stratosphere - troposphere coupling; teleconnections; ocean - atmosphere coupling; weather and climate; midlatitude weather; sub-seasonal to seasonal variability; stakeholder involvement; statistical forecasting; extreme weather events; tropical - extratropical coupling
Domeisen Daniela I.V. (2019), Estimating the Frequency of Sudden Stratospheric Warming Events From Surface Observations of the North Atlantic Oscillation, in Journal of Geophysical Research: Atmospheres
, 124(6), 3180-3194.
Jiménez-Esteve B., Domeisen D. I. V. (2019), Nonlinearity in the North Pacific Atmospheric Response to a Linear ENSO Forcing, in Geophysical Research Letters
, 46(4), 2271-2281.
ButlerAmy H. (2019), Sub-seasonal Predictability and the Stratosphere, in Sub-Seasonal to Seasonal Prediction. The Gap Between Weather and Climate Forecasting
, 223 - 241.
Wengel Christian, Bayr Tobias, Domeisen Daniela (2019), The effect of the equatorial Pacific cold SST bias on simulated ENSO teleconnections to the North Pacific and California, in Climate Dynamics
, in press.
Domeisen Daniela I.V., Garfinkel Chaim I., Butler Amy H. (2019), The Teleconnection of El Niño Southern Oscillation to the Stratosphere, in Reviews of Geophysics
, 57(1), 5-47.
Garfinkel Chaim I., Schwartz Chen, Butler Amy H., Domeisen Daniela I.V., Son Seok-Woo, White Ian P. (2019), Weakening of the Teleconnection From El Niño–Southern Oscillation to the Arctic Stratosphere Over the Past Few Decades: What Can Be Learned From Subseasonal Forecast Models?, in Journal of Geophysical Research: Atmospheres
, 124(14), 7683-7696.
Jiménez-Esteve Bernat, Domeisen Daniela I. V. (2018), The Tropospheric Pathway of the ENSO–North Atlantic Teleconnection, in Journal of Climate
, 31(11), 4563-4584.
Dobrynin Mikhail, Domeisen Daniela I. V., Müller Wolfgang A., Bell Louisa, Brune Sebastian, Bunzel Felix, Düsterhus André, Fröhlich Kristina, Pohlmann Holger, Baehr Johanna (2018), Improved Teleconnection-Based Dynamical Seasonal Predictions of Boreal Winter, in Geophysical Research Letters
, 45(8), 3605-3614.
Domeisen Daniela I. V., Martius Olivia, Jiménez-Esteve Bernat (2018), Rossby Wave Propagation into the Northern Hemisphere Stratosphere: The Role of Zonal Phase Speed, in Geophysical Research Letters
, 45(4), 2064-2071.
Domeisen Daniela I. V., Badin Gualtiero, Koszalka Inga M. (2018), How Predictable Are the Arctic and North Atlantic Oscillations? Exploring the Variability and Predictability of the Northern Hemisphere, in Journal of Climate
, 31(3), 997-1014.
Garfinkel Chaim I., Schwartz Chen, Domeisen Daniela I.V., Son Seok-Woo, Butler Amy H. (2018), Extratropical atmospheric predictability from the Quasi-Biennial Oscillation in subseasonal forecast models, in Journal of Geophysical Research
Sheshadri Aditi, Plumb R.A., Lindgren E.A., Domeisen D.I.V. (2018), The vertical structure of annular modes, in Journal of the Atmospheric Sciences
, 3507 - 351.
Wulff C. Ole, Greatbatch Richard J., Domeisen Daniela I. V., Gollan Gereon, Hansen Felicitas (2017), Tropical Forcing of the Summer East Atlantic Pattern, in Geophysical Research Letters
, 44(21), 11,166-11,173.
Sub-seasonal to seasonal (S2S) weather predictions, covering the timescale of a few weeks to several months, are of crucial importance for decision making, e.g. for the harvest of agricultural products and for setting the price of renewable energy. One reason for the low skill lies in the inherent difficulty to predict atmospheric variability in the range between short-term weather prediction (up to about 15 days), where initial conditions are a major factor, and long-term prediction (years to decades), where boundary conditions such as the ocean exert their major influence on the atmosphere. However, prediction is a seamless problem, and all timescales exhibit phenomena in the Earth system that contribute to predictability: The processes that contribute to predictability at S2S timescales are however still not sufficiently understood, leading to a model prediction skill that is often too low for decision making. Due to the complexity of the problem, there has been a lack of attention to the time window of S2S prediction over the past decades. Only very recently has the low skill in S2S prediction come to the attention of the national weather services, and efforts have been started to publicly provide suitable model data for the international research community to start tackling these issues. The proposed research work aims to contribute to the global effort of evaluating possible predictors on S2S timescales by making use of the newly available data. The proposed research will focus on the North Atlantic / Europe region and analyze predictors ranging from the stratosphere to the land surface. A statistical forecasting tool will be developed for specific applications motivated by stakeholders. The overall goal is to develop a strong contribution to the international effort of understanding S2S variability and thereby improving S2S forecasts and their use in research, government, and industry.