7 resultados para sea surface temperature anomaly (SSTA)

em Chinese Academy of Sciences Institutional Repositories Grid Portal


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C-37 unsaturated alkenones were analyzed on a core retrieved from the middle Okinawa Trough. The calculated U-37(K') displays a trend generally parallel with those of the oxygen isotopic compositions of two planktonic foraminiferal species, Neogloboquadrina dutertrei and Globigerinoides sacculifer, suggesting that in this region, SST has varied in phase with global ice volume change since the last glacial -interglacial cycle. The U-37(K')-derived SST ranged from ca. 24.0 to 27.5 degrees C, with the highest value 27.5 degrees C occurring in marine isotope stage 5 and the lowest similar to 24.0 degrees C in marine isotope stage 2. This trend is consistent with the continental records from the East Asian monsoon domain and the marine records from the Equatorial Pacific. The deglacial increase of the U-37(K')-derived SST is similar to 2.4 degrees C from the Last Glacial Maximum to the Holocene. (c) 2007 Elsevier B.V. All rights reserved.

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[ 1] Intraseasonal variability of Indian Ocean sea surface temperature (SST) during boreal winter is investigated by analyzing available data and a suite of solutions to an ocean general circulation model for 1998 - 2004. This period covers the QuikSCAT and Tropical Rainfall Measuring Mission (TRMM) observations. Impacts of the 30 - 90 day and 10 - 30 day atmospheric intraseasonal oscillations (ISOs) are examined separately, with the former dominated by the Madden-Julian Oscillation (MJO) and the latter dominated by convectively coupled Rossby and Kelvin waves. The maximum variation of intraseasonal SST occurs at 10 degrees S - 2 degrees S in the wintertime Intertropical Convergence Zone (ITCZ), where the mixed layer is thin and intraseasonal wind speed reaches its maximum. The observed maximum warming ( cooling) averaged over ( 60 degrees E - 85 degrees E, 10 degrees S - 3 degrees S) is 1.13 degrees C ( - 0.97 degrees C) for the period of interest, with a standard deviation of 0.39 degrees C in winter. This SST change is forced predominantly by the MJO. While the MJO causes a basin-wide cooling ( warming) in the ITCZ region, submonthly ISOs cause a more complex SST structure that propagates southwestward in the western-central basin and southeastward in the eastern ocean. On both the MJO and submonthly timescales, winds are the deterministic factor for the SST variability. Short-wave radiation generally plays a secondary role, and effects of precipitation are negligible. The dominant role of winds results roughly equally from wind speed and stress forcing. Wind speed affects SST by altering turbulent heat fluxes and entrainment cooling. Wind stress affects SST via several local and remote oceanic processes.

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We investigate the influence of low-frequency Rossby waves on the thermal structure of the upper southwestern tropical Indian Ocean (SWTIO) using Argo profiles, satellite altimetric data, sea surface temperature, wind field data and the theory of linear vertical normal mode decomposition. Our results show that the SWTIO is generally dominated by the first baroclinic mode motion. As strong downwelling Rossby waves reach the SWTIO, the contribution of the second baroclinic mode motion in this region can be increased mainly because of the reduction in the vertical stratification of the upper layer above thermocline, and the enhancement in the vertical stratification of the lower layer under thermocline also contributes to it. The vertical displacement of each isothermal is enlarged and the thermal structure of the upper level is modulated, which is indicative of strong vertical mixing. However, the cold Rossby waves increase the vertical stratification of the upper level, restricting the variability related to the second baroclinic mode. On the other hand, during decaying phase of warm Rossby waves, Ekman upwelling and advection processes associated with the surface cyclonic wind circulation can restrain the downwelling processes, carrying the relatively colder water to the near-surface, which results in an out-of-phase phenomenon between sea surface temperature anomaly (SSTA) and sea surface height anomaly (SSHA) in the SWTIO.

