scholarly journals Thermodynamic processes affecting the winter sea ice changes in the Bering Sea in the Norwegian Earth System Model

2021 ◽  
Author(s):  
Huiling Zou ◽  
Yongqi Gao ◽  
Helene R. Langehaug ◽  
Lei Yu ◽  
Dong Guo
Keyword(s):  
Sea Ice ◽  
Bering Sea ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Bering Sea ◽  
Thermodynamic Processes

Abstract. The Arctic sea ice has changed largely over the last decades and is expected to change in the future. In this study, we assess sea ice changes in the Pacific sector of the Arctic in an Earth System Model. In winter, the first Empirical Orthogonal Function of sea ice concentration in the Pacific sector of the Arctic based on observations are significantly opposite to that in the Atlantic sector during a period from 1976 to 2004, describing 13.4 % of the total Arctic winter sea ice variability. The similar pattern is also confirmed in the Norwegian Earth System Model (NorESM1-M) (15.8 %). Thermodynamics is found to be vital to winter sea ice variability. In this study, we analyze the relationships between some thermodynamical processes (congelation ice, frazil ice, bottom and top ice melting, and conversion of snow to ice) and sea ice changes in the Bering Sea, based on the NorESM1-M coupled climate model results. All these studied thermodynamical processes can influence the variability in winter sea ice concentration and thickness in the Bering Sea. Considering the mean seasonal cycle over the 30-year time period, conversion of snow to ice contributes about 69 % to the increase in sea ice mass during winter in the Bering Sea, and it is thus the main source to the growth of the winter sea ice in NorESM1-M in the Bering Sea. On the interannual time scales, winter sea ice concentration and thickness variability in the Bering Sea are highly related with the studied thermodynamic processes. Among these thermodynamic processes, congelation ice shows the most important effect on the simulated variability in the Bering Sea, especially in the northeastern part.

The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O)

2020 ◽  
Author(s):  
Clara Burgard ◽  
Dirk Notz ◽  
Leif T. Pedersen ◽  
Rasmus T. Tonboe
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Sea Ice Concentration ◽  
Brightness Temperatures ◽  
Observational Uncertainty ◽  
Ice Concentration

<p>The diversity in sea-ice concentration observational estimates retrieved from brightness temperatures measured from space is a challenge for our understanding of past and future sea-ice evolution as it inhibits reliable climate model evaluation and initialisation. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). </p><p>ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarisation from standard output of an Earth System Model to be compared to observations from space at this frequency. We use simple temperature and salinity profiles inside the snow and ice column based on the output of the Earth System Model to compute these brightness temperatures. </p><p>In this study, we evaluate ARC3O by simulating brightness temperatures based on three assimilation runs of the MPI Earth System Model (MPI-ESM) assimilated with three different sea-ice concentration products. We then compare these three sets of simulated brightness temperatures to brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that they differ up to 10 K in the period between October and June, depending on the region and the assimilation run. However, we show that these discrepancies between simulated and observed brightness temperature can be mainly attributed to the underlying observational uncertainty in sea-ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea-ice concentration but also by the melt-pond parametrisation in MPI-ESM, which is not necessarily realistic. </p><p>ARC3O is therefore capable to realistically translate the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialisation, an exciting perspective for further developing this and similar methods.</p>

scholarly journals Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model

2018 ◽  
Vol 70 (1) ◽  
pp. 1-23 ◽  
Author(s):  
M. Kimmritz ◽  
F. Counillon ◽  
C.M. Bitz ◽  
F. Massonnet ◽  
I. Bethke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Ice Model

Aerosols and their impacts on future Arctic climate change under different emission projections in the GISS-E2.1 Earth system model

2021 ◽  
Author(s):  
Ulas Im ◽  
Kostas Tsigaridis ◽  
Gregory S. Faluvegi ◽  
Peter L. Langen ◽  
Joshua P. French ◽  
...  
Keyword(s):  
Sea Ice ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Anthropogenic Aerosols ◽  
Emission Reductions ◽  
Air Temperatures ◽  
The Future ◽  
Emission Projections

