顾及PWV的广西地区多尺度PM_(2.5)浓度预测

针对现有的雾霾预测方法较少考虑可降水量的影响,且大部分预测方法都没有对模型回归残差进行有效处理因而预测精度不是很高的问题,利用广西南宁、桂林、梧州和百色四市2017年的PM_(2.5)日均值数据,结合大气污染物、气象因子和大气可降水量PWV等因素,分别建立全年和分季度的ARIMA模型对该地区PM_(2.5)日均浓度进行短期预测,并将ARIMA模型预测残差分别用前馈神经网络径向基函数RBF和多层感知器MLP进行拟合,以达到优化ARIMA模型的目的。结果表明,除桂林外,分季度ARIMA模型预测效果优于全年ARIMA模型,季度ARIMA-MLP神经网络预测精度优于分季度ARIMA模型,表明该类模型可以用于区域PM_(2.5)浓度预测。

基于水汽垂直指数分布特征的PWV快速层析方法

GNSS水汽层析技术可以反演对流层水汽三维时空变化情况,但该技术比较复杂、运算量大,需要消耗一定的时间.故本文提出了一种利用地基GNSS反演的大气可降水量(precipitable water vapor,PWV)结合水汽在垂直方向上的指数分布特性来计算大气水汽三维分布的快速层析方法.该方法利用香港地区2022年8月的GNSS数据开展试验,与传统GNSS水汽层析方法进行对比.试验结果表明:两种方法的层析解算结果与探空数据均具有良好的一致性.虽然快速层析方法的解算结果在底层区域缺少一些水汽变化的细节信息,精度略逊于传统层析方法,但是在中、高层时精度会有所提升,层析解算结果良好.而且本文提出的快速层析方法无需构建和解算复杂的层析方程组,可以在大量GNSS测站参与水汽层析时减少计算复杂度,提升运算能力,同时可以更快地得到任意高度层的水汽密度,是一种简便、高效的层析方法.

Hydrographic Characteristics and Oceanic Heat Flux in the Upper Arctic Ocean over the Alpha Ridge Observed by the DTOP Platform in 2018 and 2021

In 2018 and 2021,the Drift-Towing Ocean Profilers(DTOP)provided extensive temperature and salinity data on the upper 120m ocean through their drifts over the Alpha Ridge north of the Canada Basin.The thickness and temperature maximum of Alaska Coastal Water(ACW)ranged from 20m to 40m and-1.5℃to-0.8℃,respectively,and the salinity generally maintained from 30.2 to 32.5.Comparison with World Ocean Atlas 2018’s climatology manifested a 40m-thick and warm ACW roughly ex-ceeding the temperature maximum by 0.4–0.5℃in June–August 2021.This anomalously warm ACW was highly related to the ex-pansion of the Beaufort Gyre in the negative Arctic Oscillation phase.During summer,the under-ice oceanic heat flux F_(w)^(OHF)was elevated,with a maximum value of above 25Wm^(-2).F_(w)^(OHF)was typically low in the freezing season,with an average value of 1.2Wm^(-2).The estimates of upward heat flux contributed by ACW to the sea ice bottom F_(w)^(OHF)were in the range of 3–4Wm^(-2)in June–August 2021,when ACW contained a heat content of more than 80MJm^(-2).The heat loss over this period was driven by a weak stratification upon the ACW layer associated with a surface mixed layer(SML)approaching the ACW core.After autumn,F_(w)^(OHF)was reduced(<2 Wm^(-2))except during rare events when it elevated F_(w)^(OHF)slightly.In addition,the intensive and widespread Ekman suction,which created a violent upwelling north of the Canada Basin,was largely responsible for the substantial cooling and thinning of the ACW layer in the summer of 2021.

Impacts of Ice-Ocean Stress on the Subpolar Southern Ocean:Role of the Ocean Surface Current

The mechanical influences involved in the interaction between the Antarctic sea ice and ocean surface current(OSC)on the subpolar Southern Ocean have been systematically investigated for the first time by conducting two simulations that include and exclude the OSC in the calculation of the ice-ocean stress(IOS), using an eddy-permitting coupled ocean-sea ice global model. By comparing the results of these two experiments, significant increases of 5%, 27%, and 24%, were found in the subpolar Southern Ocean when excluding the OSC in the IOS calculation for the ocean surface stress,upwelling, and downwelling, respectively. Excluding the OSC in the IOS calculation also visibly strengthens the total mechanical energy input to the OSC by about 16%, and increases the eddy kinetic energy and mean kinetic energy by about38% and 12%, respectively. Moreover, the response of the meridional overturning circulation in the Southern Ocean yields respective increases of about 16% and 15% for the upper and lower branches;and the subpolar gyres are also found to considerably intensify, by about 12%, 11%, and 11% in the Weddell Gyre, the Ross Gyre, and the Australian-Antarctic Gyre, respectively. The strengthened ocean circulations and Ekman pumping result in a warmer sea surface temperature(SST), and hence an incremental surface heat loss. The increased sea ice drift and warm SST lead to an expansion of the sea ice area and a reduction of sea ice volume. These results emphasize the importance of OSCs in the air-sea-ice interactions on the global ocean circulations and the mass balance of Antarctic ice shelves, and this component may become more significant as the rapid change of Antarctic sea ice.

Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations

Accurately estimating the ocean subsurface salinity structure(OSSS)is crucial for understanding ocean dynamics and predicting climate variations.We present a convolutional neural network(CNN)model to estimate the OSSS in the Indian Ocean using satellite data and Argo observations.We evaluated the performance of the CNN model in terms of its vertical and spatial distribution,as well as seasonal variation of OSSS estimation.Results demonstrate that the CNN model accurately estimates the most significant salinity features in the Indian Ocean using sea surface data with no significant differences from Argo-derived OSSS.However,the estimation accuracy of the CNN model varies with depth,with the most challenging depth being approximately 70 m,corresponding to the halocline layer.Validations of the CNN model’s accuracy in estimating OSSS in the Indian Ocean are also conducted by comparing Argo observations and CNN model estimations along two selected sections and four selected boxes.The results show that the CNN model effectively captures the seasonal variability of salinity,demonstrating its high performance in salinity estimation using sea surface data.Our analysis reveals that sea surface salinity has the strongest correlation with OSSS in shallow layers,while sea surface height anomaly plays a more significant role in deeper layers.These preliminary results provide valuable insights into the feasibility of estimating OSSS using satellite observations and have implications for studying upper ocean dynamics using machine learning techniques.

New Record Ocean Temperatures and Related Climate Indicators in 2023

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities.In 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record highs.The 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the 1950s.Associated with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of 2023.The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

Seasonal variation of mesoscale eddy intensity in the global ocean

Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion.Characterizing by rotational speed,the eddy intensity is one of the most fundamental properties of an eddy.However,the seasonal spatiotemporal variation in eddy intensity has not been examined from a global ocean perspective.In this study,we unveil the seasonal spatiotemporal characteristics of eddy intensity in the global ocean by using the latest satellite-altimetry-derived eddy trajectory data set.The results suggest that the eddy intensity has a distinct seasonal variation,reaching a peak in spring while attaining a minimum in autumn in the Northern Hemisphere and the opposite in the Southern Hemisphere.The seasonal variation of eddy intensity is more intense in the tropical-subtropical transition zones within latitudinal bands between 15°and 30°in the western Pacific Ocean,the northwestern Atlantic Ocean,and the eastern Indian Ocean because baroclinic instability in these areas changes sharply.Further analysis found that the seasonal variation of baroclinic instability precedes the eddy intensity by a phase of 2–3 months due to the initial perturbations needing time to grow into mesoscale eddies.

Predictability of the upper ocean heat content in a Community Earth System Model ensemble prediction system

Upper ocean heat content(OHC)has been widely recognized as a crucial precursor to high-impact climate variability,especially for that being indispensable to the long-term memory of the ocean.Assessing the predictability of OHC using state-of-the-art climate models is invaluable for improving and advancing climate forecasts.Recently developed retrospective forecast experiments,based on a Community Earth System Model ensemble prediction system,offer a great opportunity to comprehensively explore OHC predictability.Our results indicate that the skill of actual OHC predictions varies across different oceans and diminishes as the lead time of prediction extends.The spatial distribution of the actual prediction skill closely resembles the corresponding persistence skill,indicating that the persistence of OHC serves as the primary predictive signal for its predictability.The decline in actual prediction skill is more pronounced in the Indian and Atlantic oceans than in the Pacific Ocean,particularly within tropical regions.Additionally,notable seasonal variations in the actual prediction skills across different oceans align well with the phase-locking features of OHC variability.The potential predictability of OHC generally surpasses the actual prediction skill at all lead times,highlighting significant room for improvement in current OHC predictions,especially for the North Indian Ocean and the Atlantic Ocean.Achieving such improvements necessitates a collaborative effort to enhance the quality of ocean observations,develop effective data assimilation methods,and reduce model bias.

