From d70b7c8025ceaa257315f439cebf172c1ce31eec Mon Sep 17 00:00:00 2001 From: malmans2 Date: Mon, 4 Nov 2024 09:54:31 +0000 Subject: [PATCH] =?UTF-8?q?Deploy=20preview=20for=20PR=20258=20?= =?UTF-8?q?=F0=9F=9B=AB?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../Climate_Projections/CMIP6/CMIP6.html | 2 +- .../Climate_Projections/CORDEX/CORDEX.html | 2 +- .../pr-258/Climate_Projections/climate.html | 2 +- pr-preview/pr-258/In_Situ/insitu.html | 2 +- pr-preview/pr-258/Reanalyses/reanalysis.html | 8 +- ...ra-single-levels_trend-assessment_q02.html | 69 +++++----- .../Atmosphere_Physics.html | 2 +- .../Atmospheric_Composition.html | 2 +- .../Satellite_ECVs/Cryosphere/Cryosphere.html | 2 +- .../Land_Biosphere/Land_Biosphere.html | 2 +- .../Land_Hydrology/Land_Hydrology.html | 2 +- .../pr-258/Satellite_ECVs/Ocean/Ocean.html | 2 +- .../pr-258/Satellite_ECVs/satellite.html | 2 +- .../pr-258/Seasonal_Forecasts/seasonal.html | 6 +- .../70f49cc4-fe64-4ce4-a33b-5f2065faf915.png | Bin 0 -> 197446 bytes ...a-single-levels_trend-assessment_q02.ipynb | 118 +++++++++++------- .../pr-258/_sources/templates/template.ipynb | 4 +- pr-preview/pr-258/genindex.html | 2 +- pr-preview/pr-258/intro.html | 2 +- pr-preview/pr-258/objects.inv | Bin 857 -> 921 bytes pr-preview/pr-258/search.html | 2 +- pr-preview/pr-258/searchindex.js | 2 +- pr-preview/pr-258/templates/template.html | 2 +- .../templates/template_instructions.html | 2 +- 24 files changed, 137 insertions(+), 102 deletions(-) create mode 100644 pr-preview/pr-258/_images/70f49cc4-fe64-4ce4-a33b-5f2065faf915.png diff --git a/pr-preview/pr-258/Climate_Projections/CMIP6/CMIP6.html b/pr-preview/pr-258/Climate_Projections/CMIP6/CMIP6.html index bf64ca58..617ff979 100644 --- a/pr-preview/pr-258/Climate_Projections/CMIP6/CMIP6.html +++ b/pr-preview/pr-258/Climate_Projections/CMIP6/CMIP6.html @@ -204,7 +204,7 @@

➡️ Reanalysis

diff --git a/pr-preview/pr-258/Climate_Projections/CORDEX/CORDEX.html b/pr-preview/pr-258/Climate_Projections/CORDEX/CORDEX.html index 7e036cb0..15d2e936 100644 --- a/pr-preview/pr-258/Climate_Projections/CORDEX/CORDEX.html +++ b/pr-preview/pr-258/Climate_Projections/CORDEX/CORDEX.html @@ -204,7 +204,7 @@

➡️ Reanalysis

diff --git a/pr-preview/pr-258/Climate_Projections/climate.html b/pr-preview/pr-258/Climate_Projections/climate.html index 8045c225..8e5aeb4a 100644 --- a/pr-preview/pr-258/Climate_Projections/climate.html +++ b/pr-preview/pr-258/Climate_Projections/climate.html @@ -204,7 +204,7 @@

➡️ Reanalysis

diff --git a/pr-preview/pr-258/In_Situ/insitu.html b/pr-preview/pr-258/In_Situ/insitu.html index 4f1ccf4c..bd88cb9e 100644 --- a/pr-preview/pr-258/In_Situ/insitu.html +++ b/pr-preview/pr-258/In_Situ/insitu.html @@ -204,7 +204,7 @@

➡️ Reanalysis

diff --git a/pr-preview/pr-258/Reanalyses/reanalysis.html b/pr-preview/pr-258/Reanalyses/reanalysis.html index bf8f802b..9544e14d 100644 --- a/pr-preview/pr-258/Reanalyses/reanalysis.html +++ b/pr-preview/pr-258/Reanalyses/reanalysis.html @@ -63,7 +63,7 @@ - + @@ -204,7 +204,7 @@

➡️ Reanalysis

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3. ReanalysisAvailable assessments

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3. Reanalysis

next

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3.1. Unique quality assessment title (to be finalised by C3S), including [data stream]-[quality area]-for-[application area]

+

3.1. Validation of CERRA climatology and trends for applications in the tourism sector

 diff --git a/pr-preview/pr-258/Reanalyses/reanalysis_reanalysis-cerra-single-levels_trend-assessment_q02.html b/pr-preview/pr-258/Reanalyses/reanalysis_reanalysis-cerra-single-levels_trend-assessment_q02.html index a82abfa9..175ee427 100644 --- a/pr-preview/pr-258/Reanalyses/reanalysis_reanalysis-cerra-single-levels_trend-assessment_q02.html +++ b/pr-preview/pr-258/Reanalyses/reanalysis_reanalysis-cerra-single-levels_trend-assessment_q02.html @@ -8,7 +8,7 @@ - 3.1. Unique quality assessment title (to be finalised by C3S), including [data stream]-[quality area]-for-[application area] — eqcbook + 3.1. Validation of CERRA climatology and trends for applications in the tourism sector — eqcbook @@ -204,7 +204,7 @@

➡️ Reanalysis

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-

Unique quality assessment title (to be finalised by C3S), including [data stream]-[quality area]-for-[application area]

+

Validation of CERRA climatology and trends for applications in the tourism sector

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Contents

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-

📢 Quality assessment statement#

+
+

📢 Quality assessment statements#

These are the key outcomes of this assessment

📈 Analysis and results#

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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.

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Define data request -

2. Datasets retrieval and computation of trend#

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2. Datasets retrieval and computation of trend#

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Define functions#

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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.

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Datasets retrieval and processing -

3. Plot and describe results#

+

3. Plot and describe results#

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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.

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Discussion

  • Climatology of snow depth

    • -

    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.

    • Linear trend of snow depth

    -

    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.

    • Interannual variability of snow depth

    -

    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.

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    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.

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    References
  • 🌍 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