Simulation of the Late Maunder Minimum with global and regional climate models
Hans von Storch, Beate MüllerGKSS Research Center
MUSCAD workshop, 20-21 October 203, Uppsala
Simulation with ECHO-G (ECHAM4/HOPE-G)
- 500 years (Columbus-run), and - 1000 years (Erik run)
forced with
- variable solar output- volcanic aerosol load- GHG concentrations
Simulated global 1675-1710 temperature anomaly
deMenocal et al.
(2000)
Ice conditions off Iceland (Koch, 1945)
Information provided by
Fidel González-Rouco andSimon Tett
HadCM3
ECHO-G
differences relative to the 1550-1800 average;25-year running averages.
Late Maunder Minimum
Cold winters and springs, 1675-1710
Late Maunder Minimum
Cold winters and springs, 1675-1710
Analysis of Columbus run, only.
Baltic Sea ice winter index after Koslowski (1998)
grey: raw index, red: 5 year mean, blue:20 year mean
Mean winter temperatures in TallinMean winter temperatures in Tallin as reconstructed by Tarand & Nordli, 2001as reconstructed by Tarand & Nordli, 2001
-12
-10
-8
-6
-4
-2
0
2
1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Tem
pera
tur [
°C]
Tarand & Nordli Gauß Filter (10 Jahre)
LMM
The Late Maunder Minimum (LMM) is the coldest phase of the so-called ‘Little Ice Age’ with marked climatic variability over wide parts of Europe.
Temperature conditions in Switzerland according to Pfister‘s classification (1999).
1675-1710vs. 1550-1800
Reconstruction from historical evidence, from Luterbacher et al.
1675-1710vs. 1550-1800
Reconstruction from historical evidence, from Luterbacher et al.
Late Maunder Minimum
Model-based reconstuction
LMM – reference period, winter
Erik
Columbus
Eduardo Zorita, pers. comm.
LMM1 1671-1684 NAO- and Cooling
LMM2 1685-1708 NAO+ and Warming
Irene Fischer-Bruns, pers. comm.
gridding:
Spatial: 0.5° x 0.5°Temporal: 6 h
REMO Model AreaREMO Model Area
ECHO-G griddingECHO-G gridding
3
8
5
4
6
1
9
2
11
10
7N
EW
S
Eisbedeckungsgrad [%]3. März 1692
0 - 1010 - 2020 - 3030 - 4040 - 5050 - 6060 - 7070 - 8080 - 9090 - 100
0 200 400 600
Kilometers
Regionalized sea ice dataRegionalized sea ice data
0
10
20
30
40
50
60
70
80
90
100
650 750 850 950 1050 1150 1250 1350 1450 1550 1650 1750 1850 1950
forest farmland greenland infrastructure water
Main land use types in Germany from 7th centuryMain land use types in Germany from 7th century
area
[%
]
DifferencesDifferences
reconstructed data - REMO reconstructed data - ECHO
January
DifferencesDifferences
reconstructed data - REMO reconstructed data - ECHO
July
Winter
-4
-3
-2
-1
0
1
2
5 10 15 20 25 30
Ano
mal
ie [K
]
REMO Luterbacher
Frühling
-4
-3
-2
-1
0
1
2
5 10 15 20 25 30
Anom
alie
[K]
Sommer
-4
-3
-2
-1
0
1
2
5 10 15 20 25 30
Anom
alie
[K]
Herbst
-4
-3
-2
-1
0
1
2
5 10 15 20 25 30
Anom
alie
[K]
REMO und Luterbacher
Seasonal temperature anomalies in Seasonal temperature anomalies in areas with skill of reconstruction > 0.5areas with skill of reconstruction > 0.5
WinterWinter
HerbstHerbst
FrühlingFrühling
SommerSommer
Temperaturanomalie des LMM Temperaturanomalie des LMM für Zentral-Englandfür Zentral-England
WinterWinter
-4
-3
-2
-1
0
1
2
5 10 15 20 25 30
Anom
alie
[K]
Manley REMO Luterbacher
256
260
264
268
272
5 10 15 20 25 30
Tem
pera
tur [
K]
LMM Tarand&Nordlivorind. P. Tarand&NordliLMM REMONP REMO
Winterliche Mitteltemperatur und Anomalie des Winterliche Mitteltemperatur und Anomalie des LMMLMM
für den Tallinner Hafenfür den Tallinner Hafen
MittelMittel
Conclusions• Two multi-century simulations concluded. Natural forcing plus GHGs. Work done by a European consortium.
• Model simulations are consistent.
• Tools for dynamical downscaling for the Baltic catchment area developed.
• Models simulates significant winter cooling during LMM; similar event in HadCM3.
• Similarity to observed features, but variations much stronger than empirical reconstructions by Mann and others.
• LMM winter cooling is global event. Cooling in Europe is just a manifestation, and not mainly related to anomalous easterlies in Northern Europe.