Airplanes spend about 1% of cruise time in Moderate-Or-Greater (MOG) CAT (Sharman et al. 2006), which is defined as any turbulence occurring in the atmosphere away from a visible convective activity and which is particularly difficult to detect. MOG CAT events can injure passengers, cause structural damage to planes, and induce considerable economic loss. A major source of CAT is the Kelvin–Helmholtz instability (KHI), which is often induced by vertical wind shear associated with the jet stream and upper-level fronts. Recent studies have shown that under climate change, jet streams could be strengthened, and CAT frequency and intensity could significantly increase (Williams 2017). Assessing future CAT changes is a relatively new research topic and there are a lot of open questions. In particular, there is a need to understand the CAT trends in the present climate in atmospheric reanalysis and climate models and the mechanisms at play. The second step is to investigate the CAT sensitivity to global warming and the associated uncertainties.

In this study, we characterize present and future climate CAT trends in the Northern Hemisphere. For this purpose, we rely on a set of CAT indices computed with five different reanalysis datasets (among whom ERA5) and experiments performed by two CMIP6 climate models (CNRM-CM6-1 and IPSL-CM6A-LR). 

In present climate, the analysis of the CAT indices over the last four decades shows that CAT is more frequent over the North Atlantic, the Pacific Northwest, the Himalayas and the Rocky Mountains. We find that the spatial distribution of CAT over the North Atlantic is strongly related to the variability of large-scale circulation patterns. In particular, the occurrence of CAT is clearly associated with the positive phase of the North Atlantic Oscillation (NAO+) and the Atlantic Ridge weather regimes. A significant positive trend of CAT frequency is found using reanalysis in different regions of the northern hemisphere. However, the signal-to-noise ratio estimated from the climate models is still very weak in the present climate except over Northeast Asia.

We find that positive trends of CAT frequency are enhanced in response to global warming for the ssp8.5 worst-case scenario over the midlatitudes at the level 200hPa. This is coherent with previous studies. However, results also suggest that CAT future changes highly depend on altitude level and the region considered. For example, over the North Atlantic, CAT frequency significantly increases at the 200hPa (about 11 km) and 300hPa (about 9 km) levels, while it decreases at the 250hPa (about 10 km) level. This highlights the importance of study future changes in the vertical structure of the atmosphere.

Sharman R., Tebaldi C., Wiener G. et Wolff J., 2006, « An Integrated Approach to Mid- and Upper-Level Turbulence Forecasting », Weather and Forecasting, vol. 21, n^o 3, p. 268‑287.

Williams Paul D., 2017, « Increased light, moderate, and severe clear-air turbulence in response to climate change », Advances in Atmospheric Sciences, vol. 34, n^o 5, p. 576‑586.

How to cite: Foudad, M., Sanchez Gomez, E., Rochoux, M., and Jaravel, T.: Present climate characterization and future changes in Clear-Air Turbulence (CAT) over the northern hemisphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2796, https://doi.org/10.5194/egusphere-egu22-2796, 2022.

Columns of differential reflectivity, the difference between the horizontal and vertical reflectivity,
hereafter Zdr columns, are vertical columns of enhanced Zdr that extend above the environmental 0°C
level. These are easily identified when observed by polarimetric radars. Physically, these columns consist
of rain dominated by large drops that are being lofted above the freezing level and have been recognized
as a proxy for the location of updrafts. Their potential for nowcasting severe weather has been shown in
several past studies. We have developed an algorithm that identifies and tracks Zdr columns from
volumetric radar data along with 3D wind fields from MultiDoppler analysis to spatially correlate Zdr
columns with updrafts. Since Zdr columns are a manifestation of an updraft, different Zdr columns
properties for example Zdr column maximum height, volume, and area are expected to be related to
updraft intensity levels. In turn intensification of updrafts, as indicated by changes in the Zdr columns
properties, should be translated in intensification of observed precipitation at the surface. For the
estimation of rain rates, we used a radar-based polarimetric approach, which will allow us to monitor the
temporal evolution for a number of identified convective rain cells. These cells will be identified from
summer events observed by the C-band polarimetric German network. For each cell, the properties of Zdr
columns are correlated with rain rate values. Similarly, correlations are also calculated for updraft
volumes, updraft intensity, and other updraft properties. For the nowcasting of observed rain rates, an
extrapolation algorithm based on spatial and temporal properties of rain was used. Preliminary results
have shown that higher precipitation rates are generally associated with Zdr columns, and cells without a
Zdr column produce lower precipitation rates, as expected. Zdr column height and volume show a
positive correlation with precipitation intensity at the surface. The time lag between the intensification of
the Zdr column and associated increase in precipitation at the surface varies significantly between cells,
but it is generally short compared to previous studies, varying mostly between 5 to 15 minutes. An early
identification of cells associated with ZDR columns could benefit the skill of the nowcasting of localized
rain cells, which often are smoothed during extrapolation.

