scholarly journals Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

2014 ◽  
Vol 27 (7) ◽  
pp. 2588-2606 ◽  
Author(s):  
R. Lindsay ◽  
M. Wensnahan ◽  
A. Schweiger ◽  
J. Zhang
Keyword(s):  
Arctic Region ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Surface Fluxes ◽  
Department Of Energy ◽  
The Arctic ◽  
Ice Thickness ◽  
Ice Volume ◽  
Independent Observations ◽  
Environmental Prediction

Abstract Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice–ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981–2010: National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP–U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice–ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R = 0.82). The trend in the total ice volume in September is greatest with MERRA (−4.1 × 103 km3 decade−1) and least with CFSR (−2.7 × 103 km3 decade−1).

scholarly journals Uncertainties in Arctic Sea Ice Thickness Associated with Different Atmospheric Reanalysis Datasets Using the CICE5 Model

Atmosphere ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 361
Author(s):  
Su-Bong Lee ◽  
Baek-Min Kim ◽  
Jinro Ukita ◽  
Joong-Bae Ahn
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Reanalysis Data ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Volume ◽  
Arctic Sea

Reanalysis data are known to have relatively large uncertainties in the polar region than at lower latitudes. In this study, we used a single sea-ice model (Los Alamos’ CICE5) and three sets of reanalysis data to quantify the sensitivities of simulated Arctic sea ice area and volume to perturbed atmospheric forcings. The simulated sea ice area and thickness thus volume were clearly sensitive to the selection of atmospheric reanalysis data. Among the forcing variables, changes in radiative and sensible/latent heat fluxes caused significant amounts of sensitivities. Differences in sea-ice concentration and thickness were primarily caused by differences in downward shortwave and longwave radiations. 2-m air temperature also has a significant influence on year-to-year variability of the sea ice volume. Differences in precipitation affected the sea ice volume by causing changes in the insulation effect of snow-cover on sea ice. The diversity of sea ice extent and thickness responses due to uncertainties in atmospheric variables highlights the need to carefully evaluate reanalysis data over the Arctic region.

scholarly journals Arctic Sea Ice Retreat in 2007 Follows Thinning Trend

2009 ◽  
Vol 22 (1) ◽  
pp. 165-176 ◽  
Author(s):  
R. W. Lindsay ◽  
J. Zhang ◽  
A. Schweiger ◽  
M. Steele ◽  
H. Stern
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Extent ◽  
Pacific Side ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract The minimum of Arctic sea ice extent in the summer of 2007 was unprecedented in the historical record. A coupled ice–ocean model is used to determine the state of the ice and ocean over the past 29 yr to investigate the causes of this ice extent minimum within a historical perspective. It is found that even though the 2007 ice extent was strongly anomalous, the loss in total ice mass was not. Rather, the 2007 ice mass loss is largely consistent with a steady decrease in ice thickness that began in 1987. Since then, the simulated mean September ice thickness within the Arctic Ocean has declined from 3.7 to 2.6 m at a rate of −0.57 m decade−1. Both the area coverage of thin ice at the beginning of the melt season and the total volume of ice lost in the summer have been steadily increasing. The combined impact of these two trends caused a large reduction in the September mean ice concentration in the Arctic Ocean. This created conditions during the summer of 2007 that allowed persistent winds to push the remaining ice from the Pacific side to the Atlantic side of the basin and more than usual into the Greenland Sea. This exposed large areas of open water, resulting in the record ice extent anomaly.

scholarly journals Multidecadal Arctic sea ice thickness and volume derived from ice age

