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Estimating global mean sea-level rise and its uncertainties by 2100 and 2300 from an expert survey
(2020)
Sea-level rise projections and knowledge of their uncertainties are vital to make informed mitigation and adaptation decisions. To elicit projections from members of the scientific community regarding future global mean sea-level (GMSL) rise, we repeated a survey originally conducted five years ago. Under Representative Concentration Pathway (RCP) 2.6, 106 experts projected a likely (central 66% probability) GMSL rise of 0.30-0.65 m by 2100, and 0.54-2.15 m by 2300, relative to 1986-2005. Under RCP 8.5, the same experts projected a likely GMSL rise of 0.63-1.32 m by 2100, and 1.67-5.61 m by 2300. Expert projections for 2100 are similar to those from the original survey, although the projection for 2300 has extended tails and is higher than the original survey. Experts give a likelihood of 42% (original survey) and 45% (current survey) that under the high-emissions scenario GMSL rise will exceed the upper bound (0.98 m) of the likely range estimated by the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, which is considered to have an exceedance likelihood of 17%. Responses to open-ended questions suggest that the increases in upper-end estimates and uncertainties arose from recent influential studies about the impact of marine ice cliff instability on the meltwater contribution to GMSL rise from the Antarctic Ice Sheet.
Modeling and data analysis of large-scale atmosphere dynamics associated with extreme weather
(2018)
In the last decades the frequency and intensity of extreme weather events like heat waves and heavy rainfall have increased and are at least partly linked to global warming. These events can have a strong impact on agricultural and economic production and, thereby, on society. Thus, it is important to improve our understanding of the physical processes leading to those extreme events in order to provide accurate near-term and long-term forecasts. Thermodynamic drivers associated with global warming are well understood, but dynamical aspects of the atmosphere much less so. The dynamical aspects, while less important than the thermodynamic drivers in regards to large-scale and long-time averaged effects, play a critical role in the formation of extremes.
The overall aim of this thesis is to improve our understanding of patterns, variability and trends in the global atmospheric circulation under a changing climate. In particular, in this dissertation I developed two new data-driven methods to quantitatively describe the dynamics of jet streams, Hadley cells and storm tracks. In addition, I introduce and validate a new statistical-dynamical atmosphere model that can be used to efficiently model the large-scale circulation.
First, I developed a scheme based on the Dijkstra ‘shortest-path’ algorithm to identify jet stream cores. Using reanalysis data, I found a significant change in jet stream strength and position over the last decades: Specifically, a decrease in wind speeds and a spatial shift toward the poles. This work also shows that the splitting or merging of the polar front jet stream and the subtropical jet stream depends on the season and longitudinal position. In a follow-up study, I analyzed trends in the latitudinal position of the poleward edge of the Hadley cell and subtropical jet stream core for all longitudes. These trends depend strongly on longitude and thus the impacts of tropical expansion might be pronounced in some regions and absent in others.
The second approach was to develop an empirical forecast method for European and Mediterranean winter precipitation. This prediction algorithm innovatively incorporates the spatial patterns of predictors in autumn using clustering analyses. I identified the most important precursors (snow cover in Eurasia, Barents and Kara sea ice concentrations as well as sea surface temperature in the Atlantic and Mediterranean region) for the precipitation prediction. This forecast algorithm had higher forecast skills than conventionally employed methods such as Canonical Correlation Analysis or operational systems using climate models.
The last approach was to examine the atmospheric circulation using the novel statisticaldynamical atmosphere model Aeolus. First, I validated the model’s depiction of the largescale circulation in terms of Hadley circulation, jet streams, storm tracks and planetary waves. To do so, I performed a parameter optimization using simulated annealing. Next, I investigated the sensitivity of the large-scale circulation to three different temperature components: global mean temperature, meridional temperature gradient and zonal temperature gradient. The model experiment showed that the strength of the Hadley cell, storm tracks and jet streams depend almost linearly on both the global mean temperature and the meridional temperature gradient, whereas the zonal temperature gradient is shown to have little or no influence. The magnitude of planetary waves is clearly affected by all three temperature components. Finally, the width of the Hadley cell behaves nonlinearly with respect to all three temperature components.
These findings might have profound consequences for climate modeling of the Mediterranean region. The latitudinal poleward trend of the Hadley cell edge position might become stronger under climate change according to the results with Aeolus. These changes would lead to a substantial reduction of the winter precipitation in the Mediterranean region. In this case seasonal empirical forecast methods, like the clustering-based prediction scheme, will play an important role for forecasting seasonal droughts in advance such that water managers and politicians can mitigate impacts.