Contributors to linkage between Arctic warming and East Asian winter climate

AbstractPrevious modelling and observational studies have shown discrepancies in the interannual relationship of winter surface air temperature (SAT) between Arctic and East Asia, stimulating the debate about whether Arctic change can influence midlatitude climate. This study uses two sets of coordinated experiments (EXP1 and EXP2) from six different atmospheric general circulation models. Both EXP1 and EXP2 consist of 130 ensemble members, each of which in EXP1 (EXP2) was forced by the same observed daily varying sea ice and daily varying (daily climatological) sea surface temperature (SST) for 1982–2014 but with different atmospheric initial conditions. Large spread exists among ensemble members in simulating the Arctic–East Asian SAT relationship. Only a fraction of ensemble members can reproduce the observed deep Arctic warming–cold continent pattern which extends from surface to upper troposphere, implying the important role of atmospheric internal variability. The mechanisms of deep Arctic warming and shallow Arctic warming are further distinguished. Arctic warming aloft is caused primarily by poleward moisture transport, which in conjunction with the surface warming coupled with sea ice melting constitutes the surface-amplified deep Arctic warming throughout the troposphere. These processes associated with the deep Arctic warming may be related to the forcing of remote SST when there is favorable atmospheric circulation such as Rossby wave train propagating from the North Atlantic into the Arctic.

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Forecast skill of autumn snow for European winter climate during the 20th century: A multi member seasonal prediction experiment

10.5194/egusphere-egu21-2586 ◽

2021 ◽

Author(s):

Martin Wegmann ◽

Yvan Orsolini ◽

Antje Weisheimer ◽

Bart van den Hurk ◽

Gerrit Lohmann

Keyword(s):

Snow Cover ◽

General Circulation ◽

Initial Conditions ◽

Internal Variability ◽

Seasonal Prediction ◽

General Circulation Models ◽

Reanalysis Data ◽

Winter Climate ◽

European Continent ◽

Near Surface

As the leading climate mode to explain wintertime climate variability over Europe, the North Atlantic Oscillation (NAO) has been extensively studied over the last decades. Recently, studies highlighted the state of the Northern Hemispheric cryosphere as possible predictor for the wintertime NAO (Cohen et al. 2014). Although several studies could find seasonal prediction skill in reanalysis data (Orsolini et al. 2016, Duville et al. 2017,Han & Sun 2018), experiments with ocean-atmosphere general circulation models (AOGCMs) still show conflicting results (Furtado et al. 2015, Handorf et al. 2015, Francis 2017, Gastineau et al. 2017).&#160;Here we use two kinds ECMWF seasonal prediction ensembles starting with November initial conditions taken from the long-term reanalysis ERA-20C and forecasting the following three winter months. Besides the 110-year ensemble of 50 members representing internal variability of the atmosphere, we investigate a second ensemble of 20 members where initial conditions are split between low and high snow cover years for the Northern Hemisphere. We compare two recently used Eurasian snow cover indices for their skill in predicting winter climate for the European continent. Analyzing the two forecast experiments, we found that prediction runs starting with high snow index values in November result in significantly more negative NAO states in the following winter (DJF), which in turn modulates near surface temperatures. We track the atmospheric anomalies triggered by the high snow index through the tropo- and stratosphere as well as for the individual winter months to provide a physical explanation for the formation of this particular climate state.&#160;

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Constraining projections of summer Arctic sea ice

The Cryosphere ◽

10.5194/tc-6-1383-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 1383-1394 ◽

Cited By ~ 158

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.

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

Advances in Meteorology ◽

10.1155/2015/481834 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 1

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.

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Constraining projections of summer Arctic sea ice

The Cryosphere Discussions ◽

10.5194/tcd-6-2931-2012 ◽

2012 ◽

Vol 6 (4) ◽

pp. 2931-2959 ◽

Cited By ~ 15

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).

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The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1

Geoscientific Model Development ◽

10.5194/gmd-9-2255-2016 ◽

2016 ◽

Vol 9 (6) ◽

pp. 2255-2270 ◽

Cited By ~ 20

Author(s):

Jonathan J. Day ◽

Steffen Tietsche ◽

Mat Collins ◽

Helge F. Goessling ◽

Virginie Guemas ◽

...

Keyword(s):

Sea Ice ◽

General Circulation ◽

Southern Oscillation ◽

General Circulation Models ◽

Arctic Sea Ice ◽

Arctic Climate ◽

The Arctic ◽

Initial State ◽

Data Set ◽

Archived Data

Abstract. Recent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state. The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions. We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric. Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño–Southern Oscillation.

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A multimodel investigation of atmospheric mechanisms for driving Arctic amplification in warmer climates

Journal of Climate ◽

10.1175/jcli-d-20-0354.1 ◽

2021 ◽

pp. 1-55

Author(s):

Deepashree Dutta ◽

Steven C. Sherwood ◽

Katrin J. Meissner ◽

Alex Sen Gupta ◽

Daniel J. Lunt ◽

...

