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The Antarctic sea ice extent, though varying considerably with season and region, had been on a slight increasing trend from late 1970s until mid-2010s when the trend was suddenly reversed. The sea surface temperature anomalies related to the multi-decadal expansion and recent contraction in the Antarctic sea ice extent remain to be uncovered. Here, we demonstrate that the variations in the Antarctic sea ice extent from 1979 through 2020, including the abrupt change in direction that occurred in mid-2010's, can be explained at least partially by the sea surface temperature (SST) oscillations in the Pacific and Atlantic Oceans. Specifically, we show that the changes in the Antarctic sea ice extent are significantly correlated with the Interdecadal Pacific Oscillation (IPO) in all seasons and to the Atlantic Multidecadal Oscillation (AMO) in austral winter and spring. We further demonstrate that SST anomalies trigger planetary wavetrains of different magnitudes and propagating paths depending on seasons. These planetary wavetrains induce anomalous atmospheric circulations over the Southern Ocean that, through transport and melting/freezing, ultimately change sea ice extent. [ABSTRACT FROM AUTHOR]

Based on statistical analyses of gridded data over the past four decades (1979–2020), we examined the teleconnection between the variability of the Antarctic sea ice and the leading modes of subtropical sea surface temperature (SST) variability known as the Indian Ocean subtropical dipole (IOSD), the South Atlantic subtropical dipole (SASD) and the South Pacific subtropical dipole (SPSD). We show that the pattern and strength of the teleconnection differ with the SST modes. For each mode, while the regional distributions of significant Antarctic sea ice anomalies are broadly similar throughout the year, the areal extent and the magnitudes of the anomalies display a strong seasonality. Larger areas and magnitudes of significant sea ice anomalies occur in austral winter for the SASD, autumn and spring for the SPSD and non‐summer seasons for the IOSD. We demonstrate that the spatial and seasonal variations of the sea ice anomalies associated with each of the three subtropical SST variability modes are largely consistent with the patterns of the anomalous temperature advection and sea ice transport by the anomalous atmospheric circulations induced by planetary wavetrains that are triggered by anomalous convective activities over different regions of subtropical oceans. These relationships between subtropical SST modes and Antarctic sea ice may serve as a valuable reference for predicting seasonal to interannual scale variations of Antarctic sea ice concentrations across each austral season. [ABSTRACT FROM AUTHOR]

Both the Southern Annular Mode (SAM) and El Niño–Southern Oscillation (ENSO) are critical factors contributing to Antarctic sea ice variability on interannual time scales. However, their joint effects on sea ice are complex and remain unclear for each austral season. In this study, satellite sea ice concentration (SIC) observations and atmospheric reanalysis data are utilized to assess the impacts of combined SAM and ENSO on seasonal Antarctic sea ice changes. The joint SAM–ENSO impacts on southern high latitudes are principally controlled by the strength and position of the wave activity and associated atmospheric circulation anomalies affected by their interactions. In-phase events (La Niña/positive SAM and El Niño/negative SAM) are characterized with an SIC dipole located in the Weddell/Bellingshausen Seas and Amundsen/Ross Seas, while out-of-phase events (El Niño/positive SAM and La Niña/negative SAM) experience significant SIC anomalies in the Indian Ocean and western Pacific Ocean. Sea ice budget analyses are conducted to separate the dynamic and thermodynamic contributions inducing the sea ice intensification anomalies. The results show that in-phase intensification anomalies also display a pattern similar to the SIC dipole and are mainly driven by the direct thermodynamic forcing at the ice edge and thermodynamic responses to meridional sea ice drift in the inner pack, especially in autumn and winter. Dynamic processes caused by zonal sea ice drift also play an important role during out-of-phase conditions in addition to the same mechanisms during in-phase conditions. [ABSTRACT FROM AUTHOR]

