Allowable CO2 emissions based on regional and impact-related climate targets.

Global temperature targets, such as the widely accepted limit of an increase above pre-industrial temperatures of two degrees Celsius, may fail to communicate the urgency of reducing carbon dioxide (CO2) emissions. The translation of CO2 emissions into regional- and impact-related climate targets could be more powerful because such targets are more directly aligned with individual national interests. We illustrate this approach using regional changes in extreme temperatures and precipitation. These scale robustly with global temperature across scenarios, and thus with cumulative CO2 emissions. This is particularly relevant for changes in regional extreme temperatures on land, which are much greater than changes in the associated global mean.

Communicating the deadly consequences of global warming for human heat stress.

In December of 2015, the international community pledged to limit global warming to below 2 °C above preindustrial (PI) to prevent dangerous climate change. However, to what extent, and for whom, is danger avoided if this ambitious target is realized? We address these questions by scrutinizing heat stress, because the frequency of extremely hot weather is expected to continue to rise in the approach to the 2 °C limit. We use analogs and the extreme South Asian heat of 2015 as a focusing event to help interpret the increasing frequency of deadly heat under specified amounts of global warming. Using a large ensemble of climate models, our results confirm that global mean air temperature is nonlinearly related to heat stress, meaning that the same future warming as realized to date could trigger larger increases in societal impacts than historically experienced. This nonlinearity is higher for heat stress metrics that integrate the effect of rising humidity. We show that, even in a climate held to 2 °C above PI, Karachi (Pakistan) and Kolkata (India) could expect conditions equivalent to their deadly 2015 heatwaves every year. With only 1.5 °C of global warming, twice as many megacities (such as Lagos, Nigeria, and Shanghai, China) could become heat stressed, exposing more than 350 million more people to deadly heat by 2050 under a midrange population growth scenario. The results underscore that, even if the Paris targets are realized, there could still be a significant adaptation imperative for vulnerable urban populations.

Using a Scenario-Neutral Framework to Avoid Potential Maladaptation to Future Flood Risk.

This study develops a coherent framework to detect those catchment types associated with a high risk of maladaptation to future flood risk. Using the "scenario-neutral" approach to impact assessment the sensitivity of Irish catchments to fluvial flooding is examined in the context of national climate change allowances. A predefined sensitivity domain is used to quantify flood responses to +2 °C mean annual temperature with incremental changes in the seasonality and mean of the annual precipitation cycle. The magnitude of the 20-year flood is simulated at each increment using two rainfall-runoff models (GR4J, NAM), then concatenated as response surfaces for 35 sample catchments. A typology of catchment sensitivity is developed using clustering and discriminant analysis of physical attributes. The same attributes are used to classify 215 ungauged/data-sparse catchments. To address possible redundancies, the exposure of different catchment types to projected climate is established using an objectively selected subset of the Coupled Model Intercomparison Project Phase 5 ensemble. Hydrological model uncertainty is shown to significantly influence sensitivity and have a greater effect than ensemble bias. A national flood risk allowance of 20%, considering all 215 catchments is shown to afford protection against ~48% to 98% of the uncertainty in the Coupled Model Intercomparison Project Phase 5 subset (Representative Concentration Pathway 8.5; 2070-2099), irrespective of hydrological model and catchment type. However, results indicate that assuming a standard national or regional allowance could lead to local over/under adaptation. Herein, catchments with relatively less storage are sensitive to seasonal amplification in the annual cycle of precipitation and warrant special attention.

The impacts of sport emissions on climate: Measurement, mitigation, and making a difference.

As a global industry, sport makes potentially significant contributions to climate change through both carbon emissions and influence over sustainability practices. Yet, evidence regarding impacts is uneven and spread across many disciplines. This paper investigates the impacts of sport emissions on climate and identifies knowledge gaps. We undertook a systematic and iterative meta-analysis of relevant literature (1992-2022) on organized and individual sports. Using a defined search protocol, 116 sources were identified that map to four sport-related themes: (1) carbon emissions and their measurement; (2) emissions control and decarbonization; (3) carbon sinks and offsets; and (4) behavior change. We find that mega sport events, elite sport, soccer, skiing, and golf have received most attention, whereas grass-roots and women's sport, activity in Africa and South America, cricket, tennis, and volleyball are understudied. Other knowledge gaps include carbon accounting tools and indicators for smaller sports clubs and active participants; cobenefits and tradeoffs between mitigation-adaptation efforts in sport, such as around logistics, venues, sports equipment, and facilities; geopolitical influence; and scope for climate change litigation against hosts and/or sponsors of carbon-intensive events. Among these, researchers should target cobenefits given their scope to deliver wins for both climate mitigation and risk management of sport.

