.

Description: We have analyzed near-global stratospheric data (and mesospheric data as well for H2O) in terms of absolute abundances, variability, and trends for O3, H2O, HCl, N2O, and HNO3, based on Aura Microwave Limb Sounder (MLS) data, as well as longer-term series from the Global OZone Chemistry And Related trace gas Data records for the Stratosphere (GOZCARDS). While we emphasize the evaluation of stratospheric models via data comparisons through 2014 to free-running (FR-WACCM) and specified dynamics (SD-WACCM) versions of the Community Earth System Model version 1 (CESM1) Whole Atmosphere Community Climate Model (WACCM), we also highlight observed stratospheric changes, using the most recent data from MLS. Regarding highlights from the satellite data, we have used multiple linear regression to derive trends based on zonal mean time series from Aura MLS data alone, between 60∘ S and 60∘ N. In the upper stratosphere, MLS O3 shows increases over 2005–2018 at ∼0.1–0.3 % yr−1 (depending on altitude and latitude) with 2σ errors of ∼0.2 % yr−1. For the lower stratosphere (LS), GOZCARDS O3 data for 1998–2014 point to small decreases between 60∘ S and 60∘ N, but the trends are more positive if the starting year is 2005. Southern midlatitudes (30–60∘ S) exhibit near-zero or slightly positive LS trends for 1998–2018. The LS O3 trends based on 2005–2018 MLS data are most positive (0.1–0.2 % yr−1) at these southern midlatitudes, although marginally statistically significant, in contrast to slightly negative or near-zero trends for 2005–2014. Given the high variability in LS O3, and the high sensitivity of trends to the choice of years used, especially for short periods, further studies are required for a robust longer-term LS trend result. For H2O, upper-stratospheric and mesospheric trends from GOZCARDS 1992–2010 data are near zero (within ∼0.2 % yr−1) and significantly smaller than trends (within ∼0.4–0.7 % yr−1) from MLS for 2005–2014 or 2005–2018. The latter short-term positive H2O trends are larger than expected from changes resulting from long-term increases in methane. We note that the very shallow solar flux maximum of solar cycle 24 has contributed to fairly large short-term mesospheric and upper-stratospheric H2O trends since 2005. However, given known drifts in the MLS H2O time series, MLS H2O trend results, especially after 2010, should be viewed as upper limits. The MLS data also show regions and periods of small HCl increases in the lower stratosphere, within the context of the longer-term stratospheric decrease in HCl, as well as interhemispheric–latitudinal differences in short-term HCl tendencies. We observe similarities in such short-term tendencies, and interhemispheric asymmetries therein, for lower-stratospheric HCl and HNO3, while N2O trend profiles exhibit anti-correlated patterns. In terms of the model evaluation, climatological averages for 2005–2014 from both FR-WACCM and SD-WACCM for O3, H2O, HCl, N2O, and HNO3 compare favorably with Aura MLS data averages over this period. However, the models at mid- to high latitudes overestimate mean MLS LS O3 values and seasonal amplitudes by as much as 50 %–60 %; such differences appear to implicate, in part, a transport-related model issue. At lower-stratospheric high southern latitudes, variations in polar winter and spring composition observed by MLS are well matched by SD-WACCM, with the main exception being for the early winter rate of decrease in HCl, which is too slow in the model. In general, we find that the latitude–pressure distributions of annual and semiannual oscillation amplitudes derived from MLS data are properly captured by the model amplitudes. In terms of closeness of fit diagnostics for model–data anomaly series, not surprisingly, SD-WACCM (driven by realistic dynamics) generally matches the observations better than FR-WACCM does. We also use root mean square variability as a more valuable metric to evaluate model–data differences. We find, most notably, that FR-WACCM underestimates observed interannual variability for H2O; this has implications for the time period needed to detect small trends, based on model predictions. The WACCM O3 trends generally agree (within 2σ uncertainties) with the MLS data trends, although LS trends are typically not statistically different from zero. The MLS O3 trend dependence on latitude and pressure is matched quite well by the SD-WACCM results. For H2O, MLS and SD-WACCM positive trends agree fairly well, but FR-WACCM shows significantly smaller increases; this discrepancy for FR-WACCM is even more pronounced for longer-term GOZCARDS H2O records. The larger discrepancies for FR-WACCM likely arise from its poorer correlations with cold point temperatures and with quasi-biennial oscillation (QBO) variability. For HCl, while some expected decreases in the global LS are seen in the observations, there are interhemispheric differences in the trends, and increasing tendencies are suggested in tropical MLS data at 68 hPa, where there is only a slight positive trend in SD-WACCM. Although the vertical gradients in MLS HCl trends are well duplicated by SD-WACCM, the model trends are always somewhat more negative; this deserves further investigation. The original MLS N2O product time series yield small positive LS tropical trends (2005–2012), consistent with models and with rates of increase in tropospheric N2O. However, longer-term series from the more current MLS N2O standard product are affected by instrument-related drifts that have also impacted MLS H2O. The LS short-term trend profiles from MLS N2O and HNO3 at midlatitudes in the two hemispheres have different signs; these patterns are well matched by SD-WACCM trends for these species. These model–data comparisons provide a reminder that the QBO and other dynamical factors affect decadal variability in a major way, notably in the lower stratosphere, and can thus significantly hinder the goals of robustly extracting (and explaining) small underlying long-term trends. The data sets and tools discussed here for model evaluation could be expanded to comparisons of species or regions not included here, as well as to comparisons between a variety of chemistry–climate models.

