Anthropogenic contributions to Australia's record summer temperatures of 2013

Anthropogenic contributions to the record hot 2013 Australian summer are investigated using a suite of climate model experiments. This was the hottest Australian summer in the observational record. Australian area-average summer temperatures for simulations with natural forcings only were compared to simulations with anthropogenic and natural forcings for the period 1976-2005 and the RCP8.5 high emission simulation (2006-2020) from nine Coupled Model Intercomparison Project phase 5 models. Using fraction of attributable risk to compare the likelihood of extreme Australian summer temperatures between the experiments, it was very likely (>90% confidence) there was at least a 2.5 times increase in the odds of extreme heat due to human influences using simulations to 2005, and a fivefold increase in this risk using simulations for 2006-2020. The human contribution to the increased odds of Australian summer extremes like 2013 was substantial, while natural climate variations alone, including El Niño Southern Oscillation, are unlikely to explain the record temperature. © 2013. American Geophysical Union. All Rights Reserved

Solar UV Forecasts: A Randomized Trial Assessing Their Impact on Adults' Sun-Protection Behavior

This study examined the effectiveness of solar UV forecasts and supporting communications in assisting adults to protect themselves from excessive weekend sun exposure. The study was conducted in Australia, where 557 adult participants with workplace e-mail and Internet access were randomly allocated to one of three weather forecast conditions: standard forecast (no UV), standard forecast + UV, standard forecast + UV + sun-protection messages. From late spring through summer and early autumn, they were e-mailed weekend weather forecasts late in the working week. Each Monday they were e-mailed a prompt to complete a Web-based questionnaire to report sun-related behavior and any sunburn experienced during the previous weekend. There were no significant differences between weather forecast conditions in reported hat use, sunscreen use, sun avoidance, or sunburn. Results indicate that provision of solar-UV forecasts in weather forecasts did not promote markedly enhanced personal sun-protection practices among the adults surveyed.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

A multiregion assessment of observed changes in the areal extent of temperature and precipitation extremes

This study examines trends in the area affected by temperature and precipitation extremes across five large-scale regions using the climate extremes index (CEI) framework. Analyzing changes in temperature and precipitation extremes in terms of areal fraction provides information from a different perspective and can be useful for climate monitoring. Trends in five temperature and precipitation components are analyzed, calculated using a new method based on standard extreme indices. These indices, derived from daily meteorological station data, are obtained from two global land-based gridded extreme indices datasets. The four continental-scale regions of Europe, North America, Asia, and Australia are analyzed over the period from 1951 to 2010, where sufficient data coverage is available. These components are also computed for the entire Northern Hemisphere, providing the first CEI results at the hemispheric scale. Results show statistically significant increases in the percentage area experiencing much-above-average warm days and nights and much-below-average cool days and nights for all regions, with the exception of North America for maximum temperature extremes. Increases in the area affected by precipitation extremes are also found for the Northern Hemisphere regions, particularly Europe and North America

Understanding the role of sea surface temperature-forcing for variability in global temperature and precipitation extremes

The oceans are a well-known source of natural variability in the climate system, although their ability to account for inter-annual variations of temperature and precipitation extremes over land remains unclear. In this study, the role of sea-surface temperature (SST)-forcing is investigated for variability and trends in a range of commonly used temperature and precipitation extreme indices over the period 1959 to 2013. Using atmospheric simulations forced by observed SST and sea-ice concentrations (SIC) from three models participating in the Climate of the Twentieth Century Plus (C20C+) Project, results show that oceanic boundary conditions drive a substantial fraction of inter-annual variability in global average temperature extreme indices, as well as, to a lower extent, for precipitation extremes. The observed trends in temperature extremes are generally well captured by the SST-forced simulations although some regional features such as the lack of warming in daytime warm temperature extremes over South America are not reproduced in the model simulations. Furthermore, the models simulate too strong increases in warm day frequency compared to observations over North America. For extreme precipitation trends, the accuracy of the simulated trend pattern is regionally variable, and a thorough assessment is difficult due to the lack of locally significant trends in the observations. This study shows that prescribing SST and SIC holds potential predictability for extremes in some (mainly tropical) regions at the inter-annual time-scale

A multiregion model evaluation and attribution study of historical changes in the area affected by temperature and precipitation extremes

