short introduction to climate change modellingusers.auth.gr/vmarios/erasmus/short introduction to...

18/10/18Marios VafiadisDIVISION OF HYDRAULICS AND ENVIRONMENTAL ENGINEERING

Short Introduction to Climate Change Modelling


http://www.ipcc.ch/


Atmospheric circulation

Wikipedia


A General Circulation Model (GCM) is a mathematical model of the general circulation of the Earth’s atmosphere based on the Navier-Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). Sea-ice and land-surface components can be also combined with GCM in order to provide a more complete Global climate model.

These equations are the basis for complex computer programs commonly used for simulating the atmosphere or ocean.

These computationally intensive numerical models are based on the integration of a variety of fluid dynamical, chemical, and sometimes biological equations.

GCM (General Circulation Models)


GCM (General Circulation Models) Model structure

Three-dimensional + time (four-dimensional) GCMs discretise the equations for fluid motion and integrate these forward in time. They also contain parametrisations for processes - such as convection - that occur on scales too small to be resolved directly. More sophisticated models may include representations of the carbon and other cycles.

GCMs gemerally produce data at least for 5 parameters:

1. Atmospheric Pressure2. U wind3. V wind4. Temperature5. Humidity



Wikipedia


Atmospheric GCMs (AGCMs) model the atmosphere and impose sea surface temperatures as boundary conditions.

Coupled atmosphere-ocean GCMs (AOGCMs, e.g. HadCM3, EdGCM, GFDL CMX2, ARPEGE-Climat) combine the two models.

GCMs and global climate models are widely applied for weather forecasting, understanding the climate, and projecting climate change.



A key limitation of GCMs is the fairly coarse horizontal resolution that is ~2.5° (~200 km on Earth surface). For the practical planning of water resources, flood defences etc., this information is not useful.



Dowscaling

Downscaling refers to techniques that take output from the model and add information at scales smaller than the initial grid spacing. Global Circulation models (GCMs) are run at coarse spatial resolution (2.5° x 2.5° or ~200 x ~200 km on Earth surface [typically of the order 50,000 square kilometres]

At this resolution GCMs are unable to resolve important sub-grid scale features such as clouds and topography (relief).

As a result GCMs can’t be used for local impact studies.

To overcome this problem downscaling methods are developed to obtain local-scale surface weather from regional-scale atmospheric variables that are provided by GCMs.


There are two different ways for downscaling:

A. Statistical DownscalingB. Dynamic Downscaling

Statistical downscaling again can be divided into four categories:

1. Regression methods2. Stochastic weather generators3. Weather pattern-based approaches4. Neural networks applications

Dynamic downscaling refers to the limited-area modeling, that isweather modelling at a finer (local, regional) scale:

Regional Climate Models (RCMs)

Dowscaling


Regional climate models

RCMs work by increasing the resolution of the GCM in a small, limited area of interest. The full GCM determines the very large scale effects of changing greenhouse gas concentrations, volcanic eruptions etc. on global climate.

The climate calculated by the GCM is used as input at the edges of the RCM. RCMs can resolve the local impacts given small scale information about orography (land height), land use etc., with a spatial resolution as fine as 50 or 25km.


Comparison of GCM and RCM results


Weather types in Greece

Main Category

Symbol

Description

Continental Anticyclones

A1 Location of center in western Europe or northern Atlantic

A2 Location of center in Russian or Siberian region

A3 Location of center in Balkan

Maritimes Anticyclones

A4 Location of center in eastern Mediterranean

A5 Location of center in western Mediterranean and Africa

(after Maheras, 1989)


Weather types in Greece

Main Category Symbol Description

Cyclones with zonal orbit

W1 Cyclone passes from the Balkans over 45o latitude

W2 Cyclone passes through Greece below 45o latitude

NW1 Cyclone from W. Mediterranean through Greece

NW2 Cyclone from Scandinavia to Black Sea

Cyclones with meridional orbit

SW1 Cyclone from W. Malta-Macedonia-Ukraine

SW2 Cyclone from E. Malta-Macedonia-Ukraine

(after Maheras, 1989)