An Overview on the Composition and Age of Upper Crust of Proto-Tethyan Lajishan Intra-oceanic Arc,NE Tibet Plateau

Identification and anatomy of oceanic arcs within ancient orogenic belt are significant for better understanding the tectonic framework and closure process of paleo-ocean basin.This article summarizes the geological,geochemical,and geochronological characteristics of upper crust of Proto-Tethyan Lajishan intra-oceanic arc and provides new data to constrain the subduction evolution of the South Qilian Ocean.The intra-oceanic arc volcanic rocks,including intermediate-mafic lava,breccia,tuff,and minor felsic rocks,are distributed along southern part of the Lajishan ophiolite belt.Geochemical and isotopic compositions indicate that the intermediate-mafic lava were originated from depleted mantle contaminated by sediment melts or hydrous fluids,whereas the felsic rocks were likely generated by partial melting of juvenile mafic crust in intra-oceanic arc setting.Zircons from felsic rocks yield consistent and concordant ages ranging from 506 to 523 Ma,suggesting these volcanic rocks represent the relicts of upper crust of the Cambrian intra-oceanic arc.Combined with the Cambrian forearc ophiolite and accretionary complex,we suggest that the Cambrian intra-oceanic arc in the Lajishan ophiolite belt is belonging to the intra-oceanic arc system which was generated by south-directed subduction in the South Qilian Ocean at a relatively short interval between approximately 530 and 480 Ma.

Evaluation of Nonbreaking Wave-Induced Mixing Parameterization Schemes Based on a One-Dimensional Ocean Model

Surface waves have a considerable effect on vertical mixing in the upper ocean.In the past two decades,the vertical mixing induced through nonbreaking surface waves has been used in ocean and climate models to improve the simulation of the upper ocean.Thus far,several nonbreaking wave-induced mixing parameterization schemes have been proposed;however,no quantitative comparison has been performed among them.In this paper,a one-dimensional ocean model was used to compare the performances of five schemes,including those of Qiao et al.(Q),Hu and Wang(HW),Huang and Qiao(HQ),Pleskachevsky et al.(P),and Ghantous and Babanin(GB).Similar to previous studies,all of these schemes can decrease the simulated sea surface temperature(SST),increase the subsurface temperature,and deepen the mixed layer,thereby alleviating the common thermal deviation problem of the ocean model for upper ocean simulation.Among these schemes,the HQ scheme exhibited the weakest wave-induced mixing effect,and the HW scheme exhibited the strongest effect;the other three schemes exhibited roughly the same effect.In particular,the Q and P schemes exhibited nearly the same effect.In the simulation based on observations from the Ocean Weather Station Papa,the HQ scheme exhibited the best performance,followed by the Q scheme.In the experiment with the HQ scheme,the root-mean-square deviation of the simulated SST from the observations was 0.43℃,and the mixed layer depth(MLD)was 2.0 m.As a contrast,the deviations of the SST and MLD reached 1.25℃ and 8.4 m,respectively,in the experiment without wave-induced mixing.

Characteristics and triggering mechanisms of early negative Indian Ocean Dipole

Negative Indian Ocean Dipole(nIOD)can exert great impacts on global climate and can also strongly influence the climate in China.Early nIOD is a major type of nIOD,which can induce more pronounced climate anomalies in summer than La Niña-related nIOD.However,the characteristics and triggering mechanisms of early nIOD are unclear.Our results based on reanalysis datasets indicate that the early nIOD and La Niña-related nIOD are the two major types of nIOD,and the former accounts for over one third of all the nIOD events in the past six decades.These two types of nIODs are similar in their intensities,but are different in their spatial patterns and seasonal cycles.The early nIOD,which develops in spring and peaks in summer,is one season earlier than the La Niña-related nIOD.The spatial pattern of the wind anomaly associated with early nIOD exhibits a winter monsoon-like pattern,with strong westerly anomalies in the equatorial Indian Ocean and eastly anomalies in the northern Indian Ocean.Opposite to the triggering mechanism of early positve IOD,the early nIOD is induced by delayed Indian summer monsoon onset.The results of this study are helpful for improving the prediction skill of IOD and its climate impacts.

Paper Analysis of Shanghai Ocean University in Bibliometric Method from Web of Science

The data of this research was mainly collected from the Web of Science (WOS) and Incites database platform, which was filtered and cataloged according to the different platforms. For tracing the change in scientific research at Shanghai Ocean University, make use of Bibliometric analysis to get the image and table of highly cited papers and hot papers. In this study, the scientific aspects in highly cited papers and hot papers, published in the last year in the core collection of Web of Science, were taken as objects, and office software was used as the main tool to carry out bibliometric and figure analysis. From the four aspects to find the difference in these fields, the production of specific fields and cited times is inconsistent. And suggest the department and management adjust the policy and method via elastic personnel and rewards to prompt the advancement of the research fields.

