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

Abstract

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.

Evaluation of the Mesospheric Polar Vortices in WACCM

“…Smith‐Johnsen et al () showed better agreement with observations when D region chemistry was added to the Whole Atmosphere Community Climate Model (WACCM) but the NO underestimate persisted between 90 and 110 km. Recent studies have shown that either constraining the model up to 90 km or assimilating mesospheric data improves the representation of the EPP IE (Pedatella, Liu, et al, ; Sassi et al, ; Siskind et al, ). In the Antarctic, Hendrickx et al () show EPP‐NO x underestimates despite MLT descent rates in WACCM that agree with observations.…”

Evaluating Different Techniques for Constraining Lower Atmospheric Variability in an Upper Atmosphere General Circulation Model: A Case Study During the 2010 Sudden Stratospheric Warming

“…Recent advancements in middle and upper atmospheric modeling have led to self‐consistent, coupled numerical models of the global stratosphere, mesosphere, thermosphere, and ionosphere that can generally incorporate the day‐to‐day variability in lower and middle atmospheric wave activity by constraining the relevant dynamical fields using observational and/or reanalysis data sets. Typically, observational data sets are incorporated into physics‐based models through data assimilation (DA; e.g., Codrescu et al, ; Fuller‐Rowell et al, ; Matsuo et al, ; Pedatella et al, ; Sutton, ; H. Wang et al, , and references therein), while reanalysis data sets are incorporated into physics‐based models via nudging. More formally nudging is referred to as Newtonian relaxation, where model fields are relaxed using a user‐specified time constant (e.g., Liu et al, ; Marsh, ; Smith, Pedatella, et al, ; Siskind & Drob, ; J. C. Wang et al, , and references therein).…”

EnKF Ionosphere and Thermosphere Data Assimilation Algorithm Through a Sparse Matrix Method

“…They indicated that DA can correct most model bias during storm times via assimilating TEC into the model and optimizing multiple state variables. Pedatella et al (2018) applied EnKF to a whole atmosphere model recently and achieved reasonable ionospheric TEC forecast during stratospheric sudden warming events. Pedatella et al (2018) applied EnKF to a whole atmosphere model recently and achieved reasonable ionospheric TEC forecast during stratospheric sudden warming events.…”

“…Hsu et al (2018) did several observing system simulation experiments (OSSEs) based on the Ensemble Square Root Filter (EnSRF) for COSMIC-2 observations and concluded that the assimilation of slant TEC data from COSMIC-2 can considerably improve the global ionospheric specification and prediction. However, in these above studies, the authors usually applied the commonly used software packages to implement EnKF/EnSRF, including the Gridpoint Statistical Interpolation (Hsu et al, 2018;Wu et al, 2002) and the Data Assimilation Research Testbed (Anderson et al, 2009;Chartier et al, 2016;Chen et al, 2017;Lee et al, 2012;Matsuo et al, 2013;Pedatella et al, 2018), which rely on large-scale parallel high-performance computing systems. However, in these above studies, the authors usually applied the commonly used software packages to implement EnKF/EnSRF, including the Gridpoint Statistical Interpolation (Hsu et al, 2018;Wu et al, 2002) and the Data Assimilation Research Testbed (Anderson et al, 2009;Chartier et al, 2016;Chen et al, 2017;Lee et al, 2012;Matsuo et al, 2013;Pedatella et al, 2018), which rely on large-scale parallel high-performance computing systems.…”

“…However, the majority of previous studies were conducted on supercomputers, such as NCAR Yellowstone (Chartier et al, 2016;Hsu et al, 2014;Lee et al, 2012;Matsuo et al, 2013) or Cheyenne supercomputer (Pedatella et al, 2018). However, the majority of previous studies were conducted on supercomputers, such as NCAR Yellowstone (Chartier et al, 2016;Hsu et al, 2014;Lee et al, 2012;Matsuo et al, 2013) or Cheyenne supercomputer (Pedatella et al, 2018).…”