An assessment of ten ocean reanalyses in the polar regions

Uotila, Petteri; Goosse, Hugues; Haines, Keith; Chevallier, Matthieu; Barthélemy, Antoine; Bricaud, Clément; Carton, J.; Fučkar, Neven S.; Garric, Gilles; Iovino, Doroteaciro; Kauker, Frank; Korhonen, Meri; Lien, Vidar S.; Marnela, Marika; Massonnet, François; Mignac, Davi; Peterson, K. Andrew; Sadikni, Remon; Shi, Li; Tietsche, Steffen; Toyoda, Takahiro; Xie, Jiping; Zhang, Zhaoru

doi:10.1007/s00382-018-4242-z

Cited by 133 publications

(158 citation statements)

References 110 publications

(147 reference statements)

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“…ORAP5 has been found to simulate the overall ice thickness in the Arctic well in com- parison with other state-of-the-art ocean reanalyses (Uotila et al, 2018).…”

Section: Oras5 Sea-ice-ocean Reanalysismentioning

confidence: 98%

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Thin Arctic sea ice in L-band observations and an ocean reanalysis

et al. 2018

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Abstract. L-band radiance measurements of the Earth's surface such as those from the SMOS satellite can be used to retrieve the thickness of thin sea ice in the range 0-1 m under cold surface conditions. However, retrieval uncertainties can be large due to assumptions in the forward model, which converts brightness temperatures into ice thickness and due to uncertainties in auxiliary fields which need to be independently modelled or observed. It is therefore advisable to perform a critical assessment with independent observational and model data before using sea-ice thickness products from L-band radiometry for model validation or data assimilation. Here, we discuss version 3.1 of the University of Hamburg SMOS sea-ice thickness data set (SMOS-SIT) from autumn 2011 to autumn 2017 and compare it to the global ocean reanalysis ORAS5, which does not assimilate the SMOS-SIT data. ORAS5 currently provides the ocean and sea-ice initial conditions for all coupled weather, monthly and seasonal forecasts issued by ECMWF. It is concluded that SMOS-SIT provides valuable and unique information on thin sea ice during winter and can under certain conditions be used to expose deficiencies in the reanalysis. Overall, there is a promising match between sea-ice thicknesses from ORAS5 and SMOS-SIT early in the freezing season (October-December), while later in winter, sea ice is consistently modelled thicker than observed. This is mostly attributable to refrozen polynyas and fracture zones, which are poorly represented in ORAS5 but easily detected by SMOS-SIT. However, there are other regions like Baffin Bay, where biases in the observational data seem to be substantial, as comparisons with independent observational data suggest. Despite considerable uncertainties and discrepancies between thin sea ice in SMOS-SIT and ORAS5 on local scales, interannual variability and trends of its large-scale distribution are in good agreement. This gives some confidence in our current ability to monitor climate variability and change in thin sea ice. With further improvements in retrieval methods, forecast models and data assimilation methods, the huge potential of L-band radiometry to derive the thickness of thin sea ice in winter will be realised and will provide an important building block for improved predictions in polar regions.

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“…ORAP5 has been found to simulate the overall ice thickness in the Arctic well in com- parison with other state-of-the-art ocean reanalyses (Uotila et al, 2018).…”

Section: Oras5 Sea-ice-ocean Reanalysismentioning

confidence: 98%

“…Le Traon et al, 2017) who might not have the resources to carry out an assessment on how the reanalysis product compares to observations. Overall assessments of several reanalyses have been carried out in the past Chevallier et al, 2016;Uotila et al, 2018) but have not addressed the specific issue of thin sea ice.…”

Section: Introductionmentioning

confidence: 99%

Thin Arctic sea ice in L-band observations and an ocean reanalysis

et al. 2018

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“…; Uotila et al . ). Others intended to cover the whole world by using interpolated observational datasets (Donat et al .…”

Section: Introductionmentioning

confidence: 99%

What global reanalysis best represents near‐surface winds?

