Snow cover in a changing climate

Introduction to Climate Change - UniTrento 2024/25

Michael Matiu

2025-05-19

Outline

  1. Snow: what is it, why is it important, how is it measured
  2. European Alps: Snow climatology and past trends
  3. The future snow cover in the European Alps

What is snow?
(for you)?

Your opinion

Other opinions

“Acqua.”

“Niederschlag.”

“Quando ero giovane, pura gioia. Adesso solo scocciatura.”

“I don’t know what snow is.”

“Stupore, allegria. In macchina paura. Bello in montagna.”

Why is snow important?

Albedo

Albedo … more examples

Albedo explanation

Snow as natural water storage

2022 Drought in the Po valley

Screenshot of guardian article on 2022 Italian snow drought

Screenshot of guardian article on 2022 Italian snow drought

2022 Drought in the Po valley -> Snow drought

How is snow measured?

(Focus here: macroscopic)

In-situ

From remote

Water equivalent, depth, bulk density

\[ SWE = HS * \rho \]

Example: 75cm depth, 300mm SWE

\[ \rho = \frac{300 mm}{75 cm} = \frac{300 \frac{kg}{m^2}}{0.75 m} = 400 \frac{kg}{m^3} \]

Common snow bulk densities

Type Density
Freshly fallen snow 50-100
Damp new snow 100-200
Settled snow 200-300
Wind packed snow 350-400
Firn 400-800
Very wet snow up to 800
Glacier ice above 800-900

Some climatological snow cover indicators

  • Snow depth (HS)
  • Depth of snowfall (HN)
  • Snow water equivalent (SWE)
  • Snow density (\(\rho\))
  • Snow presence (yes/no)
  • Snow cover duration (SCD)
  • Snow covered area (SCA)

What is a snow scientist?

  • operational / civil protection (avalanche risk, snowload)
  • engineers that develop and test instruments
  • field studies
  • modelers (numerical models of varying complexity)
  • climatologists (-> me)
  • hydrologist
  • physicists (study mechanical and physical properties)
  • …

-> there is not “the” snow scientist, often a combination

Climatolical analyses of snow cover

  1. Interdisciplinarity:
    1. atmosphere
    2. often mountains (so complex physics)
    3. accumulation (land surface)
    4. impacts (runoff, hydropower, agriculture, tourism, …)
  2. Scale issues and variability in
    1. space
    2. time
    3. both

Seasonality

Inter-annual variability

Magnitude of area covered by snow

January on average 47 million km² ~

  • 150 Italys
  • 7580 provinces of Trento
  • 1/3 Northern Hemisphere land surface

Guess the snowfall

Guess the snowfall - details 1/3

Trento Laste
46.07, 11.12, 312 m.a.s.l.

avg Jan:

  • 1.6°C
  • 42mm
  • 16cm snowfall

Bondone (viote)
46.01, 11.06, 1495 m.a.s.l.

avg Jan:

  • -2.5°C
  • 50mm
  • 51cm snowfall

Guess the snowfall - details 2/3

Sukayu Onsen
40.65, 140.85, 925 m.a.s.l.

avg Jan:

  • -7.5°C
  • 117mm
  • 454cm snowfall

Guess the snowfall - details 3/3

Vostok Station
-78.47, 106.85, 3488 m.a.s.l.

avg Jul:

  • -70.4°C
  • 2mm
  • 2-6cm snowfall

The Alps shaping the weather

Data source: Alpine-wide in-situ observations

What comes after elevation?

