Improving and Assessing Organized Convection Parameterization in the Unified Model

Copernicus Publications (2024)

Authors:

Zhixiao Zhang, Hannah Christensen, Mark Muetzelfeldt, Tim Woollings, Bob Plant, Alison Stirling, Michael Whitall, Mitchell Moncrieff, Chih-Chieh Chen

Response of winter climate and extreme weather to projected Arctic sea-ice loss in very large-ensemble climate model simulations

Copernicus Publications (2024)

Authors:

Kunhui Ye, Tim Woollings, Sarah Sparrow, Peter Watson, James Screen

Global Chemical Transport on Hot Jupiters: Insights from the 2D VULCAN Photochemical Model

The Astrophysical Journal American Astronomical Society 963:1 (2024) 41

Authors:

Shang-Min Tsai, Vivien Parmentier, João M Mendonça, Xianyu Tan, Russell Deitrick, Mark Hammond, Arjun B Savel, Xi Zhang, Raymond T Pierrehumbert, Edward W Schwieterman

Aeolus wind lidar observations of the 2019/2020 quasi-biennial oscillation disruption with comparison to radiosondes and reanalysis

Atmospheric Chemistry and Physics European Geosciences Union 24:4 (2024) 2465-2490

Authors:

Timothy P Banyard, Corwin J Wright, Scott M Osprey, Neil P Hindley, Gemma Halloran, Lawrence Coy, Paul A Newman, Neal Butchart, Martina Bramberger, M Joan Alexander

Abstract:

The quasi-biennial oscillation (QBO) was unexpectedly disrupted for only the second time in the historical record during the 2019/2020 boreal winter. As the dominant mode of atmospheric variability in the tropical stratosphere and a significant source of seasonal predictability globally, understanding the drivers behind this unusual behaviour is very important. Here, novel data from Aeolus, the first Doppler wind lidar (DWL) in space, are used to observe the 2019/2020 QBO disruption. Aeolus is the first satellite able to observe winds at high resolution on a global scale, and it is therefore a uniquely capable platform for studying the evolution of the disruption and the broader circulation changes triggered by it. This study therefore contains the first direct wind observations of the QBO from space, and it exploits measurements from a special Aeolus scanning mode, implemented to observe this disruption as it happened. Aeolus observes easterly winds of up to 20 m s−1 in the core of the disruption jet during July 2020. By co-locating with radiosonde measurements from Singapore and the ERA5 reanalysis, comparisons of the observed wind structures in the tropical stratosphere are produced, showing differences in equatorial wave activity during the disruption period. Local zonal wind biases are found in both Aeolus and ERA5 around the tropopause, and the average Aeolus-ERA5 Rayleigh horizontal line-of-sight random error is found to be 7.58 m s−1. The onset of the QBO disruption easterly jet occurs 5 d earlier in Aeolus observations compared with the reanalysis. This discrepancy is linked to Kelvin wave variances that are 3 to 6 m2 s−2 higher in Aeolus compared with ERA5, centred on regions of maximum vertical wind shear in the tropical tropopause layer that are up to twice as sharp. The enhanced lower-stratospheric westerly winds which are known to help disrupt the QBO, perhaps with increasing frequency as the climate changes, are also stronger in Aeolus observations, with important implications for the future predictability of such disruptions. An investigation into differences in the equivalent depth of the most dominant Kelvin waves suggests that slower, shorter-vertical-wavelength waves break more readily in Aeolus observations compared with the reanalysis. This analysis therefore highlights how Aeolus and future DWL satellites can deepen our understanding of the QBO, its disruptions and the tropical upper-troposphere lower-stratosphere region more generally.

Response of winter climate and extreme weather to projected Arctic sea-ice loss in very large-ensemble climate model simulations

npj Climate and Atmospheric Science Springer Nature 7:1 (2024) 20

Authors:

Kunhui Ye, Tim Woollings, Sarah N Sparrow, Peter AG Watson, James A Screen

Abstract:

Very large (~2000 members) initial-condition ensemble simulations have been performed to advance understanding of mean climate and extreme weather responses to projected Arctic sea-ice loss under 2 °C global warming above preindustrial levels. These simulations better sample internal atmospheric variability and extremes for each model compared to those from the Polar Amplification Model Intercomparison Project (PAMIP). The mean climate response is mostly consistent with that from the PAMIP multi-model ensemble, including tropospheric warming, reduced midlatitude westerlies and storm track activity, an equatorward shift of the eddy-driven jet and increased mid-to-high latitude blocking. Two resolutions of the same model exhibit significant differences in the stratospheric circulation response; however, these differences only weakly modulate the tropospheric response. The response of temperature and precipitation extremes largely follows the seasonal-mean response. Sub-sampling confirms that large ensembles (e.g. ≥400) are needed to robustly estimate the seasonal-mean large-scale circulation response, and very large ensembles (e.g. ≥1000) for regional climate and extremes.