<p>In order to study the future aerosol burdens and their radiative and climate impacts over the Arctic (>60 °N), future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model. Different future anthrpogenic emission projections have been used from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases. Results showed that Arctic BC, OC and SO<sub>4</sub><sup>2-</sup> burdens decrease significantly in all simulations following the emission projections, with the CMIP6 ensemble showing larger reductions in Arctic aerosol burdens compared to the Eclipse ensemble. For the 2030-2050 period, both the Eclipse Current Legislation (CLE) and the Maximum Feasible Reduction (MFR) ensembles simulated an aerosol top of the atmosphere (TOA) forcing of -0.39±0.01 W m<sup>-2</sup>, of which -0.24±0.01 W m<sup>-2</sup> were attributed to the anthropogenic aerosols. The CMIP6 SSP3-7.0 scenario simulated a TOA aerosol forcing of -0.35 W m<sup>-2</sup> for the same period, while SSP1-2.6 and SSP2-4.5 scenarios simulated a slightly more negative TOA forcing (-0.40 W m<sup>-2</sup>), of which the anthropogenic aerosols accounted for -0.26 W m<sup>-2</sup>. The 2030-2050 mean surface air temperatures are projected to increase by 2.1 °C and 2.4 °C compared to the 1990-2010 mean temperature according to the Eclipse CLE and MFR ensembles, respectively, while the CMIP6 simulation calculated an increase of 1.9 °C (SSP1-2.6) to 2.2 °C (SSP3-7.0). Overall, results show that even the scenarios with largest emission reductions lead to similar impact on the future Arctic surface air temperatures compared to scenarios with smaller emission reductions, while scenarios with no or little mitigation leads to much larger sea-ice loss, implying that even though the magnitude of aerosol reductions lead to similar responses in surface air temperatures, high mitigation of aerosols are still necessary to limit sea-ice loss. </p>

scholarly journals Comparison of sea ice kinematics at different resolutions modeled with a grid hierarchy in the Community Earth System Model (version 1.2.1)

2021 ◽  
Vol 14 (1) ◽  
pp. 603-628
Author(s):  
Shiming Xu ◽  
Jialiang Ma ◽  
Lu Zhou ◽  
Yan Zhang ◽  
Jiping Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Small Scale ◽  
Earth System ◽  
Atmospheric Circulation Patterns ◽  
Community Earth System Model ◽  
Climate Studies

Abstract. High-resolution sea ice modeling is becoming widely available for both operational forecasts and climate studies. In traditional Eulerian grid-based models, small-scale sea ice kinematics represent the most prominent feature of high-resolution simulations, and with rheology models such as viscous–plastic (VP) and Maxwell elasto-brittle (MEB), sea ice models are able to reproduce multi-fractal sea ice deformation and linear kinematic features that are seen in high-resolution observational datasets. In this study, we carry out modeling of sea ice with multiple grid resolutions by using the Community Earth System Model (CESM) and a grid hierarchy (22, 7.3, and 2.4 km grid stepping in the Arctic). By using atmospherically forced experiments, we simulate consistent sea ice climatology across the three resolutions. Furthermore, the model reproduces reasonable sea ice kinematics, including multi-fractal spatial scaling of sea ice deformation that partially depends on atmospheric circulation patterns and forcings. By using high-resolution runs as references, we evaluate the model's effective resolution with respect to the statistics of sea ice kinematics. Specifically, we find the spatial scale at which the probability density function (PDF) of the scaled sea ice deformation rate of low-resolution runs matches that of high-resolution runs. This critical scale is treated as the effective resolution of the coarse-resolution grid, which is estimated to be about 6 to 7 times the grid's native resolution. We show that in our model, the convergence of the elastic–viscous–plastic (EVP) rheology scheme plays an important role in reproducing reasonable kinematics statistics and, more strikingly, simulates systematically thinner sea ice than the standard, non-convergent experiments in landfast ice regions of the Canadian Arctic Archipelago. Given the wide adoption of EVP and subcycling settings in current models, it highlights the importance of EVP convergence, especially for climate studies and projections. The new grids and the model integration in CESM are openly provided for public use.

scholarly journals Multi-Scale Sea Ice Kinematics Modeling with a Grid Hierarchy in Community Earth System Model (version 1.2.1)

2020 ◽  
Author(s):  
Shiming Xu ◽  
Jialiang Ma ◽  
Lu Zhou ◽  
Yan Zhang ◽  
Jiping Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Multi Scale ◽  
Kinematics Modeling ◽  
Community Earth System Model ◽  
Climate Studies

