Evaluierung ausgewählter Klimareihen Nordwestniedersachsens

Evaluierung ausgewählter Klimareihen
Nordwestniedersachsens durch Zeitreihenanalyse zum
Vergleich lokaler Trends mit einem überregionalen
Klimamodell
Claudia Gabler
Maximilian Strer
Datengrundlage
Klimaentwicklung
1
2
3
4
Datengrundlage
Methoden
Prognose
Fazit
Methoden
Prognose
Hypothesen
▶
▶
▶
2 / 16
Fazit
Erwärmung des nordwestdeutschen
Küstenraumes im Zuge des
Klimawandels
Nachweis dieser mit einfachen
statistischen Methoden
Änderung der Verteilung von Stürmen
Datengrundlage
Methoden
Prognose
Fazit
Datengrundlage
▶
▶
DWD Klimastationen
GKSS Remo-Modell
unterschiedlicher Umfang
der Reihen,
6 bis 60 Jahre
exemplarische Vorstellung
einer Reihe
Norderney
Langeoog
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5935000
▶
W
Borkum
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Esens
Hooksiel
●
●
Jever
●
●
Wittmund Wilhelmshaven
●
Aurich
●
Emden
●
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●
●
0
3380000
10 km
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20 km
3400000
3420000
Rechtswert
3 / 16
E
S
Wangerooge
5925000
▶
N
5945000
unterschiedliche Quellen
werden Verglichen:
Hochwert
▶
3440000
Datengrundlage
Methoden
Prognose
Fazit
12
Aurich
Borkum
Emden
Esens
Hooksiel
Jever
6 7 8 9 10
mittlere Jahrestemperatur [°C]
Zeitreihen
1940
1950
1960
Langeoog
Norderney
Wangerooge
Wilhelmshaven
Wittmund
1970
1980
1990
2000
2010
2000
2010
1200
Aurich
Borkum
Emden
Esens
Hooksiel
Jever
Langeoog
Norderney
Wangerooge
Wilhelmshaven
Wittmund
400 600 800
jahres Niederschlagssumme [mm]
Jahr
1940
1950
1960
1970
1980
Jahr
4 / 16
1990
Datengrundlage
Methoden
Prognose
Fazit
Regionalklima
5980000
N
W
S
980
Wangerooge
845
Norderney
Langeoog
950
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5940000
Hochwert
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935
920
Esens
830
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●
Borkum
E
5
87
86
0
Hooksiel
905 ●
●
89
0
Jever
●
●
Wittmund Wilhelmshaven
●
Aurich
●
Emden
5900000
●
●●●●●
●
●
0
3340000
5 / 16
20 km
●●●●●●●●
●●●●●
●●●
●●●●
40 km
3380000
Rechtswert
Niederschlag [mm]
Temperatur [°C]
3420000
3460000
Datengrundlage
Methoden
Prognose
Fazit
▶
▶
10
Komponentenmodell
exemplarisch:
Borkumzeitreihe (1949-2006)
monatsspezische Werte
0
▶
Temperatur [°C]
20
Methoden: Zeitreihe & Saisonalität
1950
1960
1970
1980
Jahr
6 / 16
1990
2000
Datengrundlage
Methoden
Prognose
Fazit
▶
▶
10
Komponentenmodell
exemplarisch:
Borkumzeitreihe (1949-2006)
monatsspezische Werte
0
▶
Temperatur [°C]
20
Methoden: Zeitreihe & Saisonalität
1950
1960
1970
1980
1990
2000
0
▶
▶
−10
Temperatur [°C]
10
Jahr
1950
1960
1970
1980
Jahr
6 / 16
1990
2000
jahreszeitliche
Schwankungskomponente
Anpassung einer starren
Saisongur durch
Mittelwerte der Monate
Datengrundlage
Methoden
Prognose
Fazit
▶
0
langfristige systematische
Veränderung des mittleren
Niveaus der Zeitreihe
Trendkomponente durch
lineare Regression
−2
▶
Temperatur [°C]
2
Methoden: Trend & Rest
1950
1960
1970
1980
Jahr
7 / 16
1990
2000
Datengrundlage
Methoden
Prognose
Fazit
▶
0
langfristige systematische
Veränderung des mittleren
Niveaus der Zeitreihe
Trendkomponente durch
lineare Regression
−2
▶
Temperatur [°C]
2
Methoden: Trend & Rest
1950
1960
1970
1980
1990
2000
0
▶
▶
−5
Temperatur [°C]
5
Jahr
▶
1950
1960
1970
1980
Jahr
7 / 16
1990
2000
Zeitreihe abzüglich Saison
und Trend
durch Zufallsprozesse
gesteuerte Restkomponente
bestimmt durch
AR(1)-Prozeÿ
Datengrundlage
Methoden
Prognose
Fazit
R²=0.916, p= 2.2e−16
1950
1960
1970
1980
1990
2000
0
R²=0.916, p= 2.2e−16
1950
1960
1970
1980
1990
y=0.0021*x−0.7177, R²=0.062, p= 3.81e−11
−2
Temperatur [°C]
2
−10
Jahr
0
0
−10
Temperatur [°C]
10
Temperatur [°C]
10
Ergebnis Saison
1950
1960
1970
1980
