Methodologies to characterize uncertainties in regional reanalyses

Abstract

Abstract. When using climate data for various applications, users are confronted with the difficulty to assess the uncertainties of the data. For both in-situ and remote sensing data the issues of representativeness, homogeneity, and coverage have to be considered for the past, and their respective change over time has to be considered for any interpretation of trends. A synthesis of observations can be obtained by employing data assimilation with numerical weather prediction (NWP) models resulting in a meteorological reanalysis. Global reanalyses can be used as boundary conditions for regional reanalyses (RRAs), which run in a limited area (Europe in our case) with higher spatial and temporal resolution, and allow for assimilation of more regionally representative observations. With the spatially highly resolved RRAs, which exhibit smaller scale information, a more realistic representation of extreme events (e.g. of precipitation) compared to global reanalyses is aimed for. In this study, we discuss different methods for quantifying the uncertainty of the RRAs to answer the question to which extent the smaller scale information (or resulting statistics) provided by the RRAs can be relied on. Within the European Union's seventh Framework Programme (EU FP7) project Uncertainties in Ensembles of Regional Re-Analyses (UERRA) ensembles of RRAs (both multi-model and single model ensembles) are produced and their uncertainties are quantified. Here we explore the following methods for characterizing the uncertainties of the RRAs: (A) analyzing the feedback statistics of the assimilation systems, (B) validation against station measurements and (C) grids derived thereof, and (D) against gridded satellite data products. The RRA ensembles (E) provide the opportunity to derive ensemble scores like ensemble spread and other special probabilistic skill scores. Finally, user applications (F) are considered. The various methods are related to user questions they can help to answer.

BARRA v1.0: kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains

“…Cloud microphysics scheme Single-moment scheme based on Wilson and Ballard (1999) Wilson and Ballard (1999), with prognostic graupel (Wilkinson and Bornemann, 2014) and improved warm rain scheme (Boutle et al, 2014a) Boundary layer scheme 1D vertical turbulent mixing scheme of Lock et al (2000) Blended boundary layer parameterization (Boutle et al, 2014b) Land surface and hydrology GA6 (Walters et al, 2017), PDM subgrid-scale heterogeneity, JULES urban parameters optimized for Australia (Dharssi et al, 2015) GA7 (Walters et al, 2019) et al, 2016). Aerosol absorption and scattering in the radiation scheme assume climatological aerosol properties.…”

BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia

“…correlation spatially or between variables) of phenomena. For instance, the COSMO (Consortium for Small-scale Modelling) 6 km reanalysis has shown the potential to provide realistic sub-daily representations of winds at 10 to 40 m of height (Borsche et al, 2016) and to resolve small-scale cloud structures (Bollmeyer et al, 2015). NARR was used to define a climatology of surface wind extremes (Malloy et al, 2015) and 30-year trends in wind at hub height (Holt and Wang, 2012) over northern America.…”

BARRA v1.0: Kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains

“…Departing markedly from the global reanalyses are the regional reanalyses that use limited area models at higher horizontal resolutions over sub-regions such as North America (Mesinger et al, 2006), the Arctic polar region (Bromwich et al, 2016), Europe (Borsche et al, 2015 and references therein), India (Mahmood et al, 2018) and Australia (Su et al, 2019).…”