Exploring parameter sensitivities of the land surface using a locally coupled land-atmosphere model

This paper presents a multicriteria analysis that explores the sensitivity of the land surface to changes in both land and atmospheric parameters, in terms of reproducing surface heat fluxes and ground temperature; for the land parameters, offline sensitivity analyses were also conducted for comparison to infer the influence of land-atmosphere interactions. A simple "one-at-a-time" sensitivity analysis was conducted first to filter out some insensitive parameters, followed by a multicriteria sensitivity analysis using the multiobjective generalized sensitivity analysis algorithm. The models used were the locally coupled National Center for Atmospheric Research (NCAR) single-column community climate model and the offline NCAR land surface model, driven and evaluated by a summer intensive operational periods (IOP) data set from the southern Great Plains. As expected, the results show that land-atmosphere interactions (with or without land-atmosphere parameter interactions) can have significant influences on the sensitivity of the land surface to changes in the land parameters, and the single-criterion sensitivities can be significantly different from the multicriteria sensitivity. These findings are mostly model and data independent and can be generally useful, regardless of the model/data dependence of the sensitivities of individual parameters. The exceptionally high sensitivities of the selected atmospheric parameters in a multicriteria sense (and in particular for latent heat) appeal for adequate attention to the specification of effective values of these parameters in an atmospheric model. Overall, this study proposes an effective framework of multicriteria sensitivity analysis beneficial to future studies in the development and parameter estimation of other complex (offline or coupled) land surface models. Copyright 2004 by the American Geophysical Union

Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model

Bayesian calibration of computer models tunes unknown input parameters by comparing outputs with observations. For model outputs that are distributed over space, this becomes computationally expensive because of the output size. To overcome this challenge, we employ a basis representation of the model outputs and observations: we match these decompositions to carry out the calibration efficiently. In the second step, we incorporate the non-stationary behaviour, in terms of spatial variations of both variance and correlations, in the calibration. We insert two integrated nested Laplace approximation-stochastic partial differential equation parameters into the calibration. A synthetic example and a climate model illustration highlight the benefits of our approach

Large-scale features of Pliocene climate: results from the Pliocene Model Intercomparison Project

Climate and environments of the mid-Pliocene warm period (3.264 to 3.025 Ma) have been extensively studied. Whilst numerical models have shed light on the nature of climate at the time, uncertainties in their predictions have not been systematically examined. The Pliocene Model Intercomparison Project quantifies uncertainties in model outputs through a coordinated multi-model and multi-model/data intercomparison. Whilst commonalities in model outputs for the Pliocene are clearly evident, we show substantial variation in the sensitivity of models to the implementation of Pliocene boundary conditions. Models appear able to reproduce many regional changes in temperature reconstructed from geological proxies. However, data/model comparison highlights that models potentially underestimate polar amplification. To assert this conclusion with greater confidence, limitations in the time-averaged proxy data currently available must be addressed. Furthermore, sensitivity tests exploring the known unknowns in modelling Pliocene climate specifically relevant to the high latitudes are essential (e.g. palaeogeography, gateways, orbital forcing and trace gasses). Estimates of longer-term sensitivity to CO2 (also known as Earth System Sensitivity; ESS), support previous work suggesting that ESS is greater than Climate Sensitivity (CS), and suggest that the ratio of ESS to CS is between 1 and 2, with a "best" estimate of 1.5

A Comparison of Two Shallow Water Models with Non-Conforming Adaptive Grids: classical tests

In an effort to study the applicability of adaptive mesh refinement (AMR) techniques to atmospheric models an interpolation-based spectral element shallow water model on a cubed-sphere grid is compared to a block-structured finite volume method in latitude-longitude geometry. Both models utilize a non-conforming adaptation approach which doubles the resolution at fine-coarse mesh interfaces. The underlying AMR libraries are quad-tree based and ensure that neighboring regions can only differ by one refinement level. The models are compared via selected test cases from a standard test suite for the shallow water equations. They include the advection of a cosine bell, a steady-state geostrophic flow, a flow over an idealized mountain and a Rossby-Haurwitz wave. Both static and dynamics adaptations are evaluated which reveal the strengths and weaknesses of the AMR techniques. Overall, the AMR simulations show that both models successfully place static and dynamic adaptations in local regions without requiring a fine grid in the global domain. The adaptive grids reliably track features of interests without visible distortions or noise at mesh interfaces. Simple threshold adaptation criteria for the geopotential height and the relative vorticity are assessed.Comment: 25 pages, 11 figures, preprin