1990
2000
Jahr
0
▶
−5
Temperatur [°C]
5
Jahr
Sd.=1.524
1950
1960
1970
1980
Jahr
8 / 16
1990
2000
▶
ca. 85% der Temperatur Saison bestimmt
ca. 15% des Niederschlags Saison bestimmt
2000
Datengrundlage
Methoden
Prognose
Fazit
R²=0.916, p= 2.2e−16
1950
1960
1970
1980
1990
2000
y=0.0021*x−0.7177, R²=0.062, p= 3.81e−11
1960
1970
1980
1990
0
1950
y=0.0021*x−0.7177, R²=0.062, p= 3.81e−11
−2
Temperatur [°C]
2
−2
Jahr
0
0
−10
Temperatur [°C]
10
Temperatur [°C]
2
Ergebnis Trend
1950
1960
1970
1980
1990
Jahr
2000
Jahr
0
▶
−5
Temperatur [°C]
5
lineare Regression:
▶ Temperatur
Sd.=1.524
1950
1960
1970
1980
1990
▶
2000
Jahr
▶
9 / 16
lange Zeitreihen (> 10 a) signikant vorhanden
kurze Zeitreihen (< 10 a) nicht nachweisbar
Niederschlag nur in Einzelfällen nachweisbar
2000
Datengrundlage
Methoden
Prognose
Fazit
R²=0.916, p= 2.2e−16
1950
1960
1970
1980
1990
2000
−5
0
−2
y=0.0021*x−0.7177, R²=0.062, p= 3.81e−11
1950
1960
1970
1980
1990
Sd.=1.524
2000
Jahr
1950
1960
1970
1980
1990
2000
0
Temperatur [°C]
5
Jahr
−5
Temperatur [°C]
2
Jahr
0
Temperatur [°C]
0
−10
Temperatur [°C]
10
5
Ergebnis Rest
▶
Sd.=1.524
1950
1960
1970
1980
Jahr
10 / 16
1990
2000
▶
Restkomponente: nicht erklärbarer stochastischer Anteil
kann den Trend überlagern
Datengrundlage
Methoden
Prognose
Fazit
50
40
30
▶
▶
20
mittlere Windgeschwindigkeit [m/s]
Werden Stürme häuger auftreten?
1970 1975 1980 1985 1990 1995 2000 2005
400
▶
200
{
𝜆 ⋅ exp−𝜆
f (x ) =
0,
0
Häufigkeit [−]
Jahr
▶
Sturm vWind ≥ 20.8 ms
Anzahl von Stürmen pro
Jahr
Verteilungsänderung der
Sturmstärke
Exponentialverteilung:
20
11 / 16
25
30
35
40
45
mittlere Windgeschwindigkeit [m/s]
50
⋅
x,
x
x
≥0
<0
Datengrundlage
Methoden
Prognose
80
●
●
●
●
● ●
● ●
●
60
●
● ●
40
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
▶
●
● ●
20
●
●
●
●
0
Anzahl Stürme pro Jahr
100
Werden Stürme häuger auftreten?
1970
1975
1980
1985
1990
Jahr
12 / 16
1995
2000
2005
kein Trend nachweisbar
Fazit
Datengrundlage
Methoden
Prognose
Fazit
1.0
0.2
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
0.0
0.00
●
0.30
lambda [−]
0.8
0.6
●
0.4
lambda [−]
●
●
1970
1980
1990
2000
2010
2020
20.8
Jahr
▶
lambda = 0.3
lambda = 0.4
lambda = 0.5
0.20
y=0.003*x−5.757, R²=0.128, p=0.032
lambda − Jahr
angepasste Gerade
Prognose
95%−Konfidenzintervall
●
0.10
1.2
Werden Stürme häuger auftreten?
Trend ist signikant
vorhanden
13 / 16
30.8
40.8
Windgeschwindigkeit [m/s]
▶
Trend bewirkt häugeres Vorkommen
schwacher Stürme
Datengrundlage
Methoden
Prognose
Fazit
Prognose
12
10
8
▶
lineare Regression
Borkumzeitreihe
Komponenten im
Verhältnis
y=0.0021*x−0.717, R²=0.062, p=3.81E−11
Sd.=1.524
6
▶
Temperatur [°C]
14
Temperaturzeitreihe Borkum
Trend lineare Regression
Trend Prognose
95%−KI. lin. Reg.
Sd. AR[1]−Modell
1960
1980
2000
Jahr
14 / 16
2020
Datengrundlage
Methoden
Prognose
Fazit
Prognose
1
0
Temperatur [°C]
2
Trend Klimastationen DWD
Trend Remo−Modell GKSS
Mittlerer Trend Remo−Modell GKSS
Mittlerer Trend Klimastationen DWD
−1
▶
DWD: 1‰ ± 0.6
in 60 Jahren
GKSS: 0.75‰ ± 0.1
in 60 Jahren
−2
▶
1960
1980
2000
Jahr
15 / 16
2020
Datengrundlage
Methoden
Prognose
Fazit
DWD-Klimastationen:
▶
▶
▶
Temperaturerhöhung 1‰ ± 0.6 signikant nachweisbar
Niederschlagserhöhung nachweisbar, aber nicht zu beziern
Veränderung der Verteilung der Sturmhäugkeit signikant nachweisbar,
schwache Stürme werden häuger auftreten
GKSS-Modelldaten:
▶ Temperaturerhöhung 0.75‰ ± 0.1 signikant nachweisbar
▶ vergleichbare Werte in einer Gröÿenordnung
16 / 16
Fazit
Kontakt
[email protected]
[email protected]
Quellen
Gabler, Claudia und Strer, Maximilian: Evaluierung ausgewählter
Klimareihen Nordwestniedersachsens durch Zeitreihenanalyse zum Vergleich lokaler
Trends mit einem überregionalen Klimamodell, Studienarbeit an der TU
Braunschweig, 2009.