scholarly journals Decadal climate predictions for the period 1901-2010 with a coupled climate model

2014 ◽  
Vol 41 (6) ◽  
pp. 2100-2107 ◽  
Author(s):  
W. A. Müller ◽  
H. Pohlmann ◽  
F. Sienz ◽  
D. Smith
Keyword(s):  
Climate Model ◽  
Coupled Climate Model ◽  
Decadal Climate

scholarly journals Interannual to decadal climate variability of sea salt aerosols in the coupled climate model CESM1.0

2015 ◽  
Vol 120 (4) ◽  
pp. 1502-1519 ◽  
Author(s):  
Li Xu ◽  
David W. Pierce ◽  
Lynn M. Russell ◽  
Arthur J. Miller ◽  
Richard C. J. Somerville ◽  
...  
Keyword(s):  
Climate Variability ◽  
Climate Model ◽  
Sea Salt ◽  
Decadal Climate Variability ◽  
Coupled Climate Model ◽  
Sea Salt Aerosols ◽  
Decadal Climate

scholarly journals A realistic Greenland ice sheet and surrounding glaciers and ice caps melting in a coupled climate model

2021 ◽  
Author(s):  
Marion Devilliers ◽  
Didier Swingedouw ◽  
Juliette Mignot ◽  
Julie Deshayes ◽  
Gilles Garric ◽  
...  
Keyword(s):  
Climate Model ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Coupled Climate Model ◽  
Ice Caps

scholarly journals Effects of Ocean Biology on the Penetrative Radiation in a Coupled Climate Model

2006 ◽  
Vol 19 (16) ◽  
pp. 3973-3987 ◽  
Author(s):  
Patrick Wetzel ◽  
Ernst Maier-Reimer ◽  
Michael Botzet ◽  
Johann Jungclaus ◽  
Noel Keenlyside ◽  
...  
Keyword(s):  
Seasonal Cycle ◽  
Marine Biology ◽  
Climate Model ◽  
Global Climate ◽  
Ocean Model ◽  
Equatorial Pacific ◽  
Shortwave Radiation ◽  
Upper Ocean ◽  
Biogeochemical Model ◽  
Coupled Climate Model

Abstract The influence of phytoplankton on the seasonal cycle and the mean global climate is investigated in a fully coupled climate model. The control experiment uses a fixed attenuation depth for shortwave radiation, while the attenuation depth in the experiment with biology is derived from phytoplankton concentrations simulated with a marine biogeochemical model coupled online to the ocean model. Some of the changes in the upper ocean are similar to the results from previous studies that did not use interactive atmospheres, for example, amplification of the seasonal cycle; warming in upwelling regions, such as the equatorial Pacific and the Arabian Sea; and reduction in sea ice cover in the high latitudes. In addition, positive feedbacks within the climate system cause a global shift of the seasonal cycle. The onset of spring is about 2 weeks earlier, which results in a more realistic representation of the seasons. Feedback mechanisms, such as increased wind stress and changes in the shortwave radiation, lead to significant warming in the midlatitudes in summer and to seasonal modifications of the overall warming in the equatorial Pacific. Temperature changes also occur over land where they are sometimes even larger than over the ocean. In the equatorial Pacific, the strength of interannual SST variability is reduced by about 10%–15% and phase locking to the annual cycle is improved. The ENSO spectral peak is broader than in the experiment without biology and the dominant ENSO period is increased to around 5 yr. Also the skewness of ENSO variability is slightly improved. All of these changes lead to the conclusion that the influence of marine biology on the radiative budget of the upper ocean should be considered in detailed simulations of the earth’s climate.

scholarly journals The Interpretation and Use of Biases in Decadal Climate Predictions

2014 ◽  
Vol 27 (8) ◽  
pp. 2931-2947 ◽  
Author(s):  
Ed Hawkins ◽  
Buwen Dong ◽  
Jon Robson ◽  
Rowan Sutton ◽  
Doug Smith
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
Time Dependent ◽  
Start Time ◽  
Transient Climate Response ◽  
Global Mean Temperature ◽  
Decadal Climate ◽  
Highly Correlated ◽  
Hadley Centre

Abstract Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts. The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities.

Polar amplification in a coupled climate model with locked albedo

2009 ◽  
Vol 33 (5) ◽  
pp. 629-643 ◽  
Author(s):  
Rune Grand Graversen ◽  
Minghuai Wang
Keyword(s):  
Climate Model ◽  
Coupled Climate Model ◽  
Polar Amplification

Rapid transitions and ultra-low frequency behaviour in a 40 kyr integration with a coupled climate model of intermediate complexity

2001 ◽  
Vol 17 (7) ◽  
pp. 559-570 ◽  
Author(s):  
R. J. Haarsma ◽  
J. D. Opsteegh ◽  
F. M. Selten ◽  
X. Wang
Keyword(s):  
Climate Model ◽  
Low Frequency ◽  
Coupled Climate Model ◽  
Intermediate Complexity

scholarly journals Mechanisms causing reduced Arctic sea ice loss in a coupled climate model

2012 ◽  
Vol 6 (4) ◽  
pp. 2653-2687 ◽  
Author(s):  
A. E. West ◽  
A. B. Keen ◽  
H. T. Hewitt
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Historical Record ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Slight Reduction ◽  
Coupled Climate Model ◽  
Arctic Sea ◽  
Ensembles Project ◽  
Arctic Sea Ice Loss

Abstract. The fully-coupled climate model HadGEM1 produces one of the most accurate simulations of the historical record of Arctic sea ice seen in the IPCC AR4 multi-model ensemble. In this study, we examine projections of sea ice decline out to 2030, produced by two ensembles of HadGEM1 with natural and anthropogenic forcings included. These ensembles project a significant slowing of the rate of ice loss to occur after 2010, with some integrations even simulating a small increase in ice area. We use an energy budget of the Arctic to examine the causes of this slowdown. A negative feedback effect by which rapid reductions in ice thickness north of Greenland reduce ice export is found to play a major role. A slight reduction in ocean-to-ice heat flux in the relevant period, caused by changes in the MOC and subpolar gyre in some integrations, is also found to play a part. Finally, we assess the likelihood of a slowdown occurring in the real world due to these causes.

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