Characteristics of low-level temperature inversions over the Arctic Ocean during the CHINARE 2018 campaign in summer

2021 ◽  
pp. 118333
Author(s):  
Lei Zhang ◽  
Jian Li ◽  
Minghu Ding ◽  
Jianping Guo ◽  
Lingen Bian ◽  
...  
2021 ◽  
pp. 118537
Author(s):  
Lei Zhang ◽  
Jian Li ◽  
Minghu Ding ◽  
Jianping Guo ◽  
Lingen Bian ◽  
...  

2021 ◽  
Vol 60 (4) ◽  
pp. 493-511
Author(s):  
Liang Chang ◽  
Shiqiang Wen ◽  
Guoping Gao ◽  
Zhen Han ◽  
Guiping Feng ◽  
...  

AbstractCharacteristics of temperature inversions (TIs) and specific humidity inversions (SHIs) and their relationships in three of the latest global reanalyses—the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-I), the Japanese 55-year Reanalysis (JRA-55), and the ERA5—are assessed against in situ radiosonde (RS) measurements from two expeditions over the Arctic Ocean. All reanalyses tend to detect many fewer TI and SHI occurrences, together with much less common multiple TIs and SHIs per profile than are seen in the RS data in summer 2008, winter 2015, and spring 2015. The reanalyses generally depict well the relationships among TI characteristics seen in RS data, except for the TIs below 400 m in summer, as well as above 1000 m in summer and winter. The depth is simulated worst by the reanalyses among the SHI characteristics, which may result from its sensitivity to the uncertainties in specific humidity in the reanalyses. The strongest TI per profile in RS data exhibits more robust dependency on surface conditions than the strongest SHI per profile, and the former is better presented by the reanalyses than the latter. Furthermore, all reanalyses have difficulties simulating the relationships between TIs and SHIs, together with the correlations between the simultaneous inversions. The accuracy and vertical resolution in the reanalyses are both important to properly capture occurrence and characteristics of the Arctic inversions. In general, ERA5 performs better than ERA-I and JRA-55 in depicting the relationships among the TIs. However, the representation of SHIs is more challenging than TIs in all reanalyses over the Arctic Ocean.


2021 ◽  
pp. 1-30
Author(s):  
Lin Zhang ◽  
Minghu Ding ◽  
Tingfeng Dou ◽  
Yi Huang ◽  
Junmei Lv ◽  
...  

AbstractTemperature inversion plays an important role in various physical processes by affecting the atmospheric stability, regulating the development of clouds and fog, and controlling the transport of heat and moisture fluxes. In the past few decades, previous studies have analyzed the spatiotemporal variability of Arctic inversions, but few studies have investigated changes in temperature inversions. In this study, the changes in the depth of Arctic inversions in the mid-21st century are projected based on a 30-member ensemble from the Community Earth System Model Large Ensemble (CESM-LE) project. The ERA-Interim, JRA-55, and NCEP-NCAR reanalyses were employed to verify the model results. The CESM-LE can adequately reproduce the spatial distribution and trends of present-day inversion depth in the Arctic, and the simulation is better in winter. The mean inversion depth in the CESM-LE is slightly underestimated, and the discrepancy is less than 11 hPa within a reasonable range. The model results show that during the mid-21st century, the inversion depth will strongly decrease in autumn and slightly decrease in winter. The shallowing of inversion is most obvious over the Arctic Ocean, and the maximum decrease is over 65 hPa in the Pacific sector in autumn. In contrast, the largest decrease in the inversion depth, which is more than 45 hPa, occurs over the Barents Sea in winter. Moreover, the area where the inversion shallows is consistent with the area where the sea ice is retreating, indicating that the inversion depth over the Arctic Ocean in autumn and winter is likely regulated by the sea ice extent through modulating surface heat fluxes.


Data Series ◽  
10.3133/ds862 ◽  
2014 ◽  
Author(s):  
Lisa L. Robbins ◽  
Jonathan Wynn ◽  
Paul O. Knorr ◽  
Bogdan Onac ◽  
John T. Lisle ◽  
...  

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