A model intercomparison in terms of surface air temperature annual cycle amplitude-phase characteristics (SAT AC APC) is performed. The models included in the intercomparison belong to two groups: five atmospheric mod...A model intercomparison in terms of surface air temperature annual cycle amplitude-phase characteristics (SAT AC APC) is performed. The models included in the intercomparison belong to two groups: five atmospheric models with prescribed sea surface temperature and sea ice cover and four coupled models forced by the atmospheric abundances of anthropogenic constituents (in total six coupled model simulations). Over land, the models, simulating higher than observed time averaged SAT, also tend to simulate smaller than observed amplitude of its annual and semiannual harmonics and (outside the Tropics) later-than-observed spring and autumn moments. The models with larger (smaller) time averaged amplitudes of annual and semiannual harmonics also tend to simulate larger (smaller) interannual standard deviations. Over the oceans, the coupled models with larger interannual standard deviations of annual mean SAT tend to simulate larger interannual standard deviations of both annual and semiannual SAT harmonics amplitudes. Most model errors are located in the belts 60°–70°N and 60°–70°S and over Antarctica. These errors are larger for those coupled models which do not employ dynamical modules for sea ice. No systematic differences are found in the simulated time averaged fields of the surface air temperature annual cycle characteristics for atmospheric models on one hand and for the coupled models on the other. But the coupled models generally simulate interannual variability of SAT AC APC better than the atmospheric models (which tend to underestimate it). For the coupled models, the results are not very sensitive to the choice of the particular scenario of anthropogenic forcing. There is a strong linear positive relationship between the model simulated time averaged semiannual SAT harmonics amplitude and interannual standard deviation of annual mean SAT. It is stronger over the tropical oceans and is weaker in the extratropics. In the tropical oceanic areas, it is stronger for the coupled than for the atmospheric models.展开更多
The shrinking Arctic sea-ice area(SIA) in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively ...The shrinking Arctic sea-ice area(SIA) in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration(SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used Had ISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in Had ISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km^(2) in March and 1.5 mln km^(2) in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.展开更多
文摘A model intercomparison in terms of surface air temperature annual cycle amplitude-phase characteristics (SAT AC APC) is performed. The models included in the intercomparison belong to two groups: five atmospheric models with prescribed sea surface temperature and sea ice cover and four coupled models forced by the atmospheric abundances of anthropogenic constituents (in total six coupled model simulations). Over land, the models, simulating higher than observed time averaged SAT, also tend to simulate smaller than observed amplitude of its annual and semiannual harmonics and (outside the Tropics) later-than-observed spring and autumn moments. The models with larger (smaller) time averaged amplitudes of annual and semiannual harmonics also tend to simulate larger (smaller) interannual standard deviations. Over the oceans, the coupled models with larger interannual standard deviations of annual mean SAT tend to simulate larger interannual standard deviations of both annual and semiannual SAT harmonics amplitudes. Most model errors are located in the belts 60°–70°N and 60°–70°S and over Antarctica. These errors are larger for those coupled models which do not employ dynamical modules for sea ice. No systematic differences are found in the simulated time averaged fields of the surface air temperature annual cycle characteristics for atmospheric models on one hand and for the coupled models on the other. But the coupled models generally simulate interannual variability of SAT AC APC better than the atmospheric models (which tend to underestimate it). For the coupled models, the results are not very sensitive to the choice of the particular scenario of anthropogenic forcing. There is a strong linear positive relationship between the model simulated time averaged semiannual SAT harmonics amplitude and interannual standard deviation of annual mean SAT. It is stronger over the tropical oceans and is weaker in the extratropics. In the tropical oceanic areas, it is stronger for the coupled than for the atmospheric models.
基金partly supported by the Russian Ministry of Science and Higher Education (Agreement No.075-15-2021-577)the Russian Science Foundation (Grant No.23-47-00104)+2 种基金funded by the Research Council of Norway (Grant No.Combined 328935)the support of the Bjerknes Climate Prediction Unit with funding from the Trond Mohn Foundation (Grant No.BFS2018TMT01)the support of the National Natural Science Foundation of China (Grant No.42261134532)。
文摘The shrinking Arctic sea-ice area(SIA) in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration(SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used Had ISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in Had ISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km^(2) in March and 1.5 mln km^(2) in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.
