Analysis and Hindcast Experiments of the 2009 Sudden Stratospheric Warming in WACCMX+DART

The ability to perform data assimilation in the Whole Atmosphere Community Climate Model eXtended version (WACCMX) is implemented using the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter. Results are presented demonstrating that WACCMX+DART analysis fields reproduce the middle and upper atmosphere variability during the 2009 major sudden stratospheric warming (SSW) event. Compared to specified dynamics WACCMX, which constrains the meteorology by nudging toward an external reanalysis, the large‐scale dynamical variability of the stratosphere, mesosphere, and lower thermosphere is improved in WACCMX+DART. This leads to WACCMX+DART better representing the downward transport of chemical species from the mesosphere into the stratosphere following the SSW. WACCMX+DART also reproduces most aspects of the observed variability in ionosphere total electron content and equatorial vertical plasma drift during the SSW. Hindcast experiments initialized on 5, 10, 15, 20, and 25 January are used to assess the middle and upper atmosphere predictability in WACCMX+DART. A SSW, along with the associated middle and upper atmosphere variability, is initially predicted in the hindcast initialized on 15 January, which is ∼10 days prior to the warming. However, it is not until the hindcast initialized on 20 January that a major SSW is forecast to occur. The hindcast experiments reveal that dominant features of the total electron content can be forecasted ∼10–20 days in advance. This demonstrates that whole atmosphere models that properly account for variability in lower atmosphere forcing can potentially extend the ionosphere‐thermosphere forecast range.

[1]  Jeffrey L. Anderson.  Spatially and temporally varying adaptive covariance inflation for ensemble filters , 2009 .

[2]  Jann‐Yenq Liu,et al.  Ionospheric data assimilation with thermosphere‐ionosphere‐electrodynamics general circulation model and GPS‐TEC during geomagnetic storm conditions , 2016 .

[3]  J. Wallace,et al.  Lunar gravitational atmospheric tide, surface to 50 km in a global, gridded data set , 2014 .

[4]  S. Solomon,et al.  Solar extreme‐ultraviolet irradiance for general circulation models , 2005 .

[5]  Larry W. Esposito,et al.  Sulfur dioxide in the Venus atmosphere: Distribution and implications , 1979 .

[6]  K. Raeder,et al.  Ensemble data assimilation in the Whole Atmosphere Community Climate Model , 2014 .

[7]  Henry Rishbeth,et al.  Patterns of F2-layer variability , 2001 .

[8]  Jeffrey L. Anderson,et al.  The Data Assimilation Research Testbed: A Community Facility , 2009 .

[9]  P. Bernath,et al.  NOx descent in the Arctic middle atmosphere in early 2009 , 2009 .

[10]  Jorge L. Chau,et al.  Equatorial and low latitude ionospheric effects during sudden stratospheric warming events , 2011 .

[11]  R. Garcia,et al.  Error Growth in a Whole Atmosphere Climate Model , 2009 .

[12]  Arthur D. Richmond,et al.  A theory of ionospheric response to upward‐propagating tides: Electrodynamic effects and tidal mixing effects , 2013 .

[13]  Anthea J. Coster,et al.  Automated GPS processing for global total electron content data , 2006 .

[14]  Arun Kumar,et al.  A Comprehensive Assessment of CFS Seasonal Forecasts over the Tropics , 2012 .

[15]  M. Iredell,et al.  First forecast of a sudden stratospheric warming with a coupled whole‐atmosphere/ionosphere model IDEA , 2014 .

[16]  Han L. Liu,et al.  Atmospheric semidiurnal lunar tide climatology simulated by the Whole Atmosphere Community Climate Model , 2012 .

[17]  C. Randall,et al.  Is a high‐altitude meteorological analysis necessary to simulate thermosphere‐stratosphere coupling? , 2015 .

[18]  Han-Li Liu,et al.  Variability and predictability of the space environment as related to lower atmosphere forcing , 2016 .

[19]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[20]  Robert W. Schunk,et al.  Utah State University Global Assimilation of Ionospheric Measurements Gauss‐Markov Kalman filter model of the ionosphere: Model description and validation , 2006 .

[21]  T. Fuller‐Rowell,et al.  The neutral dynamics during the 2009 sudden stratosphere warming simulated by different whole atmosphere models , 2014 .

[22]  R. Schunk,et al.  Duration of an ionospheric data assimilation initialization of a coupled thermosphere‐ionosphere model , 2007 .

[23]  Astrid Maute,et al.  Development and Validation of the Whole Atmosphere Community Climate Model With Thermosphere and Ionosphere Extension (WACCM‐X 2.0) , 2018 .

[24]  R. Akmaev,et al.  WHOLE ATMOSPHERE MODELING: CONNECTING TERRESTRIAL AND SPACE WEATHER , 2011 .

