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Shortwave forcing and feedbacks in Last Glacial Maximum and Mid-Holocene PMIP3 simulations
Author(s) -
Pascale Braconnot,
Masa Kageyama
Publication year - 2015
Publication title -
philosophical transactions of the royal society a mathematical physical and engineering sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.074
H-Index - 169
eISSN - 1471-2962
pISSN - 1364-503X
DOI - 10.1098/rsta.2014.0424
Subject(s) - shortwave , climatology , climate model , climate sensitivity , albedo (alchemy) , forcing (mathematics) , last glacial maximum , climate state , holocene , environmental science , climate change , snow , shortwave radiation , global warming , geography , geology , meteorology , effects of global warming , oceanography , radiative transfer , art , physics , quantum mechanics , performance art , radiation , art history
Simulations of the climates of the Last Glacial Maximum (LGM), 21 000 years ago, and of the Mid-Holocene (MH), 6000 years ago, allow an analysis of climate feedbacks in climate states that are radically different from today. The analyses of cloud and surface albedo feedbacks show that the shortwave cloud feedback is a major driver of differences between model results. Similar behaviours appear when comparing the LGM and MH simulated changes, highlighting the fingerprint of model physics. Even though the different feedbacks show similarities between the different climate periods, the fact that their relative strength differs from one climate to the other prevents a direct comparison of past and future climate sensitivity. The land-surface feedback also shows large disparities among models even though they all produce positive sea-ice and snow feedbacks. Models have very different sensitivities when considering the vegetation feedback. This feedback has a regional pattern that differs significantly between models and depends on their level of complexity and model biases. Analyses of the MH climate in two versions of the IPSL model provide further indication on the possibilities to assess the role of model biases and model physics on simulated climate changes using past climates for which observations can be used to assess the model results.

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