An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data

The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort,Chukchi,East Siberian,Laptev and Kara seas and utilized the microwave polarization ratio(PR)at incidence angle of 40°.The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact,reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature.The relationship between the SIT and PR was found to be almost stable across the five selected regions.The SIT retrievals were then compared to other two existing algorithms(i.e.,UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen)and validated against independent SIT data obtained from moored upward looking sonars(ULS)and airborne electromagnetic(EM)induction sensors.The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error(RMSE)being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data.The proposed algorithm can be used for thin sea ice thickness(<1.0 m)estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.

基于PWV差值方法分析沙尘暴期间GNSSPWV与大气颗粒物相关性

针对2021年3月15日中国北方发生的沙尘暴事件,提出了一种基于大气可降水量差值的方法,旨在探究GNSS站点反演的大气可降水量与大气颗粒物浓度之间的相关性.选取了位于宁夏中卫(NXZW)、北京房山(BJFS)和吉林长春(CHAN)的3个GNSS站点及附近的大气颗粒物浓度数据进行分析.结果显示,在非沙尘暴条件下,GNSS解算的大气可降水量(precipitable water vapor,PWV)精度表现良好,其与ERA5模型的PWV的差值均值和标准差均约在2 mm,证明了解算结果的可靠性.沙尘暴发生前,各站点PWV与大气颗粒物浓度的相关性均低于20%,表现出较弱的相关性.在沙尘暴期间,该相关性显著提高,尤其在BJFS和CHAN站点,PWV与大气颗粒物浓度的相关性超过60%.相位滞后消除后,NXZW站点的相关性更是达到70.25%.进一步分析还发现,沙尘暴发生时,PWV差值与大气颗粒物浓度的相关性也显著提高,其中BJFS和CHAN站点的相关性超过70%.综合分析表明,沙尘暴发生时,PWV差值与大气颗粒物浓度的相关性进一步增高,这表明大气颗粒物对PWV差值的贡献比对PWV本身的贡献显著增加,从而说明了PWV差值方法在大气颗粒物浓度监测方面的潜在应用价值.因此,本研究提供了一种新的研究思路和方法,为大气颗粒物浓度和气象条件之间复杂交互关系的进一步研究奠定了基础.

Assessment of the Intertropical Convergence Zone over the Atlantic Ocean through an Algorithm Based on Precipitation

The Intertropical Convergence Zone(ITCZ)is a key atmospheric system on a global scale,primarily driven by trade wind convergence near the equator.The ITCZ plays a crucial role in modulating the climate of the borders of tropical continental areas.For instance,Northeastern Brazil experiences a climate influenced by the ITCZ over the Atlantic Ocean.In some periods,the ITCZ exhibits double bands,known as the double ITCZ.While the features of the ITCZ have been described using various approaches and atmospheric variables,there is still a lack of regional studies focusing on the ITCZ and double ITCZ in the Atlantic Ocean.In this context,the main goals of this study are(1)to describe a simple algorithm based on precipitation to identify the ITCZ and double ITCZ,(2)to present a climatology(1997-2022)of the position,width,and intensity of these two convective bands,and(3)to investigate variabilities in the ITCZ characteristics associated with anomalies of sea surface temperature(SST)in the tropical Pacific and Atlantic oceans.The double ITCZ typically occurs southward of the main cloud band,and between February and April,both bands are more distant(~4.5°).In the western sector of the Atlantic Ocean,the ITCZ and its double band extend to more southerly latitudes in austral autumn.Considering the entire Atlantic basin,the annual mean of the latitudinal position,width,and intensity of the ITCZ is 4.9°N,4.2°,and 11 mm/day,respectively,while for the double ITCZ,it is 0.4°N,2.6°,10.3 mm/day,respectively.While the SST anomalies in the Pacific Ocean(El Niño and La Niña episodes)affect more the ITCZ width,the SST anomalies in the Tropical South Atlantic affect both its position and width.

