The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments

By Matthew Collins, S. Tett, C. Cooper. 2001

445 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/dbc228273bf80695c1319b4fdf4108533cfbfcb5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Gab Abramowitz
2. H. Drange
3. M. Bentsen
4. Timothy J. Osborn
5. T. Jickells
6. P. Liss
7. Grant R. Bigg
8. C. Bishop

In Global and Planetary Change

1. R. Olson
2. J. Kitchell
3. T. Essington
4. Carl J Walters
5. K. Aydin
6. Gab Abramowitz
7. H. Drange
8. M. Bentsen

In Oceanography

1. H. Drange
2. M. Bentsen
3. M. Collins
4. Timothy J. Mosedale
5. J. Schröter
6. Wolfgang Hiller
7. R. Olson
8. R. Wood

Citations in the corpus

• Description and evaluation of the bergen climate model: ARPEGE coupled with MICOM (2003, background): “This is a common problem in many AOGCMs (e.g., Collins et al. 2001).”
• The effect of volcanic eruptions on global precipitation (2013, background): “The Asian monsoon is reasonably well simulated [Annamalai et al., 2007], as is ENSO variability [Collins et al., 2001; Joseph and Nigam, 2006] (see also section 3.3).”
• Present and future distribution of three aquatic plants taxa across the world: decrease in native and increase in invasive ranges (2017, methodology): “As uncertainty in forecasting future distribution is partially due to GCMs (Buisson et al. 2010), we used three different GCMs that simulated the impact of the different climate scenarios for the two future periods: the Hadley Centre Coupled Model version 3 (HADCM3) (Collins et al. 2001), Coupled Gl”
• Climate predictability on interannual to decadal time scales: the initial value problem (2002, methodology): “For the AR(1) fits to the model we use the control simulation of HadCM3 (Gordon et al. 2000; Collins et al. 2001) which has a stable surface climate and hence has no global secular trends.”
• The role of the oceans in climate (2003, methodology): “A clear demonstration of the importance of the oceans in generating a red spectrum of variability was give by Collins et al. (2001) using a 1000 year simulation with the HadCM3 coupled climate model.”
• Impacts of climate change on wheat anthesis and fusarium ear blight in the UK (2011, methodology): “These projections are in turn based on the IPCC global emissions scenarios (Nakicenovic 2000) and the HadCM3 global climate model (Collins et al. 2001; Hulme et al. 2002).”
• Global Climatic Impacts of a Collapse of the Atlantic Thermohaline Circulation (2002, methodology): “Details of the model, a validation of its control climate and assessment of its errors are given by Gordon et al. (2000), Pope et al. (2000), Collins et al. (2001) and Pardaens et al. (2001).”
• Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections (2015, background): “2000; Gneiting and Raftery 2005) and climate models’ internal variability (Collins et al. 2001; Deser et al. 2012).”
• Towards multi-resolution global climate modeling with ECHAM6–FESOM. Part I: model formulation and mean climate (2015, methodology): “The motivation to compute not only most recent trends but also trends ending in 1998 is to enable direct comparison to Collins et al. (2001).”
• Physical forcing and the dynamics of the pelagic ecosystem in the eastern tropical Pacific: simulations with ENSO-scale and global-warming climate drivers (2003, methodology): “Timmermann et al. (1999) and Collins (2000a) predict an increase in both the amplitude and frequency of the ENSO, but Collins et al. (2001) do not predict significant changes in these statistics.”
• Granger Causality of Coupled Climate Processes: Ocean Feedback on the North Atlantic Oscillation (2006, methodology): “The methods presented in this study are in no way restricted to examining daily GCM data and can equally well be applied to observations and data with different sampling rates. a.”