Spatial-temporal variations in net primary productivity in the Arctic from 2003 to 2016

The area of Arctic sea ice has dramatically decreased, and the length of the open water season has increased;these patterns have been observed by satellite remote sensing since the 1970 s. In this paper, we calculate the net primary productivity(NPP, calculated by carbon) from 2003 to 2016 based on sea ice concentration products,chlorophyll a(Chl a) concentration, photosynthetically active radiation(PAR), sea surface temperature(SST), and sunshine duration data. We then analyse the spatiotemporal changes in the Chl a concentration and NPP and further investigate the relations among NPP, the open water area, and the length of the open water season. The results indicate that(1) the Chl a concentration increased by 0.025 mg/m^3 per year;(2) the NPP increased by 4.29 mg/(m^2·d) per year, reaching a maximum of 525.74 mg/(m^2·d) in 2016;and(3) the Arctic open water area increased by 57.23×10^3 km^2/a, with a growth rate of 1.53 d/a for the length of the open water season. The annual NPP was significantly positively related to the open water area, the length of the open water season and the SST.The daily NPP was also found to have a lag correlation with the open water area, with a lag time of two months.With global warming, NPP has maintained an increasing trend, with the most significant increase occurring in the Kara Sea. In summary, this study provides a macroscopic understanding of the distribution of phytoplankton in the Arctic, which is valuable information for the evaluation and management of marine ecological environments.

Arctic sea ice volume export through the Fram Strait: variation and its effect factors

Arctic sea ice export is important for the redistribution of freshwater and sea ice mass.Here,we use the sea ice thickness,sea ice velocity,and sea ice concentration(SIC)to estimate the exported sea ice volume through the Fram Strait from 2011 to 2018.We further analyse the contributions of the sea ice thickness,velocity and concentration to sea ice volume export.Then,the relationships between atmospheric circulation indices(Arctic Oscillation(AO),North Atlantic Oscillation(NAO),and Arctic Dipole(AD))and the sea ice volume export are discussed.Finally,we analyse the impact of wind-driven oceanic circulation indices(Ekman transport(ET))on the sea ice volume export.The sea ice volume export rapidly increases in winter and decreases in spring.The exported sea ice volume in winter is likely to exceed that in spring in the future.Among sea ice thickness,velocity and SIC,the greatest contribution to sea ice export comes from the ice velocity.The exported sea ice volume through the zonal gate of the Fram Strait(which contributes 97%to the total sea ice volume export of the Fram Strait)is much higher than that through the meridional gate(3%)because the sea ice flowing out of the zonal gate has the characteristics of a high thickness(mainly thicker than 1 m),a high velocity(mainly faster than 0.06 m/s)and a high concentration(mainly higher than 80%).The AD and ET explain 53.86%and 38.37%of the variation in sea ice volume export,respectively.

An improved optical flow method to estimate Arctic sea ice velocity(winter 2014-2016)

Sea ice velocity impacts the distribution of sea ice,and the flux of exported sea ice through the Fram Strait increases with increasing ice velocity.Therefore,improving the accuracy of estimates of the sea ice velocity is important.We introduce a pyramid algorithm into the Horn-Schunck optical flow(HS-OF)method(to develop the PHS-OF method).Before calculating the sea ice velocity,we generate multilayer pyramid images from an original brightness temperature image.Then,the sea ice velocity of the pyramid layer is calculated,and the ice velocity in the original image is calculated by layer iteration.Winter Arctic sea ice velocities from 2014 to 2016 are obtained and used to discuss the accuracy of the HS-OF method and PHS-OF(specifically the 2-layer PHS-OF(2 LPHS-OF)and 4-layer PHS-OF(4 LPHS-OF))methods.The results prove that the PHS-OF method indeed improves the accuracy of sea ice velocity estimates,and the 2 LPHS-OF scheme is more appropriate for estimating ice velocity.The error is smaller for the 2 LPHS-OF velocity estimates than values from the Ocean and Sea Ice Satellite Application Facility and the Copernicus Marine Environment Monitoring Service,and estimates of changes in velocity by the 2 LPHS-OF method are consistent with those from the National Snow and Ice Data Center.Sea ice undergoes two main motion patterns,i.e.,transpolar drift and the Beaufort Gyre.In addition,cyclonic and anticyclonic ice drift occurred during winter 2016.Variations in sea ice velocity are related to the open water area,sea ice retreat time and length of the open water season.

Method for detecting soil total nitrogen contents based on pyrolysis and artificial olfaction

Soil nitrogen is an essential nutrient element for crop growth and development,and an important indicator of soil fertility characteristics.This study proposed a method based on pyrolysis and artificial olfaction to quickly and accurately determine the soil total nitrogen(STN)content.A muffle furnace was used to pyrolyze the soil samples,and ten different types of oxide semiconductor gas sensors were used to construct a sensor array to detect the soil samples’pyrolysis gas.The response curves of the sensors were tested at pyrolysis temperatures of 200℃,300℃,400℃,and 500℃ and at pyrolysis times of 1 min,3 min,5 min,and 10 min to obtain the optimal pyrolysis state of the soil samples.The optimal pyrolysis temperature was 400℃,and the pyrolysis time was 3 min.The response area,maximum value,average differential coefficient,variance value,maximum gradient value,average value,and 8th-second transient value of the sensor response curve were extracted to construct an artificial olfactory feature space of 121×10×7(121 soil samples,ten sensor numbers,seven extracted eigenvalues).Back-propagation neural network algorithm(BPNN),partial least squares regression algorithm(PLSR),and partial least squares regression combined with back-propagation neural network algorithm(PLSR-BPNN)were used to establish a prediction model of artificial olfactory feature space and STN content.Moreover,coefficient of determination(R2),root mean square error(RMSE),and the ratio of performance to deviation(RPD)were used as the performance indicators of the prediction results.The test results showed that the R2 of the PLSR,BPNN,and PLSR-BPNN models were 0.89033,0.81185,and 0.92186,and the RMSE values were 0.24297,0.37370,and 0.21781,and the RPD were 2.9964,1.9482,and 3.3426,respectively.The model established by the PLSR-BPNN algorithm has the highest R2 and RPD and the smallest RMSE,can achieve the accurate prediction of STN content,and therefore the model is rated as“excellent”.The detection method in this study achieves a low-cost,rapid,and accurate determination of STN content,and provides a new reference for the measurement of STN.