El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

Seasonal influence of freshwater discharge on spatio-temporal variations in primary productivity, sea surface temperature, and euphotic zone depth in the northern Bay of Bengal

Ocean productivity is the foundation of marine food web,which continuously removes atmospheric carbon dioxide and supports life at sea and on land.Spatio-temporal variability of net primary productivity(NPP),sea surface temperature(SST),sea surface salinity(SSS),mixed layer depth(MLD),and euphotic zone depth(EZD) in the northern B ay of Bengal(BoB) during three monsoon seasons were examined in this study based on remote sensing data for the period 2005 to 2020.To compare the NPP distribution between the coastal zones and open BoB,the study area was divided into five zones(Z1-Z5).Results suggest that most productive zones Z2 and Zl are located at the head bay area and are directly influenced by freshwater discharge together with riverine sediment and nutrient loads.Across Z1-Z5,the NPP ranges from 5 315.38 mg/(m^(2)·d) to 346.7 mg/(m^(2)·d)(carbon,since then the same).The highest monthly average NPP of 5 315.38 mg/(m^(2)·d) in February and 5 039.36 mg/(m^(2)·d) in June were observed from Z2,while the lowest monthly average of 346.72 mg/(m^(2)·d) was observed in March from Z4,which is an oceanic zone.EZD values vary from 6-154 m for the study area,and it has an inverse correlation with NPP concentration.EZD is deeper during the summer season and shallower during the wintertime,with a corresponding increase in productivity.Throughout the year,monthly SST shows slight fluctuation for the entire study area,and statistical analysis shows a significant correlation among NPP,and EZD,overall positive between NPP and MLD,whereas no significant correlation among SSS,and SST for the northern BoB.Long-term trends in SST and productivity were significantly po sitive in head bay zones but negatively productive in the open ocean.The findings in this study on the distribution of NPP,SST,SSS,MLD,and EZD and their seasonal variability in five different zones of BoB can be used to further improve the management of marine resources and overall environmental condition in response to climate changes in BoB as they are of utmost relevance to the fisheries for the three bordering countries.

Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang,China

Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover(LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.

Spatiotemporal landscape pattern changes and their effects on land surface temperature in greenbelt with semi-arid climate:A case study of the Erbil City,Iraq

Urban expansion of cities has caused changes in land use and land cover(LULC)in addition to transformations in the spatial characteristics of landscape structure.These alterations have generated heat islands and rise of land surface temperature(LST),which consequently have caused a variety of environmental issues and threated the sustainable development of urban areas.Greenbelts are employed as an urban planning containment policy to regulate urban expansion,safeguard natural open spaces,and serve adaptation and mitigation functions.And they are regarded as a powerful measure for enhancing urban environmental sustainability.Despite the fact that,the relation between landscape structure change and variation of LST has been examined thoroughly in many studies,but there is a limitation concerning this relation in semi-arid climate and in greenbelts as well,with the lacking of comprehensive research combing both aspects.Accordingly,this study investigated the spatiotemporal changes of landscape pattern of LULC and their relationship with variation of LST within an inner greenbelt in the semi-arid Erbil City of northern Iraq.The study utilized remote sensing data to retrieve LST,classified LULC,and calculated landscape metrics for analyzing spatial changes during the study period.The results indicated that both composition and configuration of LULC had an impact on the variation of LST in the study area.The Pearson's correlation showed the significant effect of Vegetation 1 type(VH),cultivated land(CU),and bare soil(BS)on LST,as increase of LST was related to the decrease of VH and the increases of CU and BS,while,neither Vegetation 2 type(VL)nor built-up(BU)had any effects.Additionally,the spatial distribution of LULC also exhibited significant effects on LST,as LST was strongly correlated with landscape indices for VH,CU,and BS.However,for BU,only aggregation index metric affected LST,while none of VL metrics had a relation.The study provides insights for landscape planners and policymakers to not only develop more green spaces in greenbelt but also optimize the spatial landscape patterns to reduce the influence of LST on the urban environment,and further promote sustainable development and enhance well-being in the cities with semi-arid climate.