-10 -5 0 5 10 15 20 25 30 35 4030

35

40

45

50

55

W.T 1 W.T 2

W.T 5 W.T 4

W.T 3


-10.00 -5.00 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.0030.00

35.00

40.00

45.00

50.00

55.00

W.T 6

W.T 8

W.T 9

W.T 7

W.T 9


Downscaling

-20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.020.0

22.5

25.0

27.5

30.0

32.5

35.0

37.5

40.0

42.5

45.0

47.5

50.0

52.5

55.0

57.5

60.0

62.5

65.0

CneCnnw

Cwnw

Cwsw

Cssw

Cse

C


Downscaling

-20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.020.0

22.5

25.0

27.5

30.0

32.5

35.0

37.5

40.0

42.5

45.0

47.5

50.0

52.5

55.0

57.5

60.0

62.5

65.0

AneAnw

Asw Ase

A


Downscaling

Mean Annual Prec (mm) Mean numbers of raindays Pq95 (mm) Abs Max Prec (mm) Ratio AbsMax/Pq95

Athens 371.0 69 20.8 82.0 3.9GridAthens 330.8 105 14.9 111.2 7.5Thessaloniki 465.8 94 19.5 98.0 5.0GridThessaloniki 389.7 115 14.1 49.0 3.5Patra 660.9 83 25.6 86.6 3.4GridPatra 659.2 137 19.5 67.7 3.5Heraklio 507.1 72 27.4 222.2 8.1GridHeraklio 403.1 125 13.0 179.5 13.8


Statistical downscaling model

• Predictor determination: 500hPa (NCEP and RCM data) spatial window (0oE – 32.5oE and 30oN – 55oN)

• Validation period: 15 years (1979-1993) common period for all the available data

• Predictants: seasonal precipitation heights for 6 stations in the Aravissos area and 2 stations in the Patra area

Artificial Neural Network approach


The Artificial neural network modelThe Artificial neural network model

IINNPPUUTT

Application during the validation

period using the final weights

x1

x2

x3

xn

Hidden Layer

(nodes)

F (NET)F (NET)

Target VectorTarget Vector

Error Minimization

Calibration Period

Weight Adjustment

ww11

ww22

ww33

wwnn

)exp(1

1)(

NETNETF


b. Dynamical downscaling model (RCM)

20 21 22 23 24 25 26 27 28

Spatial resolution of KNMI over Greece

35

36

37

38

39

40

41

20 21 22 23 24 25 26 27 28

35

36

37

38

39

40

41 Goumenissa

KariotisaK. Vrisi

Skydra

ExaplatanosTheodoraki

20 21 22 23 24 25 26 27 28

35

36

37

38

39

40

41

PatraAraksos


c. Results for the validation period 1979-1993

0.0

50.0

100.0

150.0

200.0

250.0

300.0

mm

Araksos Patra

WINTER prec _ Validation period 1979-1993

Real ANNs KNMI

0.0

50.0

100.0

150.0

200.0

250.0

mm

ExaplatanosGoumenissa Kariotisa K.Vrisi Skydra Theodoraki

Winter prec _ Validation period 1979-1993

Real ANNs KNMIWinter Period

Aravissos

Patra



90.0

100.0

110.0

120.0

130.0

140.0

mm

Araksos Patra

Spring prec _ Validation period 1979-1993

Real ANNs KNMI

0.0

50.0

100.0

150.0

200.0

250.0

mm

Exaplatanos Goumenissa Kariotisa K.Vrisi Skydra Theodoraki

Spring prec _ Validation period 1979-1993

Real ANNs KNMISpring Period

Aravissos

Patra



0.0

5.0

10.0

15.0

20.0

mm

Araksos Patra

Summer prec _ Validation period 1979-1993

Real ANNs KNMI

0.0

50.0

100.0

150.0

mm


Summer prec _ Validation period 1979-1993

Real ANNs KNMISummer Period

Aravissos

Patra



0.0

50.0

100.0

150.0

200.0

250.0

mm

Araksos Patra

Autumn prec _ Validation period 1979-1993

Real ANNs KNMI

0.0

50.0

100.0

150.0

200.0

250.0

300.0

mm


Autumn prec _ Validation period 1979-1993

Real ANNs KNMIAutumn Period

Aravissos

Patra



Araksos Patra Exaplatanos Goumenissa Kariotisa K.Vrisi Skydra TheodorakiANNs 0.7 0.6 -0.2 0.9 0.1 0.5 0.8 0.5KNMI 0.0 0.3 0.0 -0.3 0.0 -0.3 0.0 -0.1ANNs 0.4 0.4 0.2 0.2 -0.2 -0.4 0.3 0.2KNMI 0.4 0.3 0.2 0.6 0.0 0.3 0.2 0.4ANNs 0.2 0.0 0.6 0.7 0.7 0.3 0.7 0.8KNMI 0.0 0.1 0.3 0.2 0.2 0.3 0.4 0.3ANNs 0.0 0.1 0.3 0.4 0.1 0.5 0.5 0.2KNMI 0.0 -0.2 -0.1 0.1 -0.2 -0.1 -0.3 -0.2

