Contents
- 🌍 Use case: Assess the impact of climate change on Alpine ski resorts
- ❓ Quality assessment questions -
- 📢 Quality assessment statement +
- 📢 Quality assessment statements
- 📋 Methodology
- 📈 Analysis and results
- 1. Choose the data to use and setup the code
-
@@ -458,9 +458,10 @@
Contents
Please note that this repository is used for development and review, so quality assessments should be considered work in progress until they are merged into the main branch -- 3.1. Unique quality assessment title (to be finalised by C3S), including [data stream]-[quality area]-for-[application area]#
+
- 1. Choose the data to use and setup the code
3.1. Validation of CERRA climatology and trends for applications in the tourism sector#
Production date: 24-05-2024
+Produced by: Sandro Calmanti (ENEA), Chiara Volta (ENEA)
🌍 Use case: Assess the impact of climate change on Alpine ski resorts#
❓ Quality assessment questions[1]. -
One noteworthy aspect of CERRA is its utilization of a layered model for the Earth’s surface (SURFEXV8.1)[2]. This model incorporates a more comprehensive representation of surface topography and physiographic data, which is expected to enhance the accuracy of information concerning soli humidity, temperature and snow cover.
+This note presents an evaluation of the snow depth climatology of CERRA [1] with a focus on the Alps, where global warming is expected to produce contrasting impacts. For example, the interplay between the rising of the zero-degree altitude and increased winter precipitation has led to glacier expansion even under warming conditions in some regions [2].
+One noteworthy aspect of CERRA is its utilization of a layered model for the Earth’s surface (SURFEXV8.1)[3]. This model incorporates a more comprehensive representation of surface topography and physiographic data, which is expected to enhance the accuracy of information concerning soil humidity, temperature and snow cover.
+
+Fig. 3.1.1 An example of the snow depth climatology for the period 1985-2020 over central Europe. The region selected for this assessment is hilighted in red.#
+📢 Quality assessment statement#
+📢 Quality assessment statements#
These are the key outcomes of this assessment
-
@@ -487,33 +494,28 @@
-
+
1. Choose the data to use and setup the code
Import required packages
Define data request
- -
+
2. Datasets retrieval and computation of trend
Define functions
Datasets retrieval
- -
+
Display results
Discussion
- Climatology of snow depth
-Linear trend of snow depth
Interannual variability of snow depth
- 🌍 Use case: Assess the impact of climate change on Alpine ski resorts
- ❓ Quality assessment questions -
- 📢 Quality assessment statement +
- 📢 Quality assessment statements
- 📋 Methodology
- 📈 Analysis and results
📢 Quality assessment statement
📋 Methodology#
The analysis focuses on snow depth data over the Alps during the period 1986-2020. The analysis and results are organized in the following steps, which are setailed in the sections below:
-
-
📈 Analysis and results#
-1. Choose the data to use and setup the code#
+1. Choose the data to use and setup the code#
-Import required packages#
+Import required packages#
Besides the standard libraries xarray for the handling of datasets and matplotlib for the graphical outputs, cartopy is necessary for the map projection functions. One of the utils included in the package c3s_eqc_automatic_quality_control is used to crop CERRA data over the area of interest. In fact, differently from ERA5, the reanalysis CERRA does not allow the extraction of areal subsets on retrieval. We also use the package tempfile to handle temporary files for the computation of the trend.
@@ -584,9 +586,9 @@ Define data request
-2. Datasets retrieval and computation of trend#
+2. Datasets retrieval and computation of trend#
-Define functions#
+Define functions#
The analysis uses the compute_time_mean_and_linear_trend function to calculate the time mean and linear trend of the snow depth, and the compute_spatial_weighted_mean_and_std function to compute its spatial weighted mean and associated standard deviation time series.
@@ -680,9 +682,9 @@ Datasets retrieval and processing
-3. Plot and describe results#
+3. Plot and describe results#
-Display results#
+Display results#
Maps of the snow depth climatology and of the corresponding trend are shown below. The unit of the linear trend is converted into meters per decade. The time series of the average snow cover over the entire region is also reported, along with the corresponding standard deviation among all grid points in the region of interest.
