Long time memory in global warming simulations
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Citation: Blender, R., and
K. Fraedrich, Long time memory in global warming simulations, Geophys. Res.
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GEOPHYSICAL RESEARCH LETTERS, VOL. 30, NO. 14, 1769, doi:10.1029/2003GL017666, 2003
Long time memory in global warming simulations
Richard Blender and Klaus Fraedrich
Meteorologisches Institut, Universitat Hamburg, Hamburg, Germany ¨ Received 2 May 2003; revised 17 June 2003; accepted 20 June 2003; published 29 July 2003.
[1] Global power law scaling of near surface temperature spectra is determined in two scenario simulations and in NCEP re-analyses. The simulations use the coupled atmosphere-ocean models HadCM3 and ECHAM4/OPYC with greenhouse gas increase according to scenario IS92a in 1860 – 2099. Observations show a low-frequency power spectrum S( f ) $ 1/f over the oceans, a white spectrum in the inner continents, and S( f ) $ f À0.3 in the coastal areas. The global distribution of the power law exponents is reproduced in the two simulations even in the 21st century INDEX TERMS: with a pronounced temperature increase.
1620 Global Change: Climate dynamics (3309); 3319 Meteorology and Atmospheric Dynamics: General circulation; 3339 Meteorology and Atmospheric Dynamics: Ocean/atmosphere interactions (0312, 4504); KEYWORDS: Long time memory, Scenario simulations. Citation: Blender, R., and K. Fraedrich, Long time memory in global warming simulations, Geophys. Res. Lett., 30(14), 1769, doi:10.1029/2003GL017666, 2003.
1. Introduction
[2] Observed near surface temperature station time series show power law spectra S( f ) $ f Àb with positive exponents b for long time scales. This is related to long time memory, as measured by the correlation function C(t) $ tbÀ1. Continental and maritime temperature measurements show different scaling of averaged power spectra up to 10 years [Pelletier, 1997; Pelletier and Turcotte, 1999]. Employing detrended fluctuation analysis (DFA, [Peng et al., 1994]) to 16 continental stations in North America, Europe and Australia, reveals the same scaling-law from months up to decades with the power law exponent b % 0.3 [KoscielnyBunde et al., 1998]. On the Atlantic and the Pacific Monetti et al. [2003] report approximate 1/f spectra for the sea surface temperature at several sites. For time scales up to 30 million years long time memory with b roughly 1 is reported for sea level changes [Hsui et al., 1993, Figure 1]. Power law scaling of long time memory in the Indian monsoon hints to predictability [Rangarajan and Sant, 1997]. Tropical convective variability behaves as 1/f-noise for 1– 30 days [Yano et al., 2001]. For a critical assessment of the presence of power laws see Talkner and Weber [2000]. [3] Long time observations with at least 100 years in the area from North America to Eurasia, and globally distributed regions, mainly in the vicinity of the coasts, have been analysed by Fraedrich and Blender [2003] (henceforth called FB). The available data indicate that the power law exponent is b % 0 (white) over the interior continents, b % 0.3 in coastal regions, and reaches b % 1 over the oceans. FB also determined scaling and memory properties in a
Copyright 2003 by the American Geophysical Union. 0094-8276/03/2003GL017666$05.00
1000 year control-run of a coupled atmosphere-ocean general circulation model (AOGCM). This reveals the observed spatial distribution of the power law exponent, its local variability, the temporal extent of the scaling-law, and the origin of the memory in the climate system. This result is based on a single simulation and the observational validation is restricted to populated areas and Atlantic ship routes. [4] Govindan et al. [2002] and Vyushin et al. [2002] conclude that climate models are not able to reproduce the observed long time variability in global warming simulations, and thus raised concern about the relevance of the anthropogenic impact. This conclusion is based on seven simulations with increasing greenhouse gas and aerosol concentrations in 10 regions near observational stations. The authors claim to find no long time memory, i.e. white power spectra, b = 0, at several coastal stations, whereas, according to the observations of KoscielnyBunde et al. [1998], b % 0.3 is expected. The apparent failure of the comprehensive models is highly relevant since the long time memory contributes to the total variance and superposes to external natural and anthropogenic impacts. [5] The aim of this Letter is to detect the global distribution of long time memory in scenario simulations with two coupled atmosphere ocean models HadCM3 and ECHAM4/OPYC, and to compare this globally with NCEP re-analysis data. The increase of greenhouse gases is prescribed by the IPCC scenario IS92a for 1860– 2099 in both simulations. The data and the model experiments are described in section 2 and the DFA is outlined in section 3. The results for single sites and global power law exponents exponents are presented in section 4. In section 5, the outcomes are summarised and discussed.
2. Observations and Scenario Simulations
[6] The observed data set is the NCEP re-analysis data (from NMC/NCAR, [Kalnay et al., 1996]). The data is used in 1958– 1998 with a resolution of 2.5° Â 2.5°. Memory is analysed in monthly near surface (2 m) air temperature data. Over sea the spectral properties of the near surface temperature coincide with those of the sea surface temperature above one year time scale. [7] Scenario simulations with two different atmosphere ocean models are used to explore the effect of different experimental designs. The models are HadCM3 (Hadley Centre, Bracknell, UK) and ECHAM4/OPYC (Max-PlanckInstitut fur Meteorologie, Hamburg, Germany). The models ¨ simulate the greenhouse gas increase according to the IPCC scenario IS92a [Houghton et al., 1992]. In both simulations monthly near surface air temperature is analysed. The data, the models and the experiments are described at http:// www.dkrz.de/ipcc/ddc/html/ISA92_all.html.
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