Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013

Abstract

Abstract. Long dynamical atmospheric reanalyses are widely used for climate studies, but data-assimilative reanalyses of ocean and sea ice in the Arctic are less common. TOPAZ4 is a coupled ocean and sea ice data assimilation system for the North Atlantic and the Arctic that is based on the HYCOM ocean model and the ensemble Kalman filter data assimilation method using 100 dynamical members. A 23-year reanalysis has been completed for the period 1991-2013 and is the multi-year physical product in the Copernicus Marine Environment Monitoring Service (CMEMS) Arctic Marine Forecasting Center (ARC MFC). This study presents its quantitative quality assessment, compared to both assimilated and unassimilated observations available in the whole Arctic region, in order to document the strengths and weaknesses of the system for potential users. It is found that TOPAZ4 performs well with respect to near-surface ocean variables, but some limitations appear in the interior of the ocean and for ice thickness, where observations are sparse. In the course of the reanalysis, the skills of the system are improving as the observation network becomes denser, in particular during the International Polar Year. The online bias estimation successfully maintains a low bias in our system. In addition, statistics of the reduced centered random variables (RCRVs) confirm the reliability of the ensemble for most of the assimilated variables. Occasional discontinuities of these statistics are caused by the changes of the input data sets or the data assimilation settings, but the statistics remain otherwise stable throughout the reanalysis, regardless of the density of observations. Furthermore, no data type is severely less dispersed than the others, even though the lack of consistently reprocessed observation time series at the beginning of the reanalysis has proven challenging. IntroductionThe Arctic Ocean plays an important role in the global climate system, where the sea ice at the interface between atmosphere and ocean regulates the fluxes of heat, moisture and momentum. The recent warming of the Arctic and the change of its water cycle has been linked to the following manifestations: a significant reduction and thinning of the sea ice cover (Johannessen et al., 2004;Shimada et al., 2006;Rothrock et al., 2008;Kwok and Rothrock, 2009), more freshwater in the Arctic in the 2000s (Haine et al., 2015) and more mobility and faster deformations of the Arctic sea ice (Rampal et al., 2009;Spreen et al., 2011). The interpretation of such changes is severely hampered by the sparseness of the concerned observations, which should not be improved dramatically in the near future. It can be assisted by free-running model simulations, but those are usually hampered by mislocations of ice edge and certain water masses. One possibility is to study surrogate locations where similar processes are assumed to take place. Another solution is to correct the dynamical model by assimilating observations available over relevant timescales.The latter activities thu...

Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

“…The corresponding errors are smaller in winter, especially for neXtSIM (25 % smaller than FD), and down to 4 km for a 1-day drift and 7.5 km for a 3-day drift. These values seem competitive compared to the year-round average RMSE of 5.1 km per day in the TOPAZ4 reanalysis (Xie et al, 2017), even though the ice drift measurements are assimilated in TOPAZ4 (Sakov et al, 2012). The RMSEs of the free-drift model in Grumbine (2003) also seem to be higher than 5 km per day.…”

Probabilistic forecast using a Lagrangian sea ice model: application for search and rescue operations

“…The corresponding errors are smaller in winter, especially for neXtSIM (31% smaller than FD) and down to 4 km for a 1-day drift and 7.5 km for a 3-days drift. These values seem competitive compared to the year-round average RMS error of 5.1 km per day in the TOPAZ4 reanalysis (Xie et al, 2017), even though the ice drift 15 measurements are assimilated in TOPAZ4 (Sakov et al, 2012).…”

Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system

“…It should be noted that the TOPAZ4 SIT data in 2011 are strongly underestimated in early summer. This might be related to the persistence of the negative bias until 2010 (Xie et al, 2017). In the freezing season, the TOPAZ4 SIT in the ESS tends to be thinner than the PIOMAS SIT and seems comparable to the CS2SMOS SIT.…”

“…Since this system assimilates the SIC and sea ice velocity (but the latter only in cold season), one should expect adequate simulation of SIT through the ridging process (Stark et al, 2008). It has been reported that the SIT of the TOPAZ4 reanalysis data has substantial negative bias from 2001 to 2010 due to excessive snowfall, which has been modified after 2011 (Xie et al, 2017). Therefore, this study used SIT data from 1 January 2011 to 31 December 2014.…”

“…The Norwegian Meteorological Institute provides 10-day forecast products in daily mean fields, forced at the surface by the European Centre for Medium-Range Weather Forecasts (ECMWF) operational atmospheric forecasts (Persson, 2011), updated daily and distributed by the Copernicus Marine Environment Monitoring Services (Simonsen et al, 2017). The reliability of the corresponding TOPAZ4 reanalysis data has been evaluated previously through comparison with in situ and satellite SIT data (Xie et al, 2017). They showed that the SIT in the TOPAZ4 reanalysis data is comparable to observed values over the Beaufort Gyre and central Arctic Ocean, although the SIT overall shows a negative bias of several dozen centimetres throughout a year.…”