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Attribution of an extreme magnitude I-day rainfall event in Hobart is inhibited by small sample size. For moderate magnitude Hobart daily rainfall extremes, models suggest that the associated extratropical lows [...]

Introduction. The wheat belt of southwest Western Australia (SWWA) experienced several severe frosts just before harvest in September 2016, leading to a loss of one million tonnes of grain crops [...]

Global climate models (GCMs) are essential for investigating climate change, but their coarse scale limits their efficacy for climate adaptation planning at the regional scales where climate impacts manifest. Dynamical downscaling of GCM outputs better resolves regional climate and thus provides improved guidance for climate policy at regional scales. Being expensive to run, downscaling uses a subset of GCMs, necessitating careful GCM selection. This evaluation identifies a suitable subset of CMIP6 GCMs for downscaling over Australia by assessing individual GCMs against three criteria: (a) performance simulating daily climate variable distributions, climate means, extremes, and modes; (b) model independence; and (c) climate change signal diversity. Over Australia, GCMs are generally biased cold (warm) for maximum (minimum) temperature, with larger biases for minimum temperature. GCMs are generally wet biased, especially over the monsoonal north, but dry biased over eastern regions. Most GCMs show larger biases for temperature and precipitation over geographically complex, heavily populated eastern regions, relative to other regions. Evaluations identify a distinct group of 11 GCMs that perform consistently poorly across climate variables, statistics, and timescales with widespread, statistically significant biases, versus 13 GCMs that show consistent adequate‐to‐good performance with substantially reduced errors. Assessment of model independence highlights the lack of independence between several high‐performing GCMs, particularly from allied modeling groups, demonstrating the importance of careful ensemble selection when making selective samples of climate space. Once GCM climate signal diversity is considered, 6–8 mid‐to‐high‐performing, independent GCMs occupy the full range of the future climate space and, thus, are suitable for dynamical downscaling over CORDEX‐Australasia. Key Points: Per‐global climate model (GCM) performances varied markedly, though the best‐performing GCMs simulated consistently well across temperature variables and statisticsAssessment of model independence highlights the lack of independence between several high‐performing GCMsSix to eight mid‐to‐high‐performing, independent GCMs capturing a broad range of the future climate space are identified as suitable for downscaling [ABSTRACT FROM AUTHOR]

Abstract: Transient climate response (TCR), transient response at 140 years (T140), and equilibrium climate sensitivity (ECS) indices are intended as benchmarks for comparing the magnitude of climate response projected by climate models. It is generally assumed that TCR or T140 would explain more variability between models than ECS for temperature change over the 21st century, since this timescale is the realm of transient climate change. Here we find that TCR explains more variability across Coupled Model Intercomparison Project phase 5 than ECS for global temperature change since preindustrial, for 50 or 100 year global trends up to the present, and for projected change under representative concentration pathways in regions of delayed warming such as the Southern Ocean. However, unexpectedly, we find that ECS correlates higher than TCR for projected change from the present in the global mean and in most regions. This higher correlation does not relate to aerosol forcing, and the physical cause requires further investigation. [ABSTRACT FROM AUTHOR]

The article focuses on the circulation pattern that drives cold outbreaks as well as severe frost risk in southwest Western Australia in September 2016. Topics being presented include thermodynamic changes as well as the effect of human influence on cold extremes and rising temperatures of the climate mean state and forced changes to circulation is presented,

The article examines the possible causes of mean seal level pressure (MSLP) anomalies in south Australia in August 2014. Topics covered include analysis of weather@home global simulations, the impact of climate change on the monthly MSLP anomalies and the findings that human influence on climate variation increased the chances of extreme high pressure anomalies associated with reduced rainfall, lowland snowfall and frosts.