Description: This study investigates the representativeness of two types of orbital sampling applied to stratospheric temperature and trace gas fields. Model fields are sampled using real sampling patterns from the Aura Microwave Limb Sounder (MLS), the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS). The MLS sampling acts as a proxy for a dense uniform sampling pattern typical of limb emission sounders, while HALOE and ACE-FTS represent coarse non-uniform sampling patterns characteristic of solar occultation instruments. First, this study revisits the impact of sampling patterns in terms of the sampling bias, as previous studies have done. Then, it quantifies the impact of different sampling patterns on the estimation of trends and their associated detectability. In general, we find that coarse non-uniform sampling patterns may introduce non-negligible errors in the inferred magnitude of temperature and trace gas trends and necessitate considerably longer records for their definitive detection. Lastly, we explore the impact of these sampling patterns on tropical vertical velocities derived from stratospheric water vapor measurements. We find that coarse non-uniform sampling may lead to a biased depiction of the tropical vertical velocities and, hence, to a biased estimation of the impact of the mechanisms that modulate these velocities. These case studies suggest that dense uniform sampling such as that available from limb emission sounders provides much greater fidelity in detecting signals of stratospheric change (for example, fingerprints of greenhouse gas warming and stratospheric ozone recovery) than coarse non-uniform sampling such as that of solar occultation instruments.

Description: The representation of upper tropospheric/lower stratospheric (UTLS) jet and tropopause characteristics is compared in five modern high-resolution reanalyses for 1980 through 2014. Climatologies of upper tropospheric jet, subvortex jet (the lowermost part of the stratospheric vortex), and multiple tropopause frequency distributions in MERRA (Modern Era Retrospective Analysis for Research and Applications), ERA-I (the ECMWF interim reanalysis), JRA-55 (the Japanese 55-year Reanalysis), and CFSR (the Climate Forecast System Reanalysis) are compared with those in MERRA-2. Differences between alternate products from individual reanalysis systems are assessed; in particular, a comparison of CFSR data on model and pressure levels highlights the importance of vertical grid spacing. Most of the differences in distributions of UTLS jets and multiple tropopauses are consistent with the differences in assimilation model grids and resolution: For example, ERA-I (with coarsest native horizontal resolution) typically shows a significant low bias in upper tropospheric jets with respect to MERRA-2, and JRA-55 a more modest one, while CFSR (with finest native horizontal resolution) shows a high bias with respect to MERRA-2 in both upper tropospheric jets and multiple tropopauses. Vertical temperature structure and grid spacing are especially important for multiple tropopause characterization. Substantial differences between MERRA and MERRA-2 are seen in mid- to high-latitude southern hemisphere winter upper tropospheric jets and multiple tropopauses, and in the upper tropospheric jets associated with tropical circulations during the solstice seasons; some of the largest differences from the other reanalyses are seen in the same times and places. Very good qualitative agreement among the reanalyses is seen between the large scale climatological features in UTLS jet and multiple tropopause distributions. Quantitative differences may, however, have important consequences for transport and variability studies. Our results highlight the importance of considering reanalyses differences in UTLS studies, especially in relation to resolution and model grids; this is particularly critical when using high-resolution reanalyses as an observational reference for evaluating global chemistry climate models.

