Tian Tian

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Tian
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Tian
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0000-0001-9092-9128

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  • Article
    Impacts of Arctic sea ice on cold season atmospheric variability and trends estimated from observations and a multimodel large ensemble
    (American Meteorological Society, 2021-09-24) Liang, Yu-Chiao ; Frankignoul, Claude ; Kwon, Young-Oh ; Gastineau, Guillaume ; Manzini, Elisa ; Danabasoglu, Gokhan ; Suo, Lingling ; Yeager, Stephen G. ; Gao, Yongqi ; Attema, Jisk J. ; Cherchi, Annalisa ; Ghosh, Rohit ; Matei, Daniela ; Mecking, Jennifer V. ; Tian, Tian ; Zhang, Ying
    To examine the atmospheric responses to Arctic sea ice variability in the Northern Hemisphere cold season (from October to the following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily varying sea ice, sea surface temperature, and radiative forcings prescribed during the 1979–2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multimodel ensemble mean (MMEM) shows decreasing sea level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drive a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual covariability between sea ice extent in the Barents–Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the covariability in MMEMs. The interannual sea ice decline followed by a negative North Atlantic Oscillation–like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship.
  • Article
    Quantification of the arctic sea ice-driven atmospheric circulation variability in coordinated large ensemble simulations
    (American Geophysical Union, 2019-12-26) Liang, Yu‐Chiao ; Kwon, Young-Oh ; Frankignoul, Claude ; Danabasoglu, Gokhan ; Yeager, Stephen G. ; Cherchi, Annalisa ; Gao, Yongqi ; Gastineau, Guillaume ; Ghosh, Rohit ; Matei, Daniela ; Mecking, Jennifer V. ; Peano, Daniele ; Suo, Lingling ; Tian, Tian
    A coordinated set of large ensemble atmosphere‐only simulations is used to investigate the impacts of observed Arctic sea ice‐driven variability (SIDV) on the atmospheric circulation during 1979–2014. The experimental protocol permits separating Arctic SIDV from internal variability and variability driven by other forcings including sea surface temperature and greenhouse gases. The geographic pattern of SIDV is consistent across seven participating models, but its magnitude strongly depends on ensemble size. Based on 130 members, winter SIDV is ~0.18 hPa2 for Arctic‐averaged sea level pressure (~1.5% of the total variance), and ~0.35 K2 for surface air temperature (~21%) at interannual and longer timescales. The results suggest that more than 100 (40) members are needed to separate Arctic SIDV from other components for dynamical (thermodynamical) variables, and insufficient ensemble size always leads to overestimation of SIDV. Nevertheless, SIDV is 0.75–1.5 times as large as the variability driven by other forcings over northern Eurasia and Arctic.
  • Article
    Arctic troposphere warming driven by external radiative forcing and modulated by the Pacific and Atlantic
    (American Geophysical Union, 2022-12-04) Suo, Lingling ; Gao, Yongqi ; Gastineau, Guillaume ; Liang, Yu‐Chiao ; Ghosh, Rohit ; Tian, Tian ; Zhang, Ying ; Kwon, Young‐Oh ; Matei, Daniela ; Otterå, Odd Helge ; Yang, Shuting
    During the past decades, the Arctic has experienced significant tropospheric warming, with varying decadal warming rates. However, the relative contributions from potential drivers and modulators of the warming are yet to be further quantified. Here, we utilize a unique set of multi‐model large‐ensemble atmospheric simulations to isolate the respective contributions from the combined external radiative forcing (ERF‐AL), interdecadal Pacific variability (IPV), Atlantic multidecadal variability (AMV), and Arctic sea‐ice concentration changes (ASIC) to the warming during 1979–2013. In this study, the ERF‐AL impacts are the ERF impacts directly on the atmosphere and land surface, excluding the indirect effects through SST and SIC feedback. The ERF‐AL is the primary driver of the April–September tropospheric warming during 1979–2013, and its warming effects vary at decadal time scales. The IPV and AMV intensify the warming during their transitioning periods to positive phases and dampen the warming during their transitioning periods to negative phases. The IPV impacts are prominent in winter and spring and are stronger than AMV impacts on 1979–2013 temperature trends. The warming impacts of ASIC are generally restricted to below 700 hPa and are strongest in autumn and winter. The combined effects of these factors reproduce the observed accelerated and step‐down Arctic warming in different decades, but the intensities of the reproduced decadal variations are generally weaker than in the observed.