The term 'downscaling' refers to the process of translating information from global climate model simulations to a finer spatial resolution. There are numerous methods by which this translation of information can occur. For users of downscaled information, it is important to have some understanding of the properties of different methods (in terms of their capabilities and limitations to convey the change signal, as simulated by the global model), as these dictate the type of applications that the downscaled information can be used for in impact, adaptation, and vulnerability research. This article provides an appraisal of downscaling in terms of its perceived purpose and value for informing on plausible impacts due to climate change and for underpinning regional risk assessments. The concepts climate realism and physical plausibility of change are introduced to qualify the broad scale properties associated with different categories of downscaling approaches; the former concerning the skill of different approaches to represent regional climate characteristics and the latter their skill in simulating regional climate change. Aspects of change not captured by global climate models, due to resolution or regional factors, may be captured by downscaling. If these aspects are of interest, then downscaling may be useful once it has been demonstrated to add value. For cases where the broad scale change to the mean climate is of interest, or where there is no demonstrated added value from downscaling, then there is a wide range of regionalization methods that are suitable for practitioners in the impact, adaptation, and vulnerability field. WIREs Clim Change 2015, 6:301-319. doi: 10.1002/wcc.339 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [ABSTRACT FROM AUTHOR]

ABSTRACT A set of 27 global climate models from the Coupled Model Inter-comparison Project Phase 5 ( CMIP5) ensemble are assessed for their performance for the purpose of making future climate projection studies in the western tropical Pacific and differences to Coupled Model Inter-comparison Project Phase 3 ( CMIP3) are assessed. The CMIP5 models show some improvements upon CMIP3 in the simulation of the climate in the western tropical Pacific in the late 20th century. There are fewer CMIP5 models with very poor skill scores than in CMIP3 for some measures and a small group of the well-performing models in CMIP5 have lower biases than in an equivalent group from CMIP3. These best-performing models could be particularly informative for studying certain climate sensitivities and feedbacks in the region. There is evidence to reject one model as unsuitable for making regional climate projections in the region, and another two models unsuitable for analysis of the South Pacific Convergence Zone (SPCZ). However, while there have been improvements, many of the systematic model biases in the mean climate in CMIP3 are also present in the CMIP5 models. They are primarily related to the shape of the transition between the Indo-Pacific warm pool and equatorial cold tongue, and the associated biases in the position and orientation of the SPCZ and Inter-Tropical Convergence Zone, as well as in the spatial pattern, variability and teleconnections of the West Pacific monsoon, and the simulation of El Niño Southern Oscillation. Overall, the results show that careful interpretation and consideration of biases is required when using CMIP5 outputs for generating regional climate projections for the western tropical Pacific, particularly at the country scale, just as there was with CMIP3. [ABSTRACT FROM AUTHOR]

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 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [ABSTRACT FROM AUTHOR]

ABSTRACT We describe the method and performance of a bias-correction applied to high-resolution (˜10 km) simulations from a stretched-grid Regional Climate Model ( RCM) over Tasmania, Australia. The bias-correction is a quantile mapping of empirical cumulative frequency distributions. Corrections are applied at a daily time step to five variables: rainfall, potential evaporation (PE), solar radiation, maximum temperature and minimum temperature. Corrections are calculated independently for each season. We show that quantile mapping of empirical distributions can be highly effective in correcting biases in RCM outputs. Cross-validation shows biases are effectively reduced across the range of cumulative frequency distributions, with few exceptions. The bias-correction is not as effective at correcting biases for values at or near zero (e.g. in rainfall simulations), although even here the bias-correction improves biases evident in the uncorrected simulations. In addition, the bias-correction improves frequency characteristics of variables such as the number of rain days. We use a detrending technique to apply the bias-correction to 140-year time series of RCM variables. We show that the bias-correction effectively preserves long-term changes (e.g. to the mean and variance) to variables projected by the uncorrected RCM simulations. Correlations between key variables are also largely preserved, thus the bias-corrected outputs reflect the dynamics of the underlying RCM. However, the bias-corrected simulations still exhibit some of the deficiencies of the RCM simulations, e.g. the tendency to underestimate the magnitude and duration of large, multi-day rain events, and the tendency to underestimate the duration of dry spells. The bias-corrected simulations for six downscaled GCMs for the A2 and B1 emissions scenarios are available to researchers from . [ABSTRACT FROM AUTHOR]