The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations

The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Amongst other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific description of these components are in companion papers), and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present-day forcing. The suitability of the configuration for predictabiity on shorter timescales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2-AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2-AO, many aspects of the present-day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extra-tropical variability and top-of-atmosphere & surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking.

[1]  C. Wunsch,et al.  Large-Scale Ocean Heat and Freshwater Transports during the World Ocean Circulation Experiment , 2003 .

[2]  Edward W. Blockley,et al.  The sea ice model component of HadGEM3-GC3.1 , 2017 .

[3]  S. Valcke,et al.  The OASIS3 coupler: a European climate modelling community software , 2012 .

[4]  John M. Haynes,et al.  COSP: Satellite simulation software for model assessment , 2011 .

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

[6]  D. N. Walters,et al.  The Met Office Global Coupled model 2.0 (GC2) configuration , 2015 .

[7]  S. Bony,et al.  The ‘too few, too bright’ tropical low‐cloud problem in CMIP5 models , 2012 .

[8]  A. Weaver,et al.  Implementing a variational data assimilation system in an operational 1/4 degree global ocean model , 2015 .

[9]  Markus Gross,et al.  The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations , 2017 .

[10]  Till Kuhlbrodt,et al.  UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions , 2018, Geoscientific Model Development.

[11]  Cyril J. Morcrette,et al.  PC2: A prognostic cloud fraction and condensation scheme. I: Scheme description , 2008 .

[12]  M. Diamantakis,et al.  An inherently mass‐conserving semi‐implicit semi‐Lagrangian discretization of the deep‐atmosphere global non‐hydrostatic equations , 2014 .

[13]  Keith Haines,et al.  Origin and impact of initialisation shocks in coupled atmosphere-ocean forecasts , 2015 .

[14]  I. Watterson,et al.  What Influences the Skill of Climate Models over the Continents , 2014 .

[15]  S. Klein,et al.  Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator , 2012 .

[16]  Adam A. Scaife,et al.  Do seasonal-to-decadal climate predictions underestimate the predictability of the real world? , 2014, Geophysical research letters.

[17]  Elizabeth C. Kent,et al.  The Southampton Oceanography Centre (SOC) Ocean - Atmosphere, Heat, Momentum and Freshwater Flux Atlas , 1998 .

[18]  J. Janowiak,et al.  The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present) , 2003 .

[19]  Nigel Wood,et al.  An inherently mass‐conserving semi‐implicit semi‐Lagrangian discretisation of the shallow‐water equations on the sphere , 2009 .

[20]  A. W. Brewer.  Evidence for a world circulation provided by the measurements of helium and water vapour distribution in the stratosphere , 1949 .

[21]  Ali Behrangi,et al.  An Update on the Oceanic Precipitation Rate and Its Zonal Distribution in Light of Advanced Observations from Space , 2014 .

[22]  H. Rashid,et al.  Mechanisms of improved rainfall simulation over the Maritime Continent due to increased horizontal resolution in an AGCM , 2017, Climate Dynamics.

[23]  A. Keen,et al.  Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model , 2015 .

[24]  Matthew D. Collins,et al.  Improved stochastic physics schemes for global weather and climate models , 2016 .

[25]  S. Hardiman,et al.  Multi‐model analysis of Northern Hemisphere winter blocking: Model biases and the role of resolution , 2013 .

[26]  N. Abraham,et al.  Processes Controlling Tropical Tropopause Temperature and Stratospheric Water Vapor in Climate Models , 2015 .

[27]  Philip W. Jones.  First- and Second-Order Conservative Remapping Schemes for Grids in Spherical Coordinates , 1999 .

[28]  Nick Rayner,et al.  EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates , 2013 .

[29]  R. Betts,et al.  JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator , 2014 .

[30]  Brian J. Hoskins,et al.  Variability of the North Atlantic eddy‐driven jet stream , 2010 .

[31]  A. Bodas‐Salcedo,et al.  A multi-diagnostic approach to cloud evaluation , 2016 .

[32]  C. Appenzeller,et al.  Two‐dimensional indices of atmospheric blocking and their statistical relationship with winter climate patterns in the Euro‐Atlantic region , 2006 .

[33]  M. J. P. Cullen,et al.  The unified forecast/climate model , 1993 .

[34]  P. Earnshaw,et al.  The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations , 2011, Geoscientific Model Development.

[35]  Michael F. Wehner,et al.  The resolution sensitivity of Northern Hemisphere blocking in four 25-km atmospheric global circulation models. , 2017 .

[36]  Michael Mayer,et al.  Combining satellite observations and reanalysis energy transports to estimate global net surface energy fluxes 1985–2012 , 2015 .