  • Article
    Simulated contribution of the interdecadal Pacific oscillation to the west Eurasia cooling in 1998–2013
    (IOP Publishing, 2022-08-30) Suo, Lingling ; Gastineau, Guillaume ; Gao, Yongqi ; Liang, Yu-Chiao ; Ghosh, Rohit ; Tian, Tian ; Zhang, Ying ; Kwon, Young-Oh ; Otterå, Odd Helge ; Yang, Shuting ; Matei, Daniela
    Large ensemble simulations with six atmospheric general circulation models involved are utilized to verify the interdecadal Pacific oscillation (IPO) impacts on the trend of Eurasian winter surface air temperatures (SAT) during 1998–2013, a period characterized by the prominent Eurasia cooling (EC). In our simulations, IPO brings a cooling trend over west-central Eurasia in 1998–2013, about a quarter of the observed EC in that area. The cooling is associated with the phase transition of the IPO to a strong negative. However, the standard deviation of the area-averaged SAT trends in the west EC region among ensembles, driven by internal variability intrinsic due to the atmosphere and land, is more than three times the isolated IPO impacts, which can shadow the modulation of the IPO on the west Eurasia winter climate.
  • Article
    Observed winter Barents Kara Sea ice variations induce prominent sub-decadal variability and a multi-decadal trend in the warm Arctic cold Eurasia pattern
    (IOP Publishing, 2024-01-25) Ghosh, Rohit ; Manzini, Elisa ; Gao, Yongqi ; Gastineau, Guillaume ; Cherchi, Annalisa ; Frankignoul, Claude ; Liang, Yu-Chiao ; Kwon, Young-Oh ; Suo, Lingling ; Tyrlis, Evangelos ; Mecking, Jennifer V. ; Tian, Tian ; Zhang, Ying ; Matei, Daniela
    The observed winter Barents-Kara Sea (BKS) sea ice concentration (SIC) has shown a close association with the second empirical orthogonal function (EOF) mode of Eurasian winter surface air temperature (SAT) variability, known as Warm Arctic Cold Eurasia (WACE) pattern. However, the potential role of BKS SIC on this WACE pattern of variability and on its long-term trend remains elusive. Here, we show that from 1979 to 2022, the winter BKS SIC and WACE association is most prominent and statistically significant for the variability at the sub-decadal time scale for 5–6 years. We also show the critical role of the multi-decadal trend in the principal component of the WACE mode of variability for explaining the overall Eurasian winter temperature trend over the same period. Furthermore, a large multi-model ensemble of atmosphere-only experiments from 1979 to 2014, with and without the observed Arctic SIC forcing, suggests that the BKS SIC variations induce this observed sub-decadal variability and the multi-decadal trend in the WACE. Additionally, we analyse the model simulated first or the leading EOF mode of Eurasian winter SAT variability, which in observations, closely relates to the Arctic Oscillation (AO). We find a weaker association of this mode to AO and a statistically significant positive trend in our ensemble simulation, opposite to that found in observation. This contrasting nature reflects excessive hemispheric warming in the models, partly contributed by the modelled Arctic Sea ice loss.
  • Article
    Forcing and impact of the Northern Hemisphere continental snow cover in 1979–2014
    (European Geosciences Union, 2023-05-23) Gastineau, Guillaume ; Frankignoul, Claude ; Gao, Yongqi ; Liang, Yu-Chiao ; Kwon, Young-Oh ; Cherchi, Annalisa ; Ghosh, Rohit ; Manzini, Elisa ; Matei, Daniela ; Mecking, Jennifer ; Suo, Lingling ; Tian, Tian ; Yang, Shuting ; Zhang, Ying
    The main drivers of the continental Northern Hemisphere snow cover are investigated in the 1979–2014 period. Four observational datasets are used as are two large multi-model ensembles of atmosphere-only simulations with prescribed sea surface temperature (SST) and sea ice concentration (SIC). A first ensemble uses observed interannually varying SST and SIC conditions for 1979–2014, while a second ensemble is identical except for SIC with a repeated climatological cycle used. SST and external forcing typically explain 10 % to 25 % of the snow cover variance in model simulations, with a dominant forcing from the tropical and North Pacific SST during this period. In terms of the climate influence of the snow cover anomalies, both observations and models show no robust links between the November and April snow cover variability and the atmospheric circulation 1 month later. On the other hand, the first mode of Eurasian snow cover variability in January, with more extended snow over western Eurasia, is found to precede an atmospheric circulation pattern by 1 month, similar to a negative Arctic oscillation (AO). A decomposition of the variability in the model simulations shows that this relationship is mainly due to internal climate variability. Detailed outputs from one of the models indicate that the western Eurasia snow cover anomalies are preceded by a negative AO phase accompanied by a Ural blocking pattern and a stratospheric polar vortex weakening. The link between the AO and the snow cover variability is strongly related to the concomitant role of the stratospheric polar vortex, with the Eurasian snow cover acting as a positive feedback for the AO variability in winter. No robust influence of the SIC variability is found, as the sea ice loss in these simulations only drives an insignificant fraction of the snow cover anomalies, with few agreements among models.