AUTUMN

Real data

WINTER

SPRING

SUMMER

Correlation between the station and the simulated time – series for the validation

period

Scatter plot winter prec Goumenissa

0

100

200

300

400

500

0 100 200 300 400 500

Goumenissa Real

Go

um

enis

sa K

NM

IScatter plot winter prec Goumenissa

0

50

100

150

200

250

300

0 100 200 300 400 500

Goumenissa Real

Go

um

enis

sa A

NN

s


Data and MethodologyStation rainfall data

For the Aravissos area seasonal data from six meteorological stations were available and used in the study:

- Goumenissa (1955-1995)- Exaplatanos (1975-2007)- Kariotisa (1969-2008)- Kria Vrisi (1951-1998)- Skydra (1959-1993)- Theodoraki (1975-2002)

For the Patra test sites the data from two stations were employed:

- Patra (1955 – 2005)- Araxos (1949-2007)


Quality of estimation by comparison of estimated versus actual data

Season Statistical downscaling

RCM

Winter - +Spring + -

Summer OK -Autumn ~ -

Results 1. Results for the validation period


2. Results for the control run period

Aravissos-test site.

Variable results depending of season and station.

Patra-test site.

Both models give smaller precipitation heights in respect to the observational ones.


e. Future estimation for the seasonal precipitation (scenarios 2071-2100)

WinterAravissos-case study

0

50

100

150

200

250

300

350

400

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

2072

2074

2076

2078

2080

2082

2084

2086

2088

2090

2092

2094

2096

2098

2100

GRID1

meso-grid

Series3

171.6193.8

170.5

SpringAravissos-case study

0

50

100

150

200

250

300

350

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

2072

2074

2076

2078

2080

2082

2084

2086

2088

2090

2092

2094

2096

2098

2100

GRID1

meso-grid

Series3



SpringAravissos-case study

0

50

100

150

200

250

300

350

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

2072

2074

2076

2078

2080

2082

2084

2086

2088

2090

2092

2094

2096

2098

2100

GRID1

meso-grid

Series3

143.2

161.3

105.1



SummerAravissos-case study

0

20

40

60

80

100

120

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

198

9

2022

202

420

2620

28

2030

203

220

34

2036

203

820

4020

42

2044

2046

2048

205

0

2072

2074

207

620

7820

80

2082

2084

2086

2088

2090

2092

2094

2096

2098

2100

GRID1

meso-grid

Series3

16.2

24.3

13.9



WinterPatra-case study

0

50

100

150

200

250

300

350

400

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

2072

2074

2076

2078

2080

2082

2084

2086

2088

2090

2092

2094

2096

2098

2100

gridpatra

Series3

223.3

SpringPatra-case study

0

50

100

150

200

250

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

2072

2074

2076

2078

2080

2082

2084

2086

2088

2090

2092

2094

2096

2098

2100

gridpatra

Series3

222.7

183.1



SpringPatra-case study

0

50

100

150

200

250

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

2072

2074

2076

2078

2080

2082

2084

2086

2088

2090

2092

2094

2096

2098

2100

gridpatra

Series3

104.8121.0

85.6



SummerPatra-case study

0

20

40

60

80

100

120

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

2072

2074

2076

2078

2080

2082

2084

2086

2088

2090

2092

2094

2096

2098

2100

meso-grid

Series3

3.7

6.7

4.4



AutumnPatra-case study

0

50

100

150

200

250

300

350

400

45019

6119

6319

6519

6719

6919

7119

7319

7519

7719

79

1981

1983

198

519

8719

89

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

204

220

4420

46

204

820

50

2072

2074

2076

2078

2080

2082

2084

2086

2088

2090

2092

2094

2096

2098

210

0

gridpatra

Series3

143.4

138.7

136.6

Marios VafiadisDIVISION OF HYDRAULICS AND ENVIRONMENTAL ENGINEERING

And….That’s all for now!

Thank you for your attention!

short introduction to climate change modellingusers.auth.gr/vmarios/erasmus/short introduction to...

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