Changes in Lake Area in the Inner Mongolian Plateau under Climate Change: The Role of the Atlantic Multidecadal Oscillation and Arctic Sea Ice

AbstractThe lake area in the Inner Mongolian Plateau (IMP) has experienced a rapid reduction in recent decades. Previous studies have highlighted the important role of intensive human activities in IMP lake shrinkage. However, this study found that climate change–induced summer precipitation variations can exert great influences on the IMP lake area variations. The results suggest that the decadal shift in the IMP summer precipitation may be the predominant contributor to lake shrinkage. Further analysis reveals that the Atlantic multidecadal oscillation (AMO) and Arctic sea ice concentration (SIC) play important roles in the IMP summer precipitation variations. The AMO seems to provide beneficial large-scale circulation fields for the decadal variations in the IMP summer precipitation, and the Arctic SIC decline is favorable for weakening the IMP summer precipitation intensity after the late 1990s. Evidence indicates that the vorticity advection related to the Arctic SIC decline can result in the generation of Rossby wave resources in the midlatitudes. Then, the strengthened wave resources become favorable for enhancing the stationary wave propagation across Eurasia and inducing cyclonic circulation over the Mongolia–Baikal regions, which might bring more rainfall northward and weaken the IMP summer precipitation intensity. Consequently, due to the decreased rainfall and gradual warming after the late 1990s, the lake area in the IMP has experienced a downward trend in recent years.

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Arctic Sea Ice’s Amplification as a Complement to Anthropogenic Global Warming

10.21203/rs.3.rs-528550/v1 ◽

2021 ◽

Author(s):

Marco Morando

Keyword(s):

Climate Change ◽

Global Warming ◽

Sea Ice ◽

Arctic Sea Ice ◽

The Arctic ◽

Future Climate Change ◽

Number Count ◽

Anthropogenic Global Warming ◽

Arctic Sea ◽

Scientific Subject

Abstract Climate Change is a widely debated scientific subject and Anthropogenic Global Warming is its main cause. Nevertheless, several authors have indicated solar activity and Atlantic Multi-decadal Oscillation variations may also influence Climate Change. This article considers the amplification of solar radiation’s and Atlantic Multi-decadal Oscillation’s variations, via sea ice cover albedo feedbacks in the Arctic regions, providing a conceptual advance in the application of Arctic Amplification for modelling historical climate change. A 1-dimensional physical model, using sunspot number count and Atlantic Multi-decadal Oscillation index as inputs, can simulate the average global temperature’s anomaly and the Arctic Sea Ice Extension for the past eight centuries. This model represents an innovative progress in understanding how existing studies on Arctic sea ice’s albedo feedbacks can help complementing the Anthropogenic Global Warming models, thus helping to define more precise models for future climate change.

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The role of cyclone activity in snow accumulation on Arctic sea ice

Nature Communications ◽

10.1038/s41467-019-13299-8 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 4

Author(s):

M. A. Webster ◽

C. Parker ◽

L. Boisvert ◽

R. Kwok

Keyword(s):

Sea Ice ◽

Snow Accumulation ◽

Arctic Sea Ice ◽

Surface Energy Budget ◽

The Arctic ◽

Cyclone Activity ◽

In Situ Data ◽

Arctic Sea ◽

Spatio Temporal

AbstractIdentifying the mechanisms controlling the timing and magnitude of snow accumulation on sea ice is crucial for understanding snow’s net effect on the surface energy budget and sea-ice mass balance. Here, we analyze the role of cyclone activity on the seasonal buildup of snow on Arctic sea ice using model, satellite, and in situ data over 1979–2016. On average, 44% of the variability in monthly snow accumulation was controlled by cyclone snowfall and 29% by sea-ice freeze-up. However, there were strong spatio-temporal differences. Cyclone snowfall comprised ~50% of total snowfall in the Pacific compared to 83% in the Atlantic. While cyclones are stronger in the Atlantic, Pacific snow accumulation is more sensitive to cyclone strength. These findings highlight the heterogeneity in atmosphere-snow-ice interactions across the Arctic, and emphasize the need to scrutinize mechanisms governing cyclone activity to better understand their effects on the Arctic snow-ice system with anthropogenic warming.

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The role of moisture transport for precipitation in the inter-annual and inter-daily fluctuations of the Arctic sea ice extension

Earth System Dynamics ◽

10.5194/esd-10-121-2019 ◽

2019 ◽

Vol 10 (1) ◽

pp. 121-133 ◽

Cited By ~ 3

Author(s):

Luis Gimeno-Sotelo ◽

Raquel Nieto ◽

Marta Vázquez ◽

Luis Gimeno

Keyword(s):

Sea Ice ◽

Moisture Transport ◽

Arctic Region ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Melting ◽

Sea Ice Extent ◽

Moisture Sources ◽

Arctic Sea

Abstract. By considering the moisture transport for precipitation (MTP) for a target region to be the moisture that arrives in this region from its major moisture sources and which then results in precipitation in that region, we explore (i) whether the MTP from the main moisture sources for the Arctic region is linked with inter-annual fluctuations in the extent of Arctic sea ice superimposed on its decline and (ii) the role of extreme MTP events in the inter-daily change in the Arctic sea ice extent (SIE) when extreme MTP simultaneously arrives from the four main moisture regions that supply it. The results suggest (1) that ice melting at the scale of inter-annual fluctuations against the trend is favoured by an increase in moisture transport in summer, autumn, and winter and a decrease in spring and, (2) on a daily basis, extreme humidity transport increases the formation of ice in winter and decreases it in spring, summer, and autumn; in these three seasons extreme humidity transport therefore contributes to Arctic sea ice melting. These patterns differ sharply from that linked to the decline on a long-range scale, especially in summer when the opposite trend applies, as ice melt is favoured by a decrease in moisture transport for this season at this scale.

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Early Holocene history of the Zachariae Ice Stream, NE Greenland: Evidence from geochemistry, grain size and sedimentary parameters

10.5194/egusphere-egu2020-169 ◽

2020 ◽

Author(s):

Joanna Davies ◽

Anders Møller Mathiase ◽

Christof Pearce ◽

Marit-Solveig Seidenkrantz

Keyword(s):

Climate Change ◽

Grain Size ◽

Sea Ice ◽

Surface Waters ◽

Arctic Sea Ice ◽

Early Holocene ◽

The Arctic ◽

Cold Surface ◽

Sea Ice Extent ◽

Arctic Sea

The Arctic region exhibits some of the most visible signs of climate change globally. Arctic sea ice extent and volume has been declining sharply in recent decades; observations indicate a mean annual decrease of 3.2% since 1980. However, no extensive network of sea ice observations extends back further than the mid-18th century and satellite data since the late 1970s; this limits perspectives of sea ice variability on longer time scales. Thus, to understand the processes governing sea-ice cover and variability, predict how sea ice and ocean conditions will respond to anthropogenic climate change and to understand if the shrinking of Arctic sea ice is a unique and irreversible process, longer records of sea ice variability and oceanic conditions are required.A multi-proxy approach, involving grain size, geochemical, foraminifera and sedimentary analysis, was applied to a marine sediment core from North East Greenland to reconstruct changes in sea ice extent and palaeoceanographic conditions throughout the early Holocene (ca. 12,400-7,800 cal. yrs. BP). The study aimed to improve the understanding of the interaction between ocean circulation, sea ice and fluctuations of the Zachariae Isstr&#248;m (ZI), one of the main glacier outlets of NE Greenland. Four distinct zones have been identified: Zone 1 (12,400-11,600 cal. yrs. BP) covering the transition from the Younger Dryas into the Holocene which evidences a gradually warming climate, resulting in a retreat of the ZI; Zone 2 (11,600 &#8211; 10,300 cal. yrs. BP) which encapsulates two distinct cooling events as a result of cooler surface waters, rapid release of freshwater and local feedback mechanisms. This coincides with sudden re-advances of the ZI followed by gradual retreats; 3) Zone 3 (10,300 &#8211; 8,600 cal. yrs. BP) shows warm and stable conditions, with warm surface waters that resulted in the retreat of the ZI; 4) Zone 4 (8,600 &#8211; 7,800 cal. yrs. BP) which shows a rapid return to cooler conditions, with cold surface waters and rapid freshwater outbursts resulting in the re-advance of the ZI, forced by decreasing solar insolation and cold surface waters. Our investigation thus indicated that changes in oceanic conditions at the NE Greenland shelf had a significant impact on the extent and melting rate of the ZI glacier.

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Linkage between Arctic Sea Ice Area and Atmospheric Blocking Probability over Europe using Logistic Regression Models

10.5194/egusphere-egu21-14736 ◽

2021 ◽

Author(s):

Andy Richling ◽

Uwe Ulbrich ◽

Henning Rust ◽

Johannes Riebold ◽

Dörthe Handorf

Keyword(s):

Climate Change ◽

Sea Ice ◽

Regression Models ◽

Arctic Sea Ice ◽

Arctic Climate ◽

The Arctic ◽

Atmospheric Blocking ◽

Logistic Regression Models ◽

Arctic Sea ◽

Arctic Climate Change

Over the last decades the Arctic climate change has been observed with a much faster warming of the Arctic compared to the global average (Arctic amplification) and related sea-ice retreat. These changes in sea ice can affect the large-scale atmospheric circulation over the mid-latitudes, in particular atmospheric blocking, and thus the frequency and severity of extreme events. As a step towards a better understanding of changes in weather and climate extremes over Central Europe associated with Arctic climate change, we first analyze the linkage between recent Arctic sea ice loss and blocking variability using logistic regression models. ERA5 reanalysis data are used on a monthly and seasonal time scale, and specific regional sea ice variabilities are explored. First results indicate an increased occurrence-probability in terms of blocking frequency over Greenland in summer as well as over Scandinavia/Ural in winter during low sea ice conditions.&#160;

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Voices of the Sea Ice: engaging an Arctic community to communicate impacts of climate change

10.5194/egusphere-egu2020-12661 ◽

2020 ◽

Author(s):

David Lipson ◽

Kim Reasor ◽

Kååre Sikuaq Erickson

Keyword(s):

Climate Change ◽

Sea Ice ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

Digital Version ◽

Sea Ice Decline ◽

Arctic Sea ◽

The World ◽

Impacts Of Climate Change

The predominantly Inupiat people of Utqia&#289;vik, Alaska are among those who will be most impacted by climate change and the loss of Arctic sea ice in the near future. Subsistence hunting of marine mammals associated with sea ice is central to the Inupiat way of life. Furthermore, their coastal homes and infrastructure are increasingly subject to damage from increased wave action on ice-free Beaufort and Chukchi Seas. While the people of this region are among the most directly vulnerable to climate change, the subject is not often discussed in the elementary school curriculum. Meanwhile, in many other parts of the world, the impacts of climate change are viewed as abstract and remote. We worked with fifth grade children in Utqia&#289;vik both to educate them, but also to engage them in helping us communicate to rest of the world, in an emotionally resonant way, the direct impacts of climate change on families in this Arctic region. The team consisted of a scientist (Lipson), an artist (Reasor) and an outreach specialist (Erickson) of Inupiat heritage from a village in Alaska. We worked with four 5th grade classes of about 25 students each at Fred Ipalook Elementary in Utqia&#289;vik, AK. The scientist gave a short lecture about sea ice and climate change in the Arctic, with emphasis on local impacts to hunting and infrastructure (with interjections from the local outreach specialist). We then showed the students a large poster of historical and projected sea ice decline, and asked the students to help us fill in the white space beneath the lines. The artist led the children in making small art pieces that represent things that are important to their lives in Utqia&#289;vik (they were encouraged to paint animals, but they were free to do whatever they wanted). We returned to the class later that week and had each student briefly introduce themselves and their painting, and place it to the large graph of sea ice decline, which included the dire predictions of the RCP8.5 scenario. At the end we added the more hopeful RCP2.6 scenario to end on a positive note. The artist then painted in the more hopeful green line by hand. The result was a poster showing historical and projected Arctic sea ice cover, with 100 beautiful paintings by children of things that are dear to them about their home being squeezed into a smaller region as the sea ice cover diminishes. We scanned all the artwork to make a digital version of the poster, and left the original with the school. These materials are being converted into an interactive webpage where viewers can click on the individual painting for detail, and get selected recordings of the children&#8217;s statements about their artwork. This project can serve as a nucleus for communicating to other classes and adults about the real impacts of climate change in people&#8217;s lives.

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Deep learning for monthly Arctic sea ice concentration prediction

10.5194/egusphere-egu2020-15481 ◽

2020 ◽

Author(s):

Tom Andersson ◽

Fruzsina Agocs ◽

Scott Hosking ◽

María Pérez-Ortiz ◽

Brooks Paige ◽

...

Keyword(s):

Climate Change ◽

Global Warming ◽

Deep Learning ◽

Sea Ice ◽

Arctic Sea Ice ◽

The Arctic ◽

Prediction System ◽

Sea Ice Concentration ◽

Arctic Sea ◽

Ice Concentration

Over recent decades, the Arctic has warmed faster than any region on Earth. The rapid decline in Arctic sea ice extent (SIE) is often highlighted as a key indicator of anthropogenic climate change. Changes in sea ice disrupt Arctic wildlife and indigenous communities, and influence weather patterns as far as the mid-latitudes. Furthermore, melting sea ice attenuates the albedo effect by replacing the white, reflective ice with dark, heat-absorbing melt ponds and open sea, increasing the Sun&#8217;s radiative heat input to the Arctic and amplifying global warming through a positive feedback loop. Thus, the reliable prediction of sea ice under a changing climate is of both regional and global importance. However, Arctic sea ice presents severe modelling challenges due to its complex coupled interactions with the ocean and atmosphere, leading to high levels of uncertainty in numerical sea ice forecasts.Deep learning (a subset of machine learning) is a family of algorithms that use multiple nonlinear processing layers to extract increasingly high-level features from raw input data. Recent advances in deep learning techniques have enabled widespread success in diverse areas where significant volumes of data are available, such as image recognition, genetics, and online recommendation systems. Despite this success, and the presence of large climate datasets, applications of deep learning in climate science have been scarce until recent years. For example, few studies have posed the prediction of Arctic sea ice in a deep learning framework. We investigate the potential of a fully data-driven, neural network sea ice prediction system based on satellite observations of the Arctic. In particular, we use inputs of monthly-averaged sea ice concentration (SIC) maps since 1979 from the National Snow and Ice Data Centre, as well as climatological variables (such as surface pressure and temperature) from the European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) dataset. Past deep learning-based Arctic sea ice prediction systems tend to overestimate sea ice in recent years - we investigate the potential to learn the non-stationarity induced by climate change with the inclusion of multi-decade global warming indicators (such as average Arctic air temperature). We train the networks to predict SIC maps one month into the future, evaluating network prediction uncertainty by ensembling independent networks with different random weight initialisations. Our model accounts for seasonal variations in the drivers of sea ice by controlling for the month of the year being predicted. We benchmark our prediction system against persistence, linear extrapolation and autoregressive models, as well as September minimum SIE predictions from submissions to the Sea Ice Prediction Network's Sea Ice Outlook. Performance is evaluated quantitatively using the root mean square error and qualitatively by analysing maps of prediction error and uncertainty.

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The Role of Extratropical Ocean Warming in the Coupled Climate Response to Arctic Sea Ice Loss

Journal of Climate ◽

10.1175/jcli-d-18-0192.1 ◽

2018 ◽

Vol 31 (22) ◽

pp. 9193-9206 ◽

Cited By ~ 6

Author(s):

Russell Blackport ◽

Paul J. Kushner

Keyword(s):

Sea Ice ◽

Atmospheric Circulation ◽

General Circulation ◽

Arctic Sea Ice ◽

Ocean Warming ◽

The Arctic ◽

Climate Response ◽

Arctic Sea ◽

Arctic Sea Ice Loss

The role of extratropical ocean warming in the coupled climate response to Arctic sea ice loss is investigated using coupled atmosphere–ocean general circulation model (AOGCM) and uncoupled atmospheric-only (AGCM) experiments. Coupled AOGCM experiments driven by sea ice albedo reduction and greenhouse gas–dominated radiative forcing are used to diagnose the extratropical sea surface temperature (SST) response to sea ice loss. Sea ice loss is then imposed in AGCM experiments both with and without these extratropical SST changes, which are found to extend beyond the regions where sea ice is lost. Sea ice loss in isolation drives warming that is confined to the Arctic lower troposphere and only a weak atmospheric circulation response. When the extratropical SST response caused by sea ice loss is also included in the forcing, the warming extends into the Arctic midtroposphere during winter. This coincides with a stronger atmospheric circulation response, including an equatorward shift in the eddy-driven jet, a deepening of the Aleutian low, and an expansion of the Siberian high. Similar results are found whether the extratropical SST forcing is taken directly from the AOGCM driven by sea ice loss, or whether they are diagnosed using a two-parameter pattern scaling technique where tropical adjustment to sea ice loss is removed. These results suggest that AGCM experiments that are driven by sea ice loss and only local SST increases will underestimate the Arctic midtroposphere warming and atmospheric circulation response to sea ice loss, compared to AOGCM simulations and the real world.

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Insignificant effect of climate change on winter haze pollution in Beijing

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-17489-2018 ◽

2018 ◽

Vol 18 (23) ◽

pp. 17489-17496 ◽

Cited By ~ 16

Author(s):

Lu Shen ◽

Daniel J. Jacob ◽

Loretta J. Mickley ◽

Yuxuan Wang ◽

Qiang Zhang

Keyword(s):

Climate Change ◽

Sea Ice ◽

21St Century ◽

Climate Models ◽

Eastern China ◽

Arctic Sea Ice ◽

The Arctic ◽

Climate Projections ◽

Haze Pollution ◽

Arctic Sea

Abstract. Several recent studies have suggested that 21st century climate change will significantly worsen the meteorological conditions, leading to very high concentrations of fine particulate matter (PM2.5) in Beijing in winter (Beijing haze). We find that 81 % of the variance in observed monthly PM2.5 during 2010–2017 winters can be explained by a single meteorological mode, the first principal component (PC1) of the 850 hPa meridional wind velocity (V850) and relative humidity (RH). V850 and RH drive stagnation and chemical production of PM2.5, respectively, and thus have a clear causal link to Beijing haze. PC1 explains more of the variance in PM2.5 than either V850 or RH alone. Using additional meteorological variables does not explain more of the variance in PM2.5. Therefore PC1 can serve as a proxy for Beijing haze in the interpretation of long-term climate records and in future climate projections. Previous studies suggested that shrinking Arctic sea ice would worsen winter haze conditions in eastern China, but we show with the PC1 proxy that Beijing haze is correlated with a dipole structure in the Arctic sea ice rather than with the total amount of sea ice. Beijing haze is also correlated with dipole patterns in Pacific sea surface temperatures (SSTs). We find that these dipole patterns of Arctic sea ice and Pacific SSTs shift and change sign on interdecadal scales, so that they cannot be used reliably as future predictors for the haze. Future 21st century trends of the PC1 haze proxy computed from the CMIP5 ensemble of climate models are statistically insignificant. We conclude that climate change is unlikely to significantly offset current efforts to decrease Beijing haze through emission controls.

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Arctic Energy Budget in Relation to Sea Ice Variability on Monthly-to-Annual Time Scales

Journal of Climate ◽

10.1175/jcli-d-15-0002.1 ◽

2015 ◽

Vol 28 (16) ◽

pp. 6335-6350 ◽

Cited By ~ 5

Author(s):

F. Krikken ◽

W. Hazeleger

Keyword(s):

Sea Ice ◽

Time Scales ◽

Energy Budget ◽

Climate Models ◽

Arctic Sea Ice ◽

The Arctic ◽

Albedo Feedback ◽

Arctic Sea ◽

Cloud Ice

Abstract The large decrease in Arctic sea ice in recent years has triggered a strong interest in Arctic sea ice predictions on seasonal-to-decadal time scales. Hence, it is important to understand physical processes that provide enhanced predictability beyond persistence of sea ice anomalies. This study analyzes the natural variability of Arctic sea ice from an energy budget perspective, using 15 climate models from phase 5 of CMIP (CMIP5), and compares these results to reanalysis data. The authors quantify the persistence of sea ice anomalies and the cross correlation with the surface and top-of-atmosphere energy budget components. The Arctic energy balance components primarily indicate the important role of the seasonal ice–albedo feedback, through which sea ice anomalies in the melt season reemerge in the growth season. This is a robust anomaly reemergence mechanism among all 15 climate models. The role of the ocean lies mainly in storing heat content anomalies in spring and releasing them in autumn. Ocean heat flux variations play only a minor role. Confirming a previous (observational) study, the authors demonstrate that there is no direct atmospheric response of clouds to spring sea ice anomalies, but a delayed response is evident in autumn. Hence, there is no cloud–ice feedback in late spring and summer, but there is a cloud–ice feedback in autumn, which strengthens the ice–albedo feedback. Anomalies in insolation are positively correlated with sea ice variability. This is primarily a result of reduced multiple reflection of insolation due to an albedo decrease. This effect counteracts the ice-albedo effect up to 50%. ERA-Interim and Ocean Reanalysis System 4 (ORAS4) confirm the main findings from the climate models.

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