Multi-aspect Assessment of CMIP6 Models for Arctic Sea Ice Simulation

2020 ◽  
pp. 1-37
Author(s):  
Mengyuan Long ◽  
Lujun Zhang ◽  
Siyu Hu ◽  
Shimeng Qian
Keyword(s):  
Sea Ice ◽  
Temporal Variation ◽  
Spatial Patterns ◽  
Barents Sea ◽  
Linear Trend ◽  
Arctic Sea Ice ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
East Greenland ◽  
Arctic Sea

AbstractThis paper evaluates the ability of 35 models from the 6th phase of the Coupled Model Intercomparison Project (CMIP6) to simulate Arctic sea ice by comparing simulated results with observation from the aspects of spatial patterns and temporal variation. The simulation ability of each model is also quantified by Taylor score and e score from these two aspects. Results show that biases between observed and simulated Arctic sea ice concentration (SIC) are mainly located in the East Greenland, Barents, Bering Sea and Sea of Okhotsk. The largest difference between the observed and simulated SIC spatial patterns occurs in September. Since the beginning of the 21st century, the ability of most models to simulate summer SIC spatial patterns has decreased. We also find that models with Sea Ice Simulator (SIS) sea-ice component in CMIP6 show a consistent larger positive simulation biases of SIC in the East Greenland and Barents Sea. In addition, for most models, the higher the model resolution is, the better the match between the simulated and observed spatial patterns of winter Arctic SIC is. Furthermore, this paper makes a detailed assessment for temporal variation of Arctic sea ice extent (SIE) with regard to climatological average, seasonal SIE, multi-year linear trend and detrended standard deviation of SIE. The sensitivity of September Arctic SIE to a given change of Arctic surface air temperature (SAT) over 1979-2014 in each model has also been investigated. Most models simulate a smaller loss of September Arctic SIE per degree of warming than observed (1.37×106 km2 K-1).

Mechanisms for and Predictability of a Drastic Reduction in the Arctic Sea Ice: APPOSITE Data with Climate Model MIROC

2019 ◽  
Vol 32 (5) ◽  
pp. 1361-1380 ◽  
Author(s):  
J. Ono ◽  
H. Tatebe ◽  
Y. Komuro
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Drastic Reduction ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Arctic Sea

Abstract The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are investigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the Arctic Ocean interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

scholarly journals A simple approach to providing a more consistent Arctic sea ice extent time series from the 1950s to present

2012 ◽  
Vol 6 (6) ◽  
pp. 1359-1368 ◽  
Author(s):  
W. N. Meier ◽  
J. Stroeve ◽  
A. Barrett ◽  
F. Fetterer
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Linear Trend ◽  
Statistical Significance ◽  
Recent Decade ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Combined Time Series

Abstract. Observations from passive microwave satellite sensors have provided a continuous and consistent record of sea ice extent since late 1978. Earlier records, compiled from ice charts and other sources exist, but are not consistent with the satellite record. Here, a method is presented to adjust a compilation of pre-satellite sources to remove discontinuities between the two periods and create a more consistent combined 59-yr time series spanning 1953–2011. This adjusted combined time series shows more realistic behavior across the transition between the two individual time series and thus provides higher confidence in trend estimates from 1953 through 2011. The long-term time series is used to calculate linear trend estimates and compare them with trend estimates from the satellite period. The results indicate that trends through the 1960s were largely positive (though not statistically significant) and then turned negative by the mid-1970s and have been consistently negative since, reaching statistical significance (at the 95% confidence level) by the late 1980s. The trend for September (when Arctic extent reaches its seasonal minimum) for the satellite period, 1979–2011 is −12.9% decade−1, nearly double the 1953–2011 trend of −6.8% decade−1 (percent relative to the 1981–2010 mean). The recent decade (2002–2011) stands out as a period of persistent decline in ice extent. The combined 59-yr time series puts the strong observed decline in the Arctic sea ice cover during 1979–2011 in a longer-term context and provides a useful resource for comparisons with historical model estimates.

scholarly journals Model-data comparison and data assimilation of mid-Holocene Arctic sea-ice concentration

2013 ◽  
Vol 9 (6) ◽  
pp. 6515-6549 ◽  
Author(s):  
F. Klein ◽  
H. Goosse ◽  
A. Mairesse ◽  
A. de Vernal
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Atmospheric Circulation ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Model Data ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration

Abstract. The consistency between a new quantitative reconstruction of Arctic sea-ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The comparison shows that the simulated sea-ice changes are weaker and spatially more homogeneous than the recorded ones. Furthermore, although the model-data agreement is relatively good in some regions such as the Labrador Sea, the skill of the models at local scale is low. The response of the models follows mainly the increase in summer insolation at large scale. This is modulated by changes in atmospheric circulation leading to differences between regions in the models that are albeit smaller than in the reconstruction. Performing simulations with data assimilation using the model LOVECLIM amplifies those regional differences, mainly through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin of the Arctic. This leads to an increase in the ice concentration in the Barents and Chukchi Seas and a better agreement with the reconstructions. This underlines the potential role of atmospheric circulation to explain the reconstructed changes during the Holocene.

scholarly journals Seasonal Prediction from Arctic Sea Surface Temperatures: Opportunities and Pitfalls

2018 ◽  
Vol 31 (20) ◽  
pp. 8197-8210 ◽  
Author(s):  
Erik W. Kolstad ◽  
Marius Årthun
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
Sea Ice Extent ◽  
Norwegian Sea ◽  
The Barents Sea ◽  
Arctic Sea ◽  
Lagged Correlation ◽  
Sst Anomalies

Arctic sea ice extent and sea surface temperature (SST) anomalies have been shown to be skillful predictors of weather anomalies in the midlatitudes on the seasonal time scale. In particular, below-normal sea ice extent in the Barents Sea in fall has sometimes preceded cold winters in parts of Eurasia. Here we explore the potential for predicting seasonal surface air temperature (SAT) anomalies in Europe from seasonal SST anomalies in the Nordic seas throughout the year. First, we show that fall SST anomalies not just in the Barents Sea but also in the Norwegian Sea have the potential to predict wintertime SAT anomalies in Europe. Norwegian Sea SST anomalies in spring are also significant predictors of European SAT anomalies in summer. Second, we demonstrate that the potential for prediction is sensitive to trends in the data. In particular, the lagged correlation between Norwegian Sea SST anomalies in spring and European SAT anomalies in summer is considerably higher for raw data than linearly detrended data, largely due to warming SST and SAT trends in recent decades. Third, we show that the potential for prediction has not been stationary in time. One key result is that, according to two twentieth-century reanalyses, the strength of the negative lagged correlation between Barents Sea SST anomalies in fall and European SAT anomalies in winter after 1979 is unprecedented since 1900.

scholarly journals How does sea ice influence δ<sup>18</sup>O of Arctic precipitation?

2016 ◽  
Author(s):  
Anne-Katrine Faber ◽  
Bo Møllesøe Vinther ◽  
Jesper Sjolte ◽  
Rasmus Anker Pedersen
Keyword(s):  
Sea Ice ◽  
Ice Cover ◽  
Research Community ◽  
Arctic Sea Ice ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Atmospheric Research ◽  
Community Atmosphere Model ◽  
Arctic Sea ◽  
Ice Concentration

Abstract. This study investigates how variations in Arctic sea ice cover influence δ18O of presentday Arctic precipitation. This is done using the model isoCAM3, an isotope-equipped version of the National Center for Atmospheric Research Community Atmosphere Model version 3. Four sensitivity experiments and one control simulation are performed with prescribed SSTs and sea ice. Each of 5 the four experiments simulates the atmospheric and isotopic response to Arctic oceanic conditions for selected years after the beginning of the satellite era in 1979. Results show that δ18O of precipitation is sensitive to local changes of sea ice concentration. Reduced sea ice extent yields more enriched isotope values while increased sea ice extent yields more depleted isotope values. The configuration of the sea ice cover is essential for the spatial distribution 10 of the simulated changes in δ18O. The experiments of this study show no changes of δ18O for central Greenland. However, this does not exclude that simulations based on other sea ice configurations might yield changes in Greenland δ18O.

scholarly journals Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System

2017 ◽  
Vol 30 (21) ◽  
pp. 8429-8446 ◽  
Author(s):  
Zhiqiang Chen ◽  
Jiping Liu ◽  
Mirong Song ◽  
Qinghua Yang ◽  
Shiming Xu
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Forecast System ◽  
Climate Forecast System ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Concentration

Here sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 satellites are assimilated in the National Centers for Environmental Prediction Climate Forecast System using a localized error subspace transform ensemble Kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilation). Assessment with the assimilated satellite products and independent sea ice thickness datasets shows that assimilating sea ice concentration and thickness leads to improved Arctic sea ice prediction. LESTKF-1 improves sea ice forecast initially. The initial improvement gradually diminishes after ~3-week integration for sea ice extent but remains quite steady through the integration for sea ice thickness. Large biases in both the ice extent and thickness in CTL are remarkably reduced through the hindcast in LESTKF-E5. Additional numerical experiments suggest that the hindcast with sea ice thickness assimilation dramatically reduces systematic bias in the predicted ice thickness compared with sea ice concentration assimilation only or without any assimilation, which also benefits the prediction of sea ice extent and concentration due to their covariability. Hence, the corrected state of sea ice thickness would aid in the forecast procedure. Increasing the number of ensemble members or extending the integration period to generate estimates of initial model states and uncertainties seems to have small impacts on sea ice prediction relative to LESTKF-E5.

