Saroja Polavarapu

Is Missing Orographic Gravity Wave Drag near 60°S the Cause of the Stratospheric Zonal Wind Biases in Chemistry–Climate Models?

Nearly all chemistry‐climate models (CCMs) have a systematic bias of a delayed springtime breakdown of the Southern Hemisphere (SH) stratospheric polar vortex, implying insufficient stratospheric wave drag. In this study the Canadian Middle Atmosphere Model (CMAM) and the CMAM Data Assimilation System (CMAM-DAS) are used to investigate the cause of this bias. Zonal wind analysis increments from CMAMDAS reveal systematic negative values in the stratosphere near 608S in winter and early spring. These are interpretedas indicatinga bias in the model physics, namely, missing gravity wave drag (GWD). The negative analysisincrementsremainatanearlyconstantheightduringwinteranddescendasthevortexweakens,much like orographic GWD. This region is also where current orographic GWD parameterizations have a gap in wave drag, which is suggested to be unrealistic because of missing effects in those parameterizations. These findings motivate a pair of free-running CMAM simulations to assess the impact of extra orographic GWD at 608S. The control simulation exhibits the cold-pole bias and delayed vortex breakdown seen in the CCMs. In thesimulationwithextraGWD,the cold-polebiasis significantly reducedandthe vortexbreaks downearlier. Changes in resolved wave drag in the stratosphere also occur in response to the extra GWD, which reduce stratospheric SH polar-cap temperature biases in late spring and early summer. Reducing the dynamical biases, however, results in degraded Antarctic column ozone. This suggests that CCMs that obtain realistic columnozoneinthepresenceofanoverlystrongandpersistentvortexmaybedoingsothroughcompensating errors.

Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction.

The World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP) have identified collaborations and scientific priorities to accelerate advances in analysis and prediction at subseasonalto-seasonal time scales, which include i) advancing knowledge of mesoscale–planetary-scale interactions and their prediction; ii) developing high-resolution global–regional climate simulations, with advanced representation of physical processes, to improve the predictive skill of subseasonal and seasonal variability of high-impact events, such as seasonal droughts and floods, blocking, and tropical and extratropical cyclones; iii) contributing to the improvement of data assimilation methods for monitoring and predicting used in coupled ocean–atmosphere–land and Earth system models; and iv) developing and transferring diagnostic and prognostic information tailored to socioeconomic decision making. The document puts forward specific underpinning research, linkage, and requirements necessary to achi...

The Stratospheric Extension of the Canadian Global Deterministic Medium-Range Weather Forecasting System and Its Impact on Tropospheric Forecasts

AbstractA new system that resolves the stratosphere was implemented for operational medium-range weather forecasts at the Canadian Meteorological Centre. The model lid was raised from 10 to 0.1 hPa, parameterization schemes for nonorographic gravity wave tendencies and methane oxidation were introduced, and a new radiation scheme was implemented. Because of the higher lid height of 0.1 hPa, new measurements between 10 and 0.1 hPa were also added. This new high-top system resulted not only in dramatically improved forecasts of the stratosphere, but also in large improvements in medium-range tropospheric forecast skill. Pairs of assimilation experiments reveal that most of the stratospheric and tropospheric forecast improvement is obtained without the extra observations in the upper stratosphere. However, these observations further improve forecasts in the winter hemisphere but not in the summer hemisphere. Pairs of forecast experiments were run in which initial conditions were the same for each experiment ...

Four-Dimensional Variational Data Assimilation with Digital Filter Initialization

A four-dimensional variational (4DVAR) data assimilation problem may be constrained so that the solution closely fits the observations but is balanced. In this way, the processes of data analysis and initialization are combined. The method of initialization considered here, digital filtering, is widely used in weather forecasting centers. The digital filter was found to control high-frequency noise when implemented as a strong or as a weak constraint in the context of a global shallow water model. Implementation of a strong constraint did not result in a recovery of small scales although some recovery of intermediate scales did occur. Implementation of a weak constraint as a penalty method with a single fixed value of the penalty parameter resulted in analyses that were smooth, but depended upon the choice of the parameter. With a parameter value that was too large, the divergent kinetic energy spectrum of the analysis was excessively damped in the large scales. The rotational kinetic energy spectrum was also affected by the choice of penalty parameter. Both types of constraint were found to adequately control gravity wave noise although caution is advised in choosing the penalty parameter for the simple penalty term method.

On the Relationship between Incremental Analysis Updating and Incremental Digital Filtering

Abstract Incremental analysis updating (IAU) refers to a method of smoothly inserting instantaneous analysis increments into a numerical model by spreading the increments over a time period. In this work, this method is shown to be identical to applying a digital filter to the time evolution of analysis increments [a method known as incremental digital filtering (IDF)] for the case of linear models with time-invariant coefficients. The equivalence of the two methods can be used to show that the constant weights typically employed in IAU applications result in too much damping of long waves. For weakly nonlinear models, the two methods will not produce identical filtered states even if the filter coefficients are the same. The implications of the similarities and differences of the two methods are discussed.

