C2-WP3 - Multi-decadal variability – centennial downscaling and hindcasting

Project aims

The goal of this work package is to derive more robust estimates of the skill of decadal predictions for Europe over an extended hindcast period, following the study by Müller et al. (2014) in the MiKlip project DROUGHTCLIP. Regional hindcast experiments with CCLM, using forcing from 20th-century re-analysis and from MPI-ESM hindcast over an extended period starting 1910, will be performed to improve the coverage of different phases of the Atlantic Multi-decadal Variability and their climate impacts on Europe. These extended hindcasts will enable an improved attribution of the impacts of anthopogenic induced climate trends and natural variability over Europe and provide more robust skill estimates than is possible over general MiKlip investigation period (after 1960). The effects of the European marginal seas will be attributed as well in this context.
 

Project structure

This work package C2-WP3 is handled by the Goethe University Frankfurt (GUF) and the Karlsruhe Institute of Technology (KIT). GUF performs the ocean-atmosphere coupled experiments with CCLM/NEMO and KIT the atmosphere-only simulations with CCLM.
 

Tasks of the project

There are three sub-tasks:

  • Downscaling a global reference for the last century (CCLM EUR-20C),
  • to perform hindcast experiments for this period atmosphere-only and with a coupled regional ocean
  • and to analyse the climate impacts of the multi-decadal variability in Europe.

Deliverables

D1a: Downscaled centennial reference datasets for Europe (atmosphere-only)
D1b: Downscaled centennial reference datasets for Europe (coupled ocean/atmosphere)
D2: Centennial hindcasts (coupled and un-coupled)
D3: Information on the climate impacts of the multi-decadal variability in Europe
 

Progress so far

Within the work package a down-scaling with CCLM of the three-member MiKlip I DROUGHTCLIP ensemble of assimilation simulations has been performed over the period 1900 – 2009 (figure 2). The DROUGHTCLIP simulations were driven by the NCEP 20CR reanalysis (CCLM EUR-20C). In addition, coupled regional simulations with CCLM-NEMO have been generated for this period with both Northern and Baltic seas as well as Mediterranean Sea coupled to the atmospheric model.

A regional centennial hindcast ensemble with 3 members has been established for the starting years 1910 – 2009, based on data from the module E DROUGHTCLIP global hindcast simulations (Müller et al., 2014). The simulations show a significant correlation to observed near surface temperature data for most of Europe (figure 1). The skill is lower for the first half of the period (starting years 1910 – 1959) than for the second half (1960 – 2009, overlapping with the main MiKlip period). This can be attributed to the lower availability of reliable observation data in the first half of the 20th century needed for initialisation and verification. But it also points to a lower impact of the climate trend during this period. The spatial skill pattern is similar to those of the regional MiKlip core ensembles of C3-WP3, with higher skill in Southern and Western Europe and lower values in Northern and Eastern Europe. This indicates that the distribution is quite robust over the time.

 

Figure 1: Temperature correlation between the CCLM EUR-20C hindcasts and the CRU TS4.01 observations for the period 1912-2014 lead-years 2-5 (starting years 1910-2009).

In collaboration with C2-WP2, the multi-decadal variability of extreme values over Europe and their links to large-scale patterns has been analysed. Figure 2 shows the long-term variability (5-year running means) of the number of heavy precipitation days (> 20mm/day) in Europe derived from the CCLM EUR-20C reference simulations. This extreme value index is highly correlated with the index for the detrended North Atlantic sea surface temperature (AMO index). A high sea surface temperature leads to a moister atmosphere over the Atlantic. Downstream in Europe this causes an increase of heavy precipitation. In cold phases of the AMO the likelihood of extremes is lowered.

The coupled regional simulation shows improvements over islands (e.g. British Isles, Corsica and Sardinia) and in some coastal regions, which is expected due to the better representation of atmosphere-see interactions. There are also differences between the coupled and uncoupled simulation over the continental regions as well. For example, over a part of Central Europe the precipitation values correlate better with observation than the uncoupled results. Nevertheless, there are also regions where the uncoupled simulation fit better to the observations. The origin and further characteristics of these differences are still under investigation.

Figure 2: Multi-decadal variability for heavy precipitation days (> 20 mm/day) for the CCLM EUR-20C ensemble (red curve and grey shaded area for the ensemble spread) for Europe and the period 1900 – 2009 (5-year running means). The blue curve marks the de-trended index for the North Atlantic sea surface temperature (AMO Index).

Contact

Institut für Atmosphäre und Umwelt Goethe University Frankfurt/Main
Bodo Ahrens
bodo.ahrens(at)nospamiau.uni-frankfurt.de
+49 69 798-40244

Institut für Atmosphäre und Umwelt Goethe University Frankfurt/Main
Fanni Dora Kelemen
Kelemen(at)nospamiau.uni-frankfurt.de
+49 69 798-40234

Institute for Meteorology and Climate Research (IMK-TRO) Karlsruhe Institute of Technology (KIT)
Hendrik Feldmann
hendrik.feldmann(at)nospamkit.edu
+49 721 608-222802

Mistral and Tramontane wind systems in climate simulations from 1950 to 2100

2017 - Climate Dynamics

Obermann-Hellhund, A. | D. Conte, S. Somot, C. Zsolt Torma, and B. Ahrens

Influence of Sea Surface Roughness Length Parameterization on Mistral and Tramontane Simulations

2016 - Advances in Science and Research, Vol. 13, pp. 107-112

Obermann, A. | B. Edelmann, and B. Ahrens

Mistral and Tramontane wind speed and wind direction patterns in regional climate simulations

2016 - Climate Dynamics

Obermann, A. | S. Bastin, S. Belamari, D. Conte, M. A. Gaertner, L. Li, and B. Ahrens