Contribution of Surface Waves to Sea Surface Temperatures in the Arctic Ocean

The aim of our study was to examine the contribution of surface waves from WAVEWATCH-III(WW3)to the variation in sea surface temperature(SST)in the Arctic Ocean.The simulated significant wave height(SWH)were validated against the products from Haiyang-2B(HY-2B)in 2021,obtaining a root mean squared error(RMSE)of 0.45 with a correlation of 0.96 and scatter index of 0.18.The wave-induced effects,i.e.,wave breaking and mixing induced by nonbearing waves resulting in changes in radiation stress and Stokes drift,were calculated from WW3,ERA-5 wind,SST,and salinity data from the National Centers for Environmental Prediction and were taken as forcing fields in the Stony Brook Parallel Ocean Model.The results showed that an RMSE of 0.81℃ with wave-induced effects was less than the RMSE of 1.11℃ achieved without the wave term compared with the simulated SST with the measurements from Argos.Considering the four wave effects and sea ice freezing,the SST in the Arctic Ocean decreased by up to 1℃ in winter.Regression analysis revealed that the SWH was linear in SST(values without subtraction of waves)in summer and autumn,but this behavior was not observed in spring or winter due to the presence of sea ice.The interannual variation also presented a negative relationship between the difference in SST and SWH.

Analysis of Sea Surface Temperature Cooling in Typhoon Events Passing the Kuroshio Current

The aim of this study is to investigate the sea surface temperature(SST) cooling as typhoons pass the Kuroshio Current.A numerical circulation model,denoted as the Stony Brook Parallel Ocean Model(sbPOM),was used to simulate the SST,which includes four wave-induced effect terms(i.e.,radiation stress,nonbreaking waves,Stokes drift,and breaking waves) simulated using the third-generation wave model,called WAVEWATCH-Ⅲ(WW3).The significant wave height(SWH) measurements from the Jason-2 altimeter were used to validate the WW3-simulated results,yielding a root mean square error(RMSE) of less than 0.50 m and a correlation coefficient(COR) of approximately 0.93.The water temperature measured from the Advanced Research and Global Observation Satellite was applied to validate the model simulation.Accordingly,the RMSE of the SST is 0.92℃ with a COR of approximately 0.99.As revealed in the sbPOM-simulated SST fields,a reduction in the SST at the Kuroshio Current region was observed as a typhoon passed,although the water temperature of the Kuroshio Current is relatively high.The variation of the SST is consistent with that of the current,whereas the maximum SST lagged behind the occurrence of the peak SWH.Moreover,the Stokes drift plays an important role in the SST cooling after analyzing four wave-induced terms in the background of the Kuroshio Current.The sensitivity experiment also showed that the accuracy of the water temperature was significantly reduced when including breaking waves,which play a negative role in the inside part of the ocean.The variation in the mean mixing layer depth(MLD) showed that a typhoon could enhance the mean MLD in the Kuroshio Current area in September and October,whereas a typhoon has little influence on the mean MLD in the Kuroshio Current area in May.Moreover,the mean MLD rapidly decreased with the weakening of the strong wind force and wave-induced effects when a typhoon crossed the Kuroshio Current.

New Insight to the Surface Temperature of the Sun

Information is given on thermal radiation from the Sun, considered in practical engineering calculations of heat exchange. It was found that although the surface temperature of the Sun is assumed to be about 5800 K, the solar spectrum data measured by Kondratyev lead to a value of at least 7134 K. Such a higher value can be obtained by interpreting the Planck formula for the black radiation spectrum for the Kondratyev data. In addition, using the Stefan-Boltzmann law, the energetic emissivity of the Sun’s surface was determined to be 0.431. Furthermore, based on Petela’s formulae for exergy of thermal radiation, the exergetic emissivity of the Sun’s surface was also calculated at the level of 0.426.

The Relationship between Extreme Precipitation Events in East Africa during the Short Rainy Season and Indian Ocean Sea Surface Temperature

The East African short rainy season (October-November-December) is one of the major flood seasons in the East African region. The amount of rainfall during the short rainy season is closely related to the lives of the people and the socio-economic development of the area. By using precipitation data and sea surface temperature data, this study reveals the spatial and temporal variation patterns of extreme precipitation during the East African short rainy season. Key findings include significant rainfall variability, with Tanzania experiencing the highest amounts in December due to the southward shift of the Intertropical Convergence Zone (ITCZ), while other regions receive less than 100 mm. Extreme rainfall events (90th percentiles) are evenly distributed, averaging 2 to 10 days annually. Historical data shows maximum seasonal rainfall often peaks at 15 mm, with frequent occurrences of daily rainfall exceeding 10 mm during OND. Additionally, a positive correlation (0.48) between OND precipitation extremes and Indian Ocean Dipole (IOD) anomalies is statistically significant. These findings highlight the climatic variability and potential trends in extreme rainfall events in East Africa, providing valuable insights for regional climate adaptation strategies.

Using satellite-derived land surface temperatures to clarify the spatiotemporal warming trends of the Alborz Mountains in northern Iran

The Alborz Mountains are some of the highest in Iran,and they play an important role in controlling the climate of the country’s northern regions.The land surface temperature(LST)is an important variable that affects the ecosystem of this area.This study investigated the spatiotemporal changes and trends of the nighttime LST in the western region of the Central Alborz Mountains at elevations of 1500-4000 m above sea level.MODIS data were extracted for the period of 2000-2021,and the Mann-Kendall nonparametric test was applied to evaluating the changes in the LST.The results indicated a significant increasing trend for the monthly average LST in May-August along the southern aspect.Both the northern and southern aspects showed decreasing trends for the monthly average LST in October,November,and March and an increasing trend in other months.At all elevations,the average decadal change in the monthly average LST was more severe along the southern aspect(0.60°C)than along the northern aspect(0.37°C).The LST difference between the northern and southern aspects decreased in the cold months but increased in the hot months.At the same elevation,the difference in the lapse rate between the northern and southern aspects was greater in the hot months than in the cold months.With increasing elevation,the lapse rate between the northern and southern aspects disappeared.Climate change was concluded to greatly decrease the difference in LST at different elevations for April-July.

Comparison between Infrared and Microwave Radiometers for Retrieving Sea Surface Temperature

The basic principles of sea surface temperature （SST） remote sensing using infrared and microwave radiometers are introduced, and the differences between two sensors for retrieving sea surface temperature are investigated. The ground resolution, atmospheric effect, sea surface wind, skin depth and so on have important influence on precision of sea surface temperature retrieved by two sensors. The better understanding of the advantage and disadvantage of sea surface temperature detected by infrared and microwave radiometers would help us to imply SST remote sensing data more effectively and correctly.

Sea Surface Temperature Simulation of Tropical and North Pacific Basins Using a Hybrid Coordinate Ocean Model (HYCOM)

A Hybrid Coordinate Ocean Model （HYCOM） is used to simulate the sea surface temperature of the Tropical and North Pacific. Based on the different combinations of two air-Sea flux data sets （COADS and ECMWF） and two bulk parameter formulas （non-constant and constant）, four numerical experiments are carried out. The following conclusions can be deduced from the numerical results. （1） The numerical results using non-constant bulk parameter formula are much better than those using constant one. In the Pacific area from 40°N to 20°S, the annual average SST obtained from the experiment using non-constant bulk parameter formula is 0.21 ℃ higher than that from the satellite-based SST climatology （the pathfinder data）. However, the difference is 0.63 ℃ for the experiment when the using constant one. （2） HYCOM successfully simulates the monthly variation of climatological SST in tropical and north Pacific basins and monthly spatial variation of Western Pacific Warm Pool. Especially in the Pacific area from 40°N to 20°S, the difference of the seasonal averaged SST between pathfinder data and the result of experiment 2 （using COADS data set and non-constant bulk parameter formula） is only about 0.02 ℃. （3）The simulation results using different Air-Sea flux data are different and the difference is very large in some regions. In the northwest of the model region, the annual average SST obtained from experiment 2 （using COADS data set） is 1℃ higher than that obtained from experiment 4 （using ECMWF data set）. Contrarily, the result of experiment 4 is 1 ℃ larger than that of experiment 2 in the southeast of the model region. The largest difference is about 4 ℃ occurred near the area of 58°N, 140°E and the Bohai sea.

The Singular Value Decomposition Analysis between Summer Precipitation in the Dongting Lake Region and the Global Sea Surface Temperature

By dint of the summer precipitation data from 21 stations in the Dongting Lake region during 1960-2008 and the sea surface temperature(SST) data from NOAA,the spatial and temporal distributions of summer precipitation and their correlations with SST are analyzed.The coupling relationship between the anomalous distribution in summer precipitation and the variation of SST has between studied with the Singular Value Decomposition(SVD) analysis.The increase or decrease of summer precipitation in the Dongting Lake region is closely associated with the SST anomalies in three key regions.The variation of SST in the three key regions has been proved to be a significant previous signal to anomaly of summer rainfall in Dongting region.

