Observationally constrained multi-model atmospheric response to future Arctic sea ice loss 

2021 ◽  
Author(s):  
Doug Smith ◽  
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
Arctic Sea Ice ◽  
Model Intercomparison ◽  
Scientific Debate ◽  
Model Response ◽  
Arctic Sea ◽  
Intercomparison Project ◽  
Constrained Model ◽  
Arctic Sea Ice Loss

<p>The possibility that Arctic sea ice loss could weaken mid-latitude westerlies and promote more severe cold winters has sparked more than a decade of scientific debate, with support from observations but inconclusive modelling evidence. Here we analyse a large multi-model ensemble of coordinated experiments from the Polar Amplification Model Intercomparison Project and find that the modelled response is proportional to the simulated eddy momentum feedback, and that this is underestimated in all models. Hence, we derive an observationally constrained model response showing a modest weakening of mid-latitude tropospheric and stratospheric winds, an equatorward shift of the Atlantic and Pacific storm tracks, and a negative North Atlantic Oscillation. Although our constrained response is consistent with observed relationships which have weakened recently, we caution that emergent constraints may only provide a lower bound.</p>

Greenhouse-gas contribution to Arctic sea-ice loss

2021 ◽  
Author(s):  
Yeon-Hee Kim ◽  
Seung-Ki Min
Keyword(s):  
Sea Ice ◽  
Greenhouse Gas ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Detection Analysis ◽  
External Forcings ◽  
Arctic Sea ◽  
Intercomparison Project ◽  
Arctic Sea Ice Loss

<p>Arctic sea-ice area (ASIA) has been declining rapidly throughout the year during recent decades, but a formal quantification of greenhouse gas (GHG) contribution remains limited. This study conducts an attribution analysis of the observed ASIA changes from 1979 to 2017 by comparing three satellite observations with the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations using an optimal fingerprint method. The observed ASIA exhibits overall decreasing trends across all months with stronger trends in warm seasons. CMIP6 anthropogenic plus natural forcing (ALL) simulations and GHG-only forcing simulations successfully capture the observed temporal trend patterns. Results from detection analysis show that ALL signals are detected robustly for all calendar months for three observations. It is found that GHG signals are detectable in the observed ASIA decrease throughout the year, explaining most of the ASIA reduction, with a much weaker contribution by other external forcings. We additionally find that the Arctic Ocean will occur ice-free in September around the 2040s regardless of the emission scenario.</p>

Transient climate response to Arctic sea-ice loss with two ice-constraining methods

2021 ◽  
pp. 1-50
Author(s):  
Amélie Simon ◽  
Guillaume Gastineau ◽  
Claude Frankignoul ◽  
Clément Rousset ◽  
Francis Codron
Keyword(s):  
Thermal Conductivity ◽  
Sea Ice ◽  
North Atlantic ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
The North ◽  
Arctic Sea ◽  
The North Atlantic ◽  
Arctic Sea Ice Loss

AbstractThe impact of Arctic sea-ice loss on the ocean and atmosphere is investigated focusing on a gradual reduction of Arctic sea-ice by 20% on annual mean, occurring within 30 years, starting from present-day conditions. Two ice-constraining methods are explored to melt Arctic sea-ice in a coupled climate model, while keeping present-day conditions for external forcing. The first method uses a reduction of sea-ice albedo, which modifies the incoming surface shortwave radiation. The second method uses a reduction of thermal conductivity, which changes the heat conduction flux inside ice. Reduced thermal conductivity inhibits oceanic cooling in winter and sea-ice basal growth, reducing seasonality of sea-ice thickness. For similar Arctic sea-ice area loss, decreasing the albedo induces larger Arctic warming than reducing the conductivity, especially in spring. Both ice-constraining methods produce similar climate impacts, but with smaller anomalies when reducing the conductivity. In the Arctic, the sea-ice loss leads to an increase of the North Atlantic water inflow in the Barents Sea and Eastern Arctic, while the salinity decreases and the gyre intensifies in the Beaufort Sea. In the North Atlantic, the subtropical gyre shifts southward and the Atlantic meridional overturning circulation weakens. A dipole of sea-level pressure anomalies sets up in winter over Northern Siberia and the North Atlantic, which resembles the negative phase of the North Atlantic Oscillation. In the tropics, the Atlantic Intertropical Convergence Zone shifts southward as the South Atlantic Ocean warms. In addition, Walker circulation reorganizes and the Southeastern Pacific Ocean cools.

