Uncertainty in temperature projections reduced using carbon cycle and climate observations

Abstract

5The future behaviour of the carbon cycle is a major contributor to uncertainty in temper-6 ature projections for the 21st century. Using a simplified climate model, we show that, for 7 a given emission scenario, it is the second most important contributor to this uncertainty 8 after climate sensitivity, followed by aerosol impacts. Historical measurements of car-9 bon dioxide concentrations have been used along with global temperature observations to 10 help reduce this uncertainty. This results in an increased probability of exceeding a 2 C 11 global-mean temperature increase by 2100 while reducing the probability of surpassing 12 a 6 C threshold for non-mitigation scenarios such as the SRES A1B and A1FI scenarios 1 , 13 as compared to IPCC projections. Climate sensitivity, the response of the carbon cycle 14 and aerosol effects remain highly uncertain but historical observations of temperature 15 and carbon dioxide imply a trade-off between them so that temperature projections are 16 more certain than they would be considering each factor in isolation. As well as pointing 17 out the promise from the formal use of observational constraints in climate projection, 18 this also highlights the need for an holistic view of uncertainty. 19Introduction 20The last decade has seen a great deal of research into quantifying the uncertainty of climate 21 projections as a guide to sensible adaptation and mitigation activity. The two most common 22 techniques have been multi-model ensembles (such as the CMIP3, Third Coupled Model In-23 tercomparison Project 2 and C4MIP, Coupled Carbon Cycle Climate Model Intercomparison 24 Project 3 ) and perturbed physics experiments 4 . These experiments have allowed us to isolate 25 those factors responsible for the spread of model ensembles. They have identified factors con-26 tributing to the overall climate sensitivity and uncertainty in the radiative response to aerosol 27 changes, as well as the response of the carbon cycle, including some assessment of the feed-28 backs between climate change and carbon fluxes. 29 More recently, observations have been used to provide direct constraints on model behaviour 5, 6 . 30 Computational complexity has largely precluded the use of full Earth system models (ESMs) 31 and restricted the facets of model behaviour that can be constrained. Full ESMs generally ex-32 plore fewer model parameters or use statistical emulation to expand their sample size. Most 33 critically, the carbon cycle behaviour remains largely untreated (with a rare exception 7 ), despite 34 the sensitivity of global-mean temperature change projections to carbon cycle processes 8 . In-35 stead, typically, simple models have been calibrated against the coupled carbon cycle/climate 36 models of Friedlingstein et al. 3 for which only limited formal calibration is possible and which 37 do not address parametric uncertainty. 38 Here we apply historical observations to address the scientific uncertainty of the climate 39 system. Our general method has been to identify those mo...

Transient Earth system responses to cumulative carbon dioxide emissions: linearities, uncertainties, and probabilities in an observation-constrained model ensemble

“…However, and in contrast to reduced-form, box-type, twodimensional, linear response models; expert assumptions; or component models applied in many earlier probabilistic assessments (e.g. Wigley and Raper, 2001;Knutti et al, 2002Knutti et al, , 2003Knutti et al, , 2005Schleussner et al, 2014;Bodman et al, 2013;Little et al, 2013;Harris et al, 2013;Holden et al, 2013;Bhat et al, 2012), Bern3D-LPJ features a dynamic threedimensional ocean with physically consistent formulations for the transport of heat, carbon, and other biogeochemical tracers, similar to work by Holden et al (2010) and Olson et al (2012), and includes a state-of-the-art dynamic global vegetation model, peat carbon, and anthropogenic land-use dynamics. The model is applied directly without using an emulator (Holden et al, 2010(Holden et al, , 2015Olson et al, 2012).…”

“…Probabilistic assessments are widely used to determine probability distributions of model parameters, climate sensitivity, and many other cli-mate variables (e.g. Wigley and Raper, 2001;Knutti et al, 2002Knutti et al, , 2003Knutti et al, , 2005Murphy et al, 2004;Schleussner et al, 2014;Bodman et al, 2013;Little et al, 2013;Harris et al, 2013;Holden et al, 2010Holden et al, , 2013Bhat et al, 2012;Olson et al, 2012;Williamson et al, 2013). The Bern3D model was used in connection with an ensemble Kalman filter to constrain model parameters and regional air-sea carbon fluxes from observations (Gerber et al, 2009;Joos, 2010, 2013).…”

Pan-Eurasian Experiment (PEEX): towards a holistic understanding of the feedbacks and interactions in the land–atmosphere–ocean–society continuum in the northern Eurasian region

“…Bala et al, 2013;Bodman et al, 2013, Mukhortova et al, 2015. This process has already been observed, particularly in disturbed forests of northern Asia (Shvidenko and Schepaschenko, 2014).…”

How do carbon cycle uncertainties affect IPCC temperature projections?

“…Most of the AR5 projections were concentration-based rather than emission-based results and so it is difficult to assess how large the spread in uncertainty would have been if emission-driven scenarios were used and the carbon cycle uncertainties also accounted for. Based on previous studies (Huntingford et al, 2009;Bodman et al, 2013;Booth et al, 2013), we would expect the carbon cycle feedbacks to lead to a wider range of future feedbacks, and therefore, uncertainties in ΔGMST projections than that due to climate sensitivity alone. This is partly because the majority of complex climate models are atmosphere-ocean general circulation models (AOGCMs) and not Earth system models (ESMs), so do not include the carbon cycle.…”