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This paper shows the impact on global cooling demand of moving from a 1.5ºC to 2.0ºC temperature increase. African countries have the highest increase in cooling requirements. The United Kingdom, Switzerland, and Norway (traditionally unprepared for extreme heat) will suffer the largest relative cooling demand surges.

Open-source intelligence is a rapidly expanding area of the security and intelligence industry, involving the collection of internet located open data from various sources, turning that data into actionable intelligence, which is reused where possible and relevant. While creating or processing the raw input data capturing and managing the corresponding provenance information, e.g., workflow, state, raw evidence, reports, and summaries, that simplifies its retrieval and reuse is essential. In comparison, scientific workflows and tools that support them are routinely used in the majority of academic research disciplines, managing diverse sets of data resources and their provenance. Based on the techniques established within the academic community, we have developed a system for managing this open-source intelligence data and associated provenance information. This will enhance the efficiency of retrieving stored data products and reusing them to support intelligence-led security decision-making. The open-source intelligence company partnered within this project has an operational envelope that includes collecting and analyzing personal subject information. Therefore, they must understand the scope of their data holdings appropriately, especially in light of obligations under the General Data Protection Regulation. The system developed allows for tracking requests for intelligence products, ownership of the collection, analysis and generation of intelligence briefs, and tracking the delivery of those final products to the customer for future billing. This adds further layers of efficiency to operations and hence reduces the costs of producing intelligence products.

Air-sea coupling is critical in influencing atmospheric temperature and precipitation. The effect of greenhouse gases has influenced atmospheric variability and extreme events. Understanding and quantifying the effect of air-sea feedback on atmospheric variability and extremes remains unknown.In this work we show results obtained from two numerical experiments. We use the HadSM4 configuration that couples the HadAM4 model at N144 resolution with a Slab Ocean to generate a large ensemble (~1000 members) of realizations of the 2013-14 October-March winter season, forced with a calibrated ocean heat convergence flux.A twin experiment is performed by forcing HadAM4 with the diagnosed SST and sea ice from the ensemble, yielding a new ensemble with identical realizations of SST and sea ice. The only difference between the two ensembles is the enabling or disabling of the feedback of air-sea heat fluxes on SST.While the impact of the feedback on the mean climate is relatively small, we show that its influence has important consequences for the variability of many important quantities, including air-sea fluxes and return periods of extreme events.

Multi-thousand member climate model simulations are highly valuable for showing characteristics of extreme weather events in historical and future climates. However, until now, studies using such a physically-based approach have been limited to using models with a resolution much coarser than the most modern systems. We have developed a global atmospheric model with ~60km resolution that can be run in the climateprediction.net distributed computing system to produce such large datasets. This resolution is finer than that of many current global climate models and sufficient for good simulation of extratropical synoptic features such as storms. It also allows many extratropical extreme weather events to be simulated without requiring regional downscaling. We will show that this model's simulation of extratropical winter weather is competitive with that in other state-of-the-art models. We will also present the first results generated by this system. One application has been the production of ~2000 member simulations based on sea surface temperatures in severe future winters produced in the UK Climate Projections 2018 dataset, generating large numbers of examples of plausible extreme wet and warm UK seasons. Another is showing the increasing spatial extent of precipitation extremes in the Northern Hemisphere extratropics.

Recent extreme weather across the globe highlights the need to understand the potential for more extreme events in the present-day, and how such events may change with global warming. We present a methodology for more efficiently sampling extremes in future climate projections. As a proof-of-concept, we examine the UK’s most recent set of national Climate Projections (UKCP18). UKCP18 includes a 15-member perturbed parameter ensemble (PPE) of coupled global simulations, providing a range of climate projections incorporating uncertainty in both internal variability and forced response. However, this ensemble is too small to adequately sample extremes with very high return periods, which are of interest to policy-makers and adaptation planners. To better understand the statistics of these events, we use distributed computing to run three 1000-member initial-condition ensembles with the atmosphere-only HadAM4 model at 60km resolution on volunteers’ computers, taking boundary conditions from three distinct future extreme winters within the UKCP18 ensemble. We find that the magnitude of each winter extreme is captured within our ensembles, and that two of the three ensembles are conditioned towards producing extremes by the boundary conditions. Our ensembles contain several extremes that would only be expected to be sampled by a UKCP18 PPE of over 500 members, which would be prohibitively expensive with current supercomputing resource. The most extreme winters we simulate exceed those within UKCP18 by 0.85 K and 37% of the present-day average for UK winter means of daily maximum temperature and precipitation respectively. As such, our ensembles contain a rich set of multivariate, spatio-temporally and physically coherent samples of extreme winters with wide-ranging potential applications.

2015 saw the strongest El Niño event in the historical record, resulting in extreme drought conditions in Brazil. As drought conditions may also lead to greater fire danger, this study uses the 2015 fire in Brazil as a case study to examine whether and to what extent human-induced climate change has contributed to the fire weather conditions in the Cerrado and the southern Amazonia transitional forests known as the Arc of deforestation. Our results show that anthropogenic climate change is indeed a driver of meteorological conditions conducive to strong fire weather in these two regions, measured by fire weather index (FWI), especially on shorter timescales of daily and weekly. The anthropogenic climate change signal of FWI on short timescales corresponds to a similar order of increase in the FWI sub-indices (initial spread index and fine fuel moisture code) that can rapidly change due to the influence of the instantaneous weather conditions. For both regions the changes in fire weather in response to anthropogenic climate change are dominated by the combination of temperature and relative humidity responses. High FWI is more likely to occur under El Niño conditions, less likely under La Niña conditions, although the impacts of El Niño vs La Niña conditions are not symmetric when compared with El Niño Southern Oscillation neutral states. To summarize, both human-induced climate change and the presence of El Niño increased the likelihood of occurrence for the strong fire weather condition in 2015. Our results suggest that local and regional adaptation measures, such as improved drought monitoring and warning systems, could help with effective planning of fire prevention, firefighting actions, and disaster preparedness.