scholarly journals Seasonal co-variability of surface downwelling longwave radiation for the 1982–2009 period in the Arctic

2017 ◽  
Vol 14 ◽  
pp. 139-143 ◽  
Author(s):  
Mauro Boccolari ◽  
Flavio Parmiggiani
Keyword(s):  
Sea Ice ◽  
Spatial Patterns ◽  
Barents Sea ◽  
Longwave Radiation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Climate Oscillation ◽  
Main Technique ◽  
Downwelling Longwave Radiation

Abstract. Trends and variability of the Arctic sea ice extent depend on various physical processes, including those related to changes in radiative fluxes, which are associated with cloudiness and water vapour and, in turn, with the atmospheric moisture transport over the Arctic. Aim of this work was: (i) to extract seasonal spatial patterns of the co-variability between the sea ice concentration (SIC) and the surface downwelling longwave radiation (SDL) in the Arctic Ocean during the 1982–2009 period; and (ii) to estimate the correlation coefficients between these patterns and the indices associated to some climate oscillation modes (AO, NAO, PNA, PDO and AMO). Maximum Covariance Analysis (MCA) was the main technique used in this study. Among our results, we highlight two areas of maximum co-variability SIC/SDL centered over the Barents Sea in winter and over the Chukchi Sea in summer. In addition, some statistically significant correlations (at 95 %) between the spatial patterns of co-variability and climate oscillation indices were assessed, e.g. with PDO and AMO in November–January, with NAO and AMO in May–July, and with PNA in August–October.

scholarly journals Seasonal and Regional Manifestation of Arctic Sea Ice Loss

2018 ◽  
Vol 31 (12) ◽  
pp. 4917-4932 ◽  
Author(s):  
Ingrid H. Onarheim ◽  
Tor Eldevik ◽  
Lars H. Smedsrud ◽  
Julienne C. Stroeve
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
The Barents Sea ◽  
East Siberian Sea ◽  
Shelf Seas ◽  
Perennial Ice

The Arctic Ocean is currently on a fast track toward seasonally ice-free conditions. Although most attention has been on the accelerating summer sea ice decline, large changes are also occurring in winter. This study assesses past, present, and possible future change in regional Northern Hemisphere sea ice extent throughout the year by examining sea ice concentration based on observations back to 1950, including the satellite record since 1979. At present, summer sea ice variability and change dominate in the perennial ice-covered Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, with the East Siberian Sea explaining the largest fraction of September ice loss (22%). Winter variability and change occur in the seasonally ice-covered seas farther south: the Barents Sea, Sea of Okhotsk, Greenland Sea, and Baffin Bay, with the Barents Sea carrying the largest fraction of loss in March (27%). The distinct regions of summer and winter sea ice variability and loss have generally been consistent since 1950, but appear at present to be in transformation as a result of the rapid ice loss in all seasons. As regions become seasonally ice free, future ice loss will be dominated by winter. The Kara Sea appears as the first currently perennial ice-covered sea to become ice free in September. Remaining on currently observed trends, the Arctic shelf seas are estimated to become seasonally ice free in the 2020s, and the seasonally ice-covered seas farther south to become ice free year-round from the 2050s.

scholarly journals Model–data comparison and data assimilation of mid-Holocene Arctic sea ice concentration

2014 ◽  
Vol 10 (3) ◽  
pp. 1145-1163 ◽  
Author(s):  
F. Klein ◽  
H. Goosse ◽  
A. Mairesse ◽  
A. de Vernal
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Atmospheric Circulation ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
Model Data ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Concentration Changes

Abstract. The consistency between new quantitative reconstructions of Arctic sea ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The response of the models mainly follows the increase in summer insolation, modulated to a limited extent by changes in atmospheric circulation. This leads to differences between regions in the models that are smaller than in the reconstruction. It is, however, impossible to precisely assess the models' skills because the sea ice concentration changes at the mid-Holocene are small in both the reconstructions and the models and of the same order of magnitude as the reconstruction uncertainty. Performing simulations with data assimilation using the model LOVECLIM amplifies the regional differences and improves the model–data agreement as expected. This is mainly achieved through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin, inducing an increase in the ice concentration in the Barents and Chukchi seas. This underlines the potential role of atmospheric circulation in explaining the reconstructed changes during the Holocene.

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.

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