A comparison of Argo nominal surface and near-surface temperature for validation ofAMSR-E SST

Satellite SST(sea surface temperature) from the Advanced Microwave Scanning Radiometer for the Earth Observing System(AMSR-E) is compared with in situ temperature observations from Argo profiling floats over the global oceans to evaluate the advantages of Argo NST(near-surface temperature: water temperature less than 1 m from the surface). By comparing Argo nominal surface temperature(~5 m) with its NST, a diurnal cycle caused by daytime warming and nighttime cooling was found, along with a maximum warming of 0.08±0.36°C during 14:00–15:00 local time. Further comparisons between Argo 5-m temperature/Argo NST and AMSR-E SST retrievals related to wind speed, columnar water vapor, and columnar cloud water indicate warming biases at low wind speed(<5 m/s) and columnar water vapor >28 mm during daytime. The warming tendency is more remarkable for AMSR-E SST/Argo 5-m temperature compared with AMSR-E SST/Argo NST, owing to the effect of diurnal warming. This effect of diurnal warming events should be excluded before validation for microwave SST retrievals. Both AMSR-E nighttime SST/Argo 5-m temperature and nighttime SST/Argo NST show generally good agreement, independent of wind speed and columnar water vapor. From our analysis, Argo NST data demonstrated their advantages for validation of satellite-retrieved SST.

Is the global sea surface temperature rise accelerating?

This is an exploratory investigation to search for the presence of an acceleration in global sea surface temperature rise, which is essential to identify anthropogenic contributions to the climate change during the 20 th century. A weighted statistical model with an acceleration parameter was built progressively to reconstruct the variations in the global sea surface temperature data considering statistically significant confounders and autoregressive disturbances in the process. From the preliminary residual analysis of a weighted regression model, emerged a parsimonious model with first order autoregressive disturbances with a deterministic trend, acceleration and periodicity of 69 yr and its 138 yr subharmonic. The final model solution, selected from 29 alternative combinations of the model parameters using Mallows' s Cp metric, revealed a statistically significant deterministic trend, 0.40 ± 0.03C/c(p < 0.01), and acceleration, 0.67 ± 0.11C/c^2(p < 0.01) explaining 33% of the global sea surface temperature variations. The combined yearly trend and acceleration in global sea surface temperature as predicted by the model,exhibit a strong correlation with the yearly increase in the global CO^2 concentrations observed during the 20th century.

Evaluating a satellite-based sea surface temperature by shipboard survey in the Northwest Indian Ocean

A summer-time shipboard meteorological survey is described in the Northwest Indian Ocean. Shipboard observations are used to evaluate a satellite-based sea surface temperature（SST）, and then find the main factors that are highly correlated with errors. Two satellite data, the first is remote sensing product of a microwave, which is a Tropical Rainfall Measuring Mission Microwave Imager（TMI）, and the second is merged data from the microwave and infrared satellite as well as drifter observations, which is Operational Sea Surface Temperature and Sea Ice Analysis（OSTIA）. The results reveal that the daily mean SST of merged data has much lower bias and root mean square error as compared with that from microwave products. Therefore the results support the necessary of the merging infrared and drifter SST with a microwave satellite for improving the quality of the SST. Furthermore, the correlation coefficient between an SST error and meteorological parameters, which include a wind speed, an air temperature, a relative humidity, an air pressure, and a visibility. The results show that the wind speed has the largest correlation coefficient with the TMI SST error. However, the air temperature is the most important factor to the OSTIA SST error. Meanwhile,the relative humidity shows the high correlation with the SST error for the OSTIA product.

Understanding the Surface Temperature Cold Bias in CMIP5 AGCMs over the Tibetan Plateau

The temperature biases of 28 CMIP5 AGCMs are evaluated over the Tibetan Plateau(TP) for the period 1979–2005. The results demonstrate that the majority of CMIP5 models underestimate annual and seasonal mean surface 2-m air temperatures(Tas) over the TP. In addition, the ensemble of the 28 AGCMs and half of the individual models underestimate annual mean skin temperatures(Ts) over the TP. The cold biases are larger in Tasthan in Ts, and are larger over the western TP. By decomposing the Tsbias using the surface energy budget equation, we investigate the contributions to the cold surface temperature bias on the TP from various factors, including the surface albedo-induced bias, surface cloud radiative forcing, clear-sky shortwave radiation, clear-sky downward longwave radiation, surface sensible heat flux, latent heat flux,and heat storage. The results show a suite of physically interlinked processes contributing to the cold surface temperature bias.Strong negative surface albedo-induced bias associated with excessive snow cover and the surface heat fluxes are highly anticorrelated, and the cancelling out of these two terms leads to a relatively weak contribution to the cold bias. Smaller surface turbulent fluxes lead to colder lower-tropospheric temperature and lower water vapor content, which in turn cause negative clear-sky downward longwave radiation and cold bias. The results suggest that improvements in the parameterization of the area of snow cover, as well as the boundary layer, and hence surface turbulent fluxes, may help to reduce the cold bias over the TP in the models.