@@ -753,15 +755,15 @@ Discussion
The climatological map of snow depth (see below) reveals a wealth of geographic details, which correspond to the complex terrain of the area. The mean value reported in the statistic, displayed in the map’s legend, appears to be quite low (a few centimeters). This is due to the region being largely snow-free for most of the year. However, it is worth noting that there are significant snow depth observed, with the highest recorded value reaching nearly 30 meters. This extreme value is found in the western Italian Alps, specifically over Mont Blanc (Monte Bianco).
+The climatological map of snow depth (see below) reveals a wealth of geographic details, which correspond to the complex orography of the area. The mean value reported in the statistic, displayed in the map’s legend, appears to be quite low (a few centimeters). This is due to the region being largely snow-free for most of the year. However, it is worth noting that there are significant snow depth observed, with the highest recorded value reaching nearly 30 meters. This large value is found in the western Italian Alps, specifically over Mont Blanc (Monte Bianco), which is one of the sites with perennial snow-cover in the area.
-The linear trend map of snow depth (see below) shows a mixed pattern of positive and negative trends in different areas over the Alps, which is in line with results presented in previous studies [3]. In general, the negative trend seems mostly concentrated over mountains areas of higher elevations. This results is in contrast with the findings of Matiu et al. (2021) [4], who have analyzed a comprehensive data set of in situ observations, suggesting an overall negative trend at low altitudes (below 1000 m) and a potential, but not always, positive trend at higher altitudes (over 2000 m). Although a detailed spatial analysis of snow depth is not provided by Matiu et al. (2021), this assessment shows that snow depth data from reanalysis datasets such CERRA should be evaluated carefully, at least in this specific area. This finding confirms what already suggested by Monteiro and Morin (2023) [5], who attributed discrepancies between reanalysis and observed snow depths to different factors, such as the misrepresentation of wind-transport and glacier accumulation, among others.
+The linear trend map of snow depth (see below) shows a mixed pattern of positive and negative trends in different areas over the Alps, which is in line with results presented in previous studies [4]. In general, the negative trend seems mostly concentrated over mountains areas of higher elevations. This results is in contrast with the findings of Matiu et al. (2021) [5], who have analyzed a comprehensive data set of in situ observations, suggesting an overall negative trend at low altitudes (below 1000 m) and a potential, but not always, positive trend at higher altitudes (over 2000 m). Although a detailed spatial analysis of snow depth is not provided by Matiu et al. (2021), this assessment shows that snow depth data from reanalysis datasets such CERRA should be evaluated carefully, at least in this specific area. This finding confirms what already suggested by Monteiro and Morin (2023) [6], who attributed discrepancies between reanalysis and observed snow depths to different factors, such as the misrepresentation of wind-transport and glacier accumulation, among others.
-The time series of the mean snow depth over the Alps and the corresponding standard deviation are shown below. As expected from the previous analysis, the mean snow depth doesn’t exhibit any clear trend over the long term. Instead, we observe a significant snow depth variability, with particularly high values in 2009 when exceptional snow events have been recorded in the area.
+The time series of the mean snow depth over the Alps and the corresponding standard deviation are shown below. As expected from the previous analysis, the mean snow depth doesn’t exhibit any clear trend over the long term. Instead, we observe a significant snow depth variability, with particularly high values in 2009 when exceptional snow events have been recorded in the area.
@@ -780,11 +782,12 @@ Key resources
References#
-[1] Calmanti, S., Motta, L., Turco, M., & Provenzale, A. (2007). Impact of climate variability on Alpine glaciers in northwestern Italy. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2041-2053.
-[2] Schimanke, S., Isaksson, L., & Edvinsson, L. (2022). Copernicus European Regional ReAnalysis (CERRA): product user guide.
-[3] Masloumidis, I., Dafis, S., Kyros, G., & Lagouvardos, K. (2023). Snow depth Trends of European Ski Resorts. Environmental Sciences Proceedings, 26, 16.
-[4] Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., … & Weilguni, V. (2020). Observed snow depth trends in the European Alps 1971 to 2019. The Cryosphere Discussions, 2020, 1-50.
-[5] Monteiro, D., & Morin, S. (2023). Multi-decadal analysis of the past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets. The Cryosphere, 17, 3617-3660.
+[1] Ridal, M., Bazile, E., Le Moigne, P., Randriamampianina, R., Schimanke, S., Andrae, U., … & Wang, Z. Q. (2024). CERRA, the Copernicus European Regional Reanalysis system. Quarterly Journal of the Royal Meteorological Society.
