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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.

Finite-time growth of perturbations in the presence of moist physics (specifically, precipitation) is investigated using singular vectors (SVs) in the context of a primitive equation regional model. Two difficulties appear in the explicit consideration of the effect of moist physics when studying such optimal growth. First, the tangentlinear description of moist physics may not be as straightforward and accurate as for dry-adiabatic processes; second, because of the consideration of moisture, the design of an appropriate measure of growth (i.e., norm) is subject to even more ambiguity than in the dry situation.In this study both of these problems are addressed in the context of the moist version of the National Center for Atmospheric Research Mesoscale Adjoint Modeling System, version 2, with emphasis on the second problem. Leading SVs are computed in an iterative fashion, using a Lanczos algorithm, for three norms over an optimization interval of 24 h; these norms are based on an expression related to (total) perturbation energy. The properties of these SVs are studied for a case of explosive cyclogenesis and a case of summer convection.The consideration of moisture leads to faster growth of perturbations than in the dry situation, as well as to the appearance of new growing structures. Apparently, moist processes provide for new mechanisms of error growth and do not simply lead to a modulation of SVs obtained with the dry version of the model. Consequently, consideration of the linearized moist processes is essential for revealing all structures that might potentially grow in a moist primitive equation model. In the context of this investigation growth rates depend more on the choice of the basic state and linearized model (moist vs dry) than on the choice of the norm (moist vs dry total energy norm). A reference is cited that supports the validity of the moist tangent-linear SV perturbation growth studied here in the nonlinear regime.

The Community Atmosphere Model (CAM) has been interfaced to the Data Assimilation Research Testbed (DART), a community facility for ensemble data assimilation. This provides a large set of data assimilation tools for climate model research and development. Aspects of the interface to the Community Earth System Model (CESM) software are discussed and a variety of applications are illustrated, ranging from model development to the production of long series of analyses. CAM output is compared directly to real observations from platforms ranging from radiosondes to global positioning system satellites. Such comparisons use the temporally and spatially heterogeneous analysis error estimates available from the ensemble to provide very specific forecast quality evaluations. The ability to start forecasts from analyses, which were generated by CAM on its native grid and have no foreign model bias, contributed to the detection of a code error involving Arctic sea ice and cloud cover. The potential of parameter estimation is discussed. A CAM ensemble reanalysis has been generated for more than 15 yr. Atmospheric forcings from the reanalysis were required as input to generate an ocean ensemble reanalysis that provided initial conditions for decadal prediction experiments. The software enables rapid experimentation with differing sets of observations and state variables, and the comparison of different models against identical real observations, as illustrated by a comparison of forecasts initialized by interpolated ECMWF analyses and by DART/CAM analyses.

This study documents and evaluates the boundary layer and energy budget response to record low 2007 sea ice extents in the Community Atmosphere Model version 4 (CAM4) using 1-day observationally constrained forecasts and 10-yr runs with a freely evolving atmosphere. While near-surface temperature and humidity are minimally affected by sea ice loss in July 2007 forecasts, near-surface stability decreases and atmospheric humidity increases aloft over newly open water in September 2007 forecasts. Ubiquitous low cloud increases over the newly ice-free Arctic Ocean are found in both the July 2007 and the September 2007 forecasts. In response to the 2007 sea ice loss, net surface [top of the atmosphere (TOA)] energy budgets change by 119.4 W m 22 (121.0 W m 22 ) and 217.9 W m 22 (11.4 W m 22 ) in the July 2007 and September 2007 forecasts, respectively. While many aspects of the forecasted response to sea ice loss are consistent with physical expectations and available observations, CAM4's ubiquitous July 2007 cloud increases over newly open water are not. The unrealistic cloud response results from the global application of parameterization designed to diagnose stratus clouds based on lower-tropospheric stability (CLDST). In the Arctic, the well-mixed boundary layer assumption implicit in CLDST is violated. Requiring a well-mixed boundary layer to diagnose stratus clouds improves the CAM4 cloud response to sea ice loss and increases July 2007 surface (TOA) energy budgets over newly open water by 111 W m 22 (114.9 W m 22 ). Of importance to high-latitude climate feedbacks, unrealistic stratus cloud compensation for sea ice loss occurs only when stable and dry atmospheric conditions exist. Therefore, coupled climate projections that use CAM4 will underpredict Arctic sea ice loss only when dry and stable summer conditions occur.