Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems

Abstract

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

Reanalysis comparisons of upper tropospheric–lower stratospheric jets and multiple tropopauses

“…As described by Fujiwara et al (2017), the ANA fields are written after the analysis step, but before the IAU is applied; these products are analogous to the analyzed fields produced by other reanalysis centers (e.g., Fujiwara et al, 2017). The ASM output is the product of IAU written by the general circulation model forced by the analysis increments computed in the analysis step.…”

“…Detailed descriptions of the models, assimilation systems, and data inputs for each are given in the overview paper on the Stratosphere-troposphere Processes And their Role in Climate Reanalysis Intercomparison Project (S-RIP; Fujiwara et al, 2017). The five recent high-resolution "full-input" reanalysis climatologies are compared for 1980 through 2014, with the December-January-February, DJF, seasonal plots starting with December 1979.…”

“…We use the fields on the model grid and vertical levels, which has a resolution of ∼ 1 km in the UTLS. A reanalysis, JRA-55C, using the same assimilation system as for JRA-55, but with only "conventional" data inputs (that is, no satellite data), was run for 1972 through 2012 (Kobayashi et al, 2014;Fujiwara et al, 2017). In Sect.…”

“…Most of these studies have used older reanalyses or focused on tropopause and/or jet diagnostics based on zonal means. Peña-Ortiz et al (2013) used a three-dimensional (3D) jet characterization scheme but applied it to the NCEP/NCAR Reanalysis and the NCEP 20th Century Reanalysis (the latter assimilates only surface observations), both of which use relatively unsophisticated or outdated assimilation systems, have coarse horizontal resolution and poor vertical resolution in the UTLS, and have been shown to have limited skill in the UTLS and above (see Fujiwara et al, 2017, for a review of reanalysis system characteristics and evaluations). While Davis and Birner (2017) used four of the five modern reanalyses we will compare here, their tropopause and jet-based tropical width diagnostics were based on analysis of zonal mean fields.…”

On the suitability of current atmospheric reanalyses for regional warming studies over China

“…Specifically, observation error incorporates systematic and random errors in instruments and their replacements, errors in data reprocessing and representation error, which arises due to the spatiotemporal incompleteness of observations (Dee and Uppala, 2009;Desroziers et al, 2005). Model error refers mainly to the inadequate representation of physical processes in NWP models (Peña and Toth, 2014;Bengtsson et al, 2007), such as the lack of time-varying surface conditions such as vegetation growth (Zhou and Wang, 2016b;Trigo et al, 2015), and incomplete cloud-precipitation-radiation parameterizations (Fujiwara et al, 2017;Dolinar et al, 2016). Assimilation error describes errors that arise in the mapping of the model space to the observation space and errors in the topologies of cost functions (Dee, 2005;Dee and Da Silva, 1998;Lahoz and Schneider, 2014;Parker, 2016).…”