Applications of Geostatistics to Data Assimilation in Biogeochemical Models

The field of geostatistics offers a rich set of tools for analyzing parameters that display spatial and/or temporal autocorrelation. Historically, these methods have been used primarily for interpolating sparse measurements of in situ data. More recently, however, methods based on geostatistical framework have used in an increasing numbers of areas of earth science. This presentation will discuss a number of recent developments in geostatistics relevant to data assimilation in biogeochemical models. The overall goal of the presentation is to emphasize the need to explicitly account for spatial and temporal covariance in sampled data, and the need to translate available data between relevant spatial and temporal scales. The emphasis will be on presenting a common framework that can be used to develop problem-specific approaches. The presented examples will include (i) the identification of environmental parameters controlling observed variability in eddy covariance flux measurements, (ii) downscaling and upscaling observed spatial variability across spatial scales, (iii) geostatistical inverse modeling for constraining carbon fluxes at fine spatial resolutions, and (iv) merging of flux data and atmospheric concentration measurements for constraining parameters in biospheric models.