How to cite: Evaristo, R., Reinoso Rondinel, R., Crijnen, F., Chen, J.-Y., and Trömel, S.: Information content of differential reflectivity columns for precipitation nowcasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11210, https://doi.org/10.5194/egusphere-egu22-11210, 2022.

Aviation aims to reduce its climate impact by adopting climate-optimized aircraft trajectories, avoiding those regions of the atmosphere where aviation emission have a large climate impact. For this purpose, dedicated MET services have to be made available to the flight planning procedures, which need to be predicted with current numerical weather prediction models.

In order to represent spatially and temporally resolved information on the climate impact in terms of future temperature changes due to aviation emissions at a given time and location in such an advanced MET service, we propose to use algorithmic climate change functions (aCCFs) developed in earlier research projects. They include CO₂ and non-CO₂ effects, comprising nitrogen oxide (NO_x), water vapour and contrail-cirrus. These aCCFs allow to derive such climate impact information for flight planning directly from operational meteorological weather forecast data. By combining the individual aCCFs of water vapour, NO_x and contrail-cirrus, also merged non-CO₂ aCCFs can be generated.

With this study we aim  to identify specific weather situations which have the potential to provide a robust climate impact reduction despite uncertainties. This work is part of the SESAR project FlyATM4E. For this purpose, a systematic analysis of the meteorological conditions and situations is required. We will present the characteristic water vapour, NO_x induced and contrail-cirrus aCCFs for a set of specific weather patterns based on 2018 reanalysis data. A detailed analysis of the variation in aCCFs will be presented, including the dependency of individual and merged aCCFs to seasonal cycle, different synoptical weather situations and cruise altitude.

Acknowledgement:

The current study has been supported by FlyATM4E project, which has received funding from the SESAR Joint Undertaking under grant agreement No 891317 under European Union’s Horizon 2020 research and innovation program. 

How to cite: Dietmüller, S., Matthes, S., Grewe, V., Yamashita, H., Dahlmann, K., and Peter, P.: Quantifying the spatial and temporal non-CO2 effect of aviation by using algorithmic climate change functions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12381, https://doi.org/10.5194/egusphere-egu22-12381, 2022.

Aviation aims to reduce its climate impact, comprising CO₂ and non-CO₂ effects, by identifying climate-optimized aircraft trajectories. Such climate-optimized routes avoid regions of atmosphere where aviation emissions have a large impact on climate, e.g. by formation of contrails or strong NO_x-induced ozone formation. Implementing such climate-optimized routings requires that air traffic management has spatially and temporally resolved information on these non-CO₂ climate effects available during the trajectory planning process.

An overall modelling chain is required in order to expand the current flight planning procedure by considering climate impact during trajectory optimization in the overall optimization process. We explore a concept how to provide such information as an advanced MET Service: based on numerical weather prediction data and using algorithms climate change functions (aCCFs) such spatially and temporally resolved information can be provided. By integrating an uncertainty and risk analysis, we enable air traffic management (ATM) to identify climate-optimized aircraft trajectories which provide a robust and eco-efficient reduction in aviation’s climate impact. Climate optimization in this feasibility study, which is part of the SESAR ER project FlyATM4E, considers CO₂ as well as non-CO₂ effects, such as contrails and contrail-cirrus, water vapour, and NOx-induced effects on ozone and methane.

We will present the overall modelling concept which has been developed to explore climate-optimized aircraft trajectories considering individual weather situations in a series of one-day case studies. This concept also explores the robustness of estimated benefits in terms of mitigation of climate effects. The approach comprises a comprehensive uncertainty analysis, that provides alternative estimates as upper and lower limit estimates to reflect low level of scientific understanding or unknown efficacy of individual effects, resulting from state-of-the-art understanding from climate science. We also explore how to incorporate different physical climate metrics, as well as the usage of ensemble forecast data. We will present how these individual sources of uncertainty are statistically combined in order to provide a risk analysis together with the performance analysis of the identified alternative trajectory solutions, and hence identify robustness of mitigation gains on alternative trajectories. Finally, we will present an verification concept relying on numerical global chemistry-climate modelling with EMAC in order to explore such alternative routings during a one-year simulation.

The current study has been supported by FlyATM4E project, which has received funding from the SESAR Joint Undertaking under grant agreement No 891317 under European Union’s Horizon 2020 research and innovation program. High-performance computing simulations with the chemistry-climate model EMAC were performed at the Deutsches Klima-Rechenzentrum (DKRZ), Hamburg.

How to cite: Matthes, S., Dietmüller, S., Lührs, B., Linke, F., Grewe, V., Yin, F., Castino, F., Mendiguchia Meuser, M., Soler, M., Simorgh, A., Dahlmann, K., Gonzales, D., and Yamashita, H.: Climate optimized aircraft trajectories and risk analysis of climate impact mitigation: FlyATM4E, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12792, https://doi.org/10.5194/egusphere-egu22-12792, 2022.