The Monash Simple Climate Model experiments (MSCM-DB v1.0): an interactive database of mean climate, climate change, and scenario simulations
This study introduces the Monash Simple Climate Model (MSCM) experiment database. The simulations are based on the Globally Resolved Energy Balance (GREB) model to study three different aspects of climate model simulations: (1) understanding processes that control the mean climate, (2) the response...
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Veröffentlicht in: | Geoscientific Model Development 2019-06, Vol.12 (6), p.2155-2179 |
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Sprache: | eng |
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Zusammenfassung: | This study introduces the Monash Simple Climate Model (MSCM)
experiment database. The simulations are based on the Globally Resolved
Energy Balance (GREB) model to study three different aspects of climate model
simulations: (1) understanding processes that control the mean climate,
(2) the response of the climate to a doubling of the CO2
concentration, and (3) scenarios of external forcing (CO2
concentration and solar radiation). A series of sensitivity experiments in
which elements of the climate system are turned off in various combinations
are used to address (1) and (2). This database currently provides more than
1300 experiments and has an online web interface for fast analysis and free
access to the data. We briefly outline the design of all experiments, give a
discussion of some results, put the findings into the context of previously
published results from similar experiments, discuss the quality and
limitations of the MSCM experiments, and also give an outlook on possible
further developments. The GREB model simulation is quite realistic, but the
model without flux corrections has a root mean square error in the mean state
of the surface temperature of about 10 ∘C, which is larger than
those of general circulation models (2 ∘C). It needs to be noted
here that the GREB model does not simulate circulation changes or changes in
cloud cover (feedbacks). However, the MSCM experiments show good agreement to
previously published studies. Although GREB is a very simple model, it
delivers good first-order estimates, is very fast, highly accessible, and can
be used to quickly try many different sensitivity experiments or scenarios.
It builds a basis on which conceptual ideas can be tested to first order and
it provides a null hypothesis for understanding complex climate interactions
in the context of response to external forcing or interactions in the climate
subsystems. |
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ISSN: | 1991-9603 1991-959X 1991-962X 1991-9603 1991-962X |
DOI: | 10.5194/gmd-12-2155-2019 |