Dynamical-statistical seasonal forecasts of winter and summer precipitation for the Island of Ireland.

Seasonal precipitation forecasting is highly challenging for the northwest fringes of Europe due to complex dynamical drivers. Hybrid dynamical-statistical approaches offer potential to improve forecast skill. Here, hindcasts of mean sea level pressure (MSLP) from two dynamical systems (GloSea5 and SEAS5) are used to derive two distinct sets of indices for forecasting winter (DJF) and summer (JJA) precipitation over lead-times of 1-4 months. These indices provide predictors of seasonal precipitation via a multiple linear regression model (MLR) and an artificial neural network (ANN) applied to four Irish rainfall regions and the Island of Ireland. Forecast skill for each model, lead time, and region was evaluated using the correlation coefficient (r) and mean absolute error (MAE), benchmarked against (a) climatology, (b) bias corrected precipitation hindcasts from both GloSea5 and SEAS5, and (c) a zero-order forecast based on rainfall persistence. The MLR and ANN models produced skilful precipitation forecasts with leads of up to 4 months. In all tests, our hybrid method based on MSLP indices outperformed the three benchmarks (i.e., climatology, bias corrected, and persistence). With correlation coefficients ranging between 0.38 and 0.81 in winter, and between 0.24 and 0.78 in summer, the ANN model outperformed MLR in both seasons in most regions and lead-times. Forecast skill for summer was comparable to that in winter and for some regions/lead times even superior. Our results also show that climatology and persistence performed better than direct use of bias corrected dynamical outputs in most regions and lead-times in terms of MAE. We conclude that the hybrid dynamical-statistical approach developed here-by leveraging useful information about MSLP from dynamical systems-enables more skilful seasonal precipitation forecasts for Ireland, and possibly other locations in western Europe, in both winter and summer.

Reconstructed monthly river flows for Irish catchments 1766-2016.

A 250-year (1766-2016) archive of reconstructed river flows is presented for 51 catchments across Ireland. By leveraging meteorological data rescue efforts with gridded precipitation and temperature reconstructions, we develop monthly river flow reconstructions using the GR2M hydrological model and an Artificial Neural Network. Uncertainties in reconstructed flows associated with hydrological model structure and parameters are quantified. Reconstructions are evaluated by comparison with those derived from quality assured long-term precipitation series for the period 1850-2000. Assessment of the reconstruction performance across all 51 catchments using metrics of MAE (9.3 mm/month; 13.3%), RMSE (12.6 mm/month; 18.0%) and mean bias (-1.16 mm/month; -1.7%), indicates good skill. Notable years with highest/lowest annual mean flows across all catchments were 1877/1855. Winter 2015/16 had the highest seasonal mean flows and summer 1826 the lowest, whereas autumn 1933 had notable low flows across most catchments. The reconstructed database will enable assessment of catchment specific responses to varying climatic conditions and extremes on annual, seasonal and monthly timescales.

The forgotten drought of 1765-1768: Reconstructing and re-evaluating historical droughts in the British and Irish Isles.

Historical precipitation records are fundamental for the management of water resources, yet rainfall observations typically span 100-150 years at most, with considerable uncertainties surrounding earlier records. Here, we analyse some of the longest available precipitation records globally, for England and Wales, Scotland and Ireland. To assess the credibility of these records and extend them further back in time, we statistically reconstruct (using independent predictors) monthly precipitation series representing these regions for the period 1748-2000. By applying the Standardized Precipitation Index at 12-month accumulations (SPI-12) to the observed and our reconstructed series we re-evaluate historical meteorological droughts. We find strong agreement between observed and reconstructed drought chronologies in post-1870 records, but divergence in earlier series due to biases in early precipitation observations. Hence, the 1800s decade was less drought prone in our reconstructions relative to observations. Overall, the drought of 1834-1836 was the most intense SPI-12 event in our reconstruction for England and Wales. Newspaper accounts and documentary sources confirm the extent of impacts across England in particular. We also identify a major, "forgotten" drought in 1765-1768 that affected the British-Irish Isles. This was the most intense event in our reconstructions for Ireland and Scotland, and ranks first for accumulated deficits across all three regional series. Moreover, the 1765-1768 event was also the most extreme multi-year drought across all regional series when considering 36-month accumulations (SPI-36). Newspaper and other sources confirm the occurrence and major socio-economic impact of this drought, such as major rivers like the Shannon being fordable by foot. Our results provide new insights into historical droughts across the British Irish Isles. Given the importance of historical droughts for stress-testing the resilience of water resources, drought plans and supply systems, the forgotten drought of 1765-1768 offers perhaps the most extreme benchmark scenario in more than 250-years.

