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In this Review, the middle initial of author Kim M. Cobb was omitted. The original Review has been corrected online.
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El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño-Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.
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El Niño Oscilação Sul , Mudança Climática , Clima Tropical , Movimentos da ÁguaRESUMO
Recent studies have suggested that spring dust storm (SDS) events in northern China (NC) have exhibited substantial decline over the past 30 years. However, it is unclear which local factors are most responsible for the decline in SDS events, and the contribution of each dominant factor remains to be determined. This study utilized high-density DS records and collocated homogenized surface meteorological observations from 1982 to 2017, in conjunction with land surface products, to examine the local drivers that influence the long-term variation in SDS frequency (SDSF) over the entire NC area and its three dust-source areas: northwestern China (NWC), north-central China (NCC), and northeastern China (NEC). Results indicated that the observed SDSF averaged over NC, NWC, NCC, and NEC has decreased by 144.4 %, 109.3 %, 166.4 %, and 92.2 %, respectively, during 1982-2017. The variation in SDSF is largely explained by variation in wind speed (WS), precipitation, volumetric soil moisture, and surface bareness. A multivariable linear regression model incorporating these local drivers accounted for 81.0 %, 74.0 %, and 46.9 % of the variance in SDSF in NWC, NCC, and NEC, respectively. Statistical analyses on the local drivers suggested that weakening of WS was the dominant factor in the reduction in SDSF over recent decades, contributing 76.9 %, 54.7 %, and 33.6 % of the variation in NWC, NCC, and NEC, respectively. More importantly, we revealed that the interannual variation in regional SDSF was not only controlled by local drivers, but also influenced by cross-regional transport of dust aerosols emitted from upstream source areas.
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The Arctic has experienced several extreme springtime stratospheric ozone depletion events over the past four decades, particularly in 1997, 2011 and 2020. However, the impact of this stratospheric ozone depletion on the climate system remains poorly understood. Here we show that the stratospheric ozone depletion causes significant reductions in the sea ice concentration (SIC) and the sea ice thickness (SIT) over the Kara Sea, Laptev Sea and East Siberian Sea from spring to summer. This is partially caused by enhanced ice transport from Barents-Kara Sea and East Siberian Sea to the Fram Strait, which is induced by a strengthened and longer lived polar vortex associated with stratospheric ozone depletion. Additionally, cloud longwave radiation and surface albedo feedbacks enhance the melting of Arctic sea ice, particularly along the coast of the Eurasian continent. This study highlights the need for realistic representation of stratosphere-troposphere interactions in order to accurately predict Arctic sea ice loss.
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Perda de Ozônio , Ozônio Estratosférico , Camada de Gelo , Regiões Árticas , Estações do AnoRESUMO
The land surface temperature (LST) changes in North America are very abnormal recently, but few studies have systematically researched these anomalies from several aspects, especially the influencing forces. After reconstructing higher quality MODIS monthly LST data (0.05° * 0.05°) in 2002-2018, we analyzed the LST changes especially anomalous changes and their driving forces in North America. Here we show that North America warmed at the rate of 0.02 °C/y. The LST changes in three regions, including frigid region in the northwestern (0.12 °C/y), the west coast from 20°N-40°N (0.07 °C/y), and the tropics south of 20°N (0.04 °C/y), were extremely abnormal. The El Nino and La Nina were the main drivers for the periodical highest and lowest LST, respectively. The North Atlantic Oscillation was closed related to the opposite change of LST in the northeastern North America and the southeastern United States, and the warming trend of the Florida peninsula in winter was closely related to enhancement of the North Atlantic Oscillation index. The Pacific Decadal Oscillation index showed a positive correlation with the LST in most Alaska. Vegetation and atmospheric water vapor also had a profound influence on the LST changes, but it had obvious difference in latitude.
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Early in 2014 several forecast systems were suggesting a strong 1997/98-like El Niño event for the following northern hemisphere winter 2014/15. However the eventual outcome was a modest warming. In contrast, winter 2015/16 saw one of the strongest El Niño events on record. Here we assess the ability of two operational seasonal prediction systems to forecast these events, using the forecast ensembles to try to understand the reasons underlying the very different development and outcomes for these two years. We test three hypotheses. First we find that the continuation of neutral ENSO conditions in 2014 is associated with the maintenance of the observed cold southeast Pacific sea surface temperature anomaly; secondly that, in our forecasts at least, warm west equatorial Pacific sea surface temperature anomalies do not appear to hinder El Niño development; and finally that stronger westerly wind burst activity in 2015 compared to 2014 is a key difference between the two years. Interestingly, in these years at least, this interannual variability in wind burst activity is predictable. ECMWF System 4 tends to produce more westerly wind bursts than Met Office GloSea5 and this likely contributes to the larger SST anomalies predicted in this model in both years.