The Semiannual Oscillation of the Tropical Zonal Wind in the Middle Atmosphere Derived from Satellite Geopotential Height Retrievals

Smith, Anne K.; García, Rolando R.; Moss, Andrew C.; Mitchell, N. J.

doi:10.1175/jas-d-17-0067.1

Cited by 48 publications

(135 citation statements)

References 30 publications

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“…1c) instead we identify SAO-related variations around 5 × 10 −3 and 10 −4 hPa. Similar structures are found in satellite data (Smith 2012;Smith et al 2017) and in other model simulations (PO10). The agreement of these mesospheric SAO structures with the present simulation is only qualitative.…”

Section: Resultssupporting

confidence: 87%

Relation between Equatorial Mesospheric Wind Anomalies during Spring and Middle Atmosphere Variability Modes

Zülicke

Becker

2017

SOLA

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Strong easterly wind anomalies in the equatorial upper mesosphere were observed during northern hemispheric spring equinox. While the anomalies are mainly related to the semi-annual oscillation (SAO) in the stratopause region and to the quasi-biennial oscillation (QBO) in the stratosphere, an additional influence of sudden stratospheric warmings (SSWs) was recently suggested. In order to analyze this relation, we use an 18-year simulation of the Kühlungsborn Mechanistic Circulation Model (KMCM) that includes these middle atmosphere variability modes. We compute composites for the different phases of the QBO and intensities of SSWs. The results support the notion that easterly equatorial mesospheric wind anomalies occur during spring equinox if the QBO is in its westerly phase. In addition we show that this relation does not hold when the preceding winter is characterized by strong SSWs. We interpret this phenomenon as a result of a persistent strengthening of the residual circulation in the equatorial stratopause region.(Citation: Zülicke, C., and E. Becker, 2017: Relation between equatorial mesospheric wind anomalies during spring and middle atmosphere variability modes. SOLA, 13A, 31−35,

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Section: Resultssupporting

confidence: 87%

Relation between Equatorial Mesospheric Wind Anomalies during Spring and Middle Atmosphere Variability Modes

Zülicke

Becker

2017

SOLA

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“…The migrating semidiurnal tide (westward k = 2) is not properly sampled, but its amplitude is relatively small in the range of altitude and latitude considered here (Wu et al, 2006). However, we find that above 80 km, where the amplitude of the migrating diurnal tide grows rapidly, some aliasing of the time mean is present, which originates ultimately from the sampling of tidal variability across the slightly shifted space-time coordinates of the observations in successive yaw maneuvers (see Smith et al, 2017). Nonetheless, here we use temperature series over the pressure range 80 to 5 × 10 −4 hPa (~17 to 95-km geometric altitude), since the time mean is irrelevant for the determination of the trend.…”

Section: Saber Observationsmentioning

confidence: 80%

“…We use SABER version 2 (v.2) data for temperature, which is available from http://saber.gats-inc.com/data.php. Smith et al (2017) showed that SABER data can be binned as a function of altitude and pressure to construct continuous time series suitable for processing using Salby's (1982aSalby's ( , 1982b Fast Fourier Synoptic Mapping (FFSM) method. FFSM operates on asynoptic observations to produce synoptic spectra in zonal wave number and frequency, whence behavior in the space-time domain can be reconstructed.…”

Section: Saber Observationsmentioning

confidence: 99%

Middle Atmosphere Temperature Trends in the Twentieth and Twenty‐First Centuries Simulated With the Whole Atmosphere Community Climate Model (WACCM)

2019

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We use Whole Atmosphere Community Climate Model simulations made under various climate change scenarios to study the evolution of the global‐mean temperature trend in the late twentieth century and the twenty‐first century. Results are compared with available satellite observations, including new trend estimates derived from the Sounding of the Atmosphere using Broadband Emission Radiometry instrument on NASA's TIMED spacecraft. Modeled and observed trends are shown to be consistent throughout the entire middle atmosphere, from near the tropopause (~16 km) to the lower thermosphere (~95 km) in the period covered by the satellite data. Simulations are extended into the twenty‐first century to document the evolution of the global‐mean temperature trend profile. We find, consistent with previous studies, a marked change in the trend profile at the turn of the twenty‐first century, which is driven by the recovery of stratospheric ozone following the adoption of the Montreal Protocol. In the twenty‐first century, the trend profile becomes more uniform with altitude, but its overall shape and magnitude are conditioned by the scenario adopted for future emissions of greenhouse gases. Our results suggest that the vertical profile of temperature trends in the middle atmosphere will remain an important signature of global climate change, and they underscore the importance of global, continuous monitoring of this region of the atmosphere.

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“…Simulations used here are based on the guidelines from the International Global Atmospheric Chemistry -Stratosphere-troposphere Processes And their role in Climate (IGAC/SPARC) Chemistry-Climate Model Initiative (Morgenstern et al, 2017). Improvements in CESM1 WACCM for CCMI include a modification to the orographic gravity wave forcing, which reduced the cold bias in Antarctic polar temperatures (Garcia et al, 2017;Calvo et al, 2017), and updates to the stratospheric heterogeneous chemistry, which improved the representation of polar ozone depletion (Wegner et al, 2013;Solomon et al, 2015). In this work, there are two CCMI scenarios, spanning the 1990-2014 period.…”

Section: Gozcardsmentioning

confidence: 99%

“…S4 for the relevant time series). Uncertainties regarding lower-stratospheric heterogeneous chemistry modeling for SD-WACCM at high latitudes in the polar winter and spring have been discussed by Solomon et al (2015) for one specific year (2011), including most of the features shown in Fig. 7.…”

Section: Average Abundancesmentioning

confidence: 99%

Evaluation of CESM1 (WACCM) free-running and specified dynamics atmospheric composition simulations using global multispecies satellite data records

et al. 2019

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Abstract. 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.

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The Semiannual Oscillation of the Tropical Zonal Wind in the Middle Atmosphere Derived from Satellite Geopotential Height Retrievals

Cited by 48 publications

References 30 publications

Relation between Equatorial Mesospheric Wind Anomalies during Spring and Middle Atmosphere Variability Modes

Relation between Equatorial Mesospheric Wind Anomalies during Spring and Middle Atmosphere Variability Modes

Middle Atmosphere Temperature Trends in the Twentieth and Twenty‐First Centuries Simulated With the Whole Atmosphere Community Climate Model (WACCM)

Evaluation of CESM1 (WACCM) free-running and specified dynamics atmospheric composition simulations using global multispecies satellite data records

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