Transition Periods Between Sea Ice Concentration and Sea Surface Air Temperature in the Arctic Revealed by an Abnormal Running Correlation

This study used the synthetic running correlation coefficient calculation method to calculate the running correlation coefficients between the daily sea ice concentration(SIC) and sea surface air temperature(SSAT) in the Beaufort-Chukchi-East Siberian-Laptev Sea(BCEL Sea), Kara Sea and southern Chukchi Sea, with an aim to understand and measure the seasonally occurring changes in the Arctic climate system. The similarities and differences among these three regions were also discussed. There are periods in spring and autumn when the changes in SIC and SSAT are not synchronized, which is a result of the seasonally occurring variation in the climate system. These periods are referred to as transition periods. Spring transition periods can be found in all three regions, and the start and end dates of these periods have advancing trends. The multiyear average duration of the spring transition periods in the BCEL Sea, Kara Sea and southern Chukchi Sea is 74 days, 57 days and 34 days, respectively. In autumn, transition periods exist in only the southern Chukchi Sea, with a multiyear average duration of only 16 days. Moreover, in the Kara Sea, positive correlation events can be found in some years, which are caused by weather time scale processes.

Understanding the Soil Temperature Variability at Different Depths:Effects of Surface Air Temperature,Snow Cover,and the Soil Memory

The soil temperature(ST)is closely related to the surface air temperature(AT),but their coupling may be affected by other factors.In this study,significant effects of the AT on the underlying ST were found,and the time taken to propagate downward to 320 cm can be up to 10 months.Besides the AT,the ST is also affected by memory effects-namely,its prior thermal conditions.At deeper depth(i.e.,320 cm),the effects of the AT from a particular season may be exceeded by the soil memory effects from the last season.At shallower layers(i.e.,<80 cm),the effects of the AT may be blocked by the snow cover,resulting in a poorly synchronous correlation between the AT and the ST.In northeastern China,this snow cover blockage mainly occurs in winter and then vanishes in the subsequent spring.Due to the thermal insulation effect of the snow cover,the winter ST at layers above 80 cm in northeastern China were found to continue to increase even during the recent global warming hiatus period.These findings may be instructive for better understanding ST variations,as well as land−atmosphere interactions.

The Coordinated Influence of Indian Ocean Sea Surface Temperature and Arctic Sea Ice on Anomalous Northeast China Cold Vortex Activities with Different Paths during Late Summer

The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NCCV intensity with atmospheric circulations in late summer,the sea surface temperature(SST),and Arctic sea ice concentration(SIC)in the preceding months,are analyzed.The sensitivity tests by the Community Atmosphere Model version 5.3(CAM5.3)are used to verify the statistical results.The results show that the coordination pattern of East Asia-Pacific(EAP)and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode(NIOD)during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of NCCV.While the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer-which is forced by SST anomalies in the South Indian Ocean dipole mode(SIOD)in the preceding June and SIC anomalies in the Barents Sea in the preceding April-causes the intensity anomaly of the second type.The third type is atypical and is not analyzed in detail.Sensitivity tests,jointly forced by the SST and SIC in the preceding period,can well reproduce the observations.In contrast,the results forced separately by the SST and SIC are poor,indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months.

Comparison of summer Arctic sea ice surface temperatures from in situ and MODIS measurements

Ship-borne infrared radiometric measurements conducted during the Chinese National Arctic Research Expedition(CHINARE)in 2008,2010,2012,2014,2016 and 2017 were used for in situ validation studies of the Moderate Resolution Imaging Spectroradiometer(MODIS)sea ice surface temperature(IST)product.Observations of sea ice were made using a KT19.85 radiometer mounted on the Chinese icebreaker Xuelong between July and September over six years.The MODIS-derived ISTs from the satellites,Terra and Aqua,both show close correspondence with ISTs derived from radiometer spot measurements averaged over areas of 4 km×4 km,spanning the temperature range of 262–280 K with a±1.7 K(Aqua)and±1.6 K(Terra)variation.The consistency of the results over each year indicates that MODIS provides a suitable platform for remotely deriving surface temperature data when the sky is clear.Investigation into factors that cause the MODIS IST bias(defined as the difference between MODIS and KT19.85 ISTs)shows that large positive bias is caused by increased coverage of leads and melt ponds,while large negative bias mostly arises from undetected clouds.Thin vapor fog forming over Arctic sea ice may explain the cold bias when cloud cover is below 20%.