2020 ◽  
Vol 14 (4) ◽  
pp. 1325-1345 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key ◽  
Xuanji Wang ◽  
Mark Tschudi
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Sea ice is a key component of the Arctic climate system, and has impacts on global climate. Ice concentration, thickness, and volume are among the most important Arctic sea ice parameters. This study presents a new record of Arctic sea ice thickness and volume from 1984 to 2018 based on an existing satellite-derived ice age product. The relationship between ice age and ice thickness is first established for every month based on collocated ice age and ice thickness from submarine sonar data (1984–2000) and ICESat (2003–2008) and an empirical ice growth model. Based on this relationship, ice thickness is derived for the entire time period from the weekly ice age product, and the Arctic monthly sea ice volume is then calculated. The ice-age-based thickness and volume show good agreement in terms of bias and root-mean-square error with submarine, ICESat, and CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in February–March and October–November. More detailed comparisons with independent data from Envisat for 2003 to 2010 and CryoSat-2 from CPOM, AWI, and NASA GSFC (Goddard Space Flight Center) for 2011 to 2018 show low bias in ice-age-based thickness. The ratios of the ice volume uncertainties to the mean range from 21 % to 29 %. Analysis of the derived data shows that the ice-age-based sea ice volume exhibits a decreasing trend of −411 km3 yr−1 from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting the sea ice volume trends, changes in sea ice thickness contribute more than changes in sea ice area, with a contribution of at least 80 % from changes in sea ice thickness from November to May and nearly 50 % in August and September, while less than 30 % is from changes in sea ice area in all months.

scholarly journals Dynamics and Thermodynamics of the Mean Transpolar Drift and Ice Thickness in the Arctic Ocean

2019 ◽  
Vol 32 (24) ◽  
pp. 8449-8463 ◽  
Author(s):  
Michael A. Spall
Keyword(s):  
Heat Loss ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Ice Volume ◽  
The Arctic Ocean ◽  
Western Arctic ◽  
Transpolar Drift ◽  
The Mean ◽  
Ice Regime

Abstract A theory for the mean ice thickness and the Transpolar Drift in the Arctic Ocean is developed. Asymptotic expansions of the ice momentum and thickness equations are used to derive analytic expressions for the leading-order ice thickness and velocity fields subject to wind stress forcing and heat loss to the atmosphere. The theory is most appropriate for the eastern and central Arctic, but not for the region of the Beaufort Gyre subject to anticyclonic wind stress curl. The scale analysis reveals two distinct regimes: a thin ice regime in the eastern Arctic and a thick ice regime in the western Arctic. In the eastern Arctic, the ice drift is controlled by a balance between wind and ocean drag, while the ice thickness is controlled by heat loss to the atmosphere. In contrast, in the western Arctic, the ice thickness is determined by a balance between wind and internal ice stress, while the drift is indirectly controlled by heat loss to the atmosphere. The southward flow toward Fram Strait is forced by the across-wind gradient in ice thickness. The basic predictions for ice thickness, heat loss, ice volume, and ice export from the theory compare well with an idealized, coupled ocean–ice numerical model over a wide range of parameter space. The theory indicates that increasing atmospheric temperatures or wind speed result in a decrease in maximum ice thickness and ice volume. Increasing temperatures also result in a decrease in heat loss to the atmosphere and ice export through Fram Strait, while increasing winds drive increased heat loss and ice export.

scholarly journals A Global Atmospheric Analysis Dataset Downscaled from the NCEP–DOE Reanalysis

2012 ◽  
Vol 25 (7) ◽  
pp. 2527-2534 ◽  
Author(s):  
Jung-Eun Kim ◽  
Song-You Hong
Keyword(s):  
Large Scale ◽  
Hydrological Cycle ◽  
Analysis Data ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Department Of Energy ◽  
Quality Analysis ◽  
Resolution Model ◽  
Decadal Trend ◽  
Environmental Prediction

Abstract A global atmospheric analysis dataset is constructed via a spectral nudging technique. The 6-hourly National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis from January 1979 to February 2011 is utilized to force large-scale information, whereas a higher-resolution structure is resolved by a global model with improved physics. The horizontal resolution of the downscaled data is about 100 km, twice that of the NCEP–DOE reanalysis. A comparison of the 31-yr downscaled data with reanalysis data and observations reveals that the downscaled precipitation climatology is improved by correcting inherent biases in the lower-resolution reanalysis, and large-scale patterns are preserved. In addition, it is found that global downscaling is an efficient way to generate high-quality analysis data due to the use of a higher-resolution model with improved physics. The uniqueness of the obtained data lies in the fact that an undesirable decadal trend in the analysis due to a change in the amount of observations used in reanalysis is avoided. As such, a downscaled dataset may be used to investigate changes in the hydrological cycle and related mechanisms.

scholarly journals Recent summer Arctic atmospheric circulation anomalies in a historical perspective