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利用ERA40逐日再分析资料、NCEP/NCAR2逐日再分析资料、中国740个测站日降水资料、上海台风研究所提供的西太平洋热带气旋资料、Kaplan等重建的月平均SSTA资料、NOAA逐日长波辐射(OLR)等资料,应用离散功率谱分析、带通滤波、EOF分析等统计方法,研究了东亚夏季风(EASM)的移动特征、东亚地区季节内振荡(ISO)的基本特征、季节内振荡对东亚夏季风活动的影响、季节内振荡对东亚夏季风异常活动的影响机理。主要结论如下: (1)综合动力和热力因素定义了可动态描述东亚夏季风移动和强度的指数,并利用该指数研究了东亚夏季风的爆发和移动的季节内变化及其年际和年代际变化特征。研究发现,气候平均东亚夏季风前沿分别在28候、33候、36候、38候、40候、44候出现了明显的跳跃。东亚夏季风活动具有显著的年际变率,主要由于季风前沿在某些区域异常停滞和突然跨越北跳或南撤引起,造成中国东部旱涝灾害频繁发生。东亚夏季风的活动具有明显的年代际变化,在1965年、1980年、1994年发生了突变,造成中国东部降水由“南旱北涝”向“南涝北旱”的转变。 (2)东亚季风区季节内变化具有10~25d和30~60d两个波段的季节内振荡周期,以30-60d为主。存在三个主要低频模态,第一模态主要表征了EASM在长江中下游和华北地区活动期间的低频形势;第二模态印度洋-菲律宾由低频气旋式环流控制,主要表现了ISO在EASM爆发期间的低频形势;第三模态主要出现在EASM在华南和淮河活动期间的低频形势。第一模态和第三模态是代表东亚夏季风活动异常的主要低频形势。 (3)热带和副热带地区ISO总是沿垂直切变风的垂直方向传播。因此,在南海-菲律宾东北风垂直切变和副热带西太平洋北风垂直切变下,大气热源激发菲律宾附近交替出现的低频气旋和低频反气旋不断向西北传播,副热带西太平洋ISO以向西传播为主。中高纬度地区,乌拉尔山附近ISO以向东、向南移动或局地振荡为主;北太平洋中部ISO在某些情况下向南、向西传播。 (4)季风爆发期,伴随着热带东印度洋到菲律宾一系列低频气旋和低频反气旋, 冷空气向南输送,10~25天和30~60天季节内振荡低频气旋同时传入南海加快了南海夏季风的爆发。在气候态下,ISO活动表现的欧亚- 太平洋(EAP)以及太平洋-北美(PNA)低频波列分布特征(本文提出的EAP和PNA低频波列与传统意义上的二维定点相关得到的波列不同)。这种低频分布形式使得欧亚和太平洋中高纬度的槽、脊及太平洋副热带高压稳定、加强,东亚地区的低频波列则成为热带和中高纬度ISO相互作用影响东亚夏季风活动的纽带。不同的阶段表现不同的低频模态,30~60d低频模态的转变加快了EASM推进过程中跳跃性;30-60d低频模态的维持使得EASM前沿相对停滞。 (5)30-60d滤波场,菲律宾海域交替出现的低频气旋和低频反气旋不断向西北传播到南海-西太平洋一带。当南海-西太平洋地区低频气旋活跃时,季风槽加强、东伸,季风槽内热带气旋(TC)频数增加;当南海-西太平洋低频反气旋活跃时,季风槽减弱、西退,TC处于间歇期,生成位置不集中。 (6)在El Nino态下,大气季节内振荡偏弱,北传特征不明显,但ISO由中高纬度北太平洋中部向南和副热带西太平洋向西的传播特征显著,东亚地区ISO活动以第三模态为主,EASM集中停滞在华南和淮河流域,常伴随着持续性区域暴雨的出现,易造成华南和江淮流域洪涝灾害,长江和华北持续干旱。在La Nina态下,大气季节内振荡活跃,且具有明显的向北传播特征,PNA低频波列显著,东亚地区ISO活动以第一模态单峰为主;EASM主要停滞在长江中下游和华北地区,这些地区出现异常持续强降水,华南和淮河流域多干旱;在El Nino态向La Nina态转换期,ISO活动以第一模态双峰为主,长江中下游常常出现二度梅。

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We compared nonlinear principal component analysis (NLPCA) with linear principal component analysis (LPCA) with the data of sea surface wind anomalies (SWA), surface height anomalies (SSHA), and sea surface temperature anomalies (SSTA), taken in the South China Sea (SCS) between 1993 and 2003. The SCS monthly data for SWA, SSHA and SSTA (i.e., the anomalies with climatological seasonal cycle removed) were pre-filtered by LPCA, with only three leading modes retained. The first three modes of SWA, SSHA, and SSTA of LPCA explained 86%, 71%, and 94% of the total variance in the original data, respectively. Thus, the three associated time coefficient functions (TCFs) were used as the input data for NLPCA network. The NLPCA was made based on feed-forward neural network models. Compared with classical linear PCA, the first NLPCA mode could explain more variance than linear PCA for the above data. The nonlinearity of SWA and SSHA were stronger in most areas of the SCS. The first mode of the NLPCA on the SWA and SSHA accounted for 67.26% of the variance versus 54.7%, and 60.24% versus 50.43%, respectively for the first LPCA mode. Conversely, the nonlinear SSTA, localized in the northern SCS and southern continental shelf region, resulted in little improvement in the explanation of the variance for the first NLPCA.