scholarly journals A fully coupled Arctic sea-ice–ocean–atmosphere model (ArcIOAM v1.0) based on C-Coupler2: model description and preliminary results

2021 ◽  
Vol 14 (2) ◽  
pp. 1101-1124
Author(s):  
Shihe Ren ◽  
Xi Liang ◽  
Qizhen Sun ◽  
Hao Yu ◽  
L. Bruno Tremblay ◽  
...  
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Model Description ◽  
Preliminary Results ◽  
Arctic Sea ◽  
Ocean Atmosphere ◽  
Coupling Strategy ◽  
Atmosphere Model

Abstract. The Arctic regional coupled sea-ice–ocean–atmosphere model (ArcIOAM) has been developed to provide reliable Arctic sea ice prediction on seasonal timescales. The description and implementation of ArcIOAM and its preliminary results for the year of 2012 are presented in this paper. In the ArcIOAM configuration, the Community Coupler 2 (C-Coupler2) is used to couple the Arctic sea-ice–oceanic configuration of the MITgcm (Massachusetts Institute of Technology general circulation model) with the Arctic atmospheric configuration of the Polar WRF (Weather Research and Forecasting) model. A scalability test is performed to investigate the parallelization of the coupled model. As the first step toward reliable Arctic seasonal sea ice prediction, ArcIOAM, implemented with two-way coupling strategy along with one-way coupling strategy, is evaluated with respect to available observational data and reanalysis products for the year of 2012. A stand-alone MITgcm run with prescribed atmospheric forcing is performed for reference. From the comparison, all the experiments simulate reasonable evolution of sea ice and ocean states in the Arctic region over a 1-year simulation period. The two-way coupling has better performance in terms of sea ice extent, concentration, thickness and sea surface temperature (SST), especially in summer. This result indicates that sea-ice–ocean–atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution.

scholarly journals A fully coupled Arctic sea ice-ocean-atmosphere model (ArcIOAM v1.0) based on C-Coupler2: model description and preliminary results

2020 ◽  
Author(s):  
Shihe Ren ◽  
Xi Liang ◽  
Qizhen Sun ◽  
Hao Yu ◽  
L. Bruno Tremblay ◽  
...  
Keyword(s):  
Sea Ice ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Model Description ◽  
Preliminary Results ◽  
Comparison Results ◽  
Arctic Sea ◽  
Ocean Atmosphere ◽  
Atmosphere Model

Abstract. The implementation of a new Arctic regional coupled sea ice-ocean-atmosphere model (ArcIOAM) and its preliminary results in the year of 2012 are presented in this paper. A newly developed coupler, C-Coupler2 (the Community Coupler 2), is used to couple the Arctic sea ice-oceanic configuration of the MITgcm (Massachusetts Institute of Technology general circulation model) with the Arctic atmospheric configuration of the Polar WRF (Weather Research and Forecasting) model. ArcIOAM is demonstrated with focus on seasonal simulation of the Arctic sea ice and ocean state in the year of 2012. The results obtained by ArcIOAM, along with the experiment of one-way coupling strategy, are compared with available observational data and reanalysis products. From the comparison, results obtained from two experiments both realistically capture the sea ice and oceanic variables in the Arctic region over a 1-year simulation period. The two-way coupled model has better performance in terms of sea ice extent, concentration, thickness and SST, especially in summer. This indicates that sea ice-ocean-atmosphere interaction takes a crucial role in controlling Arctic summertime sea ice distribution. The coupled model and documentation are available at  https://doi.org/10.5281/zenodo.3742692 (last access: 9 June 2020), and the source code is maintained at  https://github.com/cdmpbp123/Coupled_Atm_Ice_Oce (last access: 7 April 2020).

scholarly journals The Impact of Regional Arctic Sea Ice Loss on Atmospheric Circulation and the NAO

2016 ◽  
Vol 29 (2) ◽  
pp. 889-902 ◽  
Author(s):  
Rasmus A. Pedersen ◽  
Ivana Cvijanovic ◽  
Peter L. Langen ◽  
Bo M. Vinther
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
General Circulation ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Near Surface ◽  
Arctic Sea ◽  
The Impact ◽  
Sea Ice Reduction

Abstract Reduction of the Arctic sea ice cover can affect the atmospheric circulation and thus impact the climate beyond the Arctic. The atmospheric response may, however, vary with the geographical location of sea ice loss. The atmospheric sensitivity to the location of sea ice loss is studied using a general circulation model in a configuration that allows combination of a prescribed sea ice cover and an active mixed layer ocean. This hybrid setup makes it possible to simulate the isolated impact of sea ice loss and provides a more complete response compared to experiments with fixed sea surface temperatures. Three investigated sea ice scenarios with ice loss in different regions all exhibit substantial near-surface warming, which peaks over the area of ice loss. The maximum warming is found during winter, delayed compared to the maximum sea ice reduction. The wintertime response of the midlatitude atmospheric circulation shows a nonuniform sensitivity to the location of sea ice reduction. While all three scenarios exhibit decreased zonal winds related to high-latitude geopotential height increases, the magnitudes and locations of the anomalies vary between the simulations. Investigation of the North Atlantic Oscillation reveals a high sensitivity to the location of the ice loss. The northern center of action exhibits clear shifts in response to the different sea ice reductions. Sea ice loss in the Atlantic and Pacific sectors of the Arctic cause westward and eastward shifts, respectively.