Abstract. High-resolution sea ice modeling is becoming widely available for both operational forecasts and climate studies. Sea ice kinematics is the most prominent feature of high-resolution simulations, and with rheology models such as Viscous-Plastic, current models are able to reproduce multi-fractality and linear kinematic features in satellite observations. In this study, we carry out multi-scale sea ice modeling with Community Earth System Model (CESM) by using a grid hierarchy (22 km, 7.3 km, and 2.5 km grid stepping in the Arctic). By using atmospherically forced experiments, we simulate consistent sea ice climatology across the 3 resolutions. Furthermore, the model reproduces reasonable sea ice kinematics, including multi-fractal deformation and scaling properties that are temporally changing and dependent on circulation patterns and forcings (e.g., Arctic Oscillation). With the grid hierarchy, we are able to evaluate the model's effective spatial resolution regarding the statistics of kinematics, which is estimated to be about 6 to 7 times that of the grid's native resolution. Besides, we show that in our model, the convergence of the Elastic-Viscous-Plastic (EVP) rheology scheme plays an important role in reproducing reasonable kinematics statistics, and more strikingly, simulates systematically thinner sea ice than the standard, non-convergent experiments in landfast ice regions of Canadian Arctic Archipelago. Given the wide adoption of EVP and subcycling settings in current models, it highlights the importance of EVP convergence especially for climate studies and projections. The new grids and the model integration in CESM are openly provided for public use.

The Interplay of Recent Vegetation and Sea Ice Dynamics—Results From a Regional Earth System Model Over the Arctic

2020 ◽  
Vol 47 (6) ◽  
Author(s):  
W. Zhang ◽  
R. Döscher ◽  
T. Koenigk ◽  
P.A. Miller ◽  
C. Jansson ◽  
...  
Keyword(s):  
Sea Ice ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Ice Dynamics ◽  
Sea Ice Dynamics

Sensitivity of organic carbon fluxes from Arctic coastal erosion to climate change

2021 ◽  
Author(s):  
David Marcolino Nielsen ◽  
Patrick Pieper ◽  
Victor Brovkin ◽  
Paul Overduin ◽  
Tatiana Ilyina ◽  
...  
Keyword(s):  
Climate Change ◽  
Organic Carbon ◽  
Sea Ice ◽  
Coastal Erosion ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Carbon Release ◽  
Arctic Coast

<p>When unprotected by sea-ice and exposed to the warm air and ocean waves, the Arctic coast erodes and releases organic carbon from permafrost to the surrounding ocean and atmosphere. This release is estimated to deliver similar amounts of organic carbon to the Arctic Ocean as all Arctic rivers combined, at the present-day climate. Depending on the degradation pathway of the eroded material, the erosion of the Arctic coast could represent a positive feedback loop in the climate system, to an extent still unknown. In addition, the organic carbon flux from Arctic coastal erosion is expected to increase in the future, mainly due to surface warming and sea-ice loss. In this work, we aim at addressing the following questions: How is Arctic coastal erosion projected to change in the future? How sensitive is Arctic coastal erosion to climate change?</p><p>To address these questions, we use a 10-member ensemble of climate change simulations performed with the Max Planck Institute Earth System Model (MPI-ESM) for the Coupled Model Intercomparison Project phase 6 (CMIP6) to make projections of coastal erosion at a pan-Arctic scale. We use a semi-empirical approach to model Arctic coastal erosion, assuming a linear contribution of its thermal and mechanical drivers. The pan-Arctic carbon release due to coastal erosion is projected to increase from 6.9 ± 5.4 TgC/year (mean estimate ± two standard deviations from the distribution of uncertainties) during the historical period (mean over 1850 -1950) to between 13.1 ± 6.7 TgC/year and 17.2 ± 8.2 TgC/year in the period 2081-2100 following an intermediate (SSP2.4-5) and a high-end (SSP5.8-5) climate change scenario, respectively. The sensitivity of the organic carbon release from Arctic coastal erosion to climate warming is estimated to range from 1.52 TgC/year/K to 2.79 TgC/year/K depending on the scenario. Our results present the first projections of Arctic coastal erosion, combining observations and Earth system model (ESM) simulations. This allows us to make first-order estimates of sensitivity and feedback magnitudes between Arctic coastal erosion and climate change, which can lay out pathways for future coupled ESM simulations.</p><p> </p>

Assessment of decadal variability in sea ice in the Community Earth System Model against a long-term regional observational record: implications for the predictability of an ice-free Arctic