+[2] Calmanti, S., Motta, L., Turco, M., & Provenzale, A. (2007). Impact of climate variability on Alpine glaciers in northwestern Italy. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2041-2053.
+[3] Schimanke, S., Isaksson, L., & Edvinsson, L. (2022). Copernicus European Regional ReAnalysis (CERRA): product user guide.
+[4] Masloumidis, I., Dafis, S., Kyros, G., & Lagouvardos, K. (2023). Snow depth Trends of European Ski Resorts. Environmental Sciences Proceedings, 26, 16.
+[5] Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., … & Weilguni, V. (2020). Observed snow depth trends in the European Alps 1971 to 2019. The Cryosphere Discussions, 2020, 1-50.
+[6] Monteiro, D., & Morin, S. (2023). Multi-decadal analysis of the past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets. The Cryosphere, 17, 3617-3660.
@@ -856,7 +859,7 @@ References
📈 Analysis and results#
1. Choose the data to use and setup the code#
+1. Choose the data to use and setup the code#
Import required packages#
+Import required packages#
Besides the standard libraries xarray for the handling of datasets and matplotlib for the graphical outputs, cartopy is necessary for the map projection functions. One of the utils included in the package c3s_eqc_automatic_quality_control is used to crop CERRA data over the area of interest. In fact, differently from ERA5, the reanalysis CERRA does not allow the extraction of areal subsets on retrieval. We also use the package tempfile to handle temporary files for the computation of the trend.
Define data request
-2. Datasets retrieval and computation of trend#
+2. Datasets retrieval and computation of trend#
-Define functions#
+Define functions#
The analysis uses the compute_time_mean_and_linear_trend function to calculate the time mean and linear trend of the snow depth, and the compute_spatial_weighted_mean_and_std function to compute its spatial weighted mean and associated standard deviation time series.
@@ -680,9 +682,9 @@ Datasets retrieval and processing
-3. Plot and describe results#
+3. Plot and describe results#
-Display results#
+Display results#
Maps of the snow depth climatology and of the corresponding trend are shown below. The unit of the linear trend is converted into meters per decade. The time series of the average snow cover over the entire region is also reported, along with the corresponding standard deviation among all grid points in the region of interest.
@@ -753,15 +755,15 @@ Discussion
The climatological map of snow depth (see below) reveals a wealth of geographic details, which correspond to the complex terrain of the area. The mean value reported in the statistic, displayed in the map’s legend, appears to be quite low (a few centimeters). This is due to the region being largely snow-free for most of the year. However, it is worth noting that there are significant snow depth observed, with the highest recorded value reaching nearly 30 meters. This extreme value is found in the western Italian Alps, specifically over Mont Blanc (Monte Bianco).
+The climatological map of snow depth (see below) reveals a wealth of geographic details, which correspond to the complex orography of the area. The mean value reported in the statistic, displayed in the map’s legend, appears to be quite low (a few centimeters). This is due to the region being largely snow-free for most of the year. However, it is worth noting that there are significant snow depth observed, with the highest recorded value reaching nearly 30 meters. This large value is found in the western Italian Alps, specifically over Mont Blanc (Monte Bianco), which is one of the sites with perennial snow-cover in the area.
-The linear trend map of snow depth (see below) shows a mixed pattern of positive and negative trends in different areas over the Alps, which is in line with results presented in previous studies [3]. In general, the negative trend seems mostly concentrated over mountains areas of higher elevations. This results is in contrast with the findings of Matiu et al. (2021) [4], who have analyzed a comprehensive data set of in situ observations, suggesting an overall negative trend at low altitudes (below 1000 m) and a potential, but not always, positive trend at higher altitudes (over 2000 m). Although a detailed spatial analysis of snow depth is not provided by Matiu et al. (2021), this assessment shows that snow depth data from reanalysis datasets such CERRA should be evaluated carefully, at least in this specific area. This finding confirms what already suggested by Monteiro and Morin (2023) [5], who attributed discrepancies between reanalysis and observed snow depths to different factors, such as the misrepresentation of wind-transport and glacier accumulation, among others.
