The computational cost required by the Ensemble Kalman Filter （EnKF） is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation （OI） or three-dimension variational as- similation ...The computational cost required by the Ensemble Kalman Filter （EnKF） is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation （OI） or three-dimension variational as- similation （3DVAR）. Ensemble optimal interpolation （EnOI）, a crudely simplified implementation of EnKF, is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF. In this paper, to compromise between computational cost and dynamic covariance, we use the idea of ＂dressing＂ a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance. The term ＂dressing＂ means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles. This dressing EnKF （DrEnKF for short） scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model （HYCOM） over a four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble. Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members. Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors （RMSE） at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.展开更多
Annual precipitation,evaporation,and calculated accumulation from reanalysis model outputs have been investigated for the Greenland Ice Sheet （GrIS）,based on the common period of 1989-2001.The ERA-40 and ERA-interim...Annual precipitation,evaporation,and calculated accumulation from reanalysis model outputs have been investigated for the Greenland Ice Sheet （GrIS）,based on the common period of 1989-2001.The ERA-40 and ERA-interim reanalysis data showed better agreement with observations than do NCEP-1 and NCEP-2 reanalyses.Further,ERA-interim showed the closest spatial distribution of accumulation to the observation.Concerning temporal variations,ERA-interim showed the best correlation with precipitation observations at five synoptic stations,and the best correlation with in situ measurements of accumulation at nine ice core sites.The mean annual precipitation averaged over the whole GrIS from ERA-interim （363 mm yr 1） and mean annual accumulation （319 mm yr 1） are very close to the observations.The validation of accumulation calculated from reanalysis data against ice-core measurements suggests that further improvements to reanalysis models are needed.展开更多
Arctic sea ice is a keystone indicator of greenhouse-gas induced global climate change, which is expected to be amplified in the Arctic. Here we directly compare observed variations in arctic sea-ice extent and CO 2 s...Arctic sea ice is a keystone indicator of greenhouse-gas induced global climate change, which is expected to be amplified in the Arctic. Here we directly compare observed variations in arctic sea-ice extent and CO 2 since the beginning of the 20th century, identifying a strengthening linkage, such that in recent decades the rate of sea-ice decrease mirrors the increase in CO 2 , with r ～ -0.95 over the last four decades, thereby indicating that 90% (r 2 ～ 0.90) of the decreasing sea-ice extent is empirically "accounted for" by the increasing CO 2 in the atmosphere. The author presents an empirical relation between annual sea-ice extent and global atmospheric CO 2 concentrations, in which sea-ice reductions are linearly, inversely proportional to the magnitude of increase of CO 2 over the last few decades. This approximates sea-ice changes during the most recent four decades, with a proportionality constant of 0.030 million km 2 per ppmv CO 2 . When applied to future emission scenarios of the Intergovernmental Panel on Climate Change (IPCC), this relationship results in substantially faster ice decreases up to 2050 than predicted by IPCC models. However, departures from this projection may arise from non-linear feedback effects and/or temporary natural variations on interannual timescales, such as the record minimum of sea-ice extent observed in September 2007.展开更多
基金supported by the Knowledge Innovation Program of Chinese Academy of Sciences (Grant No. KZCX1-YW-12-03)National Basic Research Program of China (2006CB403600)+3 种基金Project of Young Scientists Fund by National Natural Sciences Foundation of China (Grant No. 40606008)National Science and Technology Infrastructure Program(2006BAC03B04)supported by National Natural Sciences Foundation of China (Grant No.40531006)supported by a private donation from Trond Mohn c/o Frank Mohn AS, Bergenand the MERSEA project from the European Commission (Grant No. SIP3-CT-2003-502885)
文摘The computational cost required by the Ensemble Kalman Filter （EnKF） is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation （OI） or three-dimension variational as- similation （3DVAR）. Ensemble optimal interpolation （EnOI）, a crudely simplified implementation of EnKF, is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF. In this paper, to compromise between computational cost and dynamic covariance, we use the idea of ＂dressing＂ a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance. The term ＂dressing＂ means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles. This dressing EnKF （DrEnKF for short） scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model （HYCOM） over a four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble. Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members. Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors （RMSE） at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.
基金supported by the National Basic Research Program of China (Grant No. 2009CB421400)the National Science Foundation of China (Grant No. 40821092)
文摘Annual precipitation,evaporation,and calculated accumulation from reanalysis model outputs have been investigated for the Greenland Ice Sheet （GrIS）,based on the common period of 1989-2001.The ERA-40 and ERA-interim reanalysis data showed better agreement with observations than do NCEP-1 and NCEP-2 reanalyses.Further,ERA-interim showed the closest spatial distribution of accumulation to the observation.Concerning temporal variations,ERA-interim showed the best correlation with precipitation observations at five synoptic stations,and the best correlation with in situ measurements of accumulation at nine ice core sites.The mean annual precipitation averaged over the whole GrIS from ERA-interim （363 mm yr 1） and mean annual accumulation （319 mm yr 1） are very close to the observations.The validation of accumulation calculated from reanalysis data against ice-core measurements suggests that further improvements to reanalysis models are needed.
基金supported by the Mohn-Sverdrup Center for Global Ocean StudiesOperational Oceanography at the Nansen Center and the Research Council of Norway+1 种基金is a contribution to the International Polar Year―Climate of the Arcticits Role for Europe (IPY-CARE) project, headed by the author
文摘Arctic sea ice is a keystone indicator of greenhouse-gas induced global climate change, which is expected to be amplified in the Arctic. Here we directly compare observed variations in arctic sea-ice extent and CO 2 since the beginning of the 20th century, identifying a strengthening linkage, such that in recent decades the rate of sea-ice decrease mirrors the increase in CO 2 , with r ～ -0.95 over the last four decades, thereby indicating that 90% (r 2 ～ 0.90) of the decreasing sea-ice extent is empirically "accounted for" by the increasing CO 2 in the atmosphere. The author presents an empirical relation between annual sea-ice extent and global atmospheric CO 2 concentrations, in which sea-ice reductions are linearly, inversely proportional to the magnitude of increase of CO 2 over the last few decades. This approximates sea-ice changes during the most recent four decades, with a proportionality constant of 0.030 million km 2 per ppmv CO 2 . When applied to future emission scenarios of the Intergovernmental Panel on Climate Change (IPCC), this relationship results in substantially faster ice decreases up to 2050 than predicted by IPCC models. However, departures from this projection may arise from non-linear feedback effects and/or temporary natural variations on interannual timescales, such as the record minimum of sea-ice extent observed in September 2007.