scholarly journals Inter-Relations between the Arctic Sea Ice and the General Circulation of the Atmosphere (Abstract)

1987 ◽  
Vol 9 ◽  
pp. 252-252
Author(s):  
G. Wendler ◽  
M. Jeffries ◽  
Y. Nagashima
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Zonal Circulation ◽  
The Pacific ◽  
Arctic Sea ◽  
Ice Conditions ◽  
The Pacific Ocean ◽  
Opposing Effects

Satellite imagery has substantially improved the quality of sea-Ice observation over the last decades. Therefore, for a 25-year period, a statistical study based on the monthly Arctic sea-ice data and the monthly mean 700 mbar maps of the Northern Hemisphere was carried out to establish the relationships between sea-ice conditions and the general circulation of the atmosphere. It was found that sea-ice conditions have two opposing effects on the zonal circulation intensity, depending on the season. Heavier than normal ice in winter causes stronger than normal zonal circulation in the subsequent months, whereas heavier than normal ice in the summer–fall causes weaker zonal circulation in the subsequent months. Analyzing the two sectors, the Atlantic and Pacific ones separately, a negative correlation was found, which means a heavy ice year in the Atlantic Ocean is normally associated with a light one in the Pacific Ocean and vice versa.

scholarly journals Constraining projections of summer Arctic sea ice

2012 ◽  
Vol 6 (6) ◽  
pp. 1383-1394 ◽  
Author(s):  
F. Massonnet ◽  
T. Fichefet ◽  
H. Goosse ◽  
C. M. Bitz ◽  
G. Philippon-Berthier ◽  
...  
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Thickness Distribution ◽  
Coupled Model ◽  
General Circulation Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Volume Characteristics

Abstract. We examine the recent (1979–2010) and future (2011–2100) characteristics of the summer Arctic sea ice cover as simulated by 29 Earth system and general circulation models from the Coupled Model Intercomparison Project, phase 5 (CMIP5). As was the case with CMIP3, a large intermodel spread persists in the simulated summer sea ice losses over the 21st century for a given forcing scenario. The 1979–2010 sea ice extent, thickness distribution and volume characteristics of each CMIP5 model are discussed as potential constraints on the September sea ice extent (SSIE) projections. Our results suggest first that the future changes in SSIE with respect to the 1979–2010 model SSIE are related in a complicated manner to the initial 1979–2010 sea ice model characteristics, due to the large diversity of the CMIP5 population: at a given time, some models are in an ice-free state while others are still on the track of ice loss. However, in phase plane plots (that do not consider the time as an independent variable), we show that the transition towards ice-free conditions is actually occurring in a very similar manner for all models. We also find that the year at which SSIE drops below a certain threshold is likely to be constrained by the present-day sea ice properties. In a second step, using several adequate 1979–2010 sea ice metrics, we effectively reduce the uncertainty as to when the Arctic could become nearly ice-free in summertime, the interval [2041, 2060] being our best estimate for a high climate forcing scenario.

scholarly journals The Arctic and Antarctic Sea-Ice Area Index Records versus Measured and Modeled Temperature Data

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Nicola Scafetta ◽  
Adriano Mazzarella
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
General Circulation Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Climate Indices ◽  
Area Index ◽  
Antarctic Sea Ice ◽  
Arctic Sea ◽  
The Antarctic

Here we study the Arctic and Antarctic sea-ice area records provided by the National Snow and Ice Data Center (NSIDC). These records reveal an opposite climatic behavior: since 1978 the Arctic sea-ice area index decreased, that is, the region has warmed, while the Antarctic sea-ice area index increased, that is, the region has cooled. During the last 7 years the Arctic sea-ice area has stabilized while the Antarctic sea-ice area has increased at a rate significantly higher than during the previous decades; that is, the sea-ice area of both regions has experienced a positive acceleration. This result is quite robust because it is confirmed by alternative temperature climate indices of the same regions. We also found that a significant 4-5-year natural oscillation characterizes the climate of these sea-ice polar areas. On the contrary, we found that the CMIP5 general circulation models have predicted significant warming in both polar sea regions and failed to reproduce the strong 4-5-year oscillation. Because the CMIP5 GCM simulations are inconsistent with the observations, we suggest that important natural mechanisms of climate change are missing in the models.

scholarly journals Simulated Atmospheric Response to Regional and Pan-Arctic Sea Ice Loss

2017 ◽  
Vol 30 (11) ◽  
pp. 3945-3962 ◽  
Author(s):  
James A. Screen
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Large Scale ◽  
Circulation Model ◽  
Arctic Sea Ice ◽  
Ecological Impacts ◽  
The Arctic ◽  
Stratospheric Polar Vortex ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