+The linear trend map of snow depth (see below) shows a mixed pattern of positive and negative trends in different areas over the Alps, which is in line with results presented in previous studies [4]. In general, the negative trend seems mostly concentrated over mountains areas of higher elevations. This results is in contrast with the findings of Matiu et al. (2021) [5], who have analyzed a comprehensive data set of in situ observations, suggesting an overall negative trend at low altitudes (below 1000 m) and a potential, but not always, positive trend at higher altitudes (over 2000 m). Although a detailed spatial analysis of snow depth is not provided by Matiu et al. (2021), this assessment shows that snow depth data from reanalysis datasets such CERRA should be evaluated carefully, at least in this specific area. This finding confirms what already suggested by Monteiro and Morin (2023) [6], who attributed discrepancies between reanalysis and observed snow depths to different factors, such as the misrepresentation of wind-transport and glacier accumulation, among others.
-The time series of the mean snow depth over the Alps and the corresponding standard deviation are shown below. As expected from the previous analysis, the mean snow depth doesn’t exhibit any clear trend over the long term. Instead, we observe a significant snow depth variability, with particularly high values in 2009 when exceptional snow events have been recorded in the area.
+The time series of the mean snow depth over the Alps and the corresponding standard deviation are shown below. As expected from the previous analysis, the mean snow depth doesn’t exhibit any clear trend over the long term. Instead, we observe a significant snow depth variability, with particularly high values in 2009 when exceptional snow events have been recorded in the area.
@@ -780,11 +782,12 @@ Key resources
References#
-[1] Calmanti, S., Motta, L., Turco, M., & Provenzale, A. (2007). Impact of climate variability on Alpine glaciers in northwestern Italy. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2041-2053.
-[2] Schimanke, S., Isaksson, L., & Edvinsson, L. (2022). Copernicus European Regional ReAnalysis (CERRA): product user guide.
-[3] Masloumidis, I., Dafis, S., Kyros, G., & Lagouvardos, K. (2023). Snow depth Trends of European Ski Resorts. Environmental Sciences Proceedings, 26, 16.
-[4] Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., … & Weilguni, V. (2020). Observed snow depth trends in the European Alps 1971 to 2019. The Cryosphere Discussions, 2020, 1-50.
-[5] Monteiro, D., & Morin, S. (2023). Multi-decadal analysis of the past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets. The Cryosphere, 17, 3617-3660.
+[1] Ridal, M., Bazile, E., Le Moigne, P., Randriamampianina, R., Schimanke, S., Andrae, U., … & Wang, Z. Q. (2024). CERRA, the Copernicus European Regional Reanalysis system. Quarterly Journal of the Royal Meteorological Society.
+[2] Calmanti, S., Motta, L., Turco, M., & Provenzale, A. (2007). Impact of climate variability on Alpine glaciers in northwestern Italy. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2041-2053.
+[3] Schimanke, S., Isaksson, L., & Edvinsson, L. (2022). Copernicus European Regional ReAnalysis (CERRA): product user guide.
+[4] Masloumidis, I., Dafis, S., Kyros, G., & Lagouvardos, K. (2023). Snow depth Trends of European Ski Resorts. Environmental Sciences Proceedings, 26, 16.
+[5] Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., … & Weilguni, V. (2020). Observed snow depth trends in the European Alps 1971 to 2019. The Cryosphere Discussions, 2020, 1-50.
+[6] Monteiro, D., & Morin, S. (2023). Multi-decadal analysis of the past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets. The Cryosphere, 17, 3617-3660.
@@ -856,7 +859,7 @@ References
Define functions#
+Define functions#
The analysis uses the compute_time_mean_and_linear_trend function to calculate the time mean and linear trend of the snow depth, and the compute_spatial_weighted_mean_and_std function to compute its spatial weighted mean and associated standard deviation time series.
Datasets retrieval and processing
-3. Plot and describe results#
+3. Plot and describe results#
-Display results#
+Display results#
Maps of the snow depth climatology and of the corresponding trend are shown below. The unit of the linear trend is converted into meters per decade. The time series of the average snow cover over the entire region is also reported, along with the corresponding standard deviation among all grid points in the region of interest.
