dc.contributor.author | Hewitt, AJ | |
dc.contributor.author | Sansom, PG | |
dc.contributor.author | Booth, BBB | |
dc.contributor.author | Jones, CD | |
dc.contributor.author | Robertson, ES | |
dc.contributor.author | Wiltshire, AJ | |
dc.contributor.author | Stephenson, DB | |
dc.contributor.author | Yip, S | |
dc.date.accessioned | 2016-08-01T15:07:20Z | |
dc.date.issued | 2016 | |
dc.description.abstract | The inclusion of carbon cycle processes within CMIP5 Earth System Models provides the opportunity to explore the relative importance of differences in scenario and climate model representation
to future land and ocean carbon fluxes. A two-way ANOVA approach was used to quantify the
variability owing to differences between scenarios and between climate models at different lead
times.
For global ocean carbon fluxes, the variance attributed to differences between Representative
Concentration Pathway scenarios exceeds the variance attributed to differences between climate
models by around 2025, completely dominating by 2100. This contrasts with global land carbon
fluxes, where the variance attributed to differences between climate models continues to dominate
beyond 2100. This suggests that modelled processes that determine ocean fluxes are currently
better constrained than those of land fluxes, thus we can be more confident in linking different
future socio-economic pathways to consequences of ocean carbon uptake than for land carbon
uptake.
The apparent agreement in atmosphere-ocean carbon fluxes, globally, masks strong climate
model differences at a regional level. The North Atlantic and Southern Ocean are key regions,
where differences in modelled processes represent an important source of variability in projected
regional fluxes | en_GB |
dc.description.sponsorship | MOHC authors were supported by the Joint DECC / Defra Met Office Hadley Centre Cli-
mate Programme (GA01101). SY was supported by the Hong Kong Polytechnic University grant
“Bayesian Modelling for Quantifying Uncertainty in Climate Predictions” (1-ZV9Z). We acknowl-
edge use of R software package (R Core Team 2013). We acknowledge the World Climate Re-
search Programme’s Working Group on Coupled Modelling, which is responsible for CMIP and
we thank the climate modelling groups for providing their GCM output (listed in Table 1). Support
of this dataset was provided by the Office of Science, U.S. Department of Energy. | en_GB |
dc.identifier.citation | Vol. 29, pp.7203 - 7213 | en_GB |
dc.identifier.doi | 10.1175/JCLI-D-16-0161.1 | |
dc.identifier.uri | http://hdl.handle.net/10871/22818 | |
dc.language.iso | en | en_GB |
dc.publisher | American Meteorological Society | en_GB |
dc.rights.embargoreason | Publisher Policy | en_GB |
dc.title | Sources of uncertainty in future projections of the carbon cycle | en_GB |
dc.type | Article | en_GB |
dc.identifier.issn | 1520-0442 | |
dc.description | This is the final version of the article. Available from the publisher via the DOI in this record. | |
dc.identifier.journal | Journal of Climate | en_GB |