Impacts of Land Use and Cover Change on Land Surface Temperature in the Zhujiang Delta

Remote sensing and geographic information systems (GIS) technologies were used to detect land use/cover changes (LUCC) and to assess their impacts on land surface temperature (LST) in the Zhujiang Delta. Multi-temporal Landsat TM and Landsat ETM+ data were employed to identify patterns of LUCC as well as to quantify urban expansion and the associated decrease of vegetation cover. The thermal infrared bands of the data were used to retrieve LST. The results revealed a strong and uneven urban growth,which caused LST to raise 4.56℃in the newly urbanized part of the study area. Overall, remote sensing and GIS technologies were effective approaches for monitoring and analyzing urban growth patterns and evaluating their impacts on LST.

Analysis on Long-Term Change of Sea Surface Temperature in the China Seas

Long-term change of sea surface temperature （SST） in the China Seas from 1900 to 2006 is examined based on two different observation datasets （HadlSSTI and HadSST3）. Similar to the Atlantic, SST in the China Seas has been well observed during the past 107 years. A comparison between the reconstructed （HadISSTI） and un-interpolated （HadSST3） datasets shows that the SST wanning trends from both datasets are consistent with each other in most of the China Seas. The warming trends are stronger in winter than in summer, with a maximum rate of SST increase exceeding 2.7℃ （100year）-I in the East China Sea and the Taiwan Strait during winter based on HadISSTI. However, the SST from both datasets experienced a sudden decrease after 1999 in the China Seas. The estimated trend from HadlSSTI is stronger than that fi＇om HadSST3 in the East China Sea and the east of Taiwan Island, where the difference in the linear SST warming trends are as large as about 1℃ （100year）-I when using respectively HadISST1 and HadSST3 datasets. When compared to the linear winter warnling trend of the land surface air temperature （1.6℃ （100 year）-1）, HadSST3 shows a more reasonable trend of less than 2.1℃（ 100 year）-1 than HadISST 1 ＇s trend of larger than 2.7℃ （ 100 year）-1 at the mouth of the Yangtze River. The restllts also indicate large uncertainties in the estimate of SST warming patterns.

Improvement of Mono-window Algorithm for Retrieving Land Surface Temperature from HJ-1B Satellite Data

The thermal infrared channel (IRS4) of HJ-1B satellite obtains view zenith angles (VZA) up to ±33°. The view angle should be taken into account when retrieving land surface temperature (LST) from IRS4 data. This study aims at improving the mono-window algorithm for retrieving LST from IRS4 data. Based on atmospheric radiative transfer simulations,a model for correcting the VZA effects on atmospheric transmittance is proposed. In addition,a generalized model for calculating the effective mean atmospheric temperature is developed. Validation with the simulated dataset based on standard atmospheric profiles reveals that the improved mono-window algorithm for IRS4 obtains high accuracy for LST retrieval,with the mean absolute error (MAE) and root mean square error (RMSE) being 1.0 K and 1.1 K,respectively. Numerical experiment with the radiosonde profile acquired in Beijing in winter demonstrates that the improved mono-window algorithm exhibits excellent ability for LST retrieval,with MAE and RMSE being 0.6 K and 0.6 K,respectively. Further application in Qinghai Lake and comparison with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST product suggest that the improved mono-window algorithm is applicable and feasible in actual conditions.

Impact of Sea Surface Temperature Front on Stratus-Sea Fog over the Yellow and East China Seas-A Case Study with Implications for Climatology

A stratus-sea fog event that occurred over the Yellow and East China Seas on 3 June 2011 is investigated using observations and a numerical model, with a focus on the effects of background circulation and Sea Surface Temperature Front （SSTF） on the transition of stratus into sea fog. Southerly winds of a synoptic high-pressure circulation transport water vapor to the Yellow Sea, creating conditions favorable for sea fog/stratus formation. The subsidence from the high-pressure contributes to the temperature inversion at the top of the stratus. The SSTF forces a secondary circulation within the ABL （Atmospheric Boundary Layer）, the sinking branch of which on the cold flank of SSTF helps lower the stratus layer fiLrther to reach the sea surface. The cooling effect over the cold sea surface counteracts the adiabatic warming induced by subsidence. The secondary circulation becomes weak and the fog patches are shrtmk heavily with the smoothed SSTE A conceptual model is proposed for the transition of stratus into sea fog over the Yellow and East China Seas. Finally, the analyses suggest that sea fog frequency will probably decrease due to the weakened SSTF and the reduced subsidence of secondary circulation under global wanning.