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

2016 ◽  
Author(s):  
Jiping Xie ◽  
Laurent Bertino ◽  
Francois Counillon ◽  
Knut A. Lisæter ◽  
Pavel Sakov
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Quality Assessment ◽  
Arctic Region ◽  
Ocean Model ◽  
The Arctic ◽  
Bias Estimation ◽  
International Polar Year ◽  
Near Surface ◽  
The North

Abstract. Long dynamical atmospheric reanalyses are widely used for climate studies, but data assimilative reanalyses of the Arctic ocean and sea ice 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-years reanalysis has been completed for the period 1991–2013. 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.

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

Ocean Science ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 123-144 ◽  
Author(s):  
Jiping Xie ◽  
Laurent Bertino ◽  
François Counillon ◽  
Knut A. Lisæter ◽  
Pavel Sakov
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Quality Assessment ◽  
Arctic Region ◽  
Ocean Model ◽  
Observation Time ◽  
The Arctic ◽  
Bias Estimation ◽  
International Polar Year ◽  
Near Surface

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.

scholarly journals Synoptic climatological approach associated with three recent summer heatwaves in the Canadian Arctic

2020 ◽  
Vol 11 (S1) ◽  
pp. 233-250 ◽  
Author(s):  
Farahnaz Fazel-Rastgar
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Arctic Region ◽  
Low Pressure ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
The North ◽  
Pressure Centre ◽  
Temperature Records ◽  
Arctic Temperature

Abstract The observed unusually high temperatures in the Arctic during recent decades can be related to the Arctic sea ice declines in summer 2007, 2012 and 2016. Arctic dipole formation has been associated with all three heatwaves of 2007, 2012 and 2016 in the Canadian Arctic. Here, the differences in weather patterns are investigated and compared with normal climatological mean (1981–2010) structures. This study examines the high-resolution datasets from the North American Regional Reanalysis model. During the study periods, the north of Alaska has been affected by the low-pressure tongue. The maximum difference between Greenland high-pressure centre and Alaska low-pressure tongue for the summers of 2012, 2016 and 2007 are 8 hPa, 7 hPa and 6 hPa, respectively, corresponding and matching to the maximum summer surface Canadian Arctic temperature records. During anomalous summer heatwaves, low-level wind, temperatures, total clouds (%) and downward radiation flux at the surface are dramatically changed. This study shows the surface albedo has been reduced over most parts of the Canadian Arctic Ocean during the mentioned heatwaves (∼5–40%), with a higher change (specifically in the eastern Canadian Arctic region) during summer 2012 in comparison with summer 2016 and summer 2007, agreeing with the maximum surface temperature and sea ice decline records.

scholarly journals Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts

2013 ◽  
Vol 6 (4) ◽  
pp. 6219-6278 ◽  
Author(s):  
E. W. Blockley ◽  
M. J. Martin ◽  
A. J. McLaren ◽  
A. G. Ryan ◽  
J. Waters ◽  
...  
Keyword(s):  
Sea Ice ◽  
Forecast Accuracy ◽  
Deep Ocean ◽  
Ocean Model ◽  
Global Ocean ◽  
Surface Boundary ◽  
Observation Window ◽  
Sea Ice Concentration ◽  
Near Surface ◽  
The North

Abstract. The Forecast Ocean Assimilation Model (FOAM) is an operational ocean analysis and forecast system run daily at the Met Office. FOAM provides modelling capability in both deep ocean and coastal shelf seas regimes using the NEMO ocean model as its dynamical core. The FOAM Deep Ocean suite produces analyses and 7 day forecasts of ocean tracers, currents and sea ice for the global ocean at 1/4° resolution and at 1/12° resolution in the North Atlantic, Indian Ocean and Mediterranean Sea. Satellite and in-situ observations of temperature, salinity, sea level anomaly and sea ice concentration are assimilated by FOAM each day over a 48 h observation window. The FOAM Deep Ocean configurations have recently undergone a major upgrade which has involved: the implementation of a new variational, first guess at appropriate time 3D-Var, assimilation scheme (NEMOVAR); coupling to a different, multi-thickness-category, sea ice model (CICE); the use of CORE bulk formulae to specify the surface boundary condition; and an increased vertical resolution for the global model. In this paper the new FOAM Deep Ocean system is introduced and details of the recent changes are provided. Results are presented from 2 yr reanalysis integrations of the Global FOAM configuration including an assessment of forecast accuracy. Comparisons are made with both the previous FOAM system and a non-assimilative FOAM system. Assessments reveal considerable improvements in the new system to the near-surface ocean and sea ice fields. However there is some degradation to sub-surface tracer fields and in equatorial regions which highlight specific areas upon which to focus future improvements.