@@ -753,15 +755,15 @@ Discussion
The climatological map of snow depth (see below) reveals a wealth of geographic details, which correspond to the complex terrain of the area. The mean value reported in the statistic, displayed in the map’s legend, appears to be quite low (a few centimeters). This is due to the region being largely snow-free for most of the year. However, it is worth noting that there are significant snow depth observed, with the highest recorded value reaching nearly 30 meters. This extreme value is found in the western Italian Alps, specifically over Mont Blanc (Monte Bianco).
+The climatological map of snow depth (see below) reveals a wealth of geographic details, which correspond to the complex orography of the area. The mean value reported in the statistic, displayed in the map’s legend, appears to be quite low (a few centimeters). This is due to the region being largely snow-free for most of the year. However, it is worth noting that there are significant snow depth observed, with the highest recorded value reaching nearly 30 meters. This large value is found in the western Italian Alps, specifically over Mont Blanc (Monte Bianco), which is one of the sites with perennial snow-cover in the area.
-The linear trend map of snow depth (see below) shows a mixed pattern of positive and negative trends in different areas over the Alps, which is in line with results presented in previous studies [3]. In general, the negative trend seems mostly concentrated over mountains areas of higher elevations. This results is in contrast with the findings of Matiu et al. (2021) [4], who have analyzed a comprehensive data set of in situ observations, suggesting an overall negative trend at low altitudes (below 1000 m) and a potential, but not always, positive trend at higher altitudes (over 2000 m). Although a detailed spatial analysis of snow depth is not provided by Matiu et al. (2021), this assessment shows that snow depth data from reanalysis datasets such CERRA should be evaluated carefully, at least in this specific area. This finding confirms what already suggested by Monteiro and Morin (2023) [5], who attributed discrepancies between reanalysis and observed snow depths to different factors, such as the misrepresentation of wind-transport and glacier accumulation, among others.
+The linear trend map of snow depth (see below) shows a mixed pattern of positive and negative trends in different areas over the Alps, which is in line with results presented in previous studies [4]. In general, the negative trend seems mostly concentrated over mountains areas of higher elevations. This results is in contrast with the findings of Matiu et al. (2021) [5], who have analyzed a comprehensive data set of in situ observations, suggesting an overall negative trend at low altitudes (below 1000 m) and a potential, but not always, positive trend at higher altitudes (over 2000 m). Although a detailed spatial analysis of snow depth is not provided by Matiu et al. (2021), this assessment shows that snow depth data from reanalysis datasets such CERRA should be evaluated carefully, at least in this specific area. This finding confirms what already suggested by Monteiro and Morin (2023) [6], who attributed discrepancies between reanalysis and observed snow depths to different factors, such as the misrepresentation of wind-transport and glacier accumulation, among others.
-The time series of the mean snow depth over the Alps and the corresponding standard deviation are shown below. As expected from the previous analysis, the mean snow depth doesn’t exhibit any clear trend over the long term. Instead, we observe a significant snow depth variability, with particularly high values in 2009 when exceptional snow events have been recorded in the area.
+The time series of the mean snow depth over the Alps and the corresponding standard deviation are shown below. As expected from the previous analysis, the mean snow depth doesn’t exhibit any clear trend over the long term. Instead, we observe a significant snow depth variability, with particularly high values in 2009 when exceptional snow events have been recorded in the area.
@@ -780,11 +782,12 @@ Key resources
References#
-[1] Calmanti, S., Motta, L., Turco, M., & Provenzale, A. (2007). Impact of climate variability on Alpine glaciers in northwestern Italy. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2041-2053.
-[2] Schimanke, S., Isaksson, L., & Edvinsson, L. (2022). Copernicus European Regional ReAnalysis (CERRA): product user guide.
-[3] Masloumidis, I., Dafis, S., Kyros, G., & Lagouvardos, K. (2023). Snow depth Trends of European Ski Resorts. Environmental Sciences Proceedings, 26, 16.
-[4] Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., … & Weilguni, V. (2020). Observed snow depth trends in the European Alps 1971 to 2019. The Cryosphere Discussions, 2020, 1-50.
-[5] Monteiro, D., & Morin, S. (2023). Multi-decadal analysis of the past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets. The Cryosphere, 17, 3617-3660.
+[1] Ridal, M., Bazile, E., Le Moigne, P., Randriamampianina, R., Schimanke, S., Andrae, U., … & Wang, Z. Q. (2024). CERRA, the Copernicus European Regional Reanalysis system. Quarterly Journal of the Royal Meteorological Society.