scholarly journals Response of Northern Hemisphere weather and climate to Arctic sea ice decline: Resolution independence in Polar Amplification Model Intercomparison Project (PAMIP) simulations

2021 ◽  
pp. 1-39
Author(s):  
Jan Streffing ◽  
Tido Semmler ◽  
Lorenzo Zampieri ◽  
Thomas Jung
Keyword(s):  
Sea Ice ◽  
Horizontal Resolution ◽  
Arctic Sea Ice ◽  
Model Intercomparison ◽  
Polar Amplification ◽  
Sea Ice Decline ◽  
Arctic Sea ◽  
Weather And Climate ◽  
Intercomparison Project ◽  
The Impact

AbstractThe impact of Arctic sea ice decline on the weather and climate in mid-latitudes is still much debated, with observation suggesting a strong and models a much weaker link. In this study, we use the atmospheric model OpenIFS, in a set of model experiments following the protocol outlined in the Polar Amplification Model Intercomparison Project (PAMIP), to investigate whether the simulated atmospheric response to future changes in Arctic sea ice fundamentally depends on model resolution. More specifically, we increase the horizontal resolution of the model from 125km to 39km with 91 vertical levels; in a second step resolution is further increased to 16km with 137 levels in the vertical. The model does produce a response to sea ice decline with a weaker mid latitude Atlantic jet and increased blocking in the high latitude Atlantic, but no sensitivity to resolution can be detected with 100 members. Furthermore we find that the ensemble convergence toward the mean is not impacted by the model resolutions considered here.

Response of Northern Hemisphere weather and climate to Arctic sea ice decline: The role of resolution in Polar Amplification Model Intercomparison Project (PAMIP) simulations

2021 ◽  
Author(s):  
Jan Streffing ◽  
Tido Semmler ◽  
Lorenzo Zampieri ◽  
Thomas Jung
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Atmospheric Response ◽  
Model Intercomparison ◽  
Polar Amplification ◽  
Sea Ice Decline ◽  
Arctic Sea ◽  
Weather And Climate ◽  
Intercomparison Project ◽  
The Mean

<p>The impact of Arctic sea ice decline on the weather and climate in mid-latitudes is still much debated, with observation suggesting a strong and models a much weaker link. In this study, we use the atmospheric model OpenIFS, in a set of model experiments following the protocol outlined in the Polar Amplification Model Intercomparison Project (PAMIP), to investigate whether the simulated atmospheric response to future changes in Arctic sea ice fundamentally depends on model resolution. More specifically, we increase the horizontal resolution of the model from 125km to 39km with 91 vertical levels; in a second step resolution is further increased to 16km with 137 levels in the vertical. We find that neither the mean atmospheric response nor the ensemble convergence toward the mean are strongly impacted by the model resolutions considered here.</p>

scholarly journals The Polar Amplification Model Intercomparison Project (PAMIP) contribution to CMIP6: investigating the causes and consequences of polar amplification

2018 ◽  
Author(s):  
Doug M. Smith ◽  
James A. Screen ◽  
Clara Deser ◽  
Judah Cohen ◽  
John C. Fyfe ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
Model Intercomparison ◽  
Physical Processes ◽  
Polar Amplification ◽  
Arctic Sea ◽  
Intercomparison Project

Abstract. Polar amplification – the phenomenon that external radiative forcing produces a larger change in surface temperature at high latitudes than the global average – is a key aspect of anthropogenic climate change, but its causes and consequences are not fully understood. The Polar Amplification Model Intercomparison Project (PAMIP) contribution to the Sixth Coupled Model Intercomparison Project (CMIP6, Eyring et al. 2016) seeks to improve our understanding of this phenomenon through a coordinated set of numerical model experiments documented here. In particular, PAMIP will address the following primary questions: 1. What are the relative roles of local sea ice and remote sea surface temperature changes in driving polar amplification? 2. How does the global climate system respond to changes in Arctic and Antarctic sea ice? These issues will be addressed with multi-model simulations that are forced with different combinations of sea ice and/or sea surface temperatures representing present day, pre-industrial and future conditions. The use of three time periods allows the signals of interest to be diagnosed in multiple ways. Lower priority tier experiments are proposed to investigate additional aspects and provide further understanding of the physical processes. These experiments will address the following specific questions: What role does ocean-atmosphere coupling play in the response to sea ice? How and why does the atmospheric response to Arctic sea ice depend on the pattern of sea ice forcing? How and why does the atmospheric response to Arctic sea ice depend on the model background state? What are the roles of local sea ice and remote sea surface temperature in polar amplification, and the response to sea ice, over the recent period since 1979? How does the response to sea ice evolve on decadal and longer timescales? A key goal of PAMIP is to determine the real world situation using imperfect climate models. Although the experiments proposed here form a coordinated set, we anticipate a large spread across models. However, this spread will be exploited by seeking emergent constraints in which model uncertainty may be reduced by using an observable quantity that physically explains the inter-model spread. In summary, PAMIP will improve our understanding of the physical processes that drive polar amplification and its global climate impacts, thereby reducing the uncertainties in future projections and predictions of climate change and variability.