The skill of eight climate models in simulating the variability and trends in the observed areal extent of daily temperature and precipitation extremes is evaluated across five large-scale regions, using the climate extremes index (CEI) framework. Focusing on Europe, North America, Asia, Australia, and the Northern Hemisphere, results show that overall the models are generally able to simulate the decadal variability and trends of the observed temperature and precipitation components over the period 1951–2005. Climate models are able to reproduce observed increasing trends in the area experiencing warm maximum and minimum temperature extremes, as well as, to a lesser extent, increasing trends in the areas experiencing an extreme contribution of heavy precipitation to total annual precipitation for the Northern Hemisphere regions. Using simulations performed under different radiative forcing scenarios, the causes of simulated and observed trends are investigated. A clear anthropogenic signal is found in the trends in the maximum and minimum temperature components for all regions. In North America, a strong anthropogenically forced trend in the maximum temperature component is simulated despite no significant trend in the gridded observations, although a trend is detected in a reanalysis product. A distinct anthropogenic influence is also found for trends in the area affected by a much-above-average contribution of heavy precipitation to annual precipitation totals for Europe in a majority of models and to varying degrees in other Northern Hemisphere regions. However, observed trends in the area experiencing extreme total annual precipitation and extreme number of wet and dry days are not reproduced by climate models under any forcing scenario

Identifying coherent patterns of environmental change between multiple, multivariate records: an application to four 1000-year diatom records from Victoria, Australia

Empirical orthogonal functions (EOFs) of indirect archives of environmental change are increasingly used to identify coherent trends between palaeoclimate records, to separate externally forced patterns from locally driven idiosyncrasies. Lake sediments are particularly suited to such syntheses: they are abundant in most landscapes and record a wide array of information, yet local complexities often conceal or confuse the climate signal recorded at individual sites. Lake sediment parameters usually exhibit non-linear, multivariate and indirect responses to climate, therefore identifying coherent patterns between two or more lake records presents a complex challenge. Ideally, the selection of representative variables should be non-subjective and inclusive of as many different variables as possible, allowing for unexpected correlations between sites. In order to meet such demands, we propose a two-tier ordination procedure whereby site-specific (local) ordinations, obtained using Detrended Correspondence Analysis (DCA), are nested within a second, regional EOF. Using the local DCAs as representative variables allows the retention of a larger fraction of variance from each site, removes any subjectivity from variable selection and retains the potential for observing multiple, coherent signals from within and between each dataset. We explore this potential using four decadally resolved diatom records from volcanic lakes in Western Victoria, Australia. The records span the 1000 years prior to European settlement in CE 1803. Our analyses reveal at least two coherent patterns of ecological change that are manifest in each of the four datasets, patterns which may have been overlooked by a single-variable, empirical orthogonal function approach. This intra-site coherency provides a valuable step towards understanding multi-decadal hydroclimate variability in southeastern Australia

Bifurcation analysis of two coupled Jansen-Rit neural mass models

We investigate how changes in network structure can lead to pathological oscillations similar to those observed in epileptic brain. Specifically, we conduct a bifurcation analysis of a network of two Jansen-Rit neural mass models, representing two cortical regions, to investigate different aspects of its behavior with respect to changes in the input and interconnection gains. The bifurcation diagrams, along with simulated EEG time series, exhibit diverse behaviors when varying the input, coupling strength, and network structure. We show that this simple network of neural mass models can generate various oscillatory activities, including delta wave activity, which has not been previously reported through analysis of a single Jansen-Rit neural mass model. Our analysis shows that spike-wave discharges can occur in a cortical region as a result of input changes in the other region, which may have important implications for epilepsy treatment. The bifurcation analysis is related to clinical data in two case studies

Brain Model State Space Reconstruction Using an LSTM Neural Network

Objective Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to EEG. However, this approach lacks a reliable method to determine the initial filter conditions and assumes that the distribution of states remains Gaussian. This study presents an alternative, data-driven method to track the states and parameters of neural mass models (NMMs) from EEG recordings using deep learning techniques, specifically an LSTM neural network. Approach An LSTM filter was trained on simulated EEG data generated by a neural mass model using a wide range of parameters. With an appropriately customised loss function, the LSTM filter can learn the behaviour of NMMs. As a result, it can output the state vector and parameters of NMMs given observation data as the input. Main Results Test results using simulated data yielded correlations with R squared of around 0.99 and verified that the method is robust to noise and can be more accurate than a nonlinear Kalman filter when the initial conditions of the Kalman filter are not accurate. As an example of real-world application, the LSTM filter was also applied to real EEG data that included epileptic seizures, and revealed changes in connectivity strength parameters at the beginnings of seizures. Significance Tracking the state vector and parameters of mathematical brain models is of great importance in the area of brain modelling, monitoring, imaging and control. This approach has no need to specify the initial state vector and parameters, which is very difficult to do in practice because many of the variables being estimated cannot be measured directly in physiological experiments. This method may be applied using any neural mass model and, therefore, provides a general, novel, efficient approach to estimate brain model variables that are often difficult to measure