Monitoring and Assessing Arctic Climate Change

2020 ◽  
Author(s):  
Lars-Otto Reiersen ◽  
Robert W. Corell
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Global Climate ◽  
Arctic Region ◽  
The Arctic ◽  
Sea Ice Extent ◽  
The North ◽  
Meteorological Observations ◽  
Arctic Climate Change ◽  
Global Systems

This overview of climate observation, monitoring, and research for the Arctic region outlines the key elements essential to an enhanced understanding of the unprecedented climate change in the region and its global influences. The first recorded observation of sea ice extent around Svalbard date back to the whaling activities around 1600. Over the following 300 years there are periodic and inadequate observations of climate and sea ice from explorers seeking a northern sea route for sailing to Asia or reaching the North Pole. Around 1900 there were few fixed meteorological stations in the circumpolar North. During the Second World War and the following Cold War, the observation network increased significantly due to military interest. Since the 1970s the use of satellites has improved the climate and meteorological observations of Arctic areas, and advancements in marine observations (beneath the sea surface and within oceanic sediments) have contributed to a much improved network of climate and meteorological variables. Climate change in the Arctic and its possible effects within the Arctic and on global climate such as extreme weather and sea level rise were first reported in the ACIA 2005 report. Since then there has been a lot of climate-related assessments based on data from the Arctic and ongoing processes within the Arctic that are linked to global systems.

scholarly journals On the assimilation of ice velocity and concentration data into large-scale sea ice models

Ocean Science ◽  
2007 ◽  
Vol 3 (2) ◽  
pp. 321-335 ◽  
Author(s):  
V. Dulière ◽  
T. Fichefet
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Optimal Interpolation ◽  
Ice Thickness ◽  
Concentration Data ◽  
Ice Concentration ◽  
Ice Velocity

Abstract. Data assimilation into sea ice models designed for climate studies has started about 15 years ago. In most of the studies conducted so far, it is assumed that the improvement brought by the assimilation is straightforward. However, some studies suggest this might not be true. In order to elucidate this question and to find an appropriate way to further assimilate sea ice concentration and velocity observations into a global sea ice-ocean model, we analyze here results from a number of twin experiments (i.e. experiments in which the assimilated data are model outputs) carried out with a simplified model of the Arctic sea ice pack. Our objective is to determine to what degree the assimilation of ice velocity and/or concentration data improves the global performance of the model and, more specifically, reduces the error in the computed ice thickness. A simple optimal interpolation scheme is used, and outputs from a control run and from perturbed experiments without and with data assimilation are thoroughly compared. Our results indicate that, under certain conditions depending on the assimilation weights and the type of model error, the assimilation of ice velocity data enhances the model performance. The assimilation of ice concentration data can also help in improving the model behavior, but it has to be handled with care because of the strong connection between ice concentration and ice thickness. This study is first step towards real data assimilation into NEMO-LIM, a global sea ice-ocean model.

scholarly journals Impact of data assimilation of physical variables on the spring bloom from TOPAZ operational runs in the North Atlantic

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 635-647 ◽  
Author(s):  
A. Samuelsen ◽  
L. Bertino ◽  
C. Hansen
Keyword(s):  
Data Assimilation ◽  
North Atlantic ◽  
Spring Bloom ◽  
Ecosystem Model ◽  
Ocean Model ◽  
Sea Surface ◽  
The Arctic ◽  
The North ◽  
The North Atlantic ◽  
The Impact

