Spatially aggregated climate indicators over Sweden (1860–2020), part 2: Precipitation

Abstract. The Swedish Meteorology and Hydrology Institute (SMHI) provides a national aggregated climate indicator from 1860 to present. We present a new method to compute the national climate indicator based on Empirical Orthogonal Functions (EOF). EOF are computed during the1961–2018 calibration period, and later applied to the full experiment period 1860–2020. This study focuses the climate indicator for precipitation; it follows the same methodology as for the national climate indicator for temperature, described in the companion article (Sturm, 2024a).

The new method delivers results in good overall agreement with the reference method (i.e. arithmetic mean from selected stations in the reference network). Discrepancies are found prior to 1900, primarily related to the reduced number of active stations: the robustness of the indicator estimation is assessed by an ensemble computation with added random noise, which confirms that the ensemble spread increases significantly prior to 1880.

The present study establishes that the 10-year running averaged precipitation indicator rose from -8.37 mm.month^-1 in 1903 to 4.08 mm.month^-1 in 2010 (with respect to the mean value of 54.18 mm.month^-1 for the 1961–2018 calibration period), i.e. a 27 % increase over a century. Winter (DJF) precipitation rose by +20 mm.month^-1 between 1890–2010, summer precipitation by +25 mm.month^-1.

The leading EOF patterns illustrate the spatial modes of variability for climate variability. For precipitation, the first EOF pattern displays more pronounced regional features (maximum over the West coast), which is completed by a north-south seesaw pattern for the second EOF. We illustrate that EOF patterns calculated from observation data reproduce the major features of EOF calculated from GridClim, a gridded dataset over Sweden, for annual and seasonal averages. The leading EOF patterns vary significantly for seasonal averages (DJF, MAM, JJA, SON) for precipitation.

Finally, future developments of the EOF-method are discussed for calculating regional aggregated climate indicators, their relationship to synoptic circulation patterns and the benefits of homogenisation of observation series.

The EOF-based method to compute a spatially aggregated indicator for temperature is presented in a companion article (Sturm, 2024a), which includes a detailed description of the datasets and methods used in this study. The code and data for this study is available on Zenodo (Sturm, 2024b).