The Australian Community Climate and Earth System Simulator coupled model (ACCESS-CM) has been developed at the Centre for Australian Weather and Climate Research (CAWCR), a partnership between CSIRO 1 and the Bureau of Meteorology. It is built by coupling the UK Met Office atmospheric unified model (UM), and other sub-models as required, to the ACCESS ocean model, which consists of the NOAA/GFDL 2 ocean model MOM4p1 and the LANL 3 sea-ice model CICE4.1, under the CERFACS 4 OASIS3.2-5 coupling framework. The primary goal of the ACCESS-CM development is to provide the Australian climate community with a new generation fully coupled climate model for climate research, and to participate in phase five of the Coupled Model Inter-comparison Project (CMIP5). This paper describes the ACCESS-CM framework and components, and presents the control climates from two versions of the ACCESS-CM, ACCESS1.0 and AC-CESS1.3, together with some fields from the 20 th century historical experiments, as part of model evaluation. While sharing the same ocean sea-ice model (except different setups for a few parameters), ACCESS1.0 and ACCESS1.3 differ from each other in their atmospheric and land surface components: the former is configured with the UK Met Office HadGEM2 (r1.1) atmospheric physics and the Met Office Surface Exchange Scheme land surface model version 2, and the latter with atmospheric physics similar to the UK Met Office Global Atmosphere 1.0 including modifications performed at CAWCR and the CSIRO Community Atmosphere Biosphere Land Exchange land surface model version 1.8. The global average annual mean surface air temperature across the 500-year preindustrial control integrations show a warming drift of 0.35 °C in ACCESS1.0 and 0.04 °C in AC-CESS1.3. The overall skills of ACCESS-CM in simulating a set of key climatic fields both globally and over Australia significantly surpass those from the preceding CSIRO Mk3.5 model delivered to the previous coupled model inter-comparison. However, ACCESS-CM, like other CMIP5 models, has deficiencies in various aspects, and these are also discussed.

Abstract. The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 • C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 • C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climatePublished by Copernicus Publications on behalf of the European Geosciences Union. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), the second two being estimates of a similar decade but under 1.5 and 2 • C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 • C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 • C scenario.

Elevation data are fundamental to many applications, especially in geosciences. The latest global elevation data contains forest and building artifacts that limit its usefulness for applications that require precise terrain heights, in particular flood simulation. Here, we use machine learning to remove buildings and forests from the Copernicus Digital Elevation Model to produce, for the first time, a global map of elevation with buildings and forests removed at 1 arc second (∽30m) grid spacing. We train our correction algorithm on a unique set of reference elevation data from 12 countries, covering a wide range of climate zones and urban extents. Hence, this approach has much wider applicability compared to previous DEMs trained on data from a single country. Our method reduces mean absolute vertical error in built-up areas from 1.61m to 1.12m, and in forests from 5.15m to 2.88m. The new elevation map is more accurate than existing global elevation maps and will strengthen applications and models where high quality global terrain information is required.

Augmenting previous papers about the exceptional 2011–2015 California drought, we offer new perspectives on the “snow drought” that extended into Oregon in 2014 and Washington in 2015. Over 80% of measurement sites west of 115°W experienced record low snowpack in 2015, and we estimate a return period of 400–1000 years for California's snowpack under the questionable assumption of stationarity. Hydrologic modeling supports the conclusion that 2015 was the most severe on record by a wide margin. Using a crowd‐sourced superensemble of regional climate model simulations, we show that both human influence and sea surface temperature (SST) anomalies contributed strongly to the risk of snow drought in Oregon and Washington: the contribution of SST anomalies was about twice that of human influence. By contrast, SSTs and humans appear to have played a smaller role in creating California's snow drought. In all three states, the anthropogenic effect on temperature exacerbated the snow drought.