Groundwater constraints on simulated transpiration variability over Southeastern Australian forests

A land surface scheme with and without groundwater-vegetation interactions is used to explore the impact of rainfall variability on transpiration over drought-vulnerable regions of southeastern Australia. The authors demonstrate that if groundwater is included in the simulations, there is a low correlation between rainfall variability and the response of transpiration to this variability over forested regions. Groundwater reduces near-surface water variability, enabling forests to maintain transpiration through several years of low rainfall, in agreement with independent observations of vegetation greenness. If groundwater is not included, the transpiration variability matches the rainfall variability independent of land cover type. The authors' results suggest that omitting groundwater in regions where groundwater sustains forests will 1) probably overestimate the likelihood of forest dieback during drought, 2) overestimate a positive feedback linked with declining transpiration and a drying boundary layer, and 3) underestimate the impact of land cover change due to inadequately simulating the different responses to drought for different land cover types

Precipitation bias correction of very high resolution regional climate models

Regional climate models are prone to biases in precipitation that are problematic for use in impact models such as hydrology models. A large number of methods have already been proposed aimed at correcting various moments of the rainfall distribution. They all require that the model produce the same or a higher number of rain days than the observational data sets, which are usually gridded data sets. Models have traditionally met this condition because their spatial resolution was coarser than the observational grids. But recent climate simulations use higher resolution and the models are likely to systematically produce fewer rain days than the gridded observations.In this study, model outputs from a simulation at 2 km resolution are compared with gridded and in situ observational data sets to determine whether the new scenario calls for revised methodologies. The gridded observations are found to be inadequate to correct the high-resolution model at daily timescales, because they are subjected to too frequent low intensity precipitation due to spatial averaging. A histogram equalisation bias correction method was adapted to the use of station, alleviating the problems associated with relative low-resolution observational grids. The wet-day frequency condition might not be satisfied for extremely dry biases, but the proposed approach substantially increases the applicability of bias correction to high-resolution models. The method is efficient at bias correcting both seasonal and daily characteristic of precipitation, providing more accurate information that is crucial for impact assessment studies

Optimally choosing small ensemble members to produce robust climate simulations

This study examines the subset climate model ensemble size required to reproduce certain statistical characteristics from a full ensemble. The ensemble characteristics examined are the root mean square error, the ensemble mean and standard deviation. Subset ensembles are created using measures that consider the simulation performance alone or include a measure of simulation independence relative to other ensemble members. It is found that the independence measure is able to identify smaller subset ensembles that retain the desired full ensemble characteristics than either of the performance based measures. It is suggested that model independence be considered when choosing ensemble subsets or creating new ensembles