Peter B. Gibson

Evaluating synoptic systems in the CMIP5 climate models over the Australian region

Climate models are our principal tool for generating the projections used to inform climate change policy. Our confidence in projections depends, in part, on how realistically they simulate present day climate and associated variability over a range of time scales. Traditionally, climate models are less commonly assessed at time scales relevant to daily weather systems. Here we explore the utility of a self-organizing maps (SOMs) procedure for evaluating the frequency, persistence and transitions of daily synoptic systems in the Australian region simulated by state-of-the-art global climate models. In terms of skill in simulating the climatological frequency of synoptic systems, large spread was observed between models. A positive association between all metrics was found, implying that relative skill in simulating the persistence and transitions of systems is related to skill in simulating the climatological frequency. Considering all models and metrics collectively, model performance was found to be related to model horizontal resolution but unrelated to vertical resolution or representation of the stratosphere. In terms of the SOM procedure, the timespan over which evaluation was performed had some influence on model performance skill measures, as did the number of circulation types examined. These findings have implications for selecting models most useful for future projections over the Australian region, particularly for projections related to synoptic scale processes and phenomena. More broadly, this study has demonstrated the utility of the SOMs procedure in providing a process-based evaluation of climate models.

On the use of self‐organizing maps for studying climate extremes

Understanding how climate extremes are sensitive to a changing climate requires characterization of the physical mechanisms behind such events. For this purpose, the application of self-organizing maps (SOMs) has become popular in the climate science literature. One potential drawback, though not unique to SOMs, is that the background synoptic conditions represented by SOMs may be too generalized to adequately describe the atypical conditions that can co-occur during the extreme event being considered. In this paper, using the Australian region as a case study, we illustrate how the commonly used SOM training procedure can be readily modified to produce both more accurate patterns and patterns that would otherwise occur too rarely to be represented in the SOM. Even with these improvements, we illustrate that without careful treatment, the synoptic conditions that co-occur during some types of extreme events (i.e. heavy rainfall and mid-latitudinal cyclone occurrence days) risk being poorly represented by the SOM patterns. In contrast, we find that during Australian heatwave events the circulation is indeed well represented by the SOM patterns, and that this application can provide additional insight to composite analysis. While these results should not necessarily discourage researchers seeking to apply SOMs to study climate extremes, they highlight the importance of first critically evaluating the features represented by the SOM. This study has provided a methodological framework for such an evaluation which is directly applicable to other weather typing procedures, regions, and types of extremes.

Comparing Australian heat waves in the CMIP5 models through cluster analysis

Quantitative projections of climate extremes on local to regional scales are highly valuable for planners and decision makers and necessary for effective local climate change adaptation. However, in contrast to the model robustness of simulated extremes at the global scale, the robustness in simulating past and future extremes often diminishes over finer spatial scales. In this study we analyze heat waves simulated by state-of-the-art global climate models over the Australian region. For the first time we present results explicitly detailing the model spread in simulated heat wave trends and climatology for this region for the recent past (1958–2005). As expected, large intermodel spread is observed at the local to regional scale for both heat wave trends and climatology. By analyzing multiple initial condition runs from individual models, we show that model internal variability strongly influences the spatial patterns of heat wave trends, while intermodel differences in heat wave climatology appear more influenced by model uncertainty. From a model evaluation perspective, cluster analysis is shown to be useful in characterizing robust spatial features of heat waves simulated by the models. In contrast to the multimodel mean, where uncorrelated spatial features tend to be averaged out, cluster composites preserve these features. Since previous examinations have tended to focus on the multimodel mean the extent of model spread may have been overlooked. Further examination of the processes that lead to model differences and biases is needed.

Regional Variability in New Zealand’s Wind Resource Linked to Synoptic-Scale Circulation: Implications for Generation Reliability

AbstractEven in locations endowed with excellent wind resources, the intermittent nature of wind is perceived as a barrier to reliable generation. However, recent studies have demonstrated that electrically interconnecting wind farms in a meteorologically oriented network can reduce supply variability and the observed frequency of zero-generation conditions. In this study a 5-yr synthetic dataset of 15 wind farms is utilized to investigate the benefits to supply reliability from wind farm interconnection in New Zealand. An examination is carried out primarily through a synoptic climatology framework, hypothesizing that benefits to reliability are primarily related to the degree to which wind farms are influenced differently by the synoptic-scale circulation. Using a weather-typing approach and composite analysis, regionality is observed in the linkages between synoptic-scale circulation and wind resources, particularly between wind farms located in the far northern and far southern regions of the country....

The Role of Circulation and Land Surface Conditions in Current and Future Australian Heat Waves

AbstractUnderstanding the physical drivers of heat waves is essential for improving short-term forecasts of individual events and long-term projections of heat waves under climate change. This study provides the first analysis of the influence of the large-scale circulation on Australian heat waves, conditional on the land surface conditions. Circulation types, sourced from reanalysis, are used to characterize the different large-scale circulation patterns that drive heat wave events across Australia. The importance of horizontal temperature advection is illustrated in these circulation patterns, and the pattern occurrence frequency is shown to reorganize through different modes of climate variability. It is further shown that the relative likelihood of a particular synoptic situation being associated with a heat wave is strongly modulated by the localized partitioning of available energy between surface sensible and latent heat fluxes (as measured through evaporative fraction) in many regions in reanalys...

Investigating event-specific drought attribution using self-organizing maps

Previous studies evaluating anthropogenic influences on the meteorological drivers of drought have found mixed results owing to (1) the complex physical mechanisms which lead to the onset of drought, (2) differences in the characteristics and timescales of drought for different regions of the world, and (3) different approaches to the question of attribution. For a mid-latitude, temperate climate like New Zealand, strongly modulated by oceanic influences, summer droughts last on the order of three months, and are less strongly linked to persistent temperature anomalies than continental climates. Here, we demonstrate the utility of a novel approach for characterizing the meteorological conditions conducive to extreme drought over the North Island of New Zealand, using the January-March 2013 event as a case study. Specifically, we consider the use of self-organizing map (SOM) techniques in a multi-member coupled climate model ensemble to capture changes in daily circulation, between two 41-year periods (1861-1901 and 1993-2033). Comparisons are made with seasonal pressure and precipitation indices. Our results demonstrate robust (>99% confidence) increases in the likelihood of observing circulation patterns like those of the 2013 drought in the recent-climate simulations when compared with the early-climate simulations. Best-guess estimates of the fraction of attributable risk range from 0.2 to 0.4, depending on the metric used and threshold considered. Contributions to uncertainty in these attribution statements are discussed.