The spatial distribution of trends in temperature extremes over Antarctica remains largely unknown. Here we investigate the seasonal and spatial characteristics of extreme warm occurrence across Antarctica. The Antarctic inland areas show significant positive trends in the number of extreme warm days in austral spring and summer. The trends in the seasonal mean of daily maximum temperature show strong coherence with the trends in extreme warm occurrences. In addition, the long‐term longwave radiation, water vapor flux and regional atmospheric circulation changes are closely connected to the trends of extreme warm days in all seasons outside of summer; the summer longwave radiation and interior wind trends show little coherence with warm extreme trends, indicating other processes at play driving extreme warm days in summer. Plain Language Summary: Antarctica interacting with the global ocean and atmosphere is a key area in studying climate change. The extreme high temperatures in Antarctica have profound impacts on surface melt and local ecosystems. We find that the trends of extreme warm days in the Antarctic continent vary by region and season, showing significant positive trends over the inland areas in austral spring and summer. The background warming of daily maximum temperature is closely connected to the extreme warm occurrences. The trends of surface downward longwave radiation, integrated water vapor flux and the 10 m wind are to a large extent associated with the trend of extreme warm days, especially in spring. Key Points: The extreme warm days in Antarctic inland areas increase significantly in austral spring and summerThe long‐term trends in extreme warm days show strong coherence with the trends in averaged daily maximum temperatureThe temperature trends are closely connected to changes in the surface longwave radiation in all seasons except summer [ABSTRACT FROM AUTHOR]

Sea ice variability in the opposite polar regions is examined holistically by applying the self-organizing map (SOM) method to global monthly sea ice concentration data over two periods. The results show that the variability modes of sea ice decrease in the Arctic correspond to an overall sea ice increase in the Antarctic, and vice versa. In particular, the monthly sea ice anomaly patterns are dominated by in-phase variability across the Arctic that is stronger in the marginal seas particularly the Barents Sea than the central Arctic Ocean. The corresponding Antarctic sea ice variability is characterized by a zonal wavenumber-3 structure or a dipole pattern of out-of-phase variability between the Bellingshausen/Amundsen Seas and the rest of the Southern Ocean. The frequency of occurrence of these dominant patterns exhibits pronounced seasonal as well as decadal variability and the latter is closely related to the Pacific decadal oscillation and Atlantic multidecadal oscillation. Other less frequent patterns seem to be associated with the central Pacific El Niño and spatially heterogeneous interannual variability of sea surface temperature (SST) in the Indian and the Atlantic Oceans. The dominant modes explain 57% of the four-decade domain-averaged trends in the annual polar sea ice concentration, with more explained in the eastern than western Arctic Ocean and in the Weddell Sea and the Amundsen Sea in the Antarctic. The spatial patterns of the leading modes can be largely explained by the dynamic (sea ice drift) and thermodynamic (sea ice melt) effects of the anomalous atmospheric circulations associated with SST and sea level pressure anomalies. Significance Statement: The purpose of this study is to extract the main modes of monthly global sea ice concentration variability in the past four decades, explain the mechanisms behind the occurrences of these modes, and examine the contributions of these modes to the trend in annual global sea ice concentration. Sea ice extent in the past four decades has shown a significant declining trend in the Arctic and a slight, but significant increasing trend in the Antarctic. By jointly analyzing the sea ice variability and trends in the two polar regions, the results here provide a reference for what might have contributed to the opposite sea ice trends in Arctic and Antarctic and highlight the important influence of large-scale sea surface temperature anomalies on the trends in the two polar regions. [ABSTRACT FROM AUTHOR]

The anticyclonic anomaly over the Ural Mountains, or the Ural high (UH), has recently received much attention as a factor related to weather anomalies across Eurasia. Here we studied how the UH affects the occurrence of cold wintertime episodes over eastern Europe and northern China. By employing three methods to identify the UH, we found that a method based on the sea level pressure anomaly captures a stronger cooling signal over eastern Europe and this method includes nonblocking cases associated with low-level anticyclones that do not affect the upper troposphere. However, with UH occurrence, a stronger cooling over northern China is detected by a method based on 500-hPa geopotential height fields. Cold events over eastern Europe typically occur when UH formation is associated with a Rossby wave breaking in the upper level. Our results show that the horizontal temperature advection plays an important role in formation of cold episodes in both eastern Europe and northern China. The advection is balanced by diabatic processes, which show an opposite sign to the temperature advection in both regions. Also adiabatic warming contributes to balancing the advection in northern China. We find that the exact location of the positive SLP anomaly during UH episodes is the most important factor controlling whether or not eastern Europe or northern China will experience a cold episode. If the positive SLP anomaly develops more northwest than usual, eastern Europe will experience a cold episode. When the anomaly moves eastward, northern China will be cold. [ABSTRACT FROM AUTHOR]

