Dr Nicholas Leach

Abstract:

The 2021 Pacific Northwest heatwave was so extreme as to challenge conventional statistical and climate-model-based approaches to extreme weather attribution. However, state-of-the-art operational weather prediction systems are demonstrably able to simulate the detailed physics of the heatwave. Here, we leverage these systems to show that human influence on the climate made this event at least 8 [2–50] times more likely. At the current rate of global warming, the likelihood of such an event is doubling every 20 [10–50] years. Given the multi-decade lower-bound return-time implied by the length of the historical record, this rate of change in likelihood is highly relevant for decision makers. Further, forecast-based attribution can synthesise the conditional event-specific storyline and unconditional event-class probabilistic approaches to attribution. If developed as a routine service in forecasting centres, it could provide reliable estimates of human influence on extreme weather risk, which is critical to supporting effective adaptation planning.

Abstract:

<jats:title>Abstract</jats:title> <jats:p>The widespread destruction and insurance losses incurred by midlatitude storms every year makes it an imperative to study how storms change with climate change. The impact of climate change on midlatitude windstorms, however, is hard to evaluate due to the small climate change signal in variables such as windspeed compared to the noise of weather, as well as the high resolutions required to represent the dynamic processes in the storms. The midlatitude cyclone Eunice hit the South of the UK on February 18, 2022. Here, we assess how Eunice was impacted by anthropogenic climate change using the ECMWF ensemble prediction system. This system was demonstrably able to predict the storm, thus significantly increasing our confidence in its ability to model the key physical underlying processes and how they repsond to climate change. Using modified boundary conditions for the greenhouse gas concentrations and changed initial conditions for the 3D ocean temperatures, we create two counterfactual scenarios of storm Eunice in addition to the forecast for the current climate. We compare the intensity and severity of the storm between the pre-industrial, current, and future climates. Our results robustly indicate that Eunice has become more intense with climate change and similar storms will continue to intensify with further anthropogenic forcing. These results are consistent across forecast lead times of eight, four and two days, increasing our confidence in them. Analysis of storm composites shows that this process is caused by increased vorticity production through increased humidity in the warm conveyor belt of the storm. This is consistent with previous studies on extreme windstorms. Our approach of combining forecasts at different lead times for event attribution of a single event enables combining event specificity and a focus on dynamic changes with the assessment of changes in risks from strong winds. Further work is needed to develop methods to adjust the initial conditions of the atmosphere for the use in attribution studies using weather forecasts but we show that this approach is viable for reliable and fast attribution systems.&amp;#xD;</jats:p>

Abstract:

The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case thresholds or upper bounds that quantify just how hot temperatures can become. Generalized Extreme Value theory provides a data-driven estimate of extreme thresholds; however, upper bounds may be exceeded by future events, which undermines attribution and planning for heatwave impacts. Here, we show how the occurrence and relative probability of observed yet unprecedented events that exceed a priori upper bound estimates, so-called “impossible” temperatures, has changed over time. We find that many unprecedented events are actually within data-driven upper bounds, but only when using modern spatial statistical methods. Furthermore, there are clear connections between anthropogenic forcing and the “impossibility” of the most extreme temperatures. Robust understanding of heatwave thresholds provides critical information about future record-breaking events and how their extremity relates to historical measurements

Abstract:

The achievement of several sustainable development goals and the Paris Climate Agreement depends on rapid progress towards sustainable food and land systems in all countries. We have built a flexible, collaborative modeling framework to foster the development of national pathways by local research teams and their integration up to global scale. Local researchers independently customize national models to explore mid-century pathways of the food and land use system transformation in collaboration with stakeholders. An online platform connects the national models, iteratively balances global exports and imports, and aggregates results to the global level. Our results show that actions toward greater sustainability in countries could sum up to 1 Mha net forest gain per year, 950 Mha net gain in the land where natural processes predominate, and an increased CO₂ sink of 3.7 GtCO₂e yr⁻¹ over the period 2020–2050 compared to current trends, while average food consumption per capita remains above the adequate food requirements in all countries. We show examples of how the global linkage impacts national results and how different assumptions in national pathways impact global results. This modeling setup acknowledges the broad heterogeneity of socio-ecological contexts and the fact that people who live in these different contexts should be empowered to design the future they want. But it also demonstrates to local decision-makers the interconnectedness of our food and land use system and the urgent need for more collaboration to converge local and global priorities.

Abstract:

Background:Exclusive clean fuel use is essential for realizing health and other benefits but is often unaffordable. Decreasing household-level fuel needs could make exclusive clean fuel use more affordable, but there is a lack of knowledge on the amount of fuel savings that could be achieved through fuel conservation behaviors relevant to rural settings in low- and middle-income countries.Methods:Within a trial in Peru, we trained a random half of intervention participants, who had previously received a liquefied petroleum gas (LPG) stove and were purchasing their own fuel, on fuel conservation strategies. We measured the amount of fuel and mega joules (MJ) of energy consumed by all participants, including control participants who were receiving free fuel from the trial. We administered surveys on fuel conservation behaviors and assigned a score based on the number of behaviors performed.Results:Intervention participants with the training had a slightly higher conservation score than those without (7.2 vs. 6.6 points; p = 0.07). Across all participants, average daily energy consumption decreased by 9.5 MJ for each 1-point increase in conservation score (p < 0.001). Among households who used exclusively LPG (n = 99), each 1-point increase in conservation score was associated with a 0.04 kg decrease in LPG consumption per household per day (p = 0.03). Using pressure cookers and heating water in the sun decreased energy use, while using clay pots and forgetting to close stove knobs increased energy use.Conclusion:Our findings suggest that a household could save 1.16 kg of LPG per month for each additional fuel conservation behavior, for a maximum potential savings of 8.1 kg per month. Fuel conservation messaging could be integrated into national household energy policies to increase the affordability of exclusive clean fuel use, and subsequently achieve the environmental and health benefits that could accompany such a transition.D43 TW011502/TW/FIC NIH HHSUnited States/UF2 CE002402/CE/NCIPC CDC HHSUnited States/U01 TW010107/TW/FIC NIH HHSUnited States/T32 ES007141/ES/NIEHS NIH HHSUnited States/D43 TW009340/TW/FIC NIH HHSUnited States/T32 HL007534/HL/NHLBI NIH HHSUnited States