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Glaciological and geodetic methods provide independent observations of glacier mass balance. The glaciological method measures the surface mass balance, on a seasonal or annual basis, whereas the geodetic method measures surface, internal, and basal mass balances, over a period of years or decades. In this paper, we reanalyse the 10 glaciers with long-term mass-balance series in Norway. The reanalysis includes (i) homogenisation of both glaciological and geodetic observation series, (ii) uncertainty assessment, (iii) estimates of generic differences including estimates of internal and basal melt, (iv) validation, and, if needed, (v) calibration of mass-balance series. This study comprises an extensive set of data (484 mass-balance years, 34 geodetic surveys, and large volumes of supporting data, such as metadata and field notes). In total, 21 periods of data were compared and the results show discrepancies between the glaciological and geodetic methods for some glaciers, which are attributed in part to internal and basal ablation and in part to inhomogeneity in the data processing. Deviations were smaller than 0.2 m w.e.a-1 for 12 out of 21 periods. Calibration was applied to 7 out of 21 periods, as the deviations were larger than the uncertainty. The reanalysed glaciological series shows a more consistent signal of glacier change over the period of observations than previously reported: six glaciers had a significant mass loss (14-22 m w.e.) and four glaciers were nearly in balance. All glaciers have lost mass after the year 2000. More research is needed on the sources of uncertainty to reduce uncertainties and adjust the observation programmes accordingly. The study confirms the value of carrying out independent high-quality geodetic surveys to check and correct field observations. [ABSTRACT FROM AUTHOR]

Creating a century-long homogenized near-surface wind speed (WS) observation dataset is essential to improve our knowledge about the uncertainty and causes of current WS stilling and recovery. Here, we rescued paper-based WS records dating back to the 1920s at 13 stations in Sweden and established a four-step homogenization procedure to generate the first 10-member centennial homogenized WS dataset (HomogWS-se) for community use. First, background climate variation in the rescued WS series was removed, using a verified reanalysis series as a reference series to construct a difference series. A penalized maximal F test at a significance level of 0.05 was then applied to detect spurious change-points. About 38 % of the detected change-points were confirmed by the known events recorded in metadata, and the average segment length split by the change-points is ~11.3 years. A mean-matching method using up to five years of data from two adjacent segments was used to adjust the earlier segments relative to the latest segment. The homogenized WS series was finally obtained by adding the homogenized difference series back onto the subtracted reference series. Compared with the raw WS data, the homogenized WS data is more continuous and lacks significant non-climatic jumps. The homogenized WS series presents an initial WS stilling and subsequent recovery until the 1990s, whereas the raw WS fluctuates with no clear trend before the 1970s. The homogenized WS shows a 25 % reduction in the WS stilling during 1990-2005 than the raw WS, and this reduction is significant when considering the homogenization uncertainty. The homogenized WS exhibits a significantly stronger correlation with the North Atlantic Oscillation (NAO) than that of the raw WS (0.54 vs 0.29). These results highlight the importance of the century-long homogenized WS series in increasing our ability to detect and attribute multidecadal variability and changes in WS. The proposed homogenization procedure enables other countries or regions to rescue their early climate data and jointly build global long-term high-quality datasets. HomogWS-se is publicly available from the Zenodo repository at http://doi.org/10.5281/zenodo.5850264 (Zhou et al., 2022). [ABSTRACT FROM AUTHOR]