Effective Radiative Forcing in a GCM With Fixed Surface Temperatures

Effective radiative forcing (ERF) is evaluated in the ACCESS1.0 General Circulation Model (GCM) with fixed land and sea‐surface‐temperatures (SST) as well as sea‐ice. The 4xCO2 ERF is 8.0 W m−2. In contrast, a typical ERF experiment with only fixed SST and sea‐ice gives rise to an ERF of only 7.0 W m−2. This difference arises due to the influence of land warming in the commonly used fixed‐SST ERF experimental design, which results in: (i) increased emission of longwave radiation to space from the land surface (−0.45 W m−2) and troposphere (−0.90 W m−2), (ii) reduced land snow‐cover and albedo (+0.17 W m−2), (iii) increased water‐vapor (+0.49 W m−2), and (iv) a cloud adjustment (−0.26 W m−2) due to reduced stability and cloudiness over land (positive ERF) counteracted by increased lower tropospheric stability and marine cloudiness over oceans (negative ERF). The sum of these radiative adjustments to land warming is to reduce the 4xCO2 ERF in fixed‐SST experiments by ∼1.0 W m−2. CO2 stomatal effects are quantified and found to contribute just over half of the land warming effect and adjustments in the fixed‐SST ERF experimental design in this model. The basic physical mechanisms in response to land warming are confirmed in a solar ERF experiment. We test various methods that have been proposed to account for land warming in fixed‐SST ERFs against our GCM results and discuss their strengths and weaknesses.

[1]  J. Randerson,et al.  Plant Physiology Increases the Magnitude and Spread of the Transient Climate Response to CO2 in CMIP6 Earth System Models , 2020 .

[2]  M. Webb,et al.  An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence , 2020, Reviews of geophysics.

[3]  Christopher J. Smith,et al.  The HadGEM3-GA7.1 radiative kernel: the importance of a well-resolved stratosphere , 2020 .

[4]  B. Medeiros.  Aquaplanets as a Framework for Examination of Aerosol Effects , 2020, Journal of Advances in Modeling Earth Systems.

[5]  Christopher J. Smith,et al.  Effective radiative forcing and adjustments in CMIP6 models , 2020, Atmospheric Chemistry and Physics.

[6]  K. Taylor,et al.  Causes of Higher Climate Sensitivity in CMIP6 Models , 2020, Geophysical Research Letters.

[7]  Christopher J. Smith,et al.  The HadGEM3-GA7.1 radiative kernel: the importance of a well-resolved stratosphere , 2020 .

[8]  T. Andrews,et al.  Efficacy of Climate Forcings in PDRMIP Models , 2019, Journal of geophysical research. Atmospheres : JGR.

[9]  T. Andrews,et al.  Comparison of Effective Radiative Forcing Calculations Using Multiple Methods, Drivers, and Models , 2019, Journal of Geophysical Research: Atmospheres.

[10]  T. Ogura,et al.  Separating the Influences of Land Warming, the Direct CO2 Effect, the Plant Physiological Effect, and SST Warming on Regional Precipitation Changes , 2019, Journal of Geophysical Research: Atmospheres.

[11]  W. Collins,et al.  Radiative Forcing of Climate: The Historical Evolution of the Radiative Forcing Concept, the Forcing Agents and their Quantification, and Applications , 2019, Meteorological Monographs.

[12]  T. Andrews,et al.  Understanding Rapid Adjustments to Diverse Forcing Agents , 2018, Geophysical research letters.

[13]  M. Ringer,et al.  Seasonally variant low cloud adjustment over cool oceans , 2018, Climate Dynamics.

[14]  D. Dommenget,et al.  An ensemble of AMIP simulations with prescribed land surface temperatures , 2018, Geoscientific Model Development.

[15]  M. Webb,et al.  The Dependence of Global Cloud and Lapse Rate Feedbacks on the Spatial Structure of Tropical Pacific Warming , 2018 .

[16]  Benjamin Smith,et al.  Vegetation demographics in Earth System Models: A review of progress and priorities , 2018, Global change biology.

[17]  B. Soden,et al.  WATER VAPOR FEEDBACK AND GLOBAL WARMING , 2018 .

[18]  T. Andrews,et al.  PDRMIP: A Precipitation Driver and Response Model Intercomparison Project, Protocol and preliminary results. , 2017, Bulletin of the American Meteorological Society.

[19]  S. Klein,et al.  Impact of decadal cloud variations on the Earth/'s energy budget , 2016 .

[20]  T. Andrews,et al.  Recommendations for diagnosing effective radiative forcing from climate models for CMIP6 , 2016 .

[21]  Ken Caldeira,et al.  Dependence of global radiative feedbacks on evolving patterns of surface heat fluxes , 2016 .

[22]  Robert Pincus,et al.  The Radiative Forcing Model Intercomparison Project (RFMIP): Experimental Protocol for CMIP6 , 2016 .

[23]  D. Dommenget,et al.  Atmosphere-only GCM (ACCESS1.0) simulations with prescribed land surface temperatures , 2016 .