Introduction to k-means

Unsupervised classification technique

K-means convergence

Input to k-means:

Time series of daily snow depth (> 2000 stations, 30 years)

Snowfall 1921-2020

(below 2000m)

Data source

“noi prepareremo pei nostri posteri un materiale ben ordinato e prezioso per istabilire con ottimo fondamento il non facile edifizio della climatologia delle nostre regioni, al quale intendimento sono rivolti i nostri sforzi e tutti i nostri studi”
Denza F., BM 11(1), 1876

Courtesy: Daniele Cat Berro, Società Meteorologica Italiana

Snow depth trends 1971 - 2019

One station - Arabba (Veneto, 1640m)

Trend - linear

Trend - nonlinear

Trend - across the season

Trend - across the season - linear

More indicators

All stations

Results from statistical analysis: Linear Regression

\[ y_i = \beta_0 + \beta_1 * x_i + \epsilon_i \]

  • \(y_i\) predictand (to be predicted)
  • \(x_i\) predictor (used to predict, has a relationship to \(y\))
  • \(i\) observation number from \(1...n\)
  • \(\epsilon \sim N(0,\sigma^2)\) normally distributed errors (random)
  • \(\beta_.\) coefficients to be estimated
    • \(\beta_0\) intercept, value when \(x=0\)
    • \(\beta_1\) slope, change in \(y\) per 1 unit of \(x\)

Linear regression in this case

\[ y_i = \beta_0 + \beta_1 * i + \epsilon_i \]

  • \(i\) is year (1971-2019)
  • \(y_i\) average snow depth (monthly, seasonally) in year \(i\)
  • \(\beta_0\) intercept, value when \(x=0\), i.e., for year 0
    • better: \(i\) is years after first year (0 to 49)
    • then: \(\beta_0\) is average snow depth at the start year
  • \(\beta_1\) change in average snow depth per year
  • absolute trends per decade: \(10*\beta_1\)
  • relative trends per decade: \(10* \frac{\beta_1}{\beta_0}\)

The future snow cover in the European Alps

Global vs. local

5-year averages

(2018-2022)

Climate and weather models

What is what?

  • Foto from the astronauts of Apollo 17 (7 December 1972)
  • Weather model simulation (7 December 1972)

Coding errors (“bugs”)

Climate projections are not predictions

Prediction:

  • Thus it will be.

Scenario

  • If A, then X.
  • If B, then Z.
  • …

Climate scenarios strategic management

Assumptions …

CMIP6, IPCC AR6 2021: Shared Socioeconomic Pathways

  • a world of sustainability-focused growth and equality (SSP1)
  • a “middle of the road” world where trends broadly follow their historical patterns (SSP2)
  • a fragmented world of “resurgent nationalism” (SSP3)
  • a world of ever-increasing inequality (SSP4)
  • a world of rapid and unconstrained growth in economic output and energy use (SSP5)
  • plus many other socio-economic assumptions on population growth, GDP development, …

Source: Wikipedia and CarbonBrief

Shared Socioeconomic Pathways

-> Combined with radiative forcing in W/m², which can be related to mitigation targets

… translated into CO2 …

Source: Wikipedia

… translated into global climate

Source: mooc.fi

Europe’s future climate

  • CMIP6 suite of global climate models (IPCC AR6, 2021)
  • CMIP5 suite of global climate models (IPCC AR5, 2013)
  • CORDEX regional climate models, dynamically downscaled CMIP5
  • Scenario RCP8.5 (CMIP5) and ssp585 (CMIP6)
  • continuously increasing GHG concentrations in the atmosphere
  • 4-5 °C global warming until 2100

What does this mean for snow?

Temperature

  • more rain instead of snowfall
  • snow melts faster/earlier
  • more intensive snowfall

Precipitation ?

  • more rain and more snowfall

Together (at least for the Alps):

  • less snow, short snow season

Snow prophets 2023-2024?

Climate dice

very little snow, a lot of snow

In the past:

And the future?

  • ?
  • ?
  • ?

Contact

michael.matiu@unitn.it

mitmat.eu

https://mitmat.github.io/slides-v2/

Eurac Snow Dossier, CliRSnow project

Thanks to the support of: UniTrento, European Union, Eurac Research, APPA (Provincia Autonoma di Trento), Climate Action South Tyrol, Scientists4Future South Tyrol