Description: We evaluate the recently delivered Community Earth System Model version 1 (CESM1) Whole Atmosphere Community Climate Model (WACCM) using satellite-derived global composition datasets, focusing on the stratosphere. The simulations include free-running (FR-WACCM) and specified-dynamics (SD-WACCM) versions of the model. Model evaluations are made using global monthly zonal mean time series obtained by the Aura Microwave Limb Sounder (MLS), as well as longer-term global data records compiled by the Global Ozone Chemistry and Related Trace gas Data Records for the Stratosphere (GOZCARDS) project. A recent update (version 2.20) to the original GOZCARDS merged ozone (O3) data set is used. We discuss upper atmospheric climatology and zonal mean variability using O3, hydrogen chloride (HCl), nitrous oxide (N2O), nitric acid (HNO3), and water vapor (H2O) data. There are a few significant model/data mean biases, such as for lower stratospheric O3, for which the models overestimate the mean observed values and seasonal amplitudes. Another clear difference occurs for HNO3 during recurring winter periods of strong HNO3 enhancements at high latitudes; this stems from the known omission of ion chemistry relating to particle precipitation effects, in the global models used here. In the lower stratosphere at high southern latitudes, the variations in polar winter/spring composition observed by MLS are generally well matched by SD-WACCM, the main exception being for the early winter rate of decrease in HCl, which is too slow in the model. In general, the latitude/pressure distributions of annual and semi-annual oscillation amplitudes derived from the MLS data are properly captured by the corresponding model values. Nevertheless, detailed aspects of the interactions between the quasi-biennial, annual, and semi-annual ozone variations in the upper stratosphere are not as well represented by FR-WACCM as by SD-WACCM. One of the evaluation diagnostics we use represents the closeness of fit between the model/data anomaly time series, and we also consider the correlation coefficients. Not surprisingly, SD-WACCM, which is driven by realistic dynamics, generally matches observed deseasonalized anomalies better than FR-WACCM does. Other results indicate that the root mean square variability is sometimes found to be significantly smaller in FR-WACCM than in SD-WACCM and the observations. Most notably, FR-WACCM underestimates the observed interannual variability for H2O by ~30%, typically, and by as much as a factor of two in some regions; this has some implications for the time needed to detect small trends. We have derived trends using a multivariate linear regression (MLR) model, and there is a robust signal in both MLS observations and WACCM of an upper stratospheric O3 increase from 2005 to 2014 by ~0.2–0.4%/yr (±0.2%/yr, 2σ), depending on which broad latitude bin (tropics or mid-latitudes) is considered. In the lower stratosphere, while some decreases are indicated for 1998–2014 (based on merged GOZCARDS O3), we find near-zero or positive trends when using MLS O3 data alone for 2005–2014, albeit with no robust statistical significance. SD-WACCM results track such positive tendencies (albeit with no statistical significance). For H2O, the most statistically significant trend result for 2005–2014 is an upper stratospheric increase, peaking at slightly more than 0.5%/yr in the lower mesosphere, in fairly close agreement with SD-WACCM trends, but with smaller values in FR-WACCM. For HCl, while the lower stratospheric vertical gradients of MLS trends are duplicated to some extent by SD-WACCM, the model trends (decreases) are always on the low side of the data trends. There is little model-based indication (in SD-WACCM) of a significantly positive HCl trend derived from the MLS tropical series at 68hPa; this deserves further study. For N2O, the MLS-derived trends (for 2005–2012) point to negative trends (of up to about −1%/yr) in the NH mid-latitudes and positive trends (of up to about +3%/yr) in the SH mid-latitudes, in good agreement with the asymmetry that exists in SD-WACCM trend results. The small observed positive N2O trends of ~0.2%/yr in the 100 to 30hPa tropical region are also consistent with model results (SD-WACCM in particular), which in turn are very close to the known rate of increase in tropospheric N2O. In the case of HNO3, MLS-derived lower stratospheric trend differences (for 2005–2014) between hemispheres are opposite in sign to those from N2O and in reasonable agreement with both WACCM results, despite large error bars. The data sets and tools discussed here for the evaluation of the models could be expanded to additional comparisons of species not included here, as well as to model intercomparisons using a variety of CCMs, keeping in mind that there are different parameterizations and approaches for both free-running and specified-dynamics simulations.