Four-Dimensional Data Assimilation and Balanced Dynamics

Abstract The problem of spurious excitation of gravity waves in the context of four-dimensional data assimilation is investigated using a simple model of balanced dynamics. The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode, and can be initialized such that the model evolves on a so-called slow manifold, where the fast motion is suppressed. Identical twin assimilation experiments are performed, comparing the extended and ensemble Kalman filters (EKF and EnKF, respectively). The EKF uses a tangent linear model (TLM) to estimate the evolution of forecast error statistics in time, whereas the EnKF uses the statistics of an ensemble of nonlinear model integrations. Specifically, the case is examined where the true state is balanced, but observation errors project onto all degrees of freedom, including the fast modes. It is shown that the EKF and EnKF will assimilate observations in a balanced way only if certain assumptions hold, and that, outside of ideal cases (i.e., ...

Temporal Accumulation of First-Order Linearization Error for Semi-Lagrangian Passive Advection

Abstract The tangent linear model (TLM) is obtained by linearizing the governing equations around a space- and time-dependent basic state referred to as the trajectory. The TLM describes to first-order the evolution of perturbations in a nonlinear model and it is now used widely in many applications including four-dimensional data assimilation. This paper is concerned with the difficulties that arise when developing the tangent linear model for a semi-Lagrangian integration scheme. By permitting larger time steps than those of Eulerian advection schemes, the semi-Lagrangian treatment of advection improves the model efficiency. However, a potential difficulty in linearizing the interpolation algorithms commonly used in semi-Lagrangian advection schemes has been described by Polavarapu et al, who showed that for infinitesimal perturbations, the tangent linear approximation of an interpolation scheme is correct if and only if the first derivative of the interpolator is continuous at every grid point. Here, t...

Stratospheric and Mesospheric Data Assimilation: The Role of Middle Atmospheric Dynamics

The middle atmosphere refers to the stratosphere and mesosphere and features dynamics and circulations that are fundamentally different from those of the troposphere. The large-scale meridional circulations in the middle atmosphere operate on seasonal and longer time scales and are largely forced by the breaking of upward propagating waves. The winter stratosphere is dominated by large-scale waves and a polar vortex which confines constituents and which is sometimes punctuated by stratospheric sudden warmings. In contrast, the summer stratosphere is quiescent. Meanwhile, the meridional circulation in the mesosphere is mainly driven by the breaking of a broad spectrum of gravity waves that have propagated upward from the troposphere. These facets of middle atmosphere dynamics have implications for, and pose unique challenges to, data assimilation systems whose models encompass this region of the atmosphere. In this work, we provide an overview of middle atmosphere data assimilation in the context of the dynamics of this region. The purpose is to demonstrate how the dynamics can be used to explain the behavior of data assimilation systems in the middle atmosphere, and also to identify challenges in assimilating measurements from this region of the atmosphere. There are two overarching themes. Firstly, we consider the vertical propagation of information through waves, resolved and parameterized, and background error covariances . Secondly, we delve into the dynamical sources of model errors and techniques for their estimation.

A Low-Order Model Investigation of the Analysis of Gravity Waves in the Ensemble Kalman Filter

The behavior of the ensemble Kalman filter (EnKF) is examined in the context of a model that exhibits a nonlinear chaotic (slow) vortical mode coupled to a linear (fast) gravity wave of a given amplitude and frequency. It is shown that accurate recovery of both modes is enhanced when covariances between fast and slow normal-mode variables (which reflect the slaving relations inherent in balanced dynamics) are modeled correctly. More ensemble members are needed to recover the fast, linear gravity wave than the slow, vortical motion. Although the EnKF tends to diverge in the analysis of the gravity wave, the filter divergence is stable and does not lead to a great loss of accuracy. Consequently, provided the ensemble is large enough and observations are made that reflect both time scales, the EnKF is able to recover both time scales more accurately than optimal interpolation (OI), which uses a static error covariance matrix. For OI it is also found to be problematic to observe the state at a frequency that is a subharmonic of the gravity wave frequency, a problem that is in part overcome by the EnKF. However, error in the modeledgravity waveparameters can be detrimental to the performance of the EnKF and remove its implied advantages, suggesting that a modified algorithm or a method for accounting for model error is needed.

The NOy budget above Eureka, Nunavut from ground-based FTIR measurements, space-based ACE-FTS measurements, and the CMAM-DAS, GEM-BACH, and SLIMCAT models

Reactive nitrogen species, NOy, play an important role in stratospheric chemistry. Using a Bruker 125HR FTIR installed at Eureka, Nunavut, ACE-FTS satellite data, and model simulations, we study the NOy budget for this Arctic site.