My presentation was within the session salinity dynamics. The title of my presentation was: ‘The impact of the Atlantic Multidecadal Oscillation on the salinity variability of the Baltic Sea’
Description of my talk in the conference proceedings:
The salinity of the Baltic Sea is mainly driven by freshwater input and by saltwater inflows from the North Sea. The freshwater input into the Baltic Sea comes either as river runoff or as positive net precipitation (precipitation ‐ evaporation) over the sea surface. The river runoff is influenced by precipitation over the drainage basin. The discharge of the Baltic Sea drainage basin is exposed to low‐frequency variations due to changes in precipitation and evaporation (Meier and Kauker, 2003b). Hence, the mean salinity in the Baltic Sea is affected by dry and wet periods that occur due to low‐frequency changes in precipitation and evaporation patterns. Incontrast, saltwater inflows are characterized by exceptional periods of strong easterly winds and following westerly winds on time scales of about 40 days. This suggests that the Baltic Sea and its ecosystem are strongly influenced by external atmospheric forcing.
Atlantic Multidecadal Oscillation
Schimanke and Meier (2016) analyzed the decadal to centennial variability of the Baltic Sea. A wavelet analysis of the mean salinity of the Baltic Sea showed significant power in the 60‐120 year periodicity band, which we link to the Atlantic multidecadal oscillation (AMO), i.e. variations in northward heat transport in the ocean. The Atlantic multidecadal oscillation (AMO) is defined as a multidecadal climate variability (60‐90 years) of sea surface temperature (SST) in the North Atlantic with alternating warm and cool phases (Knight et al., 2006)(see Figure 1). It has been shown that regional multidecadal variability in the European region can be linked to the AMO, e.g extreme precipitation (Casanueva et al., 2014) and European summer climate (Enfield et al., 2001). In addition, there are several studies that focus on the physical mechanism behind warm AMO+ phase and cold AMO‐ affecting Europe (Peings and Magnusdottir, 2014; Zampieri et al., 2017). Even though several regional climate conditions have been related to the AMO, the origin of the AMO remains still unclearand its unique periodicity needs to be proven (Ruprich‐Robert et al., 2017). Some studies even use the term Atlantic multidecadal variability (AMV), since this does not imply that the AMV is only created by internal climate processes (Wang et al., 2017).
AMO in General Circulation Models
General circulation models have shown that the AMO signal is closely related to the Atlantic Meridional Overturning Circulation (AMOC) which appears to drive the AMO. This multi‐ decadal variability can emerge in the absence of any external forcing but is also likely to be influenced by external forcing(Sutton and Dong, 2012). Zhang and Wang (2013) analyzed 27 coupled general circulation models (GCMs) and found a large inter‐model spread in amplitudes and frequencies with respect to the AMO. The spatial structure of the AMO also varies from model to model. This leads to the conclusion that most models can reproduce an AMO like variability, but we cannot yet expect them to reproduce comparable atmospheric patterns in every detail. Still GCMs are necessary since observations of the AMO are limited to a period of roughly 150 years. With a defined frequency in the range of 60‐90 years, the results of a spectral analysis of the AMO must be considered with caution. In our study we used the Rossby Centre Ocean model (RCO) to simulate the period from 950 – 1800 (Schimanke and Meier, 2016). RCO is a primitive equation circulation model with a horizontal resolution of 2 nm and 83 vertical layers. With a thickness of 3m per vertical layer this results in a maximum depth of ~ 250m. RCO is forced by the Rossby Centre Atmosphere Model (RCA3) with a horizontal resolution of 0.44° covering nearly the whole area of Europe. RCA3 provides 10‐m wind, 2‐m air temperature 2‐m specific humidity, precipitation, total cloudiness and sea level pressure fields. RCA3 itself is driven by ECHO‐G at the lateral boundaries.
AMO and its influence on the Baltic Sea
The goal of this study is to find a coherence of the mean salinity of the Baltic Sea and the mean sea surface temperature in the North Atlantic which we define as AMO. A wavelet coherence analysis revealed a significant coherence between the AMO and the mean salinity of the Baltic Sea for the periodicity of 60‐120 years. The phase relationship is also in good agreement with a lagged response of the mean salinity. Since the salinity of the Baltic Sea is mainly driven by river runoff, we analyzed the coherence between AMO and precipitation over the Baltic Sea drainage basin. Again, the wavelet coherence revealed significant coherence between AMO and precipitation in the period of 60‐120 years with both signals in phase.This relationship is also shown in Figure 2. The correlation ofthe AMO index and precipitation over the Baltic Sea indicates apositive correlation. AMO+ phases appear to cause wetter conditions in our simulation, followed by a decrease of the mean salinity of the Baltic Sea.
The spatial response to different AMO phases is complex. Therefore, we analyzed season composites. Our study reveals that in winter the state of the AMO can make up to 7% of the mean yearly precipitation. In addition, Figure 1 shows the tendency of an increased number of warm AMO+ phases during the Medieval Climate Anomaly (MCA) compared to more AMO‐ phases during the Little Ice Age (LIA). In our simulation, these phases can be linked to dry and wet conditions over the Baltic Sea. This allows us to compare the mean salinity of the Baltic Sea during the MCA and the LIA and its relation to the AMO.In the future we will analyze in detail how the AMO signal impacts the mean salinity of the Baltic Sea and discuss in detail the influence of wind and sea level pressure patterns over the Baltic Sea area.
- Casanueva, A., Rodríguez‐Puebla, C., Frías, M. D., and González‐Reviriego, N. (2014). Variability of extreme precipitation over europe and its relationships with teleconnection patterns. Hydrology and Earth System Sciences, 18(2):709–725.
- Enfield, D. B., Mestas‐Nuñez, A. M., and Trimble, P. J. (2001). The atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental u.s. Geophysical Research Letters, 28(10):2077–2080.
- Knight, J. R., Folland, C. K., and Scaife, A. A. (2006). Climate impacts of the atlantic multidecadal oscillation. Geophysical Research Letters, 33(17):n/a–n/a. L17706.
- Landrum, L., Otto‐Bliesner, B. L., Wahl, E. R., Conley, A., Lawrence, P. J., Rosenbloom, N., and Teng, H. (2013). Last millennium climate and its variability in ccsm4. Journal of Climate, 26(4):1085–1111.
- Peings, Y. and Magnusdottir, G. (2014). Forcing of the wintertime atmospheric circulation by the multidecadal fluctuations of thenorth atlantic ocean. Environmental Research Letters, 9(3):034018.
- Ruprich‐Robert, Y., Msadek, R., Castruccio, F., Yeager, S., Delworth, T., and Danabaoglu, G. (2017). Assessing the climate impacts of theobserved atlantic multidecadal variability using the gfdl cm2.1 and ncar cesm1 global coupled models. Journal of Climate, 30(8):2785–2810.
- Schimanke, S. and Meier, H. E. M. (2016). Decadal‐to‐centennial variability of salinity in the Baltic Sea. Journal of Climate, 29(20):7173–7188.
- Sutton, R. T. and Dong, B. (2012). Atlantic Ocean influence on a shift in European climate in the 1990s. Nature Geoscience, 5:788–792.
- Trenberth, K. E. and Shea, D. J. (2006). Atlantic hurricanes and natural variability in 2005. Geophysical Research Letters, 33(12):n/a–n/a. L12704.
- Zampieri, M., Toreti, A., Schindler, A., Scoccimarro, E., and Gualdi, S. (2017). Atlantic multi‐decadal oscillation influence on weatherregimes over europe and the mediterranean in spring and summer.Global and Planetary Change, 151:92 – 100. Climate Variability and Change in the Mediterranean Region.13
- Zhang, L. and Wang, C. (2013). Multidecadal North Atlantic sea surface temperature and Atlantic meridional overturning circulation variability in CMIP5 historical simulations. 118:5772–5791.