High-resolution summer temperature reconstruction from Lake Silvaplana based on in-situ reflectance spectroscopy

Annually laminated (varved) sediments of proglacial Lake Silvaplana (46 ̊27’N, 9 ̊48’E, 1791 m a.s.l., Engadine, eastern Swiss Alps) provide an excellent archive for quantitative high-resolution (seasonal – annual) reconstruction of high- and lowfrequency climate signals back to AD 1580. The chronology of the core is based on varve counting, Cs-137, Pb-210 and event stratigraphy. In this study we present a reconstruction based on in-situ reflectance spectroscopy. In situ reflectance spectroscopy is known as a cost- and time-effective non destructtive method for semi-quantitative analysis of pigments (e.g., chlorines and carotenoids) and of lithoclastic sediment fractions. Reflectance-dependent absorption (RDA) was measured with a Gretac Macbeth spectrolino at 2 mm resolution. The spectral coverage ranges from 380 nm to 730 nm at 10 nm band resolution. In proglacial Lake Silvaplana, 99% of the sediment is lithoclastic prior to AD 1950. Therefore, we concentrate on absorption features that are characteristic for lithoclastic sediment fractions. In Lake Silvaplana, two significant correlations that are stable in time were found between RDA typical for lithoclastics and meteorological data: (1) the time series R 570 /R 630 (ratio between RDA at 570 nm and 630 nm) of varves in Lake Silvaplana and May to October temperatures at nearby station of Sils correlate highly significantly (calibration period AD 1864 – 1951, r = 0.74, p < 0.01 for 5ptsmoothed series; RMSE is 0.28 ̊C, RE = 0.41 and CE = 0.38), and (2) the minimum reflectance within the 690nm band (min690) data correlate with May to October (calibration period AD 1864 – 1951, r = 0.68, p < 0.01 for 5pt-smoothed series; RMSE = 0.22 ̊C, RE = 0.5, CE = 0.31). Both proxy series (min690nm and R 570 /R 630 values) are internally highly consistent (r = 0.8, p < 0.001). In proglacial Lake Silvaplana the largest amount of sediment is transported by glacial meltwater. The melting season spans approximately from May to October, which gives us a good understanding of the geophysical processes explaining the correlations between lithoclastic proxies and the meteorological data. The reconstructions were extended back to AD 1580 and show a broad corresponddence with fully independent reconstructions from tree rings and documentary data.

Snow variability in the Swiss Alps 1864-2009

We present a climate analysis of nine unique Swiss Alpine new snow series that have been newly digitized. The stations cover different altitudes (450–1860 m asl) and all time series cover more than 100 years (one from 1864 to 2009). In addition, data from 71 stations for the last 50–80 years for new snow and snow depth are analysed to get a more complete picture of the Swiss Alpine snow variability. Important snow climate indicators such as new snow sums (NSS), maximum new snow (MAXNS) and days with snowfall (DWSF) are calculated and variability and trends analysed. Series of days with snow pack (DWSP) ≥ 1 cm are reconstructed with useful quality for six stations using the daily new snow, local temperature and precipitation data. Our results reveal large decadal variability with phases of low and high values for NSS, DWSF and DWSP. For most stations NSS, DWSF and DWSP show the lowest values recorded and unprecedented negative trends in the late 1980s and 1990s. For MAXNS, however, no clear trends and smaller decadal variability are found but very large MAXNS values (>60 cm) are missing since the year 2000. The fraction of NSS and DWSP in different seasons (autumn, winter and spring) has changed only slightly over the ∼150 year record. Some decreases most likely attributable to temperature changes in the last 50 years are found for spring, especially for NSS at low stations. Both the NSS and DWSP snow indicators show a trend reversal in most recent years (since 2000), especially at low and medium altitudes. This is consistent with the recent ‘plateauing’ (i.e. slight relative decrease) of mean winter temperature in Switzerland and illustrates how important decadal variability is in understanding the trends in key snow indicators.