Climate projections are essential for studying ecological responses to climate change, and their use is now common in ecology. However, the lack of integration between ecology and climate science has restricted understanding of the available climate data and their appropriate use. We provide an overview of climate model outputs and issues that need to be considered when applying projections of future climate in ecological studies. We outline the strengths and weaknesses of available climate projections, the uncertainty associated with future projections at different spatial and temporal scales, the differences between available downscaling methods (dynamical, statistical downscaling, and simple scaling of global circulation model output), and the implications these have for ecological models. We describe some of the changes in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), including the new representative concentration pathways. We highlight some of the challenges in using model projections in ecological studies and suggest how to effectively address them. WIREs Clim Change 2014, 5:621–637. doi: 10.1002/wcc.291 This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models Future of Global Energy > Scenario Development and Application Climate, Ecology, and Conservation > Modeling Species and Community Interactions

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Abstract. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is the first atmospheric regional reanalysis over a large region covering Australia, New Zealand, and Southeast Asia. The production of the reanalysis with approximately 12 km horizontal resolution – BARRA-R – is well underway with completion expected in 2019. This paper describes the numerical weather forecast model, the data assimilation methods, the forcing and observational data used to produce BARRA-R, and analyses results from the 2003–2016 reanalysis. BARRA-R provides a realistic depiction of the meteorology at and near the surface over land as diagnosed by temperature, wind speed, surface pressure, and precipitation. Comparing against the global reanalyses ERA-Interim and MERRA-2, BARRA-R scores lower root mean square errors when evaluated against (point-scale) 2 m temperature, 10 m wind speed, and surface pressure observations. It also shows reduced biases in daily 2 m temperature maximum and minimum at 5 km resolution and a higher frequency of very heavy precipitation days at 5 and 25 km resolution when compared to gridded satellite and gauge analyses. Some issues with BARRA-R are also identified: biases in 10 m wind, lower precipitation than observed over the tropical oceans, and higher precipitation over regions with higher elevations in south Asia and New Zealand. Some of these issues could be improved through dynamical downscaling of BARRA-R fields using convective-scale (<2 km) models.

Daily values of McArthur Forest Fire Danger Index were generated at ~10-km resolution over Tasmania, Australia, from six dynamically downscaled CMIP3 climate models for 1961–2100, using a high (A2) emissions scenario. Multi-model mean fire danger validated well against observations for 2002–2012, with 99th percentile fire dangers having the same distribution and largely similar values to those observed over the same time. Model projections showed a broad increase in fire danger across Tasmania, but with substantial regional variation – the increase was smaller in western Tasmania (district mean cumulative fire danger increasing at 1.07 per year) compared with parts of the east (1.79 per year), for example. There was also noticeable seasonal variation, with little change occurring in autumn, but a steady increase in area subject to springtime 99th percentile fire danger from 6% in 1961–1980 to 21% by 2081–2100, again consistent with observations. In general, annually accumulated fire danger behaved similarly. Regional mean sea level pressure patterns resembled observed patterns often associated with days of dangerous fire weather. Days of elevated fire danger displaying these patterns increased in frequency during the simulated twenty-first century: in south-east Tasmania, for example, the number of such events detected rose from 101 (across all models) in 1961–1980 to 169 by 2081–2100. Correspondence of model output with observations and the regional detail available suggest that these dynamically downscaled model data are useful projections of future fire danger for landscape managers and the community.