[24]  T. Andrews,et al.  Understanding the Rapid Precipitation Response to CO2 and Aerosol Forcing on a Regional Scale , 2016 .

[25]  S. Klein,et al.  Positive tropical marine low‐cloud cover feedback inferred from cloud‐controlling factors , 2015 .

[26]  D. Hartmann,et al.  Connections Between Clouds, Radiation, and Midlatitude Dynamics: a Review , 2015, Current Climate Change Reports.

[27]  M. Webb,et al.  The Dependence of Radiative Forcing and Feedback on Evolving Patterns of Surface Temperature Change in Climate Models , 2015 .

[28]  S. Bony,et al.  Using aquaplanets to understand the robust responses of comprehensive climate models to forcing , 2015, Climate Dynamics.

[29]  M. Webb,et al.  Global‐mean radiative feedbacks and forcing in atmosphere‐only and coupled atmosphere‐ocean climate change experiments , 2014 .

[30]  Olivier Boucher,et al.  Adjustments in the Forcing-Feedback Framework for Understanding Climate Change , 2014 .

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

[32]  Gill Martin,et al.  Surface warming patterns drive tropical rainfall pattern responses to CO2 forcing on all timescales , 2014 .

[33]  D. Shindell,et al.  Anthropogenic and Natural Radiative Forcing , 2014 .

[34]  C. Bretherton,et al.  Clouds and Aerosols , 2013 .

[35]  Ian N. Harman,et al.  The land surface model component of ACCESS: description and impact on the simulated surface climatology , 2013 .

[36]  E. Kowalczyk,et al.  The ACCESS coupled model: description, control climate and evaluation , 2013 .

[37]  T. Andrews,et al.  Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models , 2013 .

[38]  K.,et al.  Carbon–Concentration and Carbon–Climate Feedbacks in CMIP5 Earth System Models , 2012 .

[39]  J. Lelieveld,et al.  Modelled suppression of boundary-layer clouds by plants in a CO2-rich atmosphere , 2012 .

[40]  Mark D. Zelinka,et al.  Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth , 2012 .

[41]  M. Webb,et al.  Sensitivity of an Earth system climate model to idealized radiative forcing , 2012 .

[42]  K. Taylor,et al.  Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere‐ocean climate models , 2012 .

[43]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[44]  C. Bretherton,et al.  Fast cloud adjustment to increasing CO2 in a superparameterized climate model , 2012 .

[45]  Stephen A. Klein,et al.  Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels , 2012 .

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

[47]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes , 2011 .

[48]  T. Andrews,et al.  A regional and global analysis of carbon dioxide physiological forcing and its impact on climate , 2011 .

[49]  W. Ingram,et al.  Climate feedbacks determined using radiative kernels in a multi-thousand member ensemble of AOGCMs , 2010 .

[50]  Jonathan M. Gregory,et al.  Understanding Land–Sea Warming Contrast in Response to Increasing Greenhouse Gases. Part I: Transient Adjustment , 2009 .

[51]  R. Nemani,et al.  Climate response to physiological forcing of carbon dioxide simulated by the coupled Community Atmosphere Model (CAM3.1) and Community Land Model (CLM3.0) , 2009 .

[52]  R. Betts,et al.  Climate response to the physiological impact of carbon dioxide on plants in the Met Office Unified Model HadCM3 , 2009 .

[53]  Olivier Boucher,et al.  Carbon dioxide induced stomatal closure increases radiative forcing via a rapid reduction in low cloud , 2009 .

[54]  Brian J. Soden,et al.  Quantifying Climate Feedbacks Using Radiative Kernels , 2008 .

[55]  Jonathan M. Gregory,et al.  Mechanisms for the land/sea warming contrast exhibited by simulations of climate change , 2008 .

[56]  S. Solomon.  The Physical Science Basis : Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[57]  S. Bony,et al.  How Well Do We Understand and Evaluate Climate Change Feedback Processes , 2006 .

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

[59]  Jonathan M. Gregory,et al.  A new method for diagnosing radiative forcing and climate sensitivity , 2004 .

[60]  B. Soden,et al.  WATER VAPOR FEEDBACK AND GLOBAL WARMING 1 , 2003 .

[61]  Manoj Joshi,et al.  An alternative to radiative forcing for estimating the relative importance of climate change mechanisms , 2003 .

[62]  Richard Essery,et al.  Explicit representation of subgrid heterogeneity in a GCM land surface scheme , 2003 .

[63]  Piers M. Forster,et al.  The effect of human activity on radiative forcing of climate change: a review of recent developments , 1999 .

[64]  R. Betts,et al.  The impact of new land surface physics on the GCM simulation of climate and climate sensitivity , 1999 .

[65]  S. M. Marlais,et al.  An Overview of the Results of the Atmospheric Model Intercomparison Project (AMIP I) , 1999 .

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

[67]  S. Klein,et al.  The Seasonal Cycle of Low Stratiform Clouds , 1993 .