Multi-century trends to wetter winters and drier summers in the England and Wales precipitation series explained by observational and sampling bias in early records.

Globally, few precipitation records extend to the 18th century. The England Wales Precipitation (EWP) series is a notable exception with continuous monthly records from 1766. EWP has found widespread use across diverse fields of research including trend detection, evaluation of climate model simulations, as a proxy for mid-latitude atmospheric circulation, a predictor in long-term European gridded precipitation data sets, the assessment of drought and extremes, tree-ring reconstructions and as a benchmark for other regional series. A key finding from EWP has been the multi-centennial trends towards wetter winters and drier summers. We statistically reconstruct seasonal EWP using independent, quality-assured temperature, pressure and circulation indices. Using a sleet and snow series for the UK derived by Profs. Gordon Manley and Elizabeth Shaw to examine winter reconstructions, we show that precipitation totals for pre-1870 winters are likely biased low due to gauge under-catch of snowfall and a higher incidence of snowfall during this period. When these factors are accounted for in our reconstructions, the observed trend to wetter winters in EWP is no longer evident. For summer, we find that pre-1820 precipitation totals are too high, likely due to decreasing network density and less certain data at key stations. A significant trend to drier summers is not robustly present in our reconstructions of the EWP series. While our findings are more certain for winter than summer, we highlight (a) that extreme caution should be exercised when using EWP to make inferences about multi-centennial trends, and; (b) that assessments of 18th and 19th Century winter precipitation should be aware of potential snow biases in early records. Our findings underline the importance of continual re-appraisal of established long-term climate data sets as new evidence becomes available. It is also likely that the identified biases in winter EWP have distorted many other long-term European precipitation series.

Impact of extreme weather conditions on healthcare provision in urban Ghana.

Extreme weather events pose significant threats to urban health in low- and middle-income countries, particularly in sub-Saharan Africa where there are systemic health challenges. This paper investigates health system vulnerabilities associated with flooding and extreme heat, along with strategies for resilience building by service providers and community members, in Accra and Tamale, Ghana. We employed field observations, rainfall records, temperature measurements, and semi-structured interviews in health facilities within selected areas of both cities. Results indicate that poor building conditions, unstable power supply, poor sanitation and hygiene, and the built environment reduce access to healthcare for residents of poor urban areas. Health facilities are sited in low-lying areas with poor drainage systems and can be 6 °C warmer at night than reported by official records from nearby weather stations. This is due to a combination of greater thermal inertia of the buildings and the urban heat island effect. Flooding and extreme heat interact with socioeconomic conditions to impact physical infrastructure and disrupt community health as well as health facility operations. Community members and health facilities make infrastructural and operational adjustments to reduce extreme weather stress and improve healthcare provision to clients. These measures include: mobilisation of residents to clear rubbish and unclog drains; elevating equipment to protect it from floods; improving ventilation during extreme heat; and using alternative power sources for emergency surgery and storage during outages. Stakeholders recommend additional actions to manage flood and heat impacts on health in their cities, such as, improving the capacity of drainage systems to carry floodwaters, and routine temperature monitoring to better manage heat in health facilities. Finally, more timely and targeted information systems and emergency response plans are required to ensure preparedness for extreme weather events in urban areas.

Small Water Bodies in Great Britain and Ireland: Ecosystem function, human-generated degradation, and options for restorative action.

Small, 1st and 2nd-order, headwater streams and ponds play essential roles in providing natural flood control, trapping sediments and contaminants, retaining nutrients, and maintaining biological diversity, which extend into downstream reaches, lakes and estuaries. However, the large geographic extent and high connectