A large-scale view of marine heatwaves revealed by archetype analysis.

Marine heatwaves can have disastrous impacts on ecosystems and marine industries. Given their potential consequences, it is important to understand how broad-scale climate variability influences the probability of localised extreme events. Here, we employ an advanced data-mining methodology, archetype analysis, to identify large scale patterns and teleconnections that lead to marine extremes in certain regions. This methodology is applied to the Australasian region, where it identifies instances of anomalous sea-surface temperatures, frequently associated with marine heatwaves, as well as the broadscale oceanic and atmospheric conditions associated with those extreme events. Additionally, we use archetype analysis to assess the ability of a low-resolution climate model to accurately represent the teleconnection patterns associated with extreme climate variability, and discuss the implications for the predictability of these impactful events.

Standard assessments of climate forecast skill can be misleading.

Assessments of climate forecast skill depend on choices made by the assessor. In this perspective, we use forecasts of the El Niño-Southern-Oscillation to outline the impact of bias-correction on skill. Many assessments of skill from hindcasts (past forecasts) are probably overestimates of attainable forecast skill because the hindcasts are informed by observations over the period assessed that would not be available to real forecasts. Differences between hindcast and forecast skill result from changes in model biases from the period used to form forecast anomalies to the period over which the forecast is made. The relative skill rankings of models can change between hindcast and forecast systems because different models have different changes in bias across periods.

Influence and seepage: An evidence-resistant minority can affect public opinion and scientific belief formation.

Some well-established scientific findings may be rejected by vocal minorities because the evidence is in conflict with political views or economic interests. For example, the tobacco industry denied the medical consensus on the harms of smoking for decades, and the clear evidence about human-caused climate change is currently being rejected by many politicians and think tanks that oppose regulatory action. We present an agent-based model of the processes by which denial of climate change can occur, how opinions that run counter to the evidence can affect the scientific community, and how denial can alter the public discourse. The model involves an ensemble of Bayesian agents, representing the scientific community, that are presented with the emerging historical evidence of climate change and that also communicate the evidence to each other. Over time, the scientific community comes to agreement that the climate is changing. When a minority of agents is introduced that is resistant to the evidence, but that enter into the scientific discussion, the simulated scientific community still acquires firm knowledge but consensus formation is delayed. When both types of agents are communicating with the general public, the public remains ambivalent about the reality of climate change. The model captures essential aspects of the actual evolution of scientific and public opinion during the last 4 decades.

On the definition and identifiability of the alleged "hiatus" in global warming.

Recent public debate and the scientific literature have frequently cited a "pause" or "hiatus" in global warming. Yet, multiple sources of evidence show that climate change continues unabated, raising questions about the status of the "hiatus". To examine whether the notion of a "hiatus" is justified by the available data, we first document that there are multiple definitions of the "hiatus" in the literature, with its presumed onset spanning a decade. For each of these definitions we compare the associated temperature trend against trends of equivalent length in the entire record of modern global warming. The analysis shows that the "hiatus" trends are encompassed within the overall distribution of observed trends. We next assess the magnitude and significance of all possible trends up to 25 years duration looking backwards from each year over the past 30 years. At every year during the past 30 years, the immediately preceding warming trend was always significant when 17 years (or more) were included in the calculation, alleged "hiatus" periods notwithstanding. If current definitions of the "pause" used in the literature are applied to the historical record, then the climate system "paused" for more than 1/3 of the period during which temperatures rose 0.6 K.

False precision, surprise and improved uncertainty assessment.

An uncertainty report describes the extent of an agent's uncertainty about some matter. We identify two basic requirements for uncertainty reports, which we call faithfulness and completeness. We then discuss two pitfalls of uncertainty assessment that often result in reports that fail to meet these requirements. The first involves adopting a one-size-fits-all approach to the representation of uncertainty, while the second involves failing to take account of the risk of surprises. In connection with the latter, we respond to the objection that it is impossible to account for the risk of genuine surprises. After outlining some steps that both scientists and the bodies who commission uncertainty assessments can take to help avoid these pitfalls, we explain why striving for faithfulness and completeness is important.

Betting strategies on fluctuations in the transient response of greenhouse warming.

We examine a series of betting strategies on the transient response of greenhouse warming, expressed by changes in 15-year mean global surface temperature from one 15-year period to the next. Over the last century, these bets are increasingly dominated by positive changes (warming), reflecting increasing greenhouse forcing and its rising contribution to temperature changes on this time scale. The greenhouse contribution to 15-year trends is now of a similar magnitude to typical naturally occurring 15-year trends. Negative 15-year changes (decreases) have not occurred since about 1970, and are still possible, but now rely on large, and therefore infrequent, natural variations. Model projections for even intermediate warming scenarios show very low likelihoods of obtaining negative 15-year changes over the coming century. Betting against greenhouse warming, even on these short time scales, is no longer a rational proposition.

Sources of heterogeneous variability and trends in Antarctic sea-ice.

While the Northern Hemisphere sea-ice has uniformly declined over the past several decades, the observed sea-ice in the Southern Hemisphere has exhibited regions of increase and decrease. Here we use a comprehensive set of ocean-sea-ice simulations (1990-2007) to elucidate the drivers of the observed heterogeneous sea-ice trends. We show wind variability is an important determinant of the heterogeneous pattern of the variability and trends in Southern Hemisphere sea-ice. Only in the West Pacific region does Southern Annular Mode wind forcing contribute significantly to the trend in sea-ice duration. El Niño Southern Oscillation wind forcing contribution to the sea-ice duration trend is confined to the Atlantic and Pacific. In the Indian Ocean, weather is a significant driver of the sea-ice duration trend. Only in the East Pacific region is wind forcing alone insufficient to give rise to the observed sea-ice decline and must be augmented by warming to reproduce the observations.

Uncertainty assessment of VOC emissions from paint in The Netherlands using the NUSAP system.

We demonstrate an innovative approach to uncertainty assessment known as the NUSAP system, to assess qualitative and quantitative uncertainty for the case of emissions of volatile organic compounds (VOC) from paint in The Netherlands. Using expert elicitation, we identified key sources of error, critical assumptions, and bias in the monitoring process. We assessed pedigree and probabilistic uncertainty of all input quantities. We used four pedigree criteria to assess the strength of the knowledge base: proxy representation, empirical basis, methodological rigour and degree of validation. Using Monte Carlo analysis, we assessed sensitivity and propagation of uncertainty. Results for sensitivity and pedigree were combined in a 'NUSAP Diagnostic Diagram', which effectively highlighted the assumption for VOC percentage of imported paint as the weakest spot in the monitoring of VOC emissions. We conclude that NUSAP facilitates systematic scrutinization of method and underlying assumptions and structures creative thinking on sources of error and bias. It provides a means to prioritise uncertainties and focus research efforts on the potentially most problematic parameters and assumptions, at the same time identifying specific weaknesses in the knowledge base. With NUSAP, nuances of meaning about quantities can be conveyed concisely and clearly, to a degree that is not possible with statistic methods only.