A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas

This paper proposes a generalised and robust multi-factor Gated Recurrent Unit (GRU) based Deep Learning (DL) model to forecast electricity load in distribution networks during wildfire seasons. The flexible modelling methods consider data input structure, calendar effects and correlation-based leading temperature conditions. Compared to the regular use of instantaneous temperature, the Mean Absolute Percentage Error (MAPE) is decreased by 30.73% by using the proposed input feature selection and leading temperature relationships. Our model is generalised and applied to eight real distribution networks in Victoria, Australia, during the wildfire seasons of 2015-2020. We demonstrate that the GRU-based model consistently outperforms another DL model, Long Short-Term Memory (LSTM), at every step, giving average improvements in Mean Squared Error (MSE) and MAPE of 10.06% and 12.86%, respectively. The sensitivity to large-scale climate variability in training data sets, e.g. El Ni\~no or La Ni\~na years, is considered to understand the possible consequences for load forecasting performance stability, showing minimal impact. Other factors such as regional poverty rate and large-scale off-peak electricity use are potential factors to further improve forecast performance. The proposed method achieves an average forecast MAPE of around 3%, giving a potential annual energy saving of AU\$80.46 million for the state of Victoria

The impact of the state of the troposphere on the response to stratospheric heating in a simplified GCM

Previous studies have made use of simplified general circulation models (sGCMs) to investigate the atmospheric response to various forcings. In particular, several studies have investigated the tropospheric response to changes in stratospheric temperature. This is potentially relevant for many climate forcings. Here the impact of changing the tropospheric climatology on the modeled response to perturbations in stratospheric temperature is investigated by the introduction of topography into the model and altering the tropospheric jet structure. The results highlight the need for very long integrations so as to determine accurately the magnitude of response. It is found that introducing topography into the model and thus removing the zonally symmetric nature of the model’s boundary conditions reduces the magnitude of response to stratospheric heating. However, this reduction is of comparable size to the variability in the magnitude of response between different ensemble members of the same 5000-day experiment. Investigations into the impact of varying tropospheric jet structure reveal a trend with lower-latitude/narrower jets having a much larger magnitude response to stratospheric heating than higher-latitude/wider jets. The jet structures that respond more strongly to stratospheric heating also exhibit longer time scale variability in their control run simulations, consistent with the idea that a feedback between the eddies and the mean flow is both responsible for the persistence of the control run variability and important in producing the tropospheric response to stratospheric temperature perturbations

Trends in Europe storm surge extremes match the rate of sea-level rise

Coastal communities across the world are already feeling the disastrous impacts of climate change through variations in extreme sea levels1. These variations reflect the combined effect of sea-level rise and changes in storm surge activity. Understanding the relative importance of these two factors in altering the likelihood of extreme events is crucial to the success of coastal adaptation measures. Existing analyses of tide gauge records2,3,4,5,6,7,8,9,10 agree that sea-level rise has been a considerable driver of trends in sea-level extremes since at least 1960. However, the contribution from changes in storminess remains unclear, owing to the difficulty of inferring this contribution from sparse data and the consequent inconclusive results that have accumulated in the literature11,12. Here we analyse tide gauge observations using spatial Bayesian methods13 to show that, contrary to current thought, trends in surge extremes and sea-level rise both made comparable contributions to the overall change in extreme sea levels in Europe since 1960 . We determine that the trend pattern of surge extremes reflects the contributions from a dominant north–south dipole associated with internal climate variability and a single-sign positive pattern related to anthropogenic forcing. Our results demonstrate that both external and internal influences can considerably affect the likelihood of surge extremes over periods as long as 60 years, suggesting that the current coastal planning practice of assuming stationary surge extremes1,14 might be inadequate

Change in cooling degree days with global mean temperature rise increasing from 1.5 °C to 2.0 °C

Limiting global mean temperature rise to 1.5 °C is increasingly out of reach. Here we show the impact on global cooling demand in moving from 1.5 °C to 2.0 °C of global warming. African countries have the highest increase in cooling requirements. Switzerland, the United Kingdom and Norway (traditionally unprepared for heat) will sufer the largest relative cooling demand surges. Immediate and unprecedented adaptation interventions are required worldwide to be prepared for a hotter world

Ensemble of global climate simulations for temperature in historical, 1.5 °C and 2.0 °C scenarios from HadAM4

