# The Influence of Gravity Waves on Ice Saturation in the Tropical Tropopause Layer over Darwin, Australia

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## Abstract

**:**

## 1. Introduction

- 1.
- When using an atmospheric state classification algorithm (as opposed to traditional seasonal analysis), how do the characteristics of gravity waves vary between convective (monsoon) and non-convective (non-monsoon) states?
- 2.
- Do gravity waves produce a measurable impact on ice saturation in the TTL?

## 2. Experiments

#### 2.1. Radiosondes

_{0}is the background mean temperature (i.e., a polynomial fit to the profile, see Section 2.3). While radiosonde measurements are excellent for measuring gravity waves in a Eulerian framework, these measurements do not resolve Lagrangian temperature fluctuations, which are also very important for contextualizing TTL dehydration [27].

#### 2.2. ARM Best-Estimate Cloud Radiation Product

#### 2.3. Wavelet Analysis

#### 2.4. Atmospheric State Classification

^{−2}) of any state (Table 1). Likewise, state 8 occurs most often near the beginning and end of the monsoon season and is the second cloudiest state based on OLR and high cloud fraction. State 1, despite occurring most often during the dry season, has the third lowest average OLR of the eight states and has the largest high cloud fraction of the four dry-season states. Convection in this state, when it does occur, is often isolated and rarely peaks above 7 km according to [21]. High clouds in any of these states, generally speaking, are more likely to be advected horizontally rather than formed locally by convection. Overall, the statistics (especially OLR) presented in Table 1 qualitatively agree with Table 1 in [21]. Our computations of KE and PE (for the range of altitudes used) are similar to previous work [29]. Eddy potential energy is larger compared to eddy kinetic energy during both monsoon states, perhaps related to changes in static stability, which is a factor relating T’ and u’ in linear gravity wave theory.

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Wavelet analysis of a radiosonde launched on 1 February 2006 (State 7), where panel (

**A**) represents the power spectrum at each wavelength bin, panel (

**B**) is the time (altitude)/frequency (wavelength) decomposition of the perturbation zonal wind, panel (

**C**) is the zonal wind (U wind) component, and panel (

**D**) is the dry-bulb temperature. The 95% confidence interval and “cone of influence”, both following [29], are also shown on panel (

**B**). The dashed line in panels (

**C**) and (

**D**) represent the background zonal wind and dry-bulb temperature (respectively), which is computed as a 5th order polynomial fit. The dashed red line represents the altitude of the tropopause, which is also computed from radiosonde temperature data.

**Figure 2.**Time-frequency wave power spectrum using perturbation zonal wind for the eight atmospheric states, with each plot numbered according to atmospheric state. Only statistically significant wave power from individual radiosondes is used in each composite. The climatological tropopause altitude for each state is plotted as a dashed red line in each plot.

**Figure 3.**Box and whiskers plots (5th through 95th percentile) of temperature perturbations, relative to the height of the tropopause, for all dry season (states 1–4; red), transition season (states 5 and 6; green) and monsoon season (states 7 and 8; blue) data.

**Figure 4.**The change in relative humidity with respect to ice (RHI) as a result of temperature perturbations. Vapor pressure is first derived from the prescribed RH and saturation vapor pressure at the given temperature (200 K is solid, 190 K is dashed). Ice saturation vapor pressure is then computed for the given starting temperature and temperature perturbation (x-axis). Finally, RHI is re-computed using the original vapor pressure and ice saturation vapor pressure for the given temperature perturbation. The shaded light-blue region highlights changes in RHI from temperature perturbations between approximately −2 K to −3 K. The difference in RHI as a result of the given temperature perturbation is shown on the y-axis. Water/ice saturation vapor pressure is computed according to [35].

**Table 1.**Mean outgoing longwave radiation (OLR) and high cloud fraction based on ARMBECLDRAD data for the TWPC3 site, categorized by atmospheric state following [21]. Mean kinetic energy (KE), potential energy (PE), total wave energy, and the ratio of KE to PE computed from TWPC3 radiosonde data using Equations (1) and (2) are also included. Only radiosonde data from 10 km to 25 km are considered in this computation.

Atmospheric State | OLR (W/m ^{2}) | High Cloud Fraction (%) | KE (J/kg) | PE (J/kg) | Wave Energy (J/kg) | Ratio of KE to PE |
---|---|---|---|---|---|---|

1 | 285.4 | 41.3 | 10.3 | 10.9 | 21.2 | 0.94 |

2 | 294.1 | 28.7 | 10.4 | 8.7 | 19.1 | 1.19 |

3 | 307.3 | 23.3 | 9.6 | 8.2 | 17.9 | 1.17 |

4 | 296.5 | 34.9 | 9.9 | 8.5 | 18.4 | 1.16 |

5 | 289.8 | 44.8 | 9.7 | 10.4 | 20.1 | 0.93 |

6 | 295.2 | 42.0 | 9.1 | 8.9 | 17.9 | 1.02 |

7 | 271.6 | 88.3 | 11.5 | 15.0 | 26.5 | 0.77 |

8 | 280.4 | 73.0 | 9.6 | 12.2 | 21.8 | 0.79 |

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**MDPI and ACS Style**

Dzambo, A.M.; Hitchman, M.H.; Chang, K.-W. The Influence of Gravity Waves on Ice Saturation in the Tropical Tropopause Layer over Darwin, Australia. *Atmosphere* **2019**, *10*, 778.
https://doi.org/10.3390/atmos10120778

**AMA Style**

Dzambo AM, Hitchman MH, Chang K-W. The Influence of Gravity Waves on Ice Saturation in the Tropical Tropopause Layer over Darwin, Australia. *Atmosphere*. 2019; 10(12):778.
https://doi.org/10.3390/atmos10120778

**Chicago/Turabian Style**

Dzambo, Andrew M., Matthew H. Hitchman, and Kai-Wei Chang. 2019. "The Influence of Gravity Waves on Ice Saturation in the Tropical Tropopause Layer over Darwin, Australia" *Atmosphere* 10, no. 12: 778.
https://doi.org/10.3390/atmos10120778