Ramón

Lledó

Torralba

et al. 2019

Quart J Royal Meteoro Soc

267

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Since global reanalysis datasets first appeared in the 1990s, they have become an essential tool to understand the climate of the past. The wind power industry uses those products extensively for wind resource assessment, while several climate services for energy rely on them as well. Nowadays various datasets coexist, which complicates the selection of the most suitable source for each purpose. In an effort to identify the products that best represent the wind speed features at turbine hub heights, five state‐of‐the‐art global reanalyses have been analysed: ERA5, ERA‐Interim, the Japanese 55‐year Reanalysis (JRA55), the Modern Era Retrospective Analysis for Research and Applications‐2 (MERRA2), and the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis 1 (R1). A multi‐reanalysis ensemble approach is used to explore the main differences amongst these datasets in terms of surface wind characteristics. Then, the quality of the surface and near‐surface winds is evaluated with a set of 77 instrumented tall towers. Results reveal that important discrepancies exist in terms of boreal winter seasonal means, interannual variability (IAV), and decadal linear trends. The differences in the computation of these parameters, which are mainly concentrated inland, reach up to the order of magnitude of the parameters themselves. Comparison with in situ observations shows that the ERA5 surface winds offer the best agreement, correlating and reproducing the observed variability better than a multi‐reanalysis mean in 35.1% of the tall tower sites on a daily time‐scale. However, none of the reanalyses stands out from the others when comparing seasonal mean winds. Regarding the IAV, near‐surface winds from ERA5 offer the values closest to the observed IAV.

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“…These were then brought together in a special issue of Climate Dynamics (Balmaseda et al, ; Chevallier et al, ; Karspeck et al, ; Palmer et al, ; Shi et al, ; Storto et al, ; Masina et al, ; Toyoda, Fujii, Kuragano, Kamachi, et al, ; Toyoda, Fujii, Kuragano, Kosugi, et al, ; Tietsche et al, ; Valdivieso et al, ). A further paper on the polar oceans was later added (Uotila et al, ). Most of these papers focused on consistency of the mean states among reanalyses although several also looked at diagnostics of variability.…”

Section: Introductionmentioning

confidence: 99%

The Mean State and Variability of the North Atlantic Circulation: A Perspective From Ocean Reanalyses

et al. 2019

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The observational network around the North Atlantic has improved significantly over the last few decades with subsurface profiling floats and satellite observations and the recent efforts to monitor the Atlantic Meridional Overturning Circulation (AMOC). These have shown decadal time scale changes across the North Atlantic including in heat content, heat transport, and the circulation. However, there are still significant gaps in the observational coverage. Ocean reanalyses integrate the observations with a dynamically consistent ocean model and can be used to understand the observed changes. However, the ability of the reanalyses to represent the dynamics must also be assessed. We use an ensemble of global ocean reanalyses to examine the time mean state and interannual-decadal variability of the North Atlantic ocean since 1993. We assess how well the reanalyses are able to capture processes and whether any understanding can be gained. In particular, we examine aspects of the circulation including convection, AMOC and gyre strengths, and transports. We find that reanalyses show some consistency, in particular showing a weakening of the subpolar gyre and AMOC at 50 • N from the mid-1990s until at least 2009 (related to decadal variability in previous studies), a strengthening and then weakening of the AMOC at 26.5 • N since 2000, and impacts of circulation changes on transports. These results agree with model studies and the AMOC observations at 26.5 • N since 2005. We also see less spread across the ensemble in AMOC strength and mixed layer depth, suggesting improvements as the observational coverage has improved.

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An assessment of ten ocean reanalyses in the polar regions

Cited by 133 publications

References 110 publications

Thin Arctic sea ice in L-band observations and an ocean reanalysis

Thin Arctic sea ice in L-band observations and an ocean reanalysis

What global reanalysis best represents near‐surface winds?

The Mean State and Variability of the North Atlantic Circulation: A Perspective From Ocean Reanalyses

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