scholarly journals Can preferred atmospheric circulation patterns over the North-Atlantic-Eurasian region be associated with arctic sea ice loss?

Polar Science ◽  
2017 ◽  
Vol 14 ◽  
pp. 9-20 ◽  
Author(s):  
Berit Crasemann ◽  
Dörthe Handorf ◽  
Ralf Jaiser ◽  
Klaus Dethloff ◽  
Tetsu Nakamura ◽  
...  
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
Arctic Sea Ice ◽  
Atmospheric Circulation Patterns ◽  
The North ◽  
Circulation Patterns ◽  
Arctic Sea ◽  
The North Atlantic ◽  
Arctic Sea Ice Loss ◽  
Eurasian Region

scholarly journals An Observational Estimate of the Direct Response of the Cold-Season Atmospheric Circulation to the Arctic Sea Ice Loss

2020 ◽  
Vol 33 (9) ◽  
pp. 3863-3882
Author(s):  
Amélie Simon ◽  
Claude Frankignoul ◽  
Guillaume Gastineau ◽  
Young-Oh Kwon
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
North Atlantic ◽  
Cold Season ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Direct Response ◽  
Atlantic Sector ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss

AbstractThe direct response of the cold-season atmospheric circulation to the Arctic sea ice loss is estimated from observed sea ice concentration (SIC) and an atmospheric reanalysis, assuming that the atmospheric response to the long-term sea ice loss is the same as that to interannual pan-Arctic SIC fluctuations with identical spatial patterns. No large-scale relationship with previous interannual SIC fluctuations is found in October and November, but a negative North Atlantic Oscillation (NAO)/Arctic Oscillation follows the pan-Arctic SIC fluctuations from December to March. The signal is field significant in the stratosphere in December, and in the troposphere and tropopause thereafter. However, multiple regressions indicate that the stratospheric December signal is largely due to concomitant Siberian snow-cover anomalies. On the other hand, the tropospheric January–March NAO signals can be unambiguously attributed to SIC variability, with an Iceland high approaching 45 m at 500 hPa, a 2°C surface air warming in northeastern Canada, and a modulation of blocking activity in the North Atlantic sector. In March, a 1°C northern Europe cooling is also attributed to SIC. An SIC impact on the warm Arctic–cold Eurasia pattern is only found in February in relation to January SIC. Extrapolating the most robust results suggests that, in the absence of other forcings, the SIC loss between 1979 and 2016 would have induced a 2°–3°C decade−1 winter warming in northeastern North America and a 40–60 m decade−1 increase in the height of the Iceland high, if linearity and perpetual winter conditions could be assumed.

scholarly journals Evaluation of Arctic sea ice thickness simulated by Arctic Ocean Model Intercomparison Project models

2012 ◽  
Vol 117 (C8) ◽  
pp. n/a-n/a ◽  
Author(s):  
Mark Johnson ◽  
Andrey Proshutinsky ◽  
Yevgeny Aksenov ◽  
An T. Nguyen ◽  
Ron Lindsay ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Model Intercomparison ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Intercomparison Project ◽  
Arctic Sea Ice Thickness

scholarly journals A multi-model CMIP6-PMIP4 study of Arctic sea ice at 127 ka: sea ice data compilation and model differences

2021 ◽  
Vol 17 (1) ◽  
pp. 37-62 ◽  
Author(s):  
Masa Kageyama ◽  
Louise C. Sime ◽  
Marie Sicard ◽  
Maria-Vittoria Guarino ◽  
Anne de Vernal ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
North Atlantic ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Nordic Seas ◽  
Arctic Sea ◽  
The Arctic Ocean ◽  
Intercomparison Project

Abstract. The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×106 km2, compared to 6.46×106 km2 for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×106 km2 for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 % yr−1) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.

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