Corrigendum: Nonlinear regional warming with increasing CO 2 concentrations

Knowledge of how climate change will affect temperatures on a regional scale is needed for effective planning and preparedness. This study uses five climate models to investigate regional warming. It shows that warming is nonlinear for doublings of atmospheric CO2 and that nonlinearity increases with higher CO2 concentrations.

[1]  Jason Lowe,et al.  A step-response approach for predicting and understanding non-linear precipitation changes , 2012, Climate Dynamics.

[2]  P. Good,et al.  A review of recent developments in climate change science. Part II: The global-scale impacts of climate change , 2011 .

[3]  B. Mcavaney,et al.  Climate feedbacks under a very broad range of forcing , 2009 .

[4]  T. Andrews,et al.  Cloud Feedbacks, Rapid Adjustments, and the Forcing-Response Relationship in a Transient CO2Reversibility Scenario , 2014 .

[5]  C. Jones,et al.  The HadGEM2 family of Met Office Unified Model climate configurations , 2011 .

[6]  S. Seneviratne,et al.  Land–atmosphere coupling and climate change in Europe , 2006, Nature.

[7]  Jonathan M. Gregory,et al.  A step‐response simple climate model to reconstruct and interpret AOGCM projections , 2011 .

[8]  S. Seneviratne,et al.  Investigating soil moisture-climate interactions in a changing climate: A review , 2010 .

[9]  N. Arnell,et al.  Hydrology and Earth System Sciences Uncertainty in climate change impacts on basin-scale freshwater resources – preface to the special issue : the QUEST-GSI methodology and synthesis of results , 2011 .

[10]  K. Calvin,et al.  The RCP greenhouse gas concentrations and their extensions from 1765 to 2300 , 2011 .

[11]  Christopher B. Field,et al.  Stomatal responses to increased CO2: implications from the plant to the global scale , 1995 .

[12]  John F. B. Mitchell,et al.  The next generation of scenarios for climate change research and assessment , 2010, Nature.

[13]  Jason Lowe,et al.  Abrupt CO2 experiments as tools for predicting and understanding CMIP5 representative concentration pathway projections , 2013, Climate Dynamics.

[14]  C. Jones,et al.  Development and evaluation of an Earth-System model - HadGEM2 , 2011 .

[15]  Pete Smith,et al.  A global assessment of the effects of climate policy on the impacts of climate change , 2013 .

[16]  Syukuro Manabe,et al.  Transient Response of a Global Ocean-Atmosphere Model to a Doubling of Atmospheric Carbon Dioxide , 1990 .

[17]  Peili Wu,et al.  Asymmetries in tropical rainfall and circulation patterns in idealised CO2 removal experiments , 2012, Climate Dynamics.

[18]  Susanne L. Weber,et al.  The stability of the MOC as diagnosed from model projections for pre-industrial, present and future climates , 2011 .

[19]  J. Hansen,et al.  Efficacy of climate forcings , 2005 .

[20]  Peter Good,et al.  Understanding nonlinear tropical precipitation responses to CO2 forcing , 2013 .

[21]  M. Oppenheimer,et al.  Defining Dangerous Anthropogenic Interference: The Role of Science, the Limits of Science , 2005, Risk analysis : an official publication of the Society for Risk Analysis.

[22]  Ian Eisenman,et al.  Factors controlling the bifurcation structure of sea ice retreat , 2012 .

[23]  Toru Nozawa,et al.  Temperature scaling pattern dependence on representative concentration pathway emission scenarios , 2012, Climatic Change.

[24]  Andy Jarvis,et al.  Long run surface temperature dynamics of an A-OGCM: the HadCM3 4×CO2 forcing experiment revisited , 2009 .

[25]  Stephen Sitch,et al.  Simulated resilience of tropical rainforests to CO2-induced climate change , 2013 .

[26]  L. K. Gohar,et al.  How well do integrated assessment models simulate climate change? , 2011 .

[27]  Benjamin M. Sanderson,et al.  Climate Feedbacks in CCSM3 under Changing CO2 Forcing. Part II: Variation of Climate Feedbacks and Sensitivity with Forcing , 2013 .

[28]  A. Hall.  The role of surface albedo feedback in climate , 2004 .