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Winter precipitation forecast in the European and mediterranean regions using cluster analysis
(2017)
The European climate is changing under global warming, and especially the Mediterranean region has been identified as a hot spot for climate change with climate models projecting a reduction in winter rainfall and a very pronounced increase in summertime heat waves. These trends are already detectable over the historic period. Hence, it is beneficial to forecast seasonal droughts well in advance so that water managers and stakeholders can prepare to mitigate deleterious impacts. We developed a new cluster-based empirical forecast method to predict precipitation anomalies in winter. This algorithm considers not only the strength but also the pattern of the precursors. We compare our algorithm with dynamic forecast models and a canonical correlation analysis-based prediction method demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.
Recent studies indicate that the tropical belt has been expanding during recent decades, which can significantly influence precipitation in subtropical climates. Often the location of the tropical border is identified using the Hadley cell edge (HCE) or the subtropical jet stream (STJ), but most studies concentrated on the zonal-mean state, thereby missing regional impacts. Here we detect longitudinal-resolved trends in STJ cores and HCEs over 1979-2016 in both hemispheres at a higher spatial and temporal resolution than previous studies. Besides pronounced regional trend differences in both sign and magnitude, we show that winter HCE and STJ variability increased in the Mediterranean region and decreased over the American and Asian continents. Rainfall variability in these regions changed likewise, and most of those changes can be explained by changes in HCE/STJ variability. This highlights the importance of understanding future tropical belt changes both regionally and in terms of variability. Plain Language Summary We applied a new network-based method to detect motion of the tropical climate border with longitudinal resolution. Depending on the longitudinal position, there are differences in both direction and magnitude of the border motion. In addition, we demonstrate that the rainfall variability is increasing in the Mediterranean region and decreasing over the American and Asian continents, which can be explained by the variability of the tropical belt location.
Climate and weather conditions in the mid-latitudes are strongly driven by the large-scale atmosphere circulation. Observational data indicate that important components of the large-scale circulation have changed in recent decades, including the strength and the width of the Hadley cell, jets, storm tracks and planetary waves. Here, we use a new statistical-dynamical atmosphere model (SDAM) to test the individual sensitivities of the large-scale atmospheric circulation to changes in the zonal temperature gradient, meridional temperature gradient and global-mean temperature. We analyze the Northern Hemisphere Hadley circulation, jet streams, storm tracks and planetary waves by systematically altering the zonal temperature asymmetry, the meridional temperature gradient and the global-mean temperature. Our results show that the strength of the Hadley cell, storm tracks and jet streams depend, in terms of relative changes, almost linearly on both the global-mean temperature and the meridional temperature gradient, whereas the zonal temperature asymmetry has little or no influence. The magnitude of planetary waves is affected by all three temperature components, as expected from theoretical dynamical considerations. The width of the Hadley cell behaves nonlinearly with respect to all three temperature components in the SDAM. Moreover, some of these observed large-scale atmospheric changes are expected from dynamical equations and are therefore an important part of model validation.
We investigate whether the distribution of maximum seasonal streamflow is significantly affected by catchment or climate state of the season/month ahead. We fit the Generalized Extreme Value (GEV) distribution to extreme seasonal streamflow for around 600 stations across Europe by conditioning the GEV location and scale parameters on 14 indices, which represent the season-ahead climate or catchment state. The comparison of these climate-informed models with the classical GEV distribution, with time-constant parameters, suggests that there is a substantial potential for seasonal forecasting of flood probabilities. The potential varies between seasons and regions. Overall, the season-ahead catchment wetness shows the highest potential, although climate indices based on large-scale atmospheric circulation, sea surface temperature or sea ice concentration also show some skill for certain regions and seasons. Spatially coherent patterns and a substantial fraction of climate-informed models are promising signs towards early alerts to increase flood preparedness already a season ahead.