Not logged in
PANGAEA.
Data Publisher for Earth & Environmental Science

Lohmann, Gerrit; Pfeiffer, Madlene; Laepple, Thomas; Leduc, Guillaume; Kim, Jung-Hyun (2013): Modelled global ocean temperature from 6 ka to present [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.815309, Supplement to: Lohmann, G et al. (2013): A model-data comparison of the Holocene global sea surface temperature evolution. Climate of the Past, 9(4), 1807-1839, https://doi.org/10.5194/cp-9-1807-2013

Always quote citation above when using data! You can download the citation in several formats below.

RIS CitationBibTeX Citation

Abstract:
We compare the ocean temperature evolution of the Holocene as simulated by climate models and reconstructed from marine temperature proxies. This site provides informations about the Holocene temperature trends as simulated by the models. We use transient simulations from a coupled atmosphere-ocean general circulation model, as well as an ensemble of time slice simulations from the Paleoclimate Modelling Intercomparison Project. The general pattern of sea surface temperature (SST) in the models shows a high latitude cooling and a low latitude warming. The proxy dataset comprises a global compilation of marine alkenone- and Mg/Ca-derived SST estimates. Independently of the choice of the climate model, we observe significant mismatches between modelled and estimated SST amplitudes in the trends for the last 6000 years. Alkenone-based SST records show a similar pattern as the simulated annual mean SSTs, but the simulated SST trends underestimate the alkenone-based SST trends by a factor of two to five. For Mg/Ca, no significant relationship between model simulations and proxy reconstructions can be detected. We tested if such discrepancies can be caused by too simplistic interpretations of the proxy data. We tested different seasons and depths in the model to compare the proxy data trends, and can reconcile only part of the mismatches on a regional scale. We therefore considered the additional environmental factor changes in the planktonic organisms' habitat depth and a time-shift in the recording season to diagnose whether invoking those environmental factors can help reconciling the proxy records and the model simulations. We find that invoking shifts in the living season and habitat depth can remove some of the model-data discrepancies in SST trends. Regardless whether such adjustments in the environmental parameters during the Holocene are realistic, they indicate that when modeled temperature trends are set up to allow drastic shifts in the ecological behavior of planktonic organisms, they do not capture the full range of reconstructed SST trends. Our findings indicate that climate model and reconstructed temperature trends are to a large degree only qualitatively comparable, thus providing a challenge for the interpretation of proxy data as well as the models' sensitivity to orbital forcing.
Funding:
German Research Foundation (DFG), grant/award no. 25575884: Integrierte Analyse zwischeneiszeitlicher Klimadynamik
Comment:
Simulated temperatures are based on the ensemble mean of two transient experiments spanning 7 to 0 ka BP, using the ECHO-G model (Lorenz and Lohmann, 2004, doi:10.1007/s00382-004-0469-y). Calculation of the orbital parameters follows the orbital solution of Berger (1978, doi:10.1175/1520-0469(1978)035<2362:LTVODI>2.0.CO;2) and is accelerated by a factor of ten (Lorenz and Lohmann, 2004, doi:10.1007/s00382-004-0469-y). The ocean model grid consists of 120 unequally spaced grid cells in latitudinal direction, and 128 equally spaced grid cells in longitudinal direction; the equatorial latitudes between ±10° latitude have a resolution of 0.5° in order to resolve the equatorial wave guide, this resolution gradually decreases polewards until 30° to approximately 2.7°.
The first set of files provided here (HOPEa2?_1271-2000_ocpt_lev_0???m_interpolated_r128x349.nc) represents ocean potential temperature from ensemble members a22 and a29 of the ECHO-G simulations. For each of the ensemble members, data from the upper ocean (mid-layer depths 10 m, 30 m, 51 m, 75 m, and 100 m) is provided. Each time series spans model years 1271 to 2000, which correspond to a real calendar time range from 7.000 ka BP to 0.290 ka in the future. For usability of the data, we interpolated the data to a common latitudinal 0.5° degree resolution. The longitudinal resolution is identical to the standard model grid (increment: 2.8125°).
In order to gain insight into the way how different climate models perform when simulating the temperature evolution during the Holocene, we also analyze the modeled SST anomalies between 6 and 0 ka BP from simulations performed in the framework of paleoclimate modelling intercomparison project phase II (PMIP2) (Braconnot et al., 2007a, doi:10.5194/cp-3-261-2007; 2007b, doi:10.5194/cp-3-279-2007) and III (PMIP3) (Taylor, 2012, doi:10.1175/BAMS-D-11-00094.1; Braconnot et al., 2012, doi:10.1038/nclimate1456). Assuming linearity of the mid- to late-Holocene temperature trends, the PMIP2 and PMIP3 temperature anomalies can be compared to the reconstructed temperature trends. Our comparison comprises 14 PMIP2 experiments from 9 AOGCMs, of which some models performed two experiments, i.e. with and without interactive vegetation (Braconnot et al., 2007a, doi:10.5194/cp-3-261-2007), and 13 PMIP3 experiments. In this data archive, we include the ensemble mean and ensemble median. The details about the models are described in the paper (Lohmann et al., 2013, doi:10.5194/cpd-8-1005-2012).
The second set of files (PMIP2_ensmean_SST.6k.anomaly.ym.nc and PMIP2_ensmedian_SST.6k.a.ym.nc) represents a statistical analysis of the PMIP2 Holocene model ensemble. Both files are based on the SST anomaly 6 ka BP - 0 ka.
The third set of files (PMIP3_ensmean_SST.6k.anomaly.ym.nc and PMIP3_ensmedian_SST.6k.a.ym.nc) represents a similar statistical analysis of the PMIP3 Holocene model ensemble. Both files are based on the SST anomaly 6 ka BP - 0 ka.
Size:
3 datasets

Download Data

Download ZIP file containing all datasets as tab-delimited text — use the following character encoding: