scholarly journals Insignificant effect of climate change on winter haze pollution in Beijing

2018 ◽  
Vol 18 (23) ◽  
pp. 17489-17496 ◽  
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.

scholarly journals Effect of climate change on winter haze pollution in Beijing: uncertain and likely small

2018 ◽  
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. 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 overall very small and variable in sign. We thus find no evidence for a significant effect of climate change on Beijing haze.

scholarly journals Arctic sea ice area in CMIP3 and CMIP5 climate model ensembles – variability and change

2015 ◽  
Vol 9 (1) ◽  
pp. 1077-1131 ◽  
Author(s):  
V. A. Semenov ◽  
T. Martin ◽  
L. K. Behrens ◽  
M. Latif
Keyword(s):  
Sea Ice ◽  
21St Century ◽  
Seasonal Cycle ◽  
Climate Models ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Cmip5 Models ◽  
The Barents Sea ◽  
Arctic Sea

Abstract. The shrinking Arctic sea ice cover observed during the last decades is probably the clearest manifestation of ongoing climate change. While climate models in general reproduce the sea ice retreat in the Arctic during the 20th century and simulate further sea ice area loss during the 21st century in response to anthropogenic forcing, the models suffer from large biases and the model results exhibit considerable spread. The last generation of climate models from World Climate Research Programme Coupled Model Intercomparison Project Phase 5 (CMIP5), when compared to the previous CMIP3 model ensemble and considering the whole Arctic, were found to be more consistent with the observed changes in sea ice extent during the recent decades. Some CMIP5 models project strongly accelerated (non-linear) sea ice loss during the first half of the 21st century. Here, complementary to previous studies, we compare results from CMIP3 and CMIP5 with respect to regional Arctic sea ice change. We focus on September and March sea ice. Sea ice area (SIA) variability, sea ice concentration (SIC) variability, and characteristics of the SIA seasonal cycle and interannual variability have been analysed for the whole Arctic, termed Entire Arctic, Central Arctic and Barents Sea. Further, the sensitivity of SIA changes to changes in Northern Hemisphere (NH) averaged temperature is investigated and several important dynamical links between SIA and natural climate variability involving the Atlantic Meridional Overturning Circulation (AMOC), North Atlantic Oscillation (NAO) and sea level pressure gradient (SLPG) in the western Barents Sea opening serving as an index of oceanic inflow to the Barents Sea are studied. The CMIP3 and CMIP5 models not only simulate a coherent decline of the Arctic SIA but also depict consistent changes in the SIA seasonal cycle and in the aforementioned dynamical links. The spatial patterns of SIC variability improve in the CMIP5 ensemble, particularly in summer. Both CMIP ensembles depict a significant link between the SIA and NH temperature changes. Our analysis suggests that, on average, the sensitivity of SIA to external forcing is enhanced in the CMIP5 models. The Arctic SIA variability response to anthropogenic forcing is different in CMIP3 and CMIP5. While the CMIP3 models simulate increased variability in March and September, the CMIP5 ensemble shows the opposite tendency. A noticeable improvement in the simulation of summer SIA by the CMIP5 models is often accompanied by worse results for winter SIA characteristics. The relation between SIA and mean AMOC changes is opposite in September and March, with March SIA changes being positively correlated with AMOC slowing. Finally, both CMIP ensembles demonstrate an ability to capture, at least qualitatively, important dynamical links of SIA to decadal variability of the AMOC, NAO and SLPG. SIA in the Barents Sea is strongly overestimated by the majority of the CMIP3 and CMIP5 models, and projected SIA changes are characterized by a large spread giving rise to high uncertainty.

scholarly journals Observation-based selection of climate models projects Arctic ice-free summers around 2035

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David Docquier ◽  
Torben Koenigk
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Arctic Sea ◽  
Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

scholarly journals What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?

Climate ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 15 ◽  
Author(s):  
Ge Peng ◽  
Jessica L. Matthews ◽  
Muyin Wang ◽  
Russell Vose ◽  
Liqiang Sun
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Adaptation ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Climate Projections ◽  
Emission Scenarios ◽  
Ice Coverage ◽  
Arctic Sea

The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.

scholarly journals Modeled Arctic sea ice evolution through 2300 in CMIP5 extended RCPs

2014 ◽  
Vol 8 (1) ◽  
pp. 1383-1406 ◽  
Author(s):  
P. J. Hezel ◽  
T. Fichefet ◽  
F. Massonnet
Keyword(s):  
Sea Ice ◽  
Radiative Forcing ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

Abstract. Almost all global climate models and Earth system models that participated in the Coupled Model Intercomparison Project 5 (CMIP5) show strong declines in Arctic sea ice extent and volume under the highest forcing scenario of the Radiative Concentration Pathways (RCPs) through 2100, including a transition from perennial to seasonal ice cover. Extended RCP simulations through 2300 were completed for a~subset of models, and here we examine the time evolution of Arctic sea ice in these simulations. In RCP2.6, the summer Arctic sea ice extent increases compared to its minimum following the peak radiative forcing in 2044 in all 9 models. RCP4.5 demonstrates continued summer Arctic sea ice decline due to continued warming on longer time scales. These two scenarios imply that summer sea ice extent could begin to recover if and when radiative forcing from greenhouse gas concentrations were to decrease. In RCP8.5 the Arctic Ocean reaches annually ice-free conditions in 7 of 9 models. The ensemble of simulations completed under the extended RCPs provide insight into the global temperature increase at which sea ice disappears in the Arctic and reversibility of declines in seasonal sea ice extent.

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

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.

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

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

<p>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-18<sup>th</sup> 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.</p><p>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 Isstrøm (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 – 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 – 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 – 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.</p>

scholarly journals How Climate Model Complexity Influences Sea Ice Stability

2015 ◽  
Vol 28 (10) ◽  
pp. 3998-4014 ◽  
Author(s):  
Till J. W. Wagner ◽  
Ian Eisenman
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Model Complexity ◽  
The Arctic ◽  
Arctic Sea ◽  
The Stability ◽  
Low Order

Abstract Record lows in Arctic sea ice extent have been making frequent headlines in recent years. The change in albedo when sea ice is replaced by open water introduces a nonlinearity that has sparked an ongoing debate about the stability of the Arctic sea ice cover and the possibility of Arctic “tipping points.” Previous studies identified instabilities for a shrinking ice cover in two types of idealized climate models: (i) annual-mean latitudinally varying diffusive energy balance models (EBMs) and (ii) seasonally varying single-column models (SCMs). The instabilities in these low-order models stand in contrast with results from comprehensive global climate models (GCMs), which typically do not simulate any such instability. To help bridge the gap between low-order models and GCMs, an idealized model is developed that includes both latitudinal and seasonal variations. The model reduces to a standard EBM or SCM as limiting cases in the parameter space, thus reconciling the two previous lines of research. It is found that the stability of the ice cover vastly increases with the inclusion of spatial communication via meridional heat transport or a seasonal cycle in solar forcing, being most stable when both are included. If the associated parameters are set to values that correspond to the current climate, the ice retreat is reversible and there is no instability when the climate is warmed. The two parameters have to be reduced by at least a factor of 3 for instability to occur. This implies that the sea ice cover may be substantially more stable than has been suggested in previous idealized modeling studies.

scholarly journals A Decade of Spaceborne Observations of the Arctic Atmosphere: Novel Insights from NASA’s AIRS Instrument

2016 ◽  
Vol 97 (11) ◽  
pp. 2163-2176 ◽  
Author(s):  
Abhay Devasthale ◽  
Joseph Sedlar ◽  
Brian H. Kahn ◽  
Michael Tjernström ◽  
Eric J. Fetzer ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Numerical Models ◽  
Lower Troposphere ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Climate Projections ◽  
Polar Regions ◽  
Arctic Atmosphere ◽  
Warming World

Abstract Arctic sea ice is declining rapidly and its annual ice extent minima reached record lows twice during the last decade. Large environmental and socioeconomic implications related to sea ice reduction in a warming world necessitate realistic simulations of the Arctic climate system, not least to formulate relevant environmental policies on an international scale. However, despite considerable progress in the last few decades, future climate projections from numerical models still exhibit the largest uncertainties over the polar regions. The lack of sufficient observations of essential climate variables is partly to blame for the poor representation of key atmospheric processes, and their coupling to the surface, in climate models. Observations from the hyperspectral Atmospheric Infrared Sounder (AIRS) instrument on board the National Aeronautics and Space Administration (NASA)’s Aqua satellite are contributing toward improved understanding of the vertical structure of the atmosphere over the poles since 2002, including the lower troposphere. This part of the atmosphere is especially important in the Arctic, as it directly impacts sea ice and its short-term variability. Although in situ measurements provide invaluable ground truth, they are spatially and temporally inhomogeneous and sporadic over the Arctic. A growing number of studies are exploiting AIRS data to investigate the thermodynamic structure of the Arctic atmosphere, with applications ranging from understanding processes to deriving climatologies—all of which are also useful to test and improve parameterizations in climate models. As the AIRS data record now extends more than a decade, a select few of many such noteworthy applications of AIRS data over this challenging and rapidly changing landscape are highlighted here.

scholarly journals Arctic sea ice-free season projected to extend into fall

2018 ◽  
Author(s):  
Marion Lebrun ◽  
Martin Vancoppenolle ◽  
Gurvan Madec ◽  
François Massonnet
Keyword(s):  
Climate Change ◽  
Solar Energy ◽  
Sea Ice ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
Arctic Ecosystems ◽  
Simple Mechanism ◽  
Arctic Sea ◽  
Ice Retreat ◽  
Sea Ice Reduction

Abstract. The recent Arctic sea-ice reduction is associated with an increase in the ice-free season duration, with comparable contributions of earlier retreat and later freeze-up. Here we show that within the next decades, the trends towards later freeze-up should progressively exceed and ultimately double the trend towards an earlier ice retreat date. This feature is robustly found in a hierarchy of climate models and is consistent with a simple mechanism: solar energy is absorbed more efficiently than it can be released in non-solar form until freeze-up. Based on climate change simulations, we envision an increase and a shift of the ice-free season towards fall, which will affect Arctic ecosystems and navigation.

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