Abstract The loss of Arctic sea ice is already having profound environmental, societal, and ecological impacts locally. A highly uncertain area of scientific research, however, is whether such Arctic change has a tangible effect on weather and climate at lower latitudes. There is emerging evidence that the geographical location of sea ice loss is critically important in determining the large-scale atmospheric circulation response and associated midlatitude impacts. However, such regional dependencies have not been explored in a thorough and systematic manner. To make progress on this issue, this study analyzes ensemble simulations with an atmospheric general circulation model prescribed with sea ice loss separately in nine regions of the Arctic, to elucidate the distinct responses to regional sea ice loss. The results suggest that in some regions, sea ice loss triggers large-scale dynamical responses, whereas in other regions sea ice loss induces only local thermodynamical changes. Sea ice loss in the Barents–Kara Seas is unique in driving a weakening of the stratospheric polar vortex, followed in time by a tropospheric circulation response that resembles the North Atlantic Oscillation. For October–March, the largest spatial-scale responses are driven by sea ice loss in the Barents–Kara Seas and the Sea of Okhotsk; however, different regions assume greater importance in other seasons. The atmosphere responds very differently to regional sea ice losses than to pan-Arctic sea ice loss, and the response to pan-Arctic sea ice loss cannot be obtained by the linear addition of the responses to regional sea ice losses. The results imply that diversity in past studies of the simulated response to Arctic sea ice loss can be partly explained by the different spatial patterns of sea ice loss imposed.

scholarly journals Constraining projections of summer Arctic sea ice

2012 ◽  
Vol 6 (4) ◽  
pp. 2931-2959 ◽  
Author(s):  
F. Massonnet ◽  
T. Fichefet ◽  
H. Goosse ◽  
C. M. Bitz ◽  
G. Philippon-Berthier ◽  
...  
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Thickness Distribution ◽  
Coupled Model ◽  
General Circulation Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Volume Characteristics

Abstract. We examine the recent (1979–2010) and future (2011–2100) characteristics of the summer Arctic sea ice cover as simulated by 29 Earth system and general circulation models from the Coupled Model Intercomparison Project, phase 5 (CMIP5). As was the case with CMIP3, a large inter-model spread persists in the simulated summer sea ice losses over the 21st century for a given forcing scenario. The initial 1979–2010 sea ice properties (including the sea ice extent, thickness distribution and volume characteristics) of each CMIP5 model are discussed as potential constraints on the September sea ice extent (SSIE) projections. Our results suggest first that the SSIE anomalies (compared to the 1979–2010 model SSIE) are related in a complicated manner to the initial 1979–2010 sea ice model characteristics, due to the large diversity of the CMIP5 population (at a given time, some models are in an ice-free state while others are still on the track of ice loss). In a new diagram (that does not consider the time as an independent variable) we show that the transition towards ice-free conditions is actually occuring in a very similar manner for all models. For these reasons, some quantities that do not explicitly depend on time, such as the year at which SSIE drops below a certain threshold, are likely to be constrained. In a second step, using several adequate 1979–2010 sea ice metrics, we effectively reduce the uncertainty as to when the Arctic could become nearly ice-free in summertime (between 2041 and 2060 for a high climate forcing scenario).

scholarly journals Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss ?

2021 ◽  
pp. 1-54
Author(s):  
Y. Peings ◽  
Z. Labe ◽  
G. Magnusdottir
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Internal Variability ◽  
General Circulation Models ◽  
Arctic Sea Ice ◽  
Model Assessment ◽  
Atmospheric Response ◽  
Arctic Sea ◽  
Ocean Atmosphere ◽  
Arctic Sea Ice Loss

AbstractThis study presents results from the Polar Amplification Multimodel Intercomparison Project (PAMIP) single-year time-slice experiments that aim to isolate the atmospheric response to Arctic sea ice loss at global warming levels of +2°C. Using two General Circulation Models (GCMs), the ensemble size is increased up to 300 ensemble members, beyond the recommended 100 members. After partitioning the response in groups of 100-ensemble members, the reproducibility of the results is evaluated, with a focus on the response of the mid-latitude jet streams in the North Atlantic and North Pacific. Both atmosphere-only and coupled ocean-atmosphere PAMIP experiments are analyzed. Substantial differences in the mid-latitude response are found among the different experiment subsets, suggesting that 100-member ensembles are still significantly influenced by internal variability, which can mislead conclusions. Despite an overall stronger response, the coupled ocean-atmosphere runs exhibit greater spread due to additional ENSO-related internal variability when the ocean is interactive. The lack of consistency in the response is true for anomalies that are statistically significant according to Student’s-t and False Discovery Rate tests. This is problematic for the multi-model assessment of the response, as some of the spread may be attributed to different model sensitivities while it is due to internal variability. We propose a method to overcome this consistency issue, that allows for more robust conclusions when only 100 ensemble members are used.

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