The most recent historic heat wave in Siberia with record‐shattering temperatures is a reflection that, as a manifestation of global warming, the Arctic is experiencing more frequent and severe warm‐temperature extremes that could have global consequences. Here, we apply the self‐organizing map (SOM) clustering method to 6‐hr data from ERA‐Interim from 1979 to 2017 to document the spatial and seasonal variations of the trends in the number of warm‐extreme days over the pan‐Arctic region, and to apportion the trends into a dynamic component representing changes in atmospheric circulation patterns, a thermodynamic component not directly related to circulations, and an interaction component. We show significant upward trends in the occurrence of warm extremes across much of the Arctic Ocean in all seasons except for summer when regions of significant upward trends move from the Arctic Ocean to the Canadian Arctic Archipelago, Greenland, and the northern North Atlantic. The direction and magnitude of the trends in seasonal warm extremes as well as their seasonal and spatial variations are dominated by the thermodynamic component, with the dynamic component and the interaction component at least an order of magnitude smaller. Although negligible to the long‐term, pan‐Arctic averaged trend, the dynamic component may be comparable with, or even larger than, the thermodynamic component at some locations and under certain atmospheric circulation patterns. [ABSTRACT FROM AUTHOR]

The relationship of the tropical Indo‐Pacific upper ocean heat content (HC) and the South China Sea (SCS) summer monsoon (SCSSM) onset during 1980–2020 was investigated. It was found that when HC anomalies in the preceding winter–spring are positive (negative) in the eastern Indian–western Pacific Ocean or negative (positive) in the southwestern Indian Ocean and eastern Pacific Ocean, SCSSM onset is usually early (late). Empirical orthogonal function (EOF) analysis suggested that the primary EOF modes of HC anomaly in both tropical Pacific and Indian Oceans are associated with El Niño–Southern Oscillation (ENSO). It is through modulating the westerly over the north Indian Ocean–SCS and the western north Pacific subtropical high (WNPSH) via the Walker circulation over the Indo‐Pacific basin, monsoon circulation in the Indian Ocean that the ENSO related Indo‐Pacific HC variation connects with the SCSSM onset. Both the second EOF modes for the tropical Pacific and Indian Ocean also show significant correlations with the SCSSM onset. The former related vertical motion over the SCS–far northwestern Pacific can influence the WNPSH and convection, thus occurrence of the SCSSM. The latter represents the intrinsic Indian Ocean HC variability; the air temperature variation associated with it can modulate the atmospheric circulation in the Indian Ocean–SCS through thermal wind balance, and thus the SCSSM onset by acting on the westerly over the SCS and the WNPSH. A western Pacific index and an Indian Ocean dipole index were defined to represent the leading HC modes in the tropical Pacific and Indian Ocean, respectively. A binary linear regression model was constructed using these two indices to improve SCSSM onset prediction. [ABSTRACT FROM AUTHOR]

The underlying mechanisms for Arctic sea ice decline can be categories as those directly related to changes in atmospheric circulations (often referred to as dynamic mechanisms) and the rest (broadly characterized as thermodynamic processes). An attribution analysis based on the self-organizing maps (SOM) method is performed to determine the relative contributions from these two types of mechanisms to the Arctic sea ice decline in August–October during 1979–2016. The daily atmospheric circulations represented by daily 500-hPa geopotential height anomalies are classified into 12 SOM patterns, which portray the spatial structures of the Arctic Oscillation and Arctic Dipole, and their transitions. Due to the counterbalance between the opposite trends among the circulation patterns, the net effect of circulation changes is small, explaining only 1.6% of the declining trend in the number of August–October sea ice days in the Arctic during 1979–2016. The majority of the trend (95.8%) is accounted for by changes in thermodynamic processes not directly related to changes in circulations, whereas for the remaining trend (2.6%) the contributions of circulation and non-circulation changes cannot be distinguished. The sea ice decline is closely associated with surface air temperature increase, which is related to increasing trends in atmospheric water vapor content, downward longwave radiation, and sea surface temperatures over the open ocean, as well as to decreasing trends in surface albedo. An analogous SOM analysis extending seasonal coverage to spring (April–October) for the same period supports the dominating role of thermodynamic forcing in decadal-scale Arctic sea ice loss. [ABSTRACT FROM AUTHOR]

