Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2018-77
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model experiment description paper
18 Apr 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
An ensemble of AMIP simulations with prescribed land surface temperatures
Duncan Ackerley1,2, Robin Chadwick3, Dietmar Dommenget1, and Paola Petrelli4 1ARC Centre of Excellence for Climate System Science, School of Earth Atmosphere and Environment, Monash University, Clayton 3800, Victoria, Australia
2Met Office, Exeter, UK
3Met Office Hadley Centre, Exeter, UK
4ARC Centre of Excellence for Climate System Science, Instit ute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia
Abstract. General circulation models (GCMs) are routinely run under Atmospheric Modelling Intercomparison Project (AMIP) conditions with prescribed sea surface temperatures (SSTs) and sea ice concentrations (SICs) from observations. These AMIP simulations are often used to evaluate the role of the land and/or atmosphere in causing the development of systematic errors in such GCMs. Extensions to the original AMIP experiment have also been developed to evaluate the response of the global climate to increased SSTs (prescribed) and carbon-dioxide (CO2) as part of the Cloud Feedback Model Intercomparison Project (CFMIP). None of these international modelling initiatives has undertaken a set of experiments where the land conditions are also prescribed, which is the focus of the work presented in this paper. Experiments are performed initially with freely varying land conditions (surface temperature and, soil temperature and mositure) under five different configurations (AMIP, AMIP with uniform 4 K added to SSTs, AMIP SST with quadrupled CO2, AMIP SST and quadrupled CO2 without the plant stomata response, and increasing the solar constant by 3.3 %). Then, the land surface temperatures from the free-land experiments are used to perform a set of “AMIP-prescribed land” (PL) simulations, which are evaluated against their free-land counterparts. The PL simulations agree well with the free-land experiments, which indicates that the land surface is prescribed in a way that is consistent with the original free-land configuration. Further experiments are also performed with different combinations of SSTs, CO2 concentrations, solar constant and land conditions. For example, SST and land conditions are used from the AMIP simulation with quadrupled CO2 in order to simulate the atmospheric response to increased CO2 concentrations without the surface temperature changing. The results of all these experiments have been made publicly available for further analysis. The main aims of this paper are to provide a description of the method used and an initial validation of these AMIP-prescribed land experiments.
Citation: Ackerley, D., Chadwick, R., Dommenget, D., and Petrelli, P.: An ensemble of AMIP simulations with prescribed land surface temperatures, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-77, in review, 2018.
Duncan Ackerley et al.
Duncan Ackerley et al.

Data sets

AMIP ACCESS 1.0 prescribed land experiment collection v1.0: PLAMIP D. Ackerley https://doi.org/10.4225/41/59521137d6c42
Duncan Ackerley et al.

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Short summary
Climate models have been run using observed sea surface temperatures to identify biases in the atmospheric circulation. In this work, land surface temperatures are also constrained, which is not routinely done. Experiments include: increasing sea surface temperatures, quadrupling atmospheric carbon-dioxide and increasing solar radiation. The response of the land surface is then allowed or suppressed and the global climate is evaluated. Information on how to obtain the model data are also given.
Climate models have been run using observed sea surface temperatures to identify biases in the...
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