2021 ◽  
Author(s):  
Amélie Desmarais ◽  
Bruno Tremblay
Keyword(s):  
Sea Ice ◽  
Decadal Variability ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Atlantic Sector ◽  
The Pacific ◽  
Community Earth System Model ◽  
Observational Record

AbstractUncertainties in the timing of a seasonal ice cover in the Arctic Ocean depend on model physics and parameterizations, natural variability at decadal timescales and uncertainties in climate scenarios and forcings. We use the Gridded Monthly Sea-Ice Extent and Concentration, 1850 Onward product to assess the simulated decadal variability from the Community Earth System Model – Large Ensemble (CESM-LE) in the Pacific, Eurasian and Atlantic sector of the Arctic where a longer observational record exists. Results show that sea-ice decadal (8-16 years) variability in CESM-LE is in agreement with the observational record in the Pacific sector of the Arctic, underestimated in the Eurasian sector of the Arctic, specifically in the East-Siberian Sea, and slightly overestimated in the Atlantic sector of the Arctic, specifically in the Greenland Sea. Results also show an increase in variability at decadal timescales in the Eurasian and Pacific sectors during the transition to a seasonally ice-free Arctic, in agreement with the observational record although this increase is delayed by 10-20 years. If the current sea-ice retreat in the Arctic continues to be Pacific-centric, results from the CESM-LE suggest that uncertainty in the timing of an ice-free Arctic associated with natural variability is realistic, but that a seasonal ice cover may occur earlier than projected.

scholarly journals The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation

2020 ◽  
Author(s):  
Clara Burgard ◽  
Dirk Notz ◽  
Leif T. Pedersen ◽  
Rasmus T. Tonboe
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Climate Model ◽  
System Model ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Brightness Temperatures ◽  
Climate Model Evaluation ◽  
Observational Uncertainty ◽  
Ice Concentration

Abstract. The observational uncertainty in sea-ice-concentration estimates from remotely-sensed passive-microwave brightness temperatures is a challenge for reliable climate model evaluation and initialization. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarisation from standard output of an Earth System Model. We evaluate ARC3O by simulating brightness temperatures based on three assimilation runs of the MPI Earth System Model (MPI-ESM) assimilated with three different sea-ice concentration products. We then compare these three sets of simulated brightness temperatures to brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that they differ up to 10 K in the period between October and June, depending on the region and the assimilation run. However, we show that these discrepancies between simulated and observed brightness temperature can be mainly attributed to the underlying observational uncertainty in sea-ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea-ice concentration but also by the melt-pond parametrization in MPI-ESM, which is not necessarily realistic. ARC3O is therefore capable to realistically translate the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialization.

scholarly journals Arctic sea ice a major determinant in Mandt's black guillemot movement and distribution during non-breeding season

2016 ◽  
Vol 12 (9) ◽  
pp. 20160275 ◽  
Author(s):  
G. J. Divoky ◽  
D. C. Douglas ◽  
I. J. Stenhouse
Keyword(s):  
Sea Ice ◽  
Bering Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Breeding Period ◽  
Sea Ice Concentration ◽  
Arctic Alaska ◽  
Black Guillemot ◽  
Arctic Sea ◽  
The Bering Sea

Mandt's black guillemot ( Cepphus grylle mandtii ) is one of the few seabirds associated in all seasons with Arctic sea ice, a habitat that is changing rapidly. Recent decreases in summer ice have reduced breeding success and colony size of this species in Arctic Alaska. Little is known about the species' movements and distribution during the nine month non-breeding period (September–May), when changes in sea ice extent and composition are also occurring and predicted to continue. To examine bird movements and the seasonal role of sea ice to non-breeding Mandt's black guillemots, we deployed and recovered ( n = 45) geolocators on individuals at a breeding colony in Arctic Alaska during 2011–2015. Black guillemots moved north to the marginal ice zone (MIZ) in the Beaufort and Chukchi seas immediately after breeding, moved south to the Bering Sea during freeze-up in December, and wintered in the Bering Sea January–April. Most birds occupied the MIZ in regions averaging 30–60% sea ice concentration, with little seasonal variation. Birds regularly roosted on ice in all seasons averaging 5 h d −1 , primarily at night. By using the MIZ, with its roosting opportunities and associated prey, black guillemots can remain in the Arctic during winter when littoral waters are completely covered by ice.

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