[25]  E. Sutton,et al.  Short‐term nonmigrating tide variability in the mesosphere, thermosphere, and ionosphere , 2016 .

[26]  M. Sigmond,et al.  The predictability of the extratropical stratosphere on monthly time‐scales and its impact on the skill of tropospheric forecasts , 2015 .

[27]  Hanli Liu.  Development and Validation of NCAR Whole Atmosphere Community Climate Model with Thermosphere/Ionosphere Extension (WACCM-X) , 2017 .

[28]  Michael J. Schwartz,et al.  Aura Microwave Limb Sounder observations of dynamics and transport during the record‐breaking 2009 Arctic stratospheric major warming , 2009 .

[29]  Lawrence L. Takacs,et al.  Data Assimilation Using Incremental Analysis Updates , 1996 .

[30]  M. Iredell,et al.  First simulations with a whole atmosphere data assimilation and forecast system: The January 2009 major sudden stratospheric warming , 2011 .

[31]  Jeffrey L. Anderson.  An Ensemble Adjustment Kalman Filter for Data Assimilation , 2001 .

[32]  Chien-Hung Lin,et al.  Assimilation of FORMOSAT-3/COSMIC electron density profiles into a coupled thermosphere/ionosphere model using ensemble Kalman filtering , 2012 .

[33]  B. Funke,et al.  Sensitivity of simulated mesospheric transport of nitrogen oxides to parameterized gravity waves , 2016 .

[34]  R. W. Spiro,et al.  A model of the high‐latitude ionospheric convection pattern , 1982 .

[35]  Daniel R. Marsh,et al.  Climate change from 1850 to 2005 simulated in CESM1(WACCM) , 2013 .

[36]  S. Cohn,et al.  Ooce Note Series on Global Modeling and Data Assimilation Construction of Correlation Functions in Two and Three Dimensions and Convolution Covariance Functions , 2022 .

[37]  Tomoko Matsuo,et al.  Thermospheric mass density specification using an ensemble Kalman filter , 2013 .

[38]  E. Kudeki,et al.  Estimating daytime vertical ExB drift velocities in the equatorial F‐region using ground‐based magnetometer observations , 2002 .

[39]  Raymond G. Roble,et al.  A thermosphere/ionosphere general circulation model with coupled electrodynamics , 1992 .

[40]  Hermann Lühr,et al.  Longitude‐dependent lunar tidal modulation of the equatorial electrojet during stratospheric sudden warmings , 2017 .

[41]  Linus Magnusson,et al.  Factors Influencing Skill Improvements in the ECMWF Forecasting System , 2013 .

[42]  D. Marsh,et al.  HEPPA-II model–measurement intercomparison project: EPP indirect effects during the dynamically perturbed NH winter 2008–2009 , 2016 .

[43]  Jeffrey L. Anderson,et al.  Implementation of new diffusion/filtering operators in the CAM-FV dynamical core , 2012, Int. J. High Perform. Comput. Appl..

[44]  J. Chau,et al.  Quiet time ionospheric variability over Arecibo during sudden stratospheric warming events , 2010 .

[45]  Kenneth F. Dymond,et al.  Simulations of the effects of vertical transport on the thermosphere and ionosphere using two coupled models , 2014 .

[46]  J. Joselyn.  Geomagnetic activity forecasting: The state of the art , 1995 .

[47]  J. Feynman,et al.  Prediction of geomagnetic activity on time scales of one to ten years , 1986 .

[48]  Jeffrey L. Anderson,et al.  THE DATA ASSIMILATION RESEARCH TESTBED , 2009 .

[49]  S. Solomon,et al.  The NCAR TIE‐GCM , 2014 .

[50]  Cathryn N. Mitchell,et al.  A comparison of the effects of initializing different thermosphere‐ionosphere model fields on storm time plasma density forecasts , 2013 .

[51]  R. Neale,et al.  The Mean Climate of the Community Atmosphere Model (CAM4) in Forced SST and Fully Coupled Experiments , 2013 .

[52]  N. Pedatella,et al.  The influence of atmospheric tide and planetary wave variability during sudden stratosphere warmings on the low latitude ionosphere , 2013 .

[53]  Anthea J. Coster,et al.  Impact of sudden stratospheric warmings on equatorial ionization anomaly , 2010 .

[54]  James J. Hack,et al.  A New Sea Surface Temperature and Sea Ice Boundary Dataset for the Community Atmosphere Model , 2008 .

[55]  E. O. Hulburt,et al.  SORCE CONTRIBUTIONS TO NEW UNDERSTANDING OF GLOBAL CHANGE AND SOLAR VARIABILITY , 2005 .

[56]  Raymond G. Roble,et al.  A coupled thermosphere/ionosphere general circulation model , 1988 .