Multi-model ensemble predictions of atmospheric turbulence

Atmospheric turbulence is a major aviation hazard, costing the aviation industry millions of dollars each year through aircraft damage and injuries to passengers and crew. In this thesis we compare reanalysis data to climate model simulations to understand how well climate models predict the location of Clear-Air Turbulence (CAT). We then model how climate change will impact CAT on a global scale, in all four seasons and at multiple flight levels. This provides ample motivation for the second half of the thesis which aims to improve aviation turbulence forecasting by testing a multi-model ensemble forecast by combing the Met Office Global and Regional Ensemble Prediction System (MOGREPS-G) and the European Centre for Medium Range Weather Forecasting (ECMWF) Ensemble. The main results found are that climate models are able to skilfully predict the location of CAT, with the main uncertainty of the location of CAT coming from which turbulence index is the best and not from the use of a climate model. We also found CAT will increase globally in the future with climate change, for multiple aviation-relevant turbulence strength categories, at multiple flight levels and in all seasons. For ensemble forecasting we started with a single-diagnostic equally weighted multi-model ensemble and found it is at least as skilful as the single-model ensembles. This lack of significant improvement in the forecast skill could be because when increasing the forecast spread, we capture more turbulence events but also more false alarms. The relative economic value of the forecast is improved for the multi-model ensemble, particularly at low cost/loss ratios. Through combining two ensembles we gain consistency, gain more operational resilience and create one authoritative forecast whilst maintaining skill and increasing value. Extending this work further, it is found that these results apply more generally for multiple turbulence diagnostics, as the multi-model ensemble was more skilful than either of the single-model ensembles. When combining the predictors, the multi-diagnostic multi-model ensemble was more skilful than the two single-model ensembles. It was also found that an optimised 12-member ECMWF and MOGREPS-G multi-diagnostic ensemble was more skilful than the 51-member multi-diagnostic ensemble. What this therefore indicates is that a smaller ensemble spread for the individual diagnostics within a multidiagnostic ensemble is important for optimising operational forecasts in the future, which could reduce computational costs for turbulence forecasting

Global response of clear-air turbulence to climate change

Clear-air turbulence (CAT) is one of the largest causes of weather-related aviation incidents. Here we use climate model simulations to study the impact that climate change could have on global CAT by the period 2050–2080. We extend previous work by analyzing eight geographic regions, two flight levels, five turbulence strength categories, and four seasons. We find large relative increases in CAT, especially in the midlatitudes in both hemispheres, with some regions experiencing several hundred per cent more turbulence. The busiest international airspace experiences the largest increases, with the volume of severe CAT approximately doubling over North America, the North Pacific, and Europe. Over the North Atlantic, severe CAT in future becomes as common as moderate CAT historically. These results highlight the increasing need to improve operational CAT forecasts and to use them effectively in flight planning, to limit discomfort and injuries among passengers and crew

Aviation turbulence: dynamics, forecasting, and response to climate change

Atmospheric turbulence is a major hazard in the aviation industry and can cause injuries to passengers and crew. Understanding the physical and dynamical generation mechanisms of turbulence aids with the development of new forecasting algorithms and, therefore, reduces the impact that it has on the aviation industry. The scope of this paper is to review the dynamics of aviation turbulence, its response to climate change, and current forecasting methods at the cruising altitude of aircraft. Aviation-affecting turbulence comes from three main sources: vertical wind shear instabilities, convection, and mountain waves. Understanding these features helps researchers to develop better turbulence diagnostics. Recent research suggests that turbulence will increase in frequency and strength with climate change, and therefore, turbulence forecasting may become more important in the future. The current methods of forecasting are unable to predict every turbulence event, and research is ongoing to find the best solution to this problem by combining turbulence predictors and using ensemble forecasts to increase skill. The skill of operational turbulence forecasts has increased steadily over recent decades, mirroring improvements in our understanding. However, more work is needed—ideally in collaboration with the aviation industry—to improve observations and increase forecast skill, to help maintain and enhance aviation safety standards in the future

Can a climate model successfully diagnose clear‐air turbulence and its response to climate change?

Atmospheric turbulence causes the majority of weatherrelated aircraft accidents. Climate models project large increases in clear-air turbulence as the jet streams become more sheared in response to climate change. However, climate models have coarser resolutions than the numerical weather prediction models that are used to forecast clearair turbulence operationally, raising questions about their suitability for this purpose. Here we provide the first rigorous demonstration that climate models are capable of successfully diagnosing clear-air turbulence and its response to climate change. We use an ensemble of seven clear-air turbulence diagnostics to compare 38 years of historic turbulence diagnosed from climate model simulations and highresolution reanalysis data. We find that the differences in turbulence between the climate model and reanalysis data are much smaller than the spread between the diagnostics. When using a climate model to calculate the probabilities (and their temporal trends) of encountering clear-air turbulence of any strength, at any flight cruising level, and in any season, we find that most of the uncertainty stems from the turbulencediagnosticsratherthantheclimatemodel. These results confirm the suitability of climate models for the task of producing future clear-air turbulence projections. The turbulence increases are generally larger when diagnosed from the reanalysis data than the climate model, suggest- ing that previous quantifications from climate models of the response of clear-air turbulence to climate change may be underestimates. Our results show that the key to reducing uncertainty in projections of future clear-air turbulence lies inimprovingtheclear-airturbulencediagnosticsratherthan the climate models

Excess centrosomes perturb dynamic endothelial cell repolarization during blood vessel formation

Blood vessel formation requires dynamic movements of endothelial cells (ECs) within sprouts. The cytoskeleton regulates migratory polarity, and centrosomes organize the microtubule cytoskeleton. However, it is not well understood how excess centrosomes, commonly found in tumor stromal cells, affect microtubule dynamics and interphase cell polarity. Here we find that ECs dynamically repolarize during sprouting angiogenesis, and excess centrosomes block repolarization and reduce migration and sprouting. ECs with excess centrosomes initially had more centrosome-derived microtubules but, paradoxically, fewer steady-state microtubules. ECs with excess centrosomes had elevated Rac1 activity, and repolarization was rescued by blockade of Rac1 or actomyosin blockers, consistent with Rac1 activity promoting cortical retrograde actin flow and actomyosin contractility, which precludes cortical microtubule engagement necessary for dynamic repolarization. Thus normal centrosome numbers are required for dynamic repolarization and migration of sprouting ECs that contribute to blood vessel formation