ALBERT

Beschreibung: An exercise in nomenclature can throw interesting light on the settlement history of an area. The location, site and situation of each of the place-names could in fact be incorporated into each place-name, which can thus authenticate, or can be authenticated by, a feature or features on the landscape. Place-names, when taken in the context of the geography of the area, can also commemorate events or personages, real or mythical, a locality being thus imbued with associations at once romantic. Coloured by romance, however, historical verisimilitude then is but an incidental consideration. Frequently, place-names just serve the mundane purpose of designation, the convenient presence of such features as trees, rocks, bays and others providing the root terms for the names. Even here though, it is possible to be adventurous and the rocks and stones and trees can be conceived fancifully, or even in the full force of belief, in terms of their semblances of animals shapes or of their magical properties. They can also be interpreted as petrifications of human forms (usually heroes or heroines, or ill-fated lovers) or as having historical pertinence in being touched by, or sat upon, or having sheltered some legendary or supernatural personalities.

Beschreibung: Various data assimilation schemes have been applied in studies on atmospheric CO2 inversion. An influence matrix based on the linear statistical analysis scheme can diagnose the impact of individual observations on a particular analysis. In this study, to estimate the effect of CO2 observations on an analysis of surface CO2 flux, both the analysis sensitivity and the information content were calculated using the influence matrix in the CarbonTracker, which is an inverse modeling system for estimating surface CO2 flux based on an ensemble Kalman filter. The experimental period was from January 2000 to December 2009. The global average self-sensitivity is 4.8%, which implies that the analysis extracts 4.8% of the information from the observations and 95.2% from the background each assimilation cycle. Because the surface CO2 flux in each week is optimized by five weeks of observations, the cumulative impact over five weeks would be greater than 4.8%. The analysis sensitivity is inversely proportional to the number of observations used in the assimilation, which is distinctly apparent in continuous observation categories with a sufficient number of observations. The time series of the globally averaged analysis sensitivities shows seasonal variations, with greater sensitivities in summer and lower sensitivities in winter, which is attributed to the surface CO2 flux uncertainty. The time-averaged analysis sensitivities in the Northern Hemisphere are greater than those in the Tropics and the Southern Hemisphere. The information content indicates an imbalance between the observation coverage in North America and that in other regions. Approximately half of the total observational information is provided by continuous observations, mainly from North America, which indicates that continuous observations are the most informative and that comprehensive coverage of additional observations in other regions is necessary to estimate the surface CO2 flux in these areas as accurately as in North America. In addition, the uncertainty of the surface CO2 flux in Asia, where observations are sparse, is reduced by assimilating five weeks of observations as opposed to one week of observations in North America, which indicates that a longer assimilation window with a lag is necessary to optimize the surface CO2 flux in Asia.

Beschreibung: In this study, the effect of CO2 observations on an analysis of surface CO2 flux was calculated using an influence matrix in the CarbonTracker, which is an inverse modeling system for estimating surface CO2 flux based on an ensemble Kalman filter. The influence matrix represents a sensitivity of the analysis to observations. The experimental period was from January 2000 to December 2009. The diagonal element of the influence matrix (i.e., analysis sensitivity) is globally 4.8% on average, which implies that the analysis extracts 4.8% of the information from the observations and 95.2% from the background each assimilation cycle. Because the surface CO2 flux in each week is optimized by 5 weeks of observations, the cumulative impact over 5 weeks is 19.1%, much greater than 4.8%. The analysis sensitivity is inversely proportional to the number of observations used in the assimilation, which is distinctly apparent in continuous observation categories with a sufficient number of observations. The time series of the globally averaged analysis sensitivities shows seasonal variations, with greater sensitivities in summer and lower sensitivities in winter, which is attributed to the surface CO2 flux uncertainty. The time-averaged analysis sensitivities in the Northern Hemisphere are greater than those in the tropics and the Southern Hemisphere. The trace of the influence matrix (i.e., information content) is a measure of the total information extracted from the observations. The information content indicates an imbalance between the observation coverage in North America and that in other regions. Approximately half of the total observational information is provided by continuous observations, mainly from North America, which indicates that continuous observations are the most informative and that comprehensive coverage of additional observations in other regions is necessary to estimate the surface CO2 flux in these areas as accurately as in North America.