+[2] Calmanti, S., Motta, L., Turco, M., & Provenzale, A. (2007). Impact of climate variability on Alpine glaciers in northwestern Italy. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2041-2053.
+[3] Schimanke, S., Isaksson, L., & Edvinsson, L. (2022). Copernicus European Regional ReAnalysis (CERRA): product user guide.
+[4] Masloumidis, I., Dafis, S., Kyros, G., & Lagouvardos, K. (2023). Snow depth Trends of European Ski Resorts. Environmental Sciences Proceedings, 26, 16.
+[5] Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., … & Weilguni, V. (2020). Observed snow depth trends in the European Alps 1971 to 2019. The Cryosphere Discussions, 2020, 1-50.
+[6] Monteiro, D., & Morin, S. (2023). Multi-decadal analysis of the past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets. The Cryosphere, 17, 3617-3660.
@@ -856,7 +859,7 @@ References
Display results#
+Display results#
Maps of the snow depth climatology and of the corresponding trend are shown below. The unit of the linear trend is converted into meters per decade. The time series of the average snow cover over the entire region is also reported, along with the corresponding standard deviation among all grid points in the region of interest.
Discussion
The climatological map of snow depth (see below) reveals a wealth of geographic details, which correspond to the complex terrain of the area. The mean value reported in the statistic, displayed in the map’s legend, appears to be quite low (a few centimeters). This is due to the region being largely snow-free for most of the year. However, it is worth noting that there are significant snow depth observed, with the highest recorded value reaching nearly 30 meters. This extreme value is found in the western Italian Alps, specifically over Mont Blanc (Monte Bianco).
+The climatological map of snow depth (see below) reveals a wealth of geographic details, which correspond to the complex orography of the area. The mean value reported in the statistic, displayed in the map’s legend, appears to be quite low (a few centimeters). This is due to the region being largely snow-free for most of the year. However, it is worth noting that there are significant snow depth observed, with the highest recorded value reaching nearly 30 meters. This large value is found in the western Italian Alps, specifically over Mont Blanc (Monte Bianco), which is one of the sites with perennial snow-cover in the area.
-The linear trend map of snow depth (see below) shows a mixed pattern of positive and negative trends in different areas over the Alps, which is in line with results presented in previous studies [3]. In general, the negative trend seems mostly concentrated over mountains areas of higher elevations. This results is in contrast with the findings of Matiu et al. (2021) [4], who have analyzed a comprehensive data set of in situ observations, suggesting an overall negative trend at low altitudes (below 1000 m) and a potential, but not always, positive trend at higher altitudes (over 2000 m). Although a detailed spatial analysis of snow depth is not provided by Matiu et al. (2021), this assessment shows that snow depth data from reanalysis datasets such CERRA should be evaluated carefully, at least in this specific area. This finding confirms what already suggested by Monteiro and Morin (2023) [5], who attributed discrepancies between reanalysis and observed snow depths to different factors, such as the misrepresentation of wind-transport and glacier accumulation, among others.
+The linear trend map of snow depth (see below) shows a mixed pattern of positive and negative trends in different areas over the Alps, which is in line with results presented in previous studies [4]. In general, the negative trend seems mostly concentrated over mountains areas of higher elevations. This results is in contrast with the findings of Matiu et al. (2021) [5], who have analyzed a comprehensive data set of in situ observations, suggesting an overall negative trend at low altitudes (below 1000 m) and a potential, but not always, positive trend at higher altitudes (over 2000 m). Although a detailed spatial analysis of snow depth is not provided by Matiu et al. (2021), this assessment shows that snow depth data from reanalysis datasets such CERRA should be evaluated carefully, at least in this specific area. This finding confirms what already suggested by Monteiro and Morin (2023) [6], who attributed discrepancies between reanalysis and observed snow depths to different factors, such as the misrepresentation of wind-transport and glacier accumulation, among others.
-The time series of the mean snow depth over the Alps and the corresponding standard deviation are shown below. As expected from the previous analysis, the mean snow depth doesn’t exhibit any clear trend over the long term. Instead, we observe a significant snow depth variability, with particularly high values in 2009 when exceptional snow events have been recorded in the area.