Keyword(s):

Heat Transport ◽

General Circulation ◽

Energy Transport ◽

Longwave Radiation ◽

General Circulation Models ◽

The Arctic ◽

Arctic Warming ◽

Surface Warming ◽

Poleward Heat Transport ◽

Warm Climates

AbstractWhen simulating past warm climates, such as the early Cretaceous and Paleogene periods, general circulation models (GCMs) underestimate the magnitude of warming in the Arctic. Additionally, model intercomparisons show a large spread in the magnitude of Arctic warming for these warmer-than-modern climates. Several mechanisms have been proposed to explain these disagreements, including the unrealistic representation of polar clouds or underestimated poleward heat transport in the models. This study provides an intercomparison of Arctic cloud and atmospheric heat transport (AHT) responses to strong imposed polar-amplified surface ocean warming across four atmosphere-only GCMs. All models simulate an increase in high clouds throughout the year; the resulting reduction in longwave radiation loss to space acts to support the imposed Arctic warming. The response of low and mid-level clouds varies considerably across the models, with models responding differently to surface warming and sea ice removal. The AHT is consistently weaker in the imposed warming experiments due to a large reduction in dry static energy transport that offsets a smaller increase in latent heat transport, thereby opposing the imposed surface warming. Our idealised polar amplification experiments require very large increases in implied ocean heat transport (OHT) to maintain steady state. Increased CO2 or tropical temperatures that likely characterised past warm climates, reduces the need for such large OHT increases.

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The impact of denying sea ice information on the predictability of atmospheric processes over the Arctic and at mid-latitude regions

10.5194/egusphere-egu2020-11692 ◽

2020 ◽

Author(s):

Leandro Ponsoni ◽

Daniela Flocco ◽

François Massonnet ◽

Steve Delhaye ◽

Ed Hawkins ◽

...

Keyword(s):

Sea Ice ◽

General Circulation ◽

Model Comparison ◽

General Circulation Models ◽

Arctic Sea Ice ◽

The Arctic ◽

Atmospheric Processes ◽

Ice Conditions ◽

The Impact

In this work, we make use of an inter-model comparison and of a perfect model approach, in which model outputs are used as true reference states, to assess the impact that denying sea ice information has on the prediction of atmospheric processes, both over the Arctic and at mid-latitude regions. To do so, two long-term control runs (longer than 250 years) were generated with two state-of-the-art General Circulation Models (GCM), namely EC-Earth and HadGEM. From these two reference states, we have identified three different years in which the Arctic sea ice volume (SIV) was (i) maximum, (ii) minimum and (iii) a representative case for the mean state. By departing from each of these three dates (not necessarily the same for the two models), we generated a set of experiments in which the control runs are restarted both from original and climatological sea ice conditions. Here, climatological sea ice conditions are estimated as the time-average of sea ice parameters from the respective long-term control runs. The experiments are 1-year long and all of them start in January when ice is still thin, snow depth is small, air-ocean temperatures contrast the most and, therefore, the heat conductive flux in sea ice (at the surface) is nearly maximum. To robustly separate the response to degrading the initial sea ice state from background internal variability, each of the two counterfactual experiments (reference and climatological) consists of 50 ensembles members. Threstatedese ensembles are generated by adding small random perturbations to the sea surface temperature (EC-Earth) or to the air temperature (HadGEM) fields. Preliminary results reinforce the importance of having the right sea ice state for improving the (sub-)seasonal prediction of atmospheric parameters (e.g., 2m-temperature and geopotential) and circulation (e.g., Westerlies and Jet Stream) not only over the Arctic, but also at mid-latitude regions.

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Local and External Moisture Sources for the Arctic Warming over the Barents–Kara Seas

Journal of Climate ◽

10.1175/jcli-d-17-0203.1 ◽

2018 ◽

Vol 31 (5) ◽

pp. 1963-1982 ◽

Cited By ~ 18

Author(s):

Linhao Zhong ◽

Lijuan Hua ◽

Dehai Luo

Keyword(s):