Sensitivity of the Number of Snow Cover Days to Surface Air Temperature over the Qinghai-Tibetan Plateau

Based on the number of snow cover days （NSCDs） and homogenized surface air temperature data for the period 1951-2004, this study performs the quantitative analysis on the sensitivity of NSCDs to surface air temperature over the Qinghai-Tibetan Plateau （QTP）. Results show that both the extreme sensitivity and sensitivity under current climate are higher in the edge than in the central area of the QTP. There exists a strong negative correlation between station＇s elevation and critical temperature, at which the sensitivity reaches extremum. The negative correlation between the elevation and the extreme sensitivity is not as strong as the former one. Currently, the climatological temperatures in quite a few stations do not reach the critical stage. The sensitivity at these stations will become greater under the current background of climate warming, which means NSCDs will be more sensitive to surface air temperature.

SATELLITE REMOTE SENSING OF SEA-ICE AND SEA SURFACE TEMPERATURE

Sea-ice and Sea Surface Temperature in offshore seas are important terms for operational monitoring and forecasting marine environment in China. The software system of regional marine environmental application designed by author is used for realtime operational monitoring sea-ice, SST, oceanic current and colours and characters of land surface. This software system processes quantitative AVHRR data from NOAA satellite to calculate calibration coefficient, solar angle correction, earth location parameter and atmospheric attenuation correction, then SST field will be produced through calculation using special SST model, and top-quality of colour composite imagery of satellite with variable spacial resolution (1, 2 or 5km) will be produced via image processing. Inside front covor Figure 1 is colour enhanced imagery with 5km resolution of NOAA satellite in offshore

Impact of land cover change on land surface temperature: A case study of Spiti Valley

Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.

Effects of sea surface temperature,cloud radiative and microphysical processes,and diurnal variations on rainfall in equilibrium cloud-resolving model simulations

The effects of sea surface temperature（SST）,cloud radiative and microphysical processes,and diurnal variations on rainfall statistics are documented with grid data from the two-dimensional equilibrium cloud-resolving model simulations.For a rain rate of higher than 3 mm.h 1,water vapor convergence prevails.The rainfall amount decreases with the decrease of SST from 29℃ to 27℃,the inclusion of diurnal variation of SST,or the exclusion of microphysical effects of ice clouds and radiative effects of water clouds,which are primarily associated with the decreases in water vapor convergence.However,the amount of rainfall increases with the increase of SST from 29℃ to 31℃,the exclusion of diurnal variation of solar zenith angle,and the exclusion of the radiative effects of ice clouds,which are primarily related to increases in water vapor convergence.For a rain rate of less than 3 mm.h 1,water vapor divergence prevails.Unlike rainfall statistics for rain rates of higher than 3 mm.h 1,the decrease of SST from 29℃ to 27℃ and the exclusion of radiative effects of water clouds in the presence of radiative effects of ice clouds increase the rainfall amount,which corresponds to the suppression in water vapor divergence.The exclusion of microphysical effects of ice clouds decreases the amount of rainfall,which corresponds to the enhancement in water vapor divergence.The amount of rainfall is less sensitive to the increase of SST from 29℃ to 31℃ and to the radiative effects of water clouds in the absence of the radiative effects of ice clouds.

An Improved Method for Modeling Spatial Distribution of δD in Surface Snow over Antarctic Ice Sheet

Using the recent compilation of the isotopic composition data of surface snow of Antarctic ice sheet, we proposed an improved interpolation method of δD, which utilizes geographical factors （i.e., latitude and altitude） as the primary predictors and incorporates inverse distance weighting （IDW） technique. The method was applied to a high-resolution digital elevation model （DEM） to produce a grid map of multi-year mean δD values with lkm spatial resolution for Antarctica. The mean absolute deviation between observed and estimated data in the map is about 5.4‰, and the standard deviation is 9‰. The resulting δD pattern resembles well known characteristics such as the depletion of the heavy isotopes with increasing latitude and distance from coast line, but also reveals the complex topographic effects.

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.

Investigation of Correlation between the Temperature on Air-Snow and Snow-Ice Interfaces and the Atmospheric Air Temperature

Solution of the practical problems of the ice engineering requires the data about the strength of the ice cover that depends upon its temperature. In most cases, the snow lies on the ice cover and the ice temperature differs from the atmospheric air temperature. To reveal the correlation of the air temperature with temperature on interfaces air-snow and snow-ice, the known in the thermophysics solution of the problem of the heat transfer through the multilayer plate was applied. Derived solution connects the temperature of air and temperature on the snow-ice interface and satisfactory correlates with data of the field measurements of the temperature within snow layer and ice cover and ice thickness on the Heilongjiang （Amur） River. Results of investigation are recommended for the ice temperature evaluation in engineering practice.

