The climate research community uses atmospheric reanalysis data sets to understand a wide range of processes and variability in the atmosphere, yet different reanalyses may give very different results for the same diagnostics. The Stratosphere–troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP) is a coordinated activity to compare reanalysis data sets using a variety of key diagnostics. The objectives of this project are to identify differences among reanalyses and understand their underlying causes, to provide guidance on appropriate usage of various reanalysis products in scientific studies, particularly those of relevance to SPARC, and to contribute to future improvements in the reanalysis products by establishing collaborative links between reanalysis centres and data users. The project focuses predominantly on differences among reanalyses, although studies that include operational analyses and studies comparing reanalyses with observations are also included when appropriate. The emphasis is on diagnostics of the upper troposphere, stratosphere, and lower mesosphere. This paper summarizes the motivation and goals of the S-RIP activity and extensively reviews key technical aspects of the reanalysis data sets that are the focus of this activity. The special issue “The SPARC Reanalysis Intercomparison Project (S-RIP)” in this journal serves to collect research with relevance to the S-RIP in preparation for the publication of the planned two (interim and full) S-RIP reports

We are adapting the global circulation model (GCM) of the UK Met Office, the so-called unified model (UM), for the study of hot Jupiters. In this work we demonstrate the successful adaptation of the most sophisticated dynamical core, the component of the GCM which solves the equations of motion for the atmosphere, available within the UM, ENDGame (Even Newer Dynamics for General atmospheric modelling of the environment). Within the same numerical scheme ENDGame supports solution to the dynamical equations under varying degrees of simplification. We present results from a simple, shallow (in atmospheric domain) hot Jupiter model (SHJ), and a more realistic (with a deeper atmosphere) HD 209458b test case. For both test cases we find that the large-scale, time-averaged (over the 1200 days prescribed test period), dynamical state of the atmosphere is relatively insensitive to the level of simplification of the dynamical equations. However, problems exist when attempting to reproduce the results for these test cases derived from other models. For the SHJ case the lower (and upper) boundary intersects the dominant dynamical features of the atmosphere meaning the results are heavily dependent on the boundary conditions. For the HD 209458b test case, when using the more complete dynamical models, the atmosphere is still clearly evolving after 1200 days, and in a transient state. Solving the complete (deep atmosphere and non-hydrostatic) dynamical equations allows exchange between the vertical and horizontal momentum of the atmosphere, via Coriolis and metric terms. Subsequently, interaction between the upper atmosphere and the deeper more slowly evolving (radiatively inactive) atmosphere significantly alters the results, and acts over timescales longer than 1200 days.

Derived Meteorological Products (DMPs, including potential temperature, potential vorticity (PV), equivalent latitude (EqL), horizontal winds and tropopause locations) from several meteorological analyses have been produced for the locations and times of measurements taken by several solar occultation instruments and the Aura Microwave Limb Sounder (MLS). MLS and solar occultation data are analyzed using DMPs to illustrate sampling issues that may affect interpretation and comparison of data sets with diverse sampling patterns and to provide guidance regarding the kinds of studies that benefit most from analyzing satellite data in relation to meteorological conditions using the DMPs. Using EqL or PV as a vortex‐centered coordinate does not alleviate all sampling problems, including those in studies using “vortex averages” of solar occultation data and in analyses of localized features (such as polar stratospheric clouds) and other fields that do not correlate well with PV. Using DMPs to view measurements with respect to their air mass characteristics is particularly valuable in studies of transport of long‐lived trace gases, polar processing in the winter lower stratosphere, and distributions and transport of O3 and other trace gases from the upper troposphere through the lower stratosphere. The comparisons shown here demonstrate good agreement between MLS and solar occultation data for O3, N2O, H2O, HNO3, and HCl; small biases are attributable to sampling effects or are consistent with detailed validation results presented elsewhere in this special section. The DMPs are valuable for many scientific studies and to facilitate validation of noncoincident measurements.

Extreme variability of the winter-and spring-time stratospheric polar vortex has been shown to affect extratropical tropospheric weather. Therefore, reducing stratospheric forecast error may be one way to improve the skill of tropospheric weather forecasts. In this review, the basis for this idea is examined. A range of studies of different stratospheric extreme vortex events shows that they can be skilfully forecasted beyond 5 days and into the sub-seasonal range (0-30 days) in some cases. Separate studies show that typical errors in forecasting a stratospheric extreme vortex event can alter tropospheric forecast skill by 5-7% in the extratropics on sub-seasonal time-scales. Thus understanding what limits stratospheric predictability is of significant interest to operational forecasting centres. Both limitations in forecasting tropospheric planetary waves and stratospheric model biases have been shown to be important in this context.

Abstract. This paper aims to summarise the current performance of ozone data assimilation (DA) systems, to show where they can be improved, and to quantify their errors. It examines 11 sets of ozone analyses from 7 different DA systems. Two are numerical weather prediction (NWP) systems based on general circulation models (GCMs); the other five use chemistry transport models (CTMs). The systems examined contain either linearised or detailed ozone chemistry, or no chemistry at all. In most analyses, MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) ozone data are assimilated; two assimilate SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography) observations instead. Analyses are compared to independent ozone observations covering the troposphere, stratosphere and lower mesosphere during the period July to November 2003.Biases and standard deviations are largest, and show the largest divergence between systems, in the troposphere, in the upper-troposphere/lower-stratosphere, in the upperstratosphere and mesosphere, and the Antarctic ozone hole region. However, in any particular area, apart from the troposphere, at least one system can be found that agrees well with independent data. In general, none of the differences can be linked to the assimilation technique (Kalman filter, three or four dimensional variational methods, direct inversion) orCorrespondence to: A. J. Geer (alan.geer@ecmwf.int) the system (CTM or NWP system). Where results diverge, a main explanation is the way ozone is modelled. It is important to correctly model transport at the tropical tropopause, to avoid positive biases and excessive structure in the ozone field. In the southern hemisphere ozone hole, only the analyses which correctly model heterogeneous ozone depletion are able to reproduce the near-complete ozone destruction over the pole. In the upper-stratosphere and mesosphere (above 5 hPa), some ozone photochemistry schemes caused large but easily remedied biases. The diurnal cycle of ozone in the mesosphere is not captured, except by the one system that includes a detailed treatment of mesospheric chemistry. These results indicate that when good observations are available for assimilation, the first priority for improving ozone DA systems is to improve the models.The analyses benefit strongly from the good quality of the MIPAS ozone observations. Using the analyses as a transfer standard, it is seen that MIPAS is ∼5% higher than HALOE (Halogen Occultation Experiment) in the mid and upper stratosphere and mesosphere (above 30 hPa), and of order 10% higher than ozonesonde and HALOE in the lower stratosphere (100 hPa to 30 hPa). Analyses based on SCIA-MACHY total column are almost as good as the MIPAS analyses; analyses based on SCIAMACHY limb profiles are worse in some areas, due to problems in the SCIAMACHY retrievals.