Sea Ice ◽

North Atlantic ◽

High Latitude ◽

Wave Train ◽

Recent Decade ◽

Arctic Sea Ice ◽

The Arctic ◽

Arctic Warming ◽

Moisture Sources ◽

Local Moisture

Water vapor is critical to Arctic sea ice loss and surface air warming, particularly in winter. Whether the local process or poleward transport from lower latitudes can explain the Arctic warming is still a controversial issue. In this work, a hydrological tool, a dynamical recycling model (DRM) based on time-backward Lagrangian moisture tracking, is applied to quantitatively evaluate the relative contributions of local evaporation and external sources to Barents–Kara Seas (BKS) moisture in winter during 1979–2015. On average, the local and external moistures explain 35.4% and 57.3% of BKS moisture, respectively. The BKS, Norwegian Sea, and midlatitude North Atlantic are the three major sources and show significant increasing trends of moisture contribution. The local moisture contribution correlates weakly to downward infrared radiation (IR) but significantly to sea ice variation, which suggests that the recent-decade increase of local moisture contribution is only a manifestation of sea ice melting. In contrast, the external moisture contribution significantly correlates to both downward IR and sea ice variation, thus suggesting that meridional moisture transport mainly explains the recent BKS warming. The moisture contributions due to different sources are governed by distinct circulation patterns. The negative Arctic Oscillation–like pattern suppresses external moisture but favors local evaporation. In the case of dominant external moisture, a well-organized wave train spanning from across the midlatitude Atlantic to mid–high-latitude Eurasia has the mid–high-latitude components similar to a positive-phase North Atlantic Oscillation with a Ural blocking to the east. Moreover, the meridional shift of the wave train pathway and the spatial scale of the wave train anomalies determine the transport passage and strength of the major external moisture sources.

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Ocean Heat Transport as a Cause for Model Uncertainty in Projected Arctic Warming

Journal of Climate ◽

10.1175/2010jcli3713.1 ◽

2011 ◽

Vol 24 (5) ◽

pp. 1451-1460 ◽

Cited By ~ 56

Author(s):

Irina Mahlstein ◽

Reto Knutti

Keyword(s):

Sea Ice ◽

Heat Transport ◽

General Circulation ◽

General Circulation Models ◽

Arctic Sea Ice ◽

The Arctic ◽

Ocean Heat Transport ◽

Sea Ice Extent ◽

Arctic Sea ◽

Future Warming

Abstract The Arctic climate is governed by complex interactions and feedback mechanisms between the atmosphere, ocean, and solar radiation. One of its characteristic features, the Arctic sea ice, is very vulnerable to anthropogenically caused warming. Production and melting of sea ice is influenced by several physical processes. The authors show that the northward ocean heat transport is an important factor in the simulation of the sea ice extent in the current general circulation models. Those models that transport more energy to the Arctic show a stronger future warming, in the Arctic as well as globally. Larger heat transport to the Arctic, in particular in the Barents Sea, reduces the sea ice cover in this area. More radiation is then absorbed during summer months and is radiated back to the atmosphere in winter months. This process leads to an increase in the surface temperature and therefore to a stronger polar amplification. The models that show a larger global warming agree better with the observed sea ice extent in the Arctic. In general, these models also have a higher spatial resolution. These results suggest that higher resolution and greater complexity are beneficial in simulating the processes relevant in the Arctic and that future warming in the high northern latitudes is likely to be near the upper range of model projections, consistent with recent evidence that many climate models underestimate Arctic sea ice decline.

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An energy budget approach to understand the Arctic warming during the Last Interglacial

10.5194/cp-2021-70 ◽

2021 ◽

Author(s):

Marie Sicard ◽

Masa Kageyama ◽

Sylvie Charbit ◽

Pascale Braconnot ◽

Jean-Baptiste Madeleine

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Air Temperature ◽

Energy Budget ◽

Surface Air Temperature ◽

The Arctic ◽

Last Interglacial ◽

Near Surface ◽

Arctic Warming ◽

The Arctic Ocean

Abstract. The Last Interglacial period (129–116 ka BP) is characterized by a strong orbital forcing which leads to a different seasonal and latitudinal distribution of insolation compared to the pre-industrial period. In particular, these changes amplify the seasonality of the insolation in the high latitudes of the northern hemisphere. Here, we investigate the Arctic climate response to this forcing by comparing the CMIP6 lig127k and pi-Control simulations performed with the IPSL-CM6A-LR model. Using an energy budget framework, we analyse the interactions between the atmosphere, ocean, sea ice and continents. In summer, the insolation anomaly reaches its maximum and causes a near-surface air temperature rise of 3.2 °C over the Arctic region. This warming is primarily due to a strong positive surface downwelling shortwave radiation anomaly over continental surfaces, followed by large heat transfers from the continents back to the atmosphere. The surface layers of the Arctic Ocean also receives more energy, but in smaller quantity than the continents due to a cloud negative feedback. Furthermore, while heat exchanges from the continental surfaces towards the atmosphere are strengthened, the ocean absorbs and stores the heat excess due to a decline in sea ice cover. However, the maximum near-surface air temperature anomaly does not peak in summer like insolation, but occurs in autumn with a temperature increase of 4.0 °C relative to the pre-industrial period. This strong warming is driven by a positive anomaly of longwave radiations over the Arctic ocean enhanced by a positive cloud feedback. It is also favoured by the summer and autumn Arctic sea ice retreat (−1.9 × 106 and −3.4 × 106 km2 respectively), which exposes the warm oceanic surface and allows heat stored by the ocean in summer and water vapour to be released. This study highlights the crucial role of the sea ice cover variations, the Arctic ocean, as well as changes in polar clouds optical properties on the Last Interglacial Arctic warming.

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