A major observed and predicted impact of climate change on marine species is the poleward shift in their distributions and the resulting changes in community structure. Here, we used a Dynamic Bioclimate Envelope Model to project range shift of exploited marine fishes and invertebrates in Western Australia. We combined published data and expert knowledge to predict current species distributions for 30 tropical, sub-tropical and temperate species that occur along the coast of Western Australia. Using outputs from both a Regional Oceanographic Model and a Global Circulation Model, we simulated change in the distribution of each species. Our study shows that under the SRES (Special Report for Emission Scenarios) A1B scenario, the median rate of distribution shift is around 19 km decade–1 towards higher latitudes and 9 m deeper decade–1 by 2055 relative to 2005. As a result, species gains and losses are expected along the south coast and north coast of Western Australia, respectively. Also, the coast of Western Australia is expected to experience a ‘tropicalisation’ of the marine community in the future, with increasing dominance of warmer-water species. Such changes in species assemblages may have large ecological and socio-economic implications through shifts in fishing grounds and unexpected trophic effects.

Ocean boundary currents are poorly represented in existing coupled climate models, partly because of their insufficient resolution to resolve narrow jets. Therefore, there is limited confidence in the simulated response of boundary currents to climate change by climate models. To address this issue, the eddy-resolving Ocean Forecasting Australia Model (OFAM) was used, forced with bias-corrected output in the 2060s under the Special Report on Emissions Scenarios (SRES) A1B from the CSIRO Mark version 3.5 (Mk3.5) climate model, to provide downscaled regional ocean projections. CSIRO Mk3.5 captures a number of robust changes that are common to most climate models that are consistent with observed changes, including the weakening of the equatorial Pacific zonal wind stress and the strengthening of the wind stress curl in the Southern Pacific, important for driving the boundary currents around Australia. The 1990s climate is downscaled using air–sea fluxes from the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40). The current speed, seasonality, and volume transports of the Australian boundary currents show much greater fidelity to the observations in the downscaled model. Between the 1990s and the 2060s, the downscaling with the OFAM simulates a 15% reduction in the Leeuwin Current (LC) transport, a 20% decrease in the Indonesian Throughflow (ITF) transport, a 12% increase in the East Australian Current (EAC) core transport, and a 35% increase in the EAC extension. The projected changes by the downscaling model are consistent with observed trends over the past several decades and with changes in wind-driven circulation derived from Sverdrup dynamics. Although the direction of change projected from downscaling is usually in agreement with CSIRO Mk3.5, there are important regional details and differences that will impact the response of ecosystems to climate change.

Accurate prediction of snowpack status is important for a range of environmental applications, yet model estimates are typically poor and in situ measurement coverage is inadequate. Moreover, remote sensing estimates are spatially and temporally limited due to complicating effects, including distance to open water, presence of wet snow, and presence of thick snow. However, through assimilation of remote sensing estimates into a land surface model, it is possible to capitalize on the strengths of both approaches. In order to achieve this, reliable estimates of the uncertainty in both remotely sensed and model simulated snow water equivalent (SWE) estimates are critical. For practical application, the remotely sensed SWE retrieval error is prescribed with a spatially constant but monthly varying value, with data omitted for (1) locations closer than 200 km to significant open water, (2) times and locations with model‐predicted presence of liquid water in the snowpack, and (3) model SWE estimates greater than 100 mm. The model error is estimated using standard error propagation with a calibrated spatially and temporally constant model error contribution. A series of tests have been performed to assess the assimilation algorithm performance. Multiyear model simulations with and without remotely sensed SWE assimilation are presented and evaluated with in situ SWE observations. The SWE estimates from assimilation were found to be superior to both the model simulation and remotely sensed estimates alone, except when model SWE estimates rapidly and erroneously crossed the 100‐mm SWE cutoff early in the snow season.