The recent increasing trend of "warm Arctic, cold continents" has attracted much attention, but it remains debatable as to what forces are behind this phenomenon. Here, we revisited surface temperature variability over the Arctic and the Eurasian continent by applying the self-organizing-map (SOM) technique to gridded daily surface temperature data. Nearly 40 % of the surface temperature trends are explained by the nine SOM patterns that depict the switch to the current warm Arctic–cold Eurasia pattern at the beginning of this century from the reversed pattern that dominated the 1980s and 1990s. Further, no cause–effect relationship is found between the Arctic sea ice loss and the cold spells in the high-latitude to midlatitude Eurasian continent suggested by earlier studies. Instead, the increasing trend in warm Arctic–cold Eurasia pattern appears to be related to the anomalous atmospheric circulations associated with two Rossby wave trains triggered by rising sea surface temperature (SST) over the central North Pacific and the North Atlantic oceans. On interdecadal timescale, the recent increase in the occurrences of the warm Arctic–cold Eurasia pattern is a fragment of the interdecadal variability of SST over the Atlantic Ocean as represented by the Atlantic Multidecadal Oscillation (AMO) and over the central Pacific Ocean. [ABSTRACT FROM AUTHOR]

Surface radiative fluxes over landfast sea ice off Zhongshan station have been measured in austral spring for five springs between 2010 and 2015. Downward and upward solar radiation vary diurnally with maximum amplitudes of 473 and 290 W m−2, respectively. The maximum and minimum long-wave radiation values of the mean diurnal cycle are 218 and 210 W m−2 for downward radiation, 277 and 259 W m−2 for upward radiation and 125 and −52 W m−2 for net radiation. The albedo has a U-shaped mean diurnal cycle with a minimum of 0.64 at noon. Sea ice thickness is in the growth phase for most spring days, but can be disturbed by synoptic processes. The surface temperature largely determines the occurrence of ice melting. Surface downward and upward long-wave radiation show synoptic oscillations with a 5–8 day period and intraseasonal variability with a 12–45 day period. The amplitudes of the diurnal, synoptic and intraseasonal variability show some differences during the five austral springs considered here. The intraseasonal and synoptic variability of downward and upward long-wave radiation are associated with the variability of cloud cover and surface temperature induced by the atmospheric circulation. [ABSTRACT FROM AUTHOR]

This study investigates the interannual variability of wind energy resources across China and how it changes with season by applying empirical orthogonal function (EOF) analyses to gridded wind data from the Climate Forecast System Reanalysis (CFSR) from January 1979 through December 2011. The first EOF mode (EOF1) represents between 22% variance for winter and 29% for summer. Spatially, the variation is largely consistent across China for summer and autumn and almost opposite between north and south for spring and winter, and the strongest variation in all seasons is found over Inner Mongolia and Tibet. The second EOF mode (EOF2) represents between 13% variance for autumn and 16% for spring, and is largely dominated by a sharp contrast between Inner Mongolia and Tibet for all seasons. The EOF1 appears to be linked statistically to the pacific decadal oscillation for summer and autumn and to the Pacific North American pattern for spring and winter, while the EOF2 seems to be connected to the Arctic Oscillation for spring and winter and to an interdecadal variability for summer and autumn. The anomalous wind fields associated with these large‐scale circulation patterns modify the climatological wind fields in different ways that lead to an increase or a decrease of the 80‐m winds in different regions of China. Understanding the low‐frequency variability of wind speeds, or how and why wind speed and wind power vary from 1 year to another, can be beneficial for seasonal outlook or long‐range forecasting of wind power generation. Using gridded wind data from a third‐generation global reanalysis, we statically identify and characterize the main modes of the interannual variability of wind speeds at the modern wind turbine level across entire China and the dominant climate forcing for these modes.Figure caption: The correlations between the 80‐m wind speed summer season anomalies across China and the Pacific Decadal Oscillation indices for 1979–2011. [ABSTRACT FROM AUTHOR]