2015 ◽  
Vol 9 (1) ◽  
pp. 53-64 ◽  
Author(s):  
A. Belleflamme ◽  
X. Fettweis ◽  
M. Erpicum
Keyword(s):  
Atmospheric Circulation ◽  
Greenland Ice Sheet ◽  
Arctic Region ◽  
Reanalysis Data ◽  
Circulation Type ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Pressure Area ◽  
Circulation Anomalies ◽  
The Arctic Region

Abstract. A significant increase in the summertime occurrence of a high pressure area over the Beaufort Sea, the Canadian Arctic Archipelago, and Greenland has been observed since the beginning of the 2000s, and particularly between 2007 and 2012. These circulation anomalies are likely partly responsible for the enhanced Greenland ice sheet melt as well as the Arctic sea ice loss observed since 2007. Therefore, it is interesting to analyse whether similar conditions might have happened since the late 19th century over the Arctic region. We have used an atmospheric circulation type classification based on daily mean sea level pressure and 500 hPa geopotential height data from five reanalysis data sets (ERA-Interim, ERA-40, NCEP/NCAR, ERA-20C, and 20CRv2) to put the recent circulation anomalies in perspective with the atmospheric circulation variability since 1871. We found that circulation conditions similar to 2007–2012 have occurred in the past, despite a higher uncertainty of the reconstructed circulation before 1940. For example, only ERA-20C shows circulation anomalies that could explain the 1920–1930 summertime Greenland warming, in contrast to 20CRv2. While the recent anomalies exceed by a factor of 2 the interannual variability of the atmospheric circulation of the Arctic region, their origin (natural variability or global warming) remains debatable.

High interannual variability of sea ice thickness in the Arctic region

Nature ◽  
2003 ◽  
Vol 425 (6961) ◽  
pp. 947-950 ◽  
Author(s):  
Seymour Laxon ◽  
Neil Peacock ◽  
Doug Smith
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Arctic Region ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
The Arctic Region

scholarly journals Snow Cover Characteristics over the Main Russian River Basins as Represented by Reanalyses and Measured Data

2008 ◽  
Vol 47 (6) ◽  
pp. 1819-1833 ◽  
Author(s):  
V. Khan ◽  
L. Holko ◽  
K. Rubinstein ◽  
M. Breiling
Keyword(s):  
Interannual Variability ◽  
River Basins ◽  
Reanalysis Data ◽  
Measured Data ◽  
Department Of Energy ◽  
Weather Forecasts ◽  
Northern European ◽  
Snow Water ◽  
Russian River ◽  
Environmental Prediction

Abstract Snow water equivalents (SWE) produced by the National Centers for Environmental Prediction–U.S. Department of Energy (NCEP–DOE) and 40-yr European Centre for Medium-Range Weather Forecasts (ERA-40) reanalyses and snow depths (SD) produced by the 25-yr Japanese “JRA-25” reanalysis over the main Russian river basins for 1979–2000 were examined against measured data. The analysis included comparisons of mean basin values and correlation of anomalies, as well as seasonal and interannual variabilities and trends. ERA-40 generally provided better estimates of mean SWE values for river basins than did the NCEP–DOE reanalysis. Mean SD values from the JRA-25 reanalysis were systematically underestimated. The best correlations among the anomalies were given by ERA-40, followed by JRA-25. All reanalyses reproduced seasonal variability well, although the differences in absolute values varied substantially. The highest differences were typically connected with the snowmelt period (April and May). Interannual variability confirmed the errors of ERA-40 and JRA-25 in 1992–94 and 1979–83, respectively. Otherwise, the reproduction of the interannual variability of SWE and SD was reasonable. Strong biases in SD data from JRA-25 that decrease with time induce artificial positive trends. Significant underestimations of SWE data by ERA-40 for 1991–94 influenced the values of the trends. NCEP–DOE reasonably represented the trend found in measured data. In general, the highest discrepancies between measured and reanalysis data were found for the northern European and eastern Asian rivers (Pechora, Lena, and Amur). The assessment of the quality of SWE and SD reanalysis data can help potential users in the selection of a particular reanalysis as being appropriate to the purpose of their studies.

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.

Sign in / Sign up

Export Citation Format

Share Document