[37]  David R. Doelling,et al.  Toward Optimal Closure of the Earth's Top-of-Atmosphere Radiation Budget , 2009 .

[38]  Daniele Iudicone,et al.  Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology , 2004 .

[39]  Malcolm Davidson,et al.  CryoSat‐2 estimates of Arctic sea ice thickness and volume , 2013 .

[40]  S. Klein,et al.  The Cloud Feedback Model , 2022 .

[41]  S. Bony,et al.  The GCM‐Oriented CALIPSO Cloud Product (CALIPSO‐GOCCP) , 2010 .

[42]  Kevin I. Hodges,et al.  Feature Tracking on the Unit Sphere , 1995 .

[43]  Andrew Ryan,et al.  Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts , 2013 .

[44]  S. Milton,et al.  The interaction between moist diabatic processes and the atmospheric circulation in African Easterly Wave propagation , 2017 .

[45]  William H. Lipscomb,et al.  Biogeochemistry of CICE: the Los Alamos Sea Ice Model Documentation and Software User's Manual zbgc_colpkg modifications to Version 5 , 2016 .

[46]  M. Huddleston,et al.  Quality control of ocean temperature and salinity profiles — Historical and real-time data , 2007 .

[47]  H. Hewitt,et al.  The location of the thermodynamic atmosphere–ice interface in fully-coupled models , 2015 .

[48]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[49]  H. Hewitt,et al.  The location of the thermodynamic atmosphere–ice interface in fully coupled models – a case study using JULES and CICE , 2016 .

[50]  Ron Kwok,et al.  Uncertainty in modeled Arctic sea ice volume , 2011 .

[51]  S. Bony,et al.  Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model , 2008 .

[52]  John Siddorn,et al.  GO5.0: The joint NERC-Met Office NEMO global ocean model for use in coupled and forced applications , 2013 .

[53]  Laura Ferranti,et al.  Flow‐dependent verification of the ECMWF ensemble over the Euro‐Atlantic sector , 2015 .

[54]  Adam A. Scaife,et al.  Tropical rainfall, Rossby waves and regional winter climate predictions , 2017 .

[55]  Yiran Peng,et al.  Dispersion bias, dispersion effect, and the aerosol–cloud conundrum , 2008 .

[56]  Chris Harris,et al.  Improved Atlantic winter blocking in a climate model , 2011 .

[57]  Brian Golding,et al.  Unified Modeling and Prediction of Weather and Climate: A 25-Year Journey , 2012 .

[58]  S. Vosper,et al.  Accounting for non‐uniform static stability in orographic drag parametrization , 2009 .

[59]  Richard P Allan,et al.  Changes in global net radiative imbalance 1985–2012 , 2014, Geophysical research letters.

[60]  Adam A. Scaife,et al.  Global Seasonal forecast system version 5 (GloSea5): a high‐resolution seasonal forecast system , 2015 .

[61]  Elizabeth C. Kent,et al.  Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century , 2003 .

[62]  Christophe Cassou,et al.  Intraseasonal interaction between the Madden–Julian Oscillation and the North Atlantic Oscillation , 2008, Nature.

[63]  A. Pier Siebesma,et al.  The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6. , 2016 .

[64]  J. Heming.  Met Office Unified Model Tropical Cyclone Performance Following Major Changes to the Initialization Scheme and a Model Upgrade , 2016 .

[65]  Adam A. Scaife,et al.  Skillful long‐range prediction of European and North American winters , 2014 .

[66]  C. Landsea,et al.  Atlantic Hurricane Database Uncertainty and Presentation of a New Database Format , 2013 .

[67]  Andrew Gettelman,et al.  Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations , 2012 .

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

[69]  Chris Harris,et al.  Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system , 2010 .

[70]  H. Treut,et al.  THE CALIPSO MISSION: A Global 3D View of Aerosols and Clouds , 2010 .

[71]  P. Xie,et al.  Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs , 1997 .

[72]  Matthew C. Wheeler,et al.  Convectively Coupled Equatorial Waves: Analysis of Clouds and Temperature in the Wavenumber–Frequency Domain , 1999 .

[73]  C. J. Morcrette,et al.  Geoscientific Model Development The Met Office Unified Model Global Atmosphere 3 . 0 / 3 . 1 and JULES Global Land 3 . 0 / 3 . 1 configurations , 2011 .

[74]  A. Bodas‐Salcedo,et al.  The Surface Downwelling Solar Radiation Surplus over the Southern Ocean in the Met Office Model: The Role of Midlatitude Cyclone Clouds , 2012 .

[75]  R. Allan,et al.  A new globally complete monthly historical gridded mean sea level pressure dataset (HadSLP2): 1850-2004 , 2006 .