Large ensembles of global temperature are provided for three climate scenarios: historical (2006–16), 1.5 °C and 2.0 °C above pre-industrial levels. Each scenario has 700 members (70 simulations per year for ten years) of 6-hourly mean temperatures at a resolution of 0.833° ´ 0.556° (longitude ´ latitude) over the land surface. The data was generated using the climateprediction.net (CPDN) climate simulation environment, to run HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Biases in simulated temperature were identified and corrected using quantile mapping with reference temperature data from ERA5. The data is stored within the UK Natural and Environmental Research Council Centre for Environmental Data Analysis repository as NetCDF V4 files

Anthropogenic influence on the changing likelihood of an exceptionally warm summer in Texas, 2011

The impact of anthropogenic forcing on the probability of high mean summer temperatures being exceeded in Texas in the year 2011 was investigated using an atmospheric circulation model to simulate large ensembles of the world with 2011 level forcing and 5 counterfactual worlds under preindustrial forcing. In Texas, drought is a strong control on summer temperature, so an increased frequency in large precipitation deficits and/or soil moisture deficits that may result from anthropogenic forcing could magnify the regional footprint of global warming. However, no simulated increase in the frequency of large precipitation deficits, or of soil moisture deficits, was detected from preindustrial to year 2011 conditions. Despite the lack of enhancement to warming via these potential changes in the hydrological cycle, the likelihood of a given unusually high summer temperature being exceeded was simulated to be about 10 times greater due to anthropogenic emissions.Keywords: likelihood, heat wave, 2011, attribution, drought, Texa

Event attribution of Parnaíba River floods in Northeastern Brazil

The climate modeling techniques of event attribution enable systematic assessments of the extent that anthropogenic climate change may be altering the probability or magnitude of extreme events. In the consecutive years of 2018, 2019, and 2020, rainfalls caused repeated flooding impacts in the lower Parnaíba River in Northeastern Brazil. We studied the effect that alterations in precipitation resulting from human influences on the climate had on the likelihood of flooding using two ensembles of the HadGEM3-GA6 atmospheric model: one driven by both natural and anthropogenic forcings; and the other driven only by natural atmospheric forcings, with anthropogenic changes removed from sea surface temperatures and sea ice patterns. We performed hydrological modeling to base our assessments on the peak annual streamflow. The change in the likelihood of flooding was expressed in terms of the ratio between probabilities of threshold exceedance estimated for each model ensemble. With uncertainty estimates at the 90% confidence level, the median (5% 95%) probability ratio at the threshold for flooding impacts in the historical period (1982–2013) was 1.12 (0.97 1.26), pointing to a marginal contribution of anthropogenic emissions by about 12%. For the 2018, 2019, and 2020 events, the median (5% 95%) probability ratios at the threshold for flooding impacts were higher at 1.25 (1.07 1.46), 1.27 (1.12 1.445), and 1.37 (1.19 1.59), respectively; indicating that precipitation change driven by anthropogenic emissions has contributed to the increase of likelihood of these events by about 30%. However, there are other intricate hydrometeorological and anthropogenic processes undergoing long-term changes that affect the flood hazard in the lower Parnaíba River. Trend and flood frequency analyses performed on observations showed a nonsignificant long-term reduction of annual peak flow, likely due to decreasing precipitation from natural climate variability and increasing evapotranspiration and flow regulation

Climate model forecast biases assessed with a perturbed physics ensemble

Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the ‘equivalent parameter perturbations’, which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies

Evaluating the Psychometric Quality of Social Skills Measures: A Systematic Review

Introduction - Impairments in social functioning are associated with an array of adverse outcomes. Social skills measures are commonly used by health professionals to assess and plan the treatment of social skills difficulties. There is a need to comprehensively evaluate the quality of psychometric properties reported across these measures to guide assessment and treatment planning. Objective - To conduct a systematic review of the literature on the psychometric properties of social skills and behaviours measures for both children and adults. Methods - A systematic search was performed using four electronic databases: CINAHL, PsycINFO, Embase and Pubmed; the Health and Psychosocial Instruments database; and grey literature using PsycExtra and Google Scholar. The psychometric properties of the social skills measures were evaluated against the COSMIN taxonomy of measurement properties using pre-set psychometric criteria. Results - Thirty-Six studies and nine manuals were included to assess the psychometric properties of thirteen social skills measures that met the inclusion criteria. Most measures obtained excellent overall methodological quality scores for internal consistency and reliability. However, eight measures did not report measurement error, nine measures did not report cross-cultural validity and eleven measures did not report criterion validity. Conclusions - The overall quality of the psychometric properties of most measures was satisfactory. The SSBS-2, HCSBS and PKBS-2 were the three measures with the most robust evidence of sound psychometric quality in at least seven of the eight psychometric properties that were appraised. A universal working definition of social functioning as an overarching construct is recommended. There is a need for ongoing research in the area of the psychometric properties of social skills and behaviours instruments