Abstract. A reanalysis of the North Atlantic spring bloom in 2007 was produced using the real-time analysis from the TOPAZ North Atlantic and Arctic forecasting system. The TOPAZ system uses a hybrid coordinate general circulation ocean model and assimilates physical observations: sea surface anomalies, sea surface temperatures, and sea-ice concentrations using the Ensemble Kalman Filter. This ocean model was coupled to an ecosystem model, NORWECOM (Norwegian Ecological Model System), and the TOPAZ-NORWECOM coupled model was run throughout the spring and summer of 2007. The ecosystem model was run online, restarting from analyzed physical fields (result after data assimilation) every 7 days. Biological variables were not assimilated in the model. The main purpose of the study was to investigate the impact of physical data assimilation on the ecosystem model. This was determined by comparing the results to those from a model without assimilation of physical data. The regions of focus are the North Atlantic and the Arctic Ocean. Assimilation of physical variables does not affect the results from the ecosystem model significantly. The differences between the weekly mean values of chlorophyll are normally within 5–10% during the summer months, and the maximum difference of ~20% occurs in the Arctic, also during summer. Special attention was paid to the nutrient input from the North Atlantic to the Nordic Seas and the impact of ice-assimilation on the ecosystem. The ice-assimilation increased the phytoplankton concentration: because there was less ice in the assimilation run, this increased both the mixing of nutrients during winter and the area where production could occur during summer. The forecast was also compared to remotely sensed chlorophyll, climatological nutrients, and in-situ data. The results show that the model reproduces a realistic annual cycle, but the chlorophyll concentrations tend to be between 0.1 and 1.0 mg chla/m3 too low during winter and spring and 1–2 mg chla/m3 too high during summer. Surface nutrients on the other hand are generally lower than the climatology throughout the year.

Advective pathways of nutrients and key ecological substances in the Arctic

2021 ◽  
Author(s):  
Myriel Vredenborg ◽  
Benjamin Rabe ◽  
Sinhue Torres-Valdès
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ocean Circulation ◽  
Environmental Changes ◽  
Marine Ecosystem ◽  
Ocean Model ◽  
The Arctic ◽  
Large Set ◽  
Sea Ice Extent ◽  
Near Surface

<p>The Arctic Ocean is undergoing remarkable environmental changes due to global warming. The rise in the Arctic near-surface air temperature during the past decades is more than twice as high as the global average, a phenomenon known as the “Arctic Amplification”. As a consequence the Arctic summer sea ice extent has decreased by more than 40 % in recent decades, and moreover a year-round sea ice loss in extent and thickness was recorded. By opening up of large areas formerly covered by sea ice, the exchange of heat, moisture and momentum between the ocean and the atmosphere intensified. This resulted in changes in the ocean circulation and the water masses impacting the marine ecosystem. We investigate these changes by using a large set of hydrographic and biogeochemical data of the entire Arctic Ocean. To better quantify the current changes in the Arctic ecosystem we will compare our observational data analysis with high-resolution biogeochemical atmosphere-ice-ocean model simulations.</p>

scholarly journals Assessing the Impact of a Future Volcanic Eruption on Decadal Predictions

2018 ◽  
Author(s):  
Sebastian Illing ◽  
Christopher Kadow ◽  
Holger Pohlmann ◽  
Claudia Timmreck
Keyword(s):  
Sea Ice ◽  
Volcanic Eruption ◽  
Volcanic Eruptions ◽  
Arctic Sea Ice ◽  
Climate System ◽  
The Arctic ◽  
Near Surface ◽  
The North ◽  
Precipitation Decrease ◽  
The Impact

Abstract. The likelihood of a large volcanic eruption in the future provides the largest uncertainty concerning the evolution of the climate system on the time scale of a few years; but also an excellent opportunity to learn about the behavior of the climate system, and our models thereof. So the question emerges how predictable is the response of the climate system to future eruptions? By this we mean, to what extent will the volcanic perturbation affect decadal climate predictions and how does the pre-eruption climate state influence the impact of the volcanic signal on the predictions? To address these questions, we performed decadal forecasts with the MiKlip prediction system in the low-resolution configuration for the initialization years 2012 and 2014, which differ in the Pacific Decadal Oscillation (PDO) phase among other things. Each forecast contains an artificial Pinatubo-like eruption starting in June of the first prediction year. For the construction of the aerosol radiative forcing, we used the global aerosol model ECHAM5-HAM in a version adapted for volcanic eruptions. We investigate the response of different climate variables, including near-surface air temperature, precipitation, frost days, and sea ice area fraction. Our results show that the average global cooling response over four years of about 0.2 K and the precipitation decrease of about 0.025 mm/day, is relatively robust throughout the different experiments and seemingly independent of the initialization state. However, on a regional scale, we find substantial differences between the initializations. The cooling effect in the North Atlantic and Europe lasts longer and the Arctic sea ice increase is stronger than in the simulations initialized in 2014. In contrast, the forecast initialized with a negative PDO shows a prolonged cooling in the North Pacific basin.

