Bibliography - Thomas E Robinson

1. Held, Isaac M., Huan Guo, Alistair Adcroft, John P Dunne, Larry W Horowitz, John P Krasting, Elena Shevliakova, Michael Winton, Ming Zhao, Mitchell Bushuk, Andrew T Wittenberg, Bruce Wyman, Baoqiang Xiang, Rong Zhang, Whit G Anderson, V Balaji, Leo J Donner, Krista A Dunne, J W Durachta, Paul P G Gauthier, Paul Ginoux, J-C Golaz, Stephen M Griffies, Robert Hallberg, Lucas Harris, Matthew J Harrison, William J Hurlin, Jasmin G John, Pu Lin, Shian-Jiann Lin, Sergey Malyshev, Raymond Menzel, P C D Milly, Yi Ming, Vaishali Naik, David J Paynter, Fabien Paulot, V Ramaswamy, Brandon G Reichl, Thomas E Robinson, Anthony Rosati, Charles J Seman, Levi G Silvers, Seth D Underwood, and Niki Zadeh, in press: Structure and Performance of GFDL's CM4.0 Climate Model. Journal of Advances in Modeling Earth Systems. DOI:10.1029/2019MS001829. October 2019.
   Abstract

   We describe GFDL's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the pre‐industrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top‐of‐atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño‐Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasi‐periodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.

2. Robinson, Thomas E., and Steven Businger, August 2019: A Novel Method for Modeling Lowest-Level Vertical Motion. Weather and Forecasting, 34(4), DOI:10.1175/WAF-D-18-0064.1.
   Abstract

   A new method for modeling the lowest model level vertical motion is described and validated. Instead of smoothing terrain heights, the new method calculates the terrain gradient on a high-resolution grid and averages the gradient values around a gridpoint location. In essence, the method provides a way to achieve some of the impact of very steep terrain on the flow without the computational overhead associated with the very high grid resolution needed to fully resolve complex terrain. The more accurate depiction of the terrain gradient leads to an increase in orographic vertical motion and causes rainfall to occur more often over the windward-facing mountain slopes, consistent with observations. Model results are compared with rain gauge data during the month of January 2016 as well as radar data from a case study on 9 March 2012. When implemented in the Weather Research and Forecasting (WRF) Model over the island of Oahu and compared with the current WRF method, the model precipitation forecast skill is improved. The new method produces more precipitation over the island during January 2016, which is closer to the observed value. On 9 March 2012, the new method clearly focuses the precipitation over the Ko‘olau Mountains, reducing the number of false alarm forecasts by nearly one-half. Although the changes to model precipitation skill were small, they were generally positive.

3. Zhao, Ming, J-C Golaz, Isaac M Held, Huan Guo, V Balaji, Rusty Benson, Jan-Huey Chen, Xi Chen, Leo J Donner, John P Dunne, Krista A Dunne, J W Durachta, Songmiao Fan, Stuart Freidenreich, Stephen T Garner, Paul Ginoux, Lucas Harris, Larry W Horowitz, John P Krasting, Amy R Langenhorst, Zhi Liang, Pu Lin, Shian-Jiann Lin, Sergey Malyshev, E Mason, P C D Milly, Yi Ming, Vaishali Naik, Fabien Paulot, David J Paynter, Peter Phillipps, Aparna Radhakrishnan, V Ramaswamy, Thomas E Robinson, M Daniel Schwarzkopf, Charles J Seman, Elena Shevliakova, Zhaoyi Shen, Hyeyum Hailey Shin, Levi G Silvers, R John Wilson, Michael Winton, Andrew T Wittenberg, Bruce Wyman, and Baoqiang Xiang, March 2018: The GFDL Global Atmosphere and Land Model AM4.0/LM4.0 - Part I: Simulation Characteristics with Prescribed SSTs. Journal of Advances in Modeling Earth Systems, 10(3), DOI:10.1002/2017MS001208.
   Abstract

   In this two-part paper, a description is provided of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). This version, with roughly 100km horizontal resolution and 33 levels in the vertical, contains an aerosol model that generates aerosol fields from emissions and a “light” chemistry mechanism designed to support the aerosol model but with prescribed ozone. In Part I, the quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode – with prescribed sea surface temperatures (SSTs) and sea ice distribution – is described and compared with previous GFDL models and with the CMIP5 archive of AMIP simulations. The model's Cess sensitivity (response in the top-of-atmosphere radiative flux to uniform warming of SSTs) and effective radiative forcing are also presented. In Part II, the model formulation is described more fully and key sensitivities to aspects of the model formulation are discussed, along with the approach to model tuning.

4. Zhao, Ming, J-C Golaz, Isaac M Held, Huan Guo, V Balaji, Rusty Benson, Jan-Huey Chen, Xi Chen, Leo J Donner, John P Dunne, Krista A Dunne, J W Durachta, Songmiao Fan, Stuart Freidenreich, Stephen T Garner, Paul Ginoux, Lucas Harris, Larry W Horowitz, John P Krasting, Amy R Langenhorst, Zhi Liang, Pu Lin, Shian-Jiann Lin, Sergey Malyshev, E Mason, P C D Milly, Yi Ming, Vaishali Naik, Fabien Paulot, David J Paynter, Peter Phillipps, Aparna Radhakrishnan, V Ramaswamy, Thomas E Robinson, M Daniel Schwarzkopf, Charles J Seman, Elena Shevliakova, Zhaoyi Shen, Hyeyum Hailey Shin, Levi G Silvers, R John Wilson, Michael Winton, Andrew T Wittenberg, Bruce Wyman, and Baoqiang Xiang, March 2018: The GFDL Global Atmosphere and Land Model AM4.0/LM4.0 - Part II: Model Description, Sensitivity Studies, and Tuning Strategies. Journal of Advances in Modeling Earth Systems, 10(3), DOI:10.1002/2017MS001209.
   Abstract

   In Part II of this two-part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part I. Part II provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.

Direct link to page: http://www.gfdl.noaa.gov/bibliography/results.php?author=7902