时空加权与再分析资料相结合的GNSS PWV时序填补方法

PWV是对流层中的重要参数之一,长时序连续的PWV对长期气候变化研究具有重要影响。但受外界因素影响,目前GNSS获取的PWV长时序数据缺失严重或分辨率较差,无法满足长时序分析的应用需求。针对该问题,本文提出了一种顾及时空加权的PWV长时序填补方法(STW)。该方法引入再分析资料,同时顾及GNSS站点上空水汽在空间和时间上的变化特征,并根据测站位置给予PWV时空变化不同的权值,选取中国地壳运动观测网络的实测数据和欧洲中尺度天气预报中心第五代全球大气再分析数据集进行试验。结果表明,本文提出的STW方法在不同时间分辨率的数据缺失和PWV长时序重采样方面均优于经典的线性插值和模型替换方法,可以得到更加可靠、精确和完整的PWV长时序集。

融合GNSS和随机森林算法的FY-3D PWV高精度校正模型

FY-3D可降水量(precipitable water vapor,PWV)是我国新一代极轨气象卫星提供的新型遥感水汽产品。针对降水、云、地表反射等因素影响导致的FY-3D PWV产品精度不高的问题,提出了一种融合GNSS和随机森林算法的FY-3D PWV高精度校正模型。该模型使用随机森林算法作为核心结构,充分考虑FY-3D PWV产品的多类型影响因素,以FY-3D像元的PWV值、时间、位置、高程、地表类型以及观测姿态等特征参数为输入变量,以高精度GNSS PWV为目标变量,利用美国西部地区2020年的实测数据进行了模型训练和测试。实验结果表明,相比于初始产品和线性校正模型,该模型分别使FY-3D PWV产品的精度提高了40.68%和36.07%,说明了其在FY-3D水汽产品校正中的优越性和稳定性。

利用GPT3模型的GNSS⁃PWV计算方法

针对部分GNSS测站缺乏实测气象参数时无法实时计算可降水量(PWV)的问题,本文以长三角地区为例提出一种将GPT3模型参数与GNSS对流层总延迟(ZTD)融合获取高精度PWV的新方法。研究结果表明,GPT3模型的气象参数和各类对流层延迟参数在长三角地区具有较好的稳定性和精度,融合GPT3模型的干延迟(ZHD)、加权平均温度(Tm)和GNSS⁃ZTD所得PWV的RMS为3.56 mm,接近GNSS⁃PWV的3.74 mm,远优于GPT3⁃PWV的11.12 mm。

Circulation Patterns Linked to the Positive Sub-Tropical Indian Ocean Dipole

The positive phase of the subtropical Indian Ocean dipole(SIOD)is one of the climatic modes in the subtropical southern Indian Ocean that influences the austral summer inter-annual rainfall variability in parts of southern Africa.This paper examines austral summer rain-bearing circulation types(CTs)in Africa south of the equator that are related to the positive SIOD and the dynamics through which specific rainfall regions in southern Africa can be influenced by this relationship.Four austral summer rain-bearing CTs were obtained.Among the four CTs,the CT that featured(i)enhanced cyclonic activity in the southwest Indian Ocean;(ii)positive widespread rainfall anomaly in the southwest Indian Ocean;and(iii)low-level convergence of moisture fluxes from the tropical South Atlantic Ocean,tropical Indian Ocean,and the southwest Indian Ocean,over the south-central landmass of Africa,was found to be related to the positive SIOD climatic mode.The relationship also implies that positive SIOD can be expected to increase the amplitude and frequency of occurrence of the aforementioned CT.The linkage between the CT related to the positive SIOD and austral summer homogeneous regions of rainfall anomalies in Africa south of the equator showed that it is the principal CT that is related to the inter-annual rainfall variability of the south-central regions of Africa,where the SIOD is already known to significantly influence its rainfall variability.Hence,through the large-scale patterns of atmospheric circulation associated with the CT,the SIOD can influence the spatial distribution and intensity of rainfall over the preferred landmass through enhanced moisture convergence.

Another Year of Record Heat for the Oceans

Changes in ocean heat content(OHC), salinity, and stratification provide critical indicators for changes in Earth’s energy and water cycles. These cycles have been profoundly altered due to the emission of greenhouse gasses and other anthropogenic substances by human activities, driving pervasive changes in Earth’s climate system. In 2022, the world’s oceans, as given by OHC, were again the hottest in the historical record and exceeded the previous 2021 record maximum.According to IAP/CAS data, the 0–2000 m OHC in 2022 exceeded that of 2021 by 10.9 ± 8.3 ZJ(1 Zetta Joules = 1021Joules);and according to NCEI/NOAA data, by 9.1 ± 8.7 ZJ. Among seven regions, four basins(the North Pacific, North Atlantic, the Mediterranean Sea, and southern oceans) recorded their highest OHC since the 1950s. The salinity-contrast index, a quantification of the “salty gets saltier–fresh gets fresher” pattern, also reached its highest level on record in 2022,implying continued amplification of the global hydrological cycle. Regional OHC and salinity changes in 2022 were dominated by a strong La Ni?a event. Global upper-ocean stratification continued its increasing trend and was among the top seven in 2022.