Impact of Initial Soil Conditions on Soil Hydrothermal and Surface Energy Fluxes in the Permafrost Region of the Tibetan Plateau

Accurate initial soil conditions play a crucial role in simulating soil hydrothermal and surface energy fluxes in land surface process modeling.This study emphasized the influence of the initial soil temperature(ST)and soil moisture(SM)conditions on a land surface energy and water simulation in the permafrost region in the Tibetan Plateau(TP)using the Community Land Model version 5.0(CLM5.0).The results indicate that the default initial schemes for ST and SM in CLM5.0 were simplistic,and inaccurately represented the soil characteristics of permafrost in the TP which led to underestimating ST during the freezing period while overestimating ST and underestimating SLW during the thawing period at the XDT site.Applying the long-term spin-up method to obtain initial soil conditions has only led to limited improvement in simulating soil hydrothermal and surface energy fluxes.The modified initial soil schemes proposed in this study comprehensively incorporate the characteristics of permafrost,which coexists with soil liquid water(SLW),and soil ice(SI)when the ST is below freezing temperature,effectively enhancing the accuracy of the simulated soil hydrothermal and surface energy fluxes.Consequently,the modified initial soil schemes greatly improved upon the results achieved through the long-term spin-up method.Three modified initial soil schemes experiments resulted in a 64%,88%,and 77%reduction in the average mean bias error(MBE)of ST,and a 13%,21%,and 19%reduction in the average root-mean-square error(RMSE)of SLW compared to the default simulation results.Also,the average MBE of net radiation was reduced by 7%,22%,and 21%.

Velocity of Surface Ice Flow on Amery Ice Shelf Determined with PPP

The main activities in the joint expedition between CHINARE and ANARE on Amery ice shelf are introduced. Five day continuous GPS observation data collected on the site which locates at the frontal part of Amery ice shelf was processed with precise point positioning (PPP) technology based on precise products from IGS. Velocity of the surface ice flow on Amery can be derived from the PPP solution. Preliminary result shows that the surface ice flow velocity of the site is 2.25 meters per day, the motion direction is northeastward. Semidiurnal oceanic tide and diurnal oceanic tide signal of that site can be recovered from the height variation series of PPP solution. These above solutions can be used to the consequent mass balance calculation.

Micro-particle in surface snow at Princess Elizabeth Land, East Antarctica

During the Austral summer of 1996/1997, the First Chinese Antarctic Inland Expedition reached the inland area about 330 km along the direction around 76°E from Zhongshan Station, and collected 84 surface snow samples at an interval of 4 km . Micro particle analysis of the samples indicates that the micro particle concentration apparently decreases with the increasing of altitude, and the amplitudes of micro particle concentration is much larger in the lower altitude than in the higher altitude. Further analysis of grain size distributions of micro particle, percentage of micro particles from different sources and variations with altitude suggest that micro particles in this area are from a considerably dominant source. Although this area is controlled by polar easterly wind and katabatic wind, transportation and deposition of the micro particles are mainly influenced by marine transportation in coastal area.

Impacts of Winter Eurasian Snow Cover Anomalies on the Surface Air Temperature Variability over West Asia

Previous research has shown that land surface thermal anomalies in West Asia(WA) can impact regional and global climate,particularly affecting China through the eastward propagation of wave trains.However,the factors driving these anomalies in WA have not been extensively studied.Based on the observation data,this work focuses on examining the impacts of Eurasian winter snow cover on winter surface air temperature(SAT) variability over WA from 1978/1979 to 2017/2018 and explores the underlying physical mechanisms.The results indicate that a crucial snow anomaly area extending from the Baltic Sea to eastern Ural significantly influences the winter SAT anomaly in WA.An anomalous increase(decrease) in winter snow cover in this key area corresponds to the anomalously warmer(cooler) SAT in WA.This relationship is primarily driven by the albedo effects of snow cover,where more(less)snow cover induces cooling(warming) of the overlying air,altering upper-level geopotential height and influencing the intensity,duration,and frequency of local blocking events.Additionally,changes in the air temperature above the key area modify the meridional temperature gradient(MTG) between high and low latitudes,affecting the mean zonal flow in the midlatitude.Diagnosis of the thermodynamic energy equation for SAT reveals that the combined effects of variations in blocking events in high latitudes and mean zonal flow in midlatitudes alter the advection of climatological temperature by anomalous winds,which is caused by the anomalous increase(decrease) of snow cover in the key area.Consequently,this leads to changes in cold advection transported to WA,contributing to the occurrence of a warmer(colder) SAT over WA in winter.

The physical structures of snow and sea ice in the Arctic section of 150°-180°W during the summer of 2010

The physical structures of snow and sea ice in the Arctic section of 150°-180°W were observed on the basis of snow-pit, ice-core, and drill-hole measurements from late July to late August 2010. Almost all the in- vestigated floes were first-year ice, except for one located north of Alaska, which was probably multi-year ice transported from north of the Canadian Arctic Archipelago during early summer. The snow covers over all the investigated floes were in the melting phase, with temperatures approaching 0℃and densities of 295-398 kg/m3. The snow covers can be divided into two to five layers of different textures, with most cases having a top layer of fresh snow, a round-grain layer in the middle, and slush and/or thin icing layers at the bottom. The first-year sea ice contained about 7%-17% granular ice at the top. There was no granular ice in the lower layers. The interior melting and desalination of sea ice introduced strong stratifications of temper- ature, salinity, density, and gas and brine volume fractions. The sea ice temperature exhibited linear cooling with depth, while the salinity and the density increased linearly with normalized depth from 0.2 to 0.9 and from 0 to 0.65, respectively. The top layer, especially the freeboard layer, had the lowest salinity and density, and consequently the largest gas content and the smallest brine content. Both the salinity and density in the ice basal layer were highly scattered due to large differences in ice porosity among the samples. The bulk average sea ice temperature, salinity, density, and gas and brine volume fractions were -0.8℃, 1.8, 837 kg/m3, 9.3% and 10.4%, respectively. The snow cover, sea ice bottom, and sea ice interior show evidences of melting during mid-August in the investigated floe located at about 87°N, 175°W.

Observed and modelled snow and ice thickness in the Arctic Ocean with CHINARE buoy data

Sea ice and the snow pack on top of it were investigated using Chinese National Arctic Research Expedition （CHINARE） buoy data. Two polar hydrometeorological drifters, known as Zeno ice stations, were deployed during CHINARE 2003. A new type of high-resolution Snow and Ice Mass Balance Arrays, known as SIMBA buoys, were deployed during CHINARE 2014. Data from those buoys were applied to investigate the thickness of sea ice and snow in the CHINARE domain. A simple approach was applied to estimate the average snow thickness on the basis of Zeno temperature data. Snow and ice thicknesses were also derived from vertical temperature profile data based on the SIMBA buoys. A one-dimensional snow and ice thermodynamic model （HIGHTSI） was applied to calculate the snow and ice thickness along the buoy drift trajectories. The model forcing was based on forecasts and analyses of the European Centre for Medium-Range Weather Forecasts （ECMWF）. The Zeno buoys drifted in a confined area during 2003-2004. The snow thickness modelled applying HIGHTSI was consistent with results based on Zeno buoy data. The SIMBA buoys drifted from 81. 1°N, 157.4°W to 73.5°N, 134.9°W in 15 months during 2014-2015. The total ice thickness increased from an initial August 2014 value of 1.97 m to a maximum value of 2.45 in before the onset of snow melt in May 2015; the last observation was approximately 1 m in late November 2015. The ice thickness based on HIGHTSI agreed with SIMBA measurements, in particular when the seasonal variation of oceanic heat flux was taken into account, but the modelled snow thickness differed from the observed one. Sea ice thickness derived from SIMBA data was reasonably good in cold conditions, but challenges remain in both snow and ice thickness in summer.

Retrieval of Snow Depth on Sea Ice in the Arctic Using the FengYun-3B Microwave Radiation Imager

Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager(MWRI) on board the FengYun(FY)-3 B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS(AMSR-E) Level 2 A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice(ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between-1.01 and-0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.

NUMERICAL EXPERIMENTS FOR CLIMATE VARIABILITY OF MONSOON UNDER THE UNITED EFFECTS OF ANTARCTIC ICE COVER AND SEA SURFACE TEMPERATURE

In order to study the mechanisms of climate natural variability under the united effects of antarc- tic ice cover(AIC)and sea surface temperature(SST),we have done a series of numerical experi- ments for the climate variability of the period from January 1981 to December 1983 by using a three- level atmospheric general circulation model(AGCM).Firstly we conduct climate integration for six years,then 3 years'control integration from January 16 of the seventh year.Moreover,we do three sensitivity experiments,which are the sensitivity experiments forced by observed SST and AIC.ob- served SST and climatic AIC,observed AIC and climatic SST respectively,to study the climate vari- ability and its mechanisms affected by SST and AIC.We put emphasis on the variability of East Asia monsoon and the Southern Hemisphere(SH)circulation.In this paper,introduction is made to the results of control test and the sensitivity experiment forced by observed SST and AIC,and the pre- dictability of the monsoon climate variability is discussed.

Retrieving Snow Surface Temperature Based on MODIS Data

On the basis of simplification of the Planck function in a low temperature range, this paper revises the practical split-window algorithm and presents a method for retrieving snow surface temperature （Ts） based on MODIS data in the middle-latitude region. The application of this method in Qinghai Lake region reveals that it is feasible for the retrieval of Ts. Results of correlation analysis indicate that there was strong negative relationship between Ts and altitude. By analyzing three typical areas in which land cover was relatively homogenous, this paper discusses the relationship between Ts and normalized difference snow index （NDSI） and then presents a new concept named ＂NDSI-Ts space＂.