Observed daily precipitation data were used to investigate the characteristics of precipitation at Antarctic Progress Station and synoptic patterns associated with extreme precipitation events during the period 2003–16. The annual precipitation, annual number of extreme precipitation events, and amount of precipitation during the extreme events have positive trends. The distribution of precipitation at Progress Station is heavily skewed with a long tail of extreme dry days and a high peak of extreme wet days. The synoptic pattern associated with extreme precipitation events is a dipole structure of negative and positive height anomalies to the west and east of Progress Station, respectively, resulting in water vapor advection to the station. For the first time, we apply self-organizing maps (SOMs) to examine thermodynamic and dynamic perspectives of trends in the frequency of occurrence of Antarctic extreme precipitation events. The changes in thermodynamic (noncirculation) processes explain 80% of the trend, followed by the changes in the interaction between thermodynamic and dynamic processes, which account for nearly 25% of the trend. The changes in dynamic processes make a negative (less than 5%) contribution to the trend. The positive trend in total column water vapor over the Southern Ocean explains the change of thermodynamic term. [ABSTRACT FROM AUTHOR]

In recent decades, the Arctic sea ice has been declining at a rapid pace as the Arctic warms at a rate of twice the global average. The underlying physical mechanisms for the Arctic warming and accelerated sea ice retreat are not fully understood. In this study, we apply a relatively novel statistical method called self-organizing maps (SOM) along with composite analysis to examine the trend and variability of autumn Arctic sea ice in the past three decades and their relationships to large-scale atmospheric circulation changes. Our statistical results show that the anomalous autumn Arctic dipole (AD) (Node 1) and the Arctic Oscillation (AO) (Node 9) could explain in a statistical sense as much as 50% of autumn sea ice decline between 1979 and 2016. The Arctic atmospheric circulation anomalies associated with anomalous sea-surface temperature (SST) patterns over the North Pacific and North Atlantic influence Arctic sea ice primarily through anomalous temperature and water vapor advection and associated radiative feedback. [ABSTRACT FROM AUTHOR]

The potential mechanisms underlying the observed increasing trend in Antarctic summertime sea ice cover for the 1979-2017 period have been investigated using a relatively novel method called the selforganizing map (SOM). Among the nine nodes generated to explain the variability of Antarctic sea ice cover, two (nodes 3 and 7) exhibit a statistically significant linear trend in the time series of the frequency of the SOM pattern occurrence that together explain 40% of the total trend in the sea ice cover. These two nodes have completely opposite spatial patterns and directions of trend and are associated with different atmospheric circulation patterns. Node 3, which represents an increase in sea ice over the Weddell Sea and the eastern Ross Sea and a decrease over the other coastal seas of West Antarctica, appears to be related to the positive phase of the southern annular mode (SAM) linked with the La Niña pattern of sea surface temperature (SST) over the tropical Pacific Ocean. The opposite spatial pattern and trend represented by node 7 is associated with a wave train originating over northern Australia. The anomalous wind field, surface downward longwave radiation, and surface air temperature generated by these circulation patterns are consistent with the spatial pattern and overall trends in the Antarctic sea ice cover. [ABSTRACT FROM AUTHOR]

We used 35-year (1979-2013) hourly rainfall data from the North American Land Data Assimilation System (NLDAS-2) to examine the relationships of the symmetric and asymmetric components of two types of El Niño-Southern Oscillation (ENSO) (El Niño and El Niño Modoki) episodes with occurrences of extreme precipitation events across the United States. During the cold season, the symmetric impacts of El Niño and El Niño Modoki are more significant than during the warm season. The asymmetric components associated with El Niño during the two seasons are comparable, while the asymmetric components associated with El Niño Modoki during the warm season are more significant than those during the cold season. The connections between the symmetric and asymmetric components of the two types of ENSO and extreme precipitation occurrences are related to atmospheric moisture convergence patterns that tend to occur in response to the anomalous large-scale atmospheric circulations during the ENSO episodes. [ABSTRACT FROM AUTHOR]

ABSTRACT The increased extreme warm and decreased extreme cold temperature events across the Arctic strongly influence the natural environment as well as the societal activities. This study investigates temporal and spatial variability of wintertime extreme high and low temperature events defined by the 95 and 5% percentiles across the Arctic and subarctic regions, respectively (north of 60°N) using data from 238 stations in the Global Summary of the Day for the period 1979-2016. Empirical orthogonal function analyses indicate that the first modes (which account for 30-35% of the total variance) are out-of-phase between northern Europe, western and central Russia, and northeastern North America, and that this appears to be related to the Arctic Oscillation ( AO) and the Northern Atlantic Oscillation. The second modes explain about 8% of the total variance. During the positive phase of the first and second modes the anomalous northeasterly and northerly winds decrease Arctic extreme high and increase extreme low temperature occurrences; while the anomalous southerly and southwesterly winds have the opposite effect. Symmetric and asymmetric effects of the AO index on extreme temperature events refer to the difference and sum between the composite of its positive and negative phases. The symmetric components of the spatial patterns are similar to those of the first modes. The asymmetric components occur mainly over western and central Russia for extreme high and low temperatures, respectively. In addition the impacts of six other large-scale climate modes are also explored. [ABSTRACT FROM AUTHOR]