Description: This study investigates the representativeness of two types of orbital sampling applied to stratospheric temperature and trace gas fields. Model fields are sampled using real sampling patterns from the Aura Microwave Limb Sounder (MLS), the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS). The MLS sampling acts as a proxy for a dense uniform sampling pattern typical of limb emission sounders, while HALOE and ACE-FTS represent coarse nonuniform sampling patterns characteristic of solar occultation instruments. First, this study revisits the impact of sampling patterns in terms of the sampling bias, as previous studies have done. Then, it quantifies the impact of different sampling patterns on the estimation of trends and their associated detectability. In general, we find that coarse nonuniform sampling patterns may introduce non-negligible errors in the inferred magnitude of temperature and trace gas trends and necessitate considerably longer records for their definitive detection. Lastly, we explore the impact of these sampling patterns on tropical vertical velocities derived from stratospheric water vapor measurements. We find that coarse nonuniform sampling may lead to a biased depiction of the tropical vertical velocities and, hence, to a biased estimation of the impact of the mechanisms that modulate these velocities. These case studies suggest that dense uniform sampling such as that available from limb emission sounders provides much greater fidelity in detecting signals of stratospheric change (for example, fingerprints of greenhouse gas warming and stratospheric ozone recovery) than coarse nonuniform sampling such as that of solar occultation instruments.

Description: The representation of upper tropospheric/lower stratospheric (UTLS) jet and tropopause characteristics is compared in five modern high-resolution reanalyses for 1980 through 2014. Climatologies of upper tropospheric jet, subvortex jet (the lowermost part of the stratospheric vortex), and multiple tropopause frequency distributions in MERRA (Modern Era Retrospective Analysis for Research and Applications), ERA-I (the ECMWF interim reanalysis), JRA-55 (the Japanese 55-year Reanalysis), and CFSR (the Climate Forecast System Reanalysis) are compared with those in MERRA-2. Differences between alternate products from individual reanalysis systems are assessed; in particular, a comparison of CFSR data on model and pressure levels highlights the importance of vertical grid spacing. Most of the differences in distributions of UTLS jets and multiple tropopauses are consistent with the differences in assimilation model grids and resolution: For example, ERA-I (with coarsest native horizontal resolution) typically shows a significant low bias in upper tropospheric jets with respect to MERRA-2, and JRA-55 a more modest one, while CFSR (with finest native horizontal resolution) shows a high bias with respect to MERRA-2 in both upper tropospheric jets and multiple tropopauses. Vertical temperature structure and grid spacing are especially important for multiple tropopause characterization. Substantial differences between MERRA and MERRA-2 are seen in mid- to high-latitude southern hemisphere winter upper tropospheric jets and multiple tropopauses, and in the upper tropospheric jets associated with tropical circulations during the solstice seasons; some of the largest differences from the other reanalyses are seen in the same times and places. Very good qualitative agreement among the reanalyses is seen between the large scale climatological features in UTLS jet and multiple tropopause distributions. Quantitative differences may, however, have important consequences for transport and variability studies. Our results highlight the importance of considering reanalyses differences in UTLS studies, especially in relation to resolution and model grids; this is particularly critical when using high-resolution reanalyses as an observational reference for evaluating global chemistry climate models.

Description: This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major components--the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products.