Load pattern categorization plays a significant role in enhancing the understanding of demand characteristics of different cohorts of energy customers on a distribution network. For a distribution network with embedded photovoltaic (PV) systems, it is also desirable to enumerate these premises since unreported PV installations can have a detrimental impact on voltage control, frequency regulation, and incur reverse power flows. In this paper, a holistic smart meter data analytics approach is proposed for classifying and characterizing the intra-day load curves of PV and non-PV households. Unlike existing studies based on raw time-series data, a series of interpretable and discriminating global and peak-period features are first developed to extract the physical information of load patterns. A two-step feature-based clustering is then applied to classify the load profiles of PV and non-PV households. A post-clustering quantitative compositional analysis is developed to characterize the energy use variability and compositional changes for each household. The proposed PV household identification method allows network owners to identify the existence of PV installations in a particular area for voltage control and ancillary service provision. In the longer term, effective load pattern categorization and post-clustering characterization can help better understand demand characteristics of different cohorts of customers, network utilization, and network-level changes in load growth.

Summer 2010 saw two simultaneous extremes linked by an atmospheric wave train: a record-breaking heatwave in Russia and severe floods in Pakistan. Here, we study this wave event using a large ensemble climate model experiment. First, we show that the circulation in 2010 reflected a recurrent wave train connecting the heatwave and flooding events. Second, we show that the occurrence of the wave train is favored by three drivers: (1) 2010 sea surface temperature anomalies increase the probability of this wave train by a factor 2-to-4 relative to the model’s climatology, (2) early-summer soil moisture deficit in Russia not only increases the probability of local heatwaves, but also enhances rainfall extremes over Pakistan by forcing an atmospheric wave response, and (3) high-latitude land warming favors wave-train occurrence and therefore rainfall and heat extremes. These findings highlight the complexity and synergistic interactions between different drivers, reconciling some seemingly contradictory results from previous studies.

Attribution of an extreme magnitude 1-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 will deliver more rainfall with weaker pressure anomalies in a warmer world.

Changing climate conditions impact ecosystem dynamics and have local to global impacts on water and carbon cycles. Many processes in dynamic vegetation models (DVMs) are parameterized, and the unknown/unknowable parameter values introduce uncertainty that has rarely been quantified in projections of forced changes. In this study, we identify processes and parameters that introduce the largest uncertainties in the vegetation state simulated by the DVM Top‐down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) coupled to a regional climate model. We adjust parameters simultaneously in an ensemble of equilibrium vegetation simulations and use statistical emulation to explore sensitivities to, and interactions among, parameters. We find that vegetation distribution is most sensitive to parameters related to carbon allocation and competition. Using a suite of statistical emulators, we identify regions of parameter space that reduce the error in modeled forest cover by 31±9%. We then generate large initial atmospheric condition ensembles with 10 improved DVM parameterizations under preindustrial, contemporary, and future climate conditions to assess uncertainty in the forced response due to parameterization. We find that while most parameterizations agree on the direction of future vegetation transitions in the western United States, the magnitude varies considerably: for example, in the northwest coast the expansion of broadleaf trees and corresponding decline of needleleaf trees ranges from 4 to 28% across 10 DVM parameterizations under projected future climate conditions. We demonstrate that model parameterization contributes to uncertainty in vegetation transition and carbon cycle feedback under nonstationary climate conditions, which has important implications for carbon stocks, ecosystem services, and climate feedback.

Predicting the electricity demand of new supermarkets will help with design, planning, and future energy management. Instead of creating complex site-specific thermal engineering models, simplified statistical energy prediction models as we propose can be useful to energy managers. We have designed and implemented a data-driven method to predict the ‘electricity daily load profile’ (EDLP) for new stores. Our preliminary work exploits a data-set of hourly electricity meter readings for 196 UK supermarkets from 2012 to 2015. Our method combines the most similar stores on a feature space (floor area split by usage such as general merchandise, food retail and offices and geographical location) to obtain a prediction of the EDLP of a new store. Computational experiments were performed separately for subsets of supermarkets that consume only electricity, both electricity and gas, and by season. The best results were obtained when predicting Summer EDLPs with stores using electricity only. In this case, the average Manhattan difference and the percentage difference are 234 kWh and 16%, respectively. We aim to develop an application tool for supermarket energy managers to automatically generate EDLP for potential new stores. EPSRC Impact Acceleration Account - University of Oxford EP/R511742/1

The simultaneous occurrence of extremely wet winters at multiple locations in the same region can contribute to widespread flooding and associated socio‐economic losses. However, the spatial extent of precipitation extremes (i.e., the area in which nearby locations experience precipitation extremes simultaneously) and its future changes are largely overlooked in climate assessments. Employing new multi‐thousand‐year climate model simulations, we show that under both 2.0 °C and 1.5 °C warming scenarios, wintertime total precipitation extreme extents would increase over about 80%–90% of the Northern Hemisphere extratropics (i.e., of the latitude band 28°–78°N). Stabilizing at 1.5 °C rather than 2.0 °C would reduce the average magnitude of the increase by 1.7–2 times. According to the climate model, the increased extents are caused by increases in precipitation intensity rather than changes in the spatial organization of the events. Relatively small percentage increases in precipitation intensities (e.g., by 4%) can drive disproportionately larger, by 1–2 orders of magnitude, growth in the spatial extents (by 93%).