Irregular oscillations of Equatorial Pacific sea surface temperatures known as El Niño-Southern Oscillation (ENSO) has been shown to affect air pollution in China by altering atmospheric circulations. In this study, we use hourly PM2.5 data from nearly 1500 monitoring stations across entire China to further explore the connection between ENSO and China's haze pollution by comparing PM2.5 concentrations for the two most recent ENSO events, the 2015–2016 El Niño and the 2017–2018 La Niña. The PM2.5 concentrations show significant differences between the two events and the differences are larger in winter and smaller in summer. In summer, the PM2.5 concentrations are higher across China during El Niño. However in winter, higher concentrations are found at most stations in northern China during El Niño, but at majority of stations in southern China during La Niña. Spring and autumn show a transitional pattern between summer and winter. Dividing the country into nine regions with similar geographical conditions, the relationship between the monthly PM2.5 concentration anomalies and the Niño3.4 indices, as revealed by linear regression, is generally positive at stations in North, Northeast and the northern parts of Central and East China, but generally negative in South, Southwest and the southern parts of Central and East China. No difference is found in the oscillation periods of the PM2.5 concentrations between the two events, except that the seasonal and intra-seasonal oscillatory signals appear to be slightly stronger during El Niño than La Niña. The regional and seasonal differences in PM2.5 concentrations between El Niño and La Niña can be partially explained by the differences in the anomalous atmospheric circulation patterns, particularly the low-level wind and surface precipitation patterns. • The influence of El Niño and La Niña on PM2.5 concentrations across China is explored. • The influence of El Niño-La Niña on PM2.5 in China differ by region and season. • The relationship between PM2.5 and Niño indices is opposite in north and south China. • PM2.5 seasonal and intra-seasonal oscillations are stronger with El Niño than La Niña. • The ENSO-PM2.5 connection is consistent with anomalous circulation and precipitation. [ABSTRACT FROM AUTHOR]

Using hourly observations of fine particulate matter (PM2.5) from a nationwide air-quality monitoring network of nearly 1500 stations, augmented with gridded atmospheric reanalysis data, we examined the differential influences of the eastern Pacific El Niño and central Pacific El Niño also known as El Niño Modoki on surface PM2.5 concentrations in different regions of China during different seasons. We show that the influence can be opposite during warm season, with PM2.5 anomalies overwhelmingly positive (negative) across China under El Niño (El Niño Modoki), although in both cases the largest anomalies appear in North China and the adjacent areas and smallest in the Qinghai-Tibet Plateau. The cold-season PM2.5 concentration is less sensitive to the El Niño type, with a general pattern of positive anomalies in the North, Northeast, Inner Mongolia, northern parts of the Central and East and along the southern and southeastern coasts and negative anomalies in most other regions. Nevertheless, negative cold-season anomalies are stronger and more widespread during El Niño Modoki compared to El Niño, and they could be opposite in some areas of the Northwest and Qinghai-Tibet Plateau where positive (negative) anomalies may prevail during El Niño (Niño Modoki). Much of these differences can be attributed to the differences in the anomalous atmospheric conditions associated with El Niño and El Niño Modoki, particularly the low-level anomalous wind direction that controls regional PM2.5 transport and precipitation leading to PM2.5 removal. These findings could assist in developing a nuanced approach to seasonal PM2.5 forecasting for different regions in China. [Display omitted] • The sensitivity of PM2.5 concentration distribution in China to ENSO type is particularly notable in the warm season. • China's warm-season PM2.5 shows opposite response to El Niño (increase) and El Niño Modoki (decrease) in most regions. • Cold-season PM2.5 response to ENSO type varies in western/southern China, but remains consistent elsewhere. • PM2.5's response to El Niño types is largely due to differences in anomalous atmospheric circulations. [ABSTRACT FROM AUTHOR]