scholarly journals Arctic Sea Ice and Freshwater Changes Driven by the Atmospheric Leading Mode in a Coupled Sea Ice–Ocean Model

2003 ◽  
Vol 16 (13) ◽  
pp. 2159-2177 ◽  
Author(s):  
Xiangdong Zhang ◽  
Moto Ikeda ◽  
John E. Walsh
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Siberian River ◽  
The North ◽  
New Findings ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract Observational and modeling studies have indicated recent large changes of sea ice and hydrographic properties in the Arctic Ocean. However, the observational database is sufficiently sparse that the mechanisms responsible for the recent changes are not fully understood. A coupled Arctic ocean–sea ice model forced by output from the NCEP–NCAR reanalysis is employed to investigate the role that the leading atmospheric mode has played in the recent changes of the Arctic Ocean. A modified Arctic Oscillation (AO) index is derived for the region poleward of 62.5°N in order to avoid ambiguities in the distinction between the conventional AO and the North Atlantic Oscillation index. The model results indicate that the AO is the driver of many of the changes manifested in the recent observations. The model shows reductions of Arctic sea ice area and volume by 3.2% and 8.8%, respectively, when the AO changes from its negative to its positive phase. Concurrently, freshwater storage decreases by about 2%, while the sea ice and freshwater exports via Fram Strait increase substantially. The changes of sea ice and freshwater storage are strikingly asymmetric between the east and the west Arctic. Notable new findings include 1) the interaction of the dynamic and thermodynamic responses in the sense that changes of sea ice growth and melt are driven by, and feed back negatively to, the dynamically (transport) driven changes of sea ice volume; and 2) the compatibility of the associated freshwater changes with recently observed changes in the salinity of the upper Arctic Ocean, thereby explaining the observed salinity variations by a mechanism that is distinct from, but complementary to, the altered circulation of Siberian river water. In addition, the enhanced freshwater export could be a contributing factor to the increased salinity in the Arctic Ocean. The results of the simulations indicate that Arctic sea ice and freshwater distributions change substantially if one phase of the AO predominates over a decadal timescale. However, such results are based on an idealization of the real-world situation, in which the pattern of forcing varies interannually and the number of positive-AO years varies among decades.

scholarly journals On the assimilation of ice velocity and concentration data into large-scale sea ice models

2007 ◽  
Vol 4 (2) ◽  
pp. 265-301 ◽  
Author(s):  
V. Dulière ◽  
T. Fichefet
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Observation Data ◽  
Concentration Data ◽  
Ice Concentration ◽  
Ice Velocity

Abstract. Data assimilation into sea ice models designed for climate studies has started about 15 years ago. In most of the studies conducted so far, it is assumed that the improvement brought by the assimilation is straightforward. However, some studies suggest this might not be true. In order to elucidate this question and to find an appropriate way to further assimilate sea ice concentration and velocity observations into a global sea ice-ocean model, we analyze here results from a number of twin experiments (i.e. experiments in which the assimilated data are model outputs) carried out with a simplified model of the Arctic sea ice pack. Our objective is to determine to what degree the assimilation of ice velocity and/or concentration data improves the global performance of the model and, more specifically, reduces the error in the computed ice thickness. A simple optimal interpolation scheme is used, and outputs from a control run and from perturbed experiments without and with data assimilation are thoroughly compared. Our results indicate that, under certain conditions depending on the assimilation weights and the type of model error, the assimilation of ice velocity data enhances the model performance. The assimilation of ice concentration data can also help in improving the model behavior, but it has to be handled with care because of the strong connection between ice concentration and ice thickness. This study is preliminary study towards real observation data assimilation into NEMOLIM, a global sea ice-ocean model.

Sign in / Sign up

Export Citation Format

Share Document