Annual mean temperature

Description of the results

The comparatively long time span of the available data series makes it possible to highlight the long-term trend: Despite some inter-annual variability, the graph shows a clear upward trend in the province‘s average annual temperature over the last 43 years. Since the 1980s, global warming has accelerated and consistently increased. In South Tyrol, the annual average temperature has increased by about 2.0 °C from 1980 to 2022, aligning with trends observed in the Alpine region. The calculated trend is statistically significant, amounting to almost +0.5 °C per decade. In 2022, it was the warmest year in the last 43 years, with approximately 1.85 °C more than the 30-year average (1981 to 2010).

Method

The graph is based on daily meteorological observations from more than 80 measuring points from the Autonomous Province of Bolzano‘s Office for Meteorology and Avalanche Warning, and is supplemented by observations from some sites in Switzerland and Austria near the national border. The collected data sets have been interpolated using a geostatistical method on a regular grid with a resolution of 1 km for the entire provincial territory, thus providing spatially distributed data. This results in a map showing the mean annual temperature for the whole of South Tyrol and, for each year, a map showing the measured annual temperature and its change.

Prior to interpolation (conversion of point temperature measurements into area data), all observation series were checked for measurement errors and temporal homogeneity. In addition, missing daily values were reconstructed using a statistical method to maximize the temporal continuity of the series.

Interpolation makes it possible to obtain a regional average that is more representative and stable than that based on individual stations. The daily mean temperature used here is calculated as the arithmetic mean of the daily maximum and minimum temperatures. The trend is calculated using the Theil-Sen method and significance is assessed using the Mann-Kendall test. The trend is considered significant if the resulting p-value is less than 0.05.

Sectors affected

The annual mean temperature directly and indirectly affects all ecosystems and human activities, especially:

• Snow and glacier

• Water

• Flora and Fauna

• Soil

• Natural hazard

• Ecosystem sevice

• Water management

• Agriculture

• Forestry

• Settlement

• Migration

• Tourism

• Infrastrucure

• Health

Related indicators

Future scenarios

Based on the available climate projections, the increase in mean annual temperature in South Tyrol is expected to continue in the coming decades, with the rate of increase depending on the emission scenario.

The most pessimistic scenario (RCP 8.5) projects an increase in the average annual temperature by around 2.3 °C by the mid-century (2041 to 2070) compared to the reference period of 1981 to 2010. For the moderate scenario RCP 4.5, the average temperature is expected to be approximately 1.6 °C higher by the mid-century than in the period 1981 to 2010.

The gap between the projections of the two emission scenarios widens towards the end of the century. The most pessimistic scenario (RCP 8.5) projects a further increase in the average annual temperature by the end of the century (2071 to 2100) to around 4.2 °C above the average values of the 30-year period 1981 to 2010. According to the moderate scenario RCP 4.5, the increase in mean average temperature slows down in the second half of the century, with average values for the period 2071 to 2100 about 2.2 °C higher than in the period 1981 to 2010.

The spatial distribution of the 30-year mean temperatures for the mid-century (2041 to 2070) and the end of the century (2071 to 2100), compared to the 30-year reference period 1981 to 2010, shows the gradual expansion of areas with annual mean temperatures above 10 °C at low and middle elevations. In addition, the results show a clear reduction of areas with annual mean temperatures below 0°C at higher altitudes, especially for the RCP 8.5 scenario.

Method

The climate scenarios for annual and seasonal precipitation in South Tyrol were derived from EURO-CORDEX climate simulations over Europe for the two emission scenarios. RCP 4.5 and RCP 8.5. RCP stands for “Representative Concentration Pathways”, which project how greenhouse gas emissions in the atmosphere will develop in the future.

RCP 4.5 is an intermediate scenario in which greenhouse gas emissions are reduced, but atmospheric concentrations continue to rise over the next 50 years and the +2°C target is not met. RCP 8.5 represents the most pessimistic scenario, in which greenhouse gas emissions continue to rise unchecked and no action is taken to combat climate change.

The daily minimum temperature projections from 1971 to 2100 provided by 15 different climate models (ensembles) for the two scenarios, were further processed by a downscaling procedure. This procedure transfers the simulated values from the original spatial resolution (in this case about 12 km) to a finer resolution (in this case 1 km). This step helps to reduce systematic errors in model simulations due to the limited spatial resolution of available models, which do not adequately represent local features, especially in mountainous regions with complex orography. The applied downscaling method is based on the Delta quantile mapping (QDM, Cannon et al., 2015). In QDM, simulated values are compared with observations over a common reference period and corrected to match probability distributions. In addition, the corrections in the QDM method are made in such a way that the original long-term climate signal in the simulations is not altered.

In this case, the reference period is 1981 to 2010. The correction was performed based on the 1 km grid observational data set.

From the corrected simulations, the annual frost days were calculated for each model in the ensemble and for both scenarios from 1971 to 2100. The indicator values for the ensemble were then aggregated by calculating the median of the 15 model simulations for each year and using the inter-quantile range, i.e., the range of values between the 25th and 75th percentiles, to estimate the variability of the model simulations.

Contact

Eurac Research: Alice Crespi , Center for Climate Change and Transformation

Data provided by: Office for Meteorology and Avalanche Warning of the Autonomous Province of Bolzano