+The time series of the mean snow depth over the Alps and the corresponding standard deviation are shown below. As expected from the previous analysis, the mean snow depth doesn’t exhibit any clear trend over the long term. Instead, we observe a significant snow depth variability, with particularly high values in 2009 when exceptional snow events have been recorded in the area.
@@ -780,11 +782,12 @@ Key resources
References#
-[1] Calmanti, S., Motta, L., Turco, M., & Provenzale, A. (2007). Impact of climate variability on Alpine glaciers in northwestern Italy. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2041-2053.
-[2] Schimanke, S., Isaksson, L., & Edvinsson, L. (2022). Copernicus European Regional ReAnalysis (CERRA): product user guide.
-[3] Masloumidis, I., Dafis, S., Kyros, G., & Lagouvardos, K. (2023). Snow depth Trends of European Ski Resorts. Environmental Sciences Proceedings, 26, 16.
-[4] Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., … & Weilguni, V. (2020). Observed snow depth trends in the European Alps 1971 to 2019. The Cryosphere Discussions, 2020, 1-50.
-[5] Monteiro, D., & Morin, S. (2023). Multi-decadal analysis of the past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets. The Cryosphere, 17, 3617-3660.
+[1] Ridal, M., Bazile, E., Le Moigne, P., Randriamampianina, R., Schimanke, S., Andrae, U., … & Wang, Z. Q. (2024). CERRA, the Copernicus European Regional Reanalysis system. Quarterly Journal of the Royal Meteorological Society.
+[2] Calmanti, S., Motta, L., Turco, M., & Provenzale, A. (2007). Impact of climate variability on Alpine glaciers in northwestern Italy. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2041-2053.
+[3] Schimanke, S., Isaksson, L., & Edvinsson, L. (2022). Copernicus European Regional ReAnalysis (CERRA): product user guide.
+[4] Masloumidis, I., Dafis, S., Kyros, G., & Lagouvardos, K. (2023). Snow depth Trends of European Ski Resorts. Environmental Sciences Proceedings, 26, 16.
+[5] Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., … & Weilguni, V. (2020). Observed snow depth trends in the European Alps 1971 to 2019. The Cryosphere Discussions, 2020, 1-50.
+[6] Monteiro, D., & Morin, S. (2023). Multi-decadal analysis of the past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets. The Cryosphere, 17, 3617-3660.
@@ -856,7 +859,7 @@ References
References#
-[1] Calmanti, S., Motta, L., Turco, M., & Provenzale, A. (2007). Impact of climate variability on Alpine glaciers in northwestern Italy. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2041-2053.
-[2] Schimanke, S., Isaksson, L., & Edvinsson, L. (2022). Copernicus European Regional ReAnalysis (CERRA): product user guide.
-[3] Masloumidis, I., Dafis, S., Kyros, G., & Lagouvardos, K. (2023). Snow depth Trends of European Ski Resorts. Environmental Sciences Proceedings, 26, 16.
-[4] Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., … & Weilguni, V. (2020). Observed snow depth trends in the European Alps 1971 to 2019. The Cryosphere Discussions, 2020, 1-50.
-[5] Monteiro, D., & Morin, S. (2023). Multi-decadal analysis of the past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets. The Cryosphere, 17, 3617-3660.
+[1] Ridal, M., Bazile, E., Le Moigne, P., Randriamampianina, R., Schimanke, S., Andrae, U., … & Wang, Z. Q. (2024). CERRA, the Copernicus European Regional Reanalysis system. Quarterly Journal of the Royal Meteorological Society.
+[2] Calmanti, S., Motta, L., Turco, M., & Provenzale, A. (2007). Impact of climate variability on Alpine glaciers in northwestern Italy. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2041-2053.
+[3] Schimanke, S., Isaksson, L., & Edvinsson, L. (2022). Copernicus European Regional ReAnalysis (CERRA): product user guide.
+[4] Masloumidis, I., Dafis, S., Kyros, G., & Lagouvardos, K. (2023). Snow depth Trends of European Ski Resorts. Environmental Sciences Proceedings, 26, 16.
+[5] Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., … & Weilguni, V. (2020). Observed snow depth trends in the European Alps 1971 to 2019. The Cryosphere Discussions, 2020, 1-50.
+[6] Monteiro, D., & Morin, S. (2023). Multi-decadal analysis of the past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets. The Cryosphere, 17, 3617-3660.
@@ -856,7 +859,7 @@