A quality control approach to better characterize the spatial distribution of snow depth over New Brunswick, Canada

Snow on the ground is an important climate variable which is normally measured either as snow depth or height of new snow. Like any other meteorological variable, manually measured snow is prone to local influences, changes in the environment or procedure of the measurements. In order to investigate the robustness of snow measurement series towards such non-climatic changes, a unique set of parallel manual snow measurements over 25 years from 23 station pairs between 490 and 1800 m a.s.l. was compiled. A sensitivity analysis based on typical snow climate indicators (e.g., mean snow depth, sum of new snow) from these parallel time series was carried out to find the most robust snow climate indicators for climatological analyses. Results show that there are only small differences in the sensitivity of the various snow climate indicators with regards to local changes. However, the indicators number of days with snow on the ground as well as the maximum snow depth are least affected by local influences and changes at station level. Median values of all station pairs reveal relative differences of about 7% for the number of days with snow cover and 11-16% for all other indicators. However, in extreme cases, the deviations within a single station pair can reach 25-40%.

Section: Introductionmentioning

confidence: 99%

Evaluating the robustness of snow climate indicators using a unique set of parallel snow measurement series

Buchmann

Begert

Brönnimann

et al. 2020

“…Quality‐control procedures are essential to detect outliers or changes in the snowfall series (Baronetti et al ., 2019). In order to study the yearly snowfall regime changes and assess climate anomalies at Parma observatory, the homogeneity of the snowfall time series has been investigated to identify breakpoints and the impact of the corrections.…”

Section: Methodsmentioning

confidence: 99%

New insights into the world's longest series of monthly snowfall (Parma, Northern Italy, 1777–2018)

Diodato¹,

Bertolin

Bellocchi

et al. 2020

The emergence of decreasing trends in snowfall frequency and snow depth highlights the challenges arising from shifts in snow regimes. In particular, snow-dependent southern Europe regions may be negatively impacted by such changes. Snow regimes strongly influence the water availability in reservoirs and groundwater. This study presents the world's longest series of monthly snowfall (fresh snow depth, snow days per year, days with snow on the ground) for the Parma Observatory, northern Italy (44 48 0 N and 10 19 0 E) between 1777 and 2018. These datasets were collected over the centuries using early rain gauges and recently, SIAP tipping bucket models. Sources of inhomogeneity were identified and analysed through the study of metadata and of nonparametric tests on datasets. Homogenized time series were obtained after correcting the observed snowfall data for the instruments response and localization characteristics, for the observing practices with the help of the standard normal homogeneity tests. The Buishand and the Mann-Kendall tests were further applied to check the correction and detect possible monotonic trends. Over the study period, days per year of snow decreased after the change point detected in 1897, in association with a rise in surface air temperature (including a distinct urban warming trend). In this study high numbers of snowy days per year (hereafter referred as snowfall frequency) and snow depth values during the latest phase of the LIA at Parma are consistent with a cycle of minimum solar activity, suggesting that enhanced, solar-induced blocking activity dominated, with the arrival into the Mediterranean of large cold air masses developing over Siberia and northern Europe. Over the last century, snow appears to have resonance-like characteristics as similar trends were observed over the Northern Hemisphere, where the extent of the snow cover has been reported to markedly decline in the transition spring and autumn seasons.

Long‐term snowfall trends and variability in the Alps

Bozzoli,

Crespi,

Matiu

et al. 2024

Snow is particularly impacted by climate change and therefore there is an urgent need to understand the temporal and spatial variability of depth of snowfall (HN) trends. However, the analysis of historical HN observations on large‐scale areas is often impeded by lack of continuous long‐term time series availability. This study investigates HN trends using observed time series spanning the period 1920–2020 from 46 sites in the Alps at different elevations. To discern patterns and variations in HN over the years, our analysis focuses also on key parameters such as precipitation (P), mean air temperature (TMEAN), and large‐scale synoptic descriptors, that is, the North Atlantic Oscillation (NAO), Arctic Oscillation (AO) and Atlantic Multidecadal Oscillation (AMO) indices. Our findings reveal that in the last 100 years and below 2000 m a.s.l., despite a slight increase in winter precipitation, there was a decrease in HN over the Alps, especially for southern and low‐elevation sites. The South‐West and South‐East regions experienced an average loss of 4.9 and 3.8%/decade, respectively. A smaller relative loss was found in the Northern region (2.3%/decade). The negative HN trends can be mainly explained by an increase of TMEAN by 0.15°C/decade. Most of the decrease in HN occurred mainly between 1980 and 2020, as a result of a more pronounced increase in TMEAN. This is also confirmed by the change of the running correlation between HN and TMEAN, NAO, AO over time, which until 1980 were not correlated at all, while the correlation increased in later years. This suggests that in more recent years favourable combinations of temperature, precipitation, and atmospheric pattern have become more crucial for snowfall to occur. On the other hand, no correlation was found with the AMO index.