No abstract

Beside its global effects, climate change is manifested in many regionally pronounced features mainly resulting from changes in the oceanic and atmospheric circulation. Here we investigate the influence of the North Atlantic SST on shaping the wintertime response to global warming. Our results are based on a long-term climate projection with the Max Planck Institute Earth System Model (MPI-ESM) to investigate the influence of North Atlantic sea surface temperature pattern changes on shaping the atmospheric climate change signal. In sensitivity experiments with the model's atmospheric component we decompose the response into components controlled by the local SST structure and components controlled by global/remote changes. MPI-ESM simulates a global warming response in SST similar to other climate models: there is a warming minimum-or "warming hole"-in the subpolar North Atlantic, and the sharp SST gradients associated with the Gulf Stream and the North Atlantic Current shift northward by a few a degrees. Over the warming hole, global warming causes a relatively weak increase in rainfall. Beyond this, our experiments show more localized effects, likely resulting from future SST gradient changes in the North Atlantic. This includes a significant precipitation decrease to the south of the Gulf Stream despite increased underlying SSTs. Since this region is characterised by a strong band of precipitation in the current climate, this is contrary to the usual case that wet regions become wetter and dry regions become drier in a warmer climate. A moisture budget analysis identifies a complex interplay of various processes in the region of modified SST gradients: reduced surface winds cause a decrease in evaporation; and thermodynamic, modified atmospheric eddy transports, and coastal processes cause a change in the moisture convergence. The changes in the the North Atlantic storm track are mainly controlled by the non-regional changes in the forcing. The impact of the local SST pattern changes on regions outside the North Atlantic is small in our setup.

Annual-to-decadal variability in northern midlatitude temperature is dominated by the cold season. However, climate field reconstructions are often based on tree rings that represent the growing season. Here we present cold-season (October-to-May average) temperature field reconstructions for the northern midlatitudes, 1701-1905, based on extensive phenological data (freezing and thawing dates of rivers, plant observations). Northern midlatitude land temperatures exceeded the variability range of the 18th and 19th centuries by the 1940s, to which recent warming has added another 1.5 °C. A sequences of cold winters 1808/9-1815/6 can be explained by two volcanic eruptions and unusual atmospheric flow. Weak southwesterlies over Western Europe in early winter caused low Eurasian temperatures, which persisted into spring even though the flow pattern did not. Twentieth century data and model simulations confirm this persistence and point to increased snow cover as a cause, consistent with sparse information on Eurasian snow in the early 19th century.

This paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000–1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. The intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based linear regressions in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941–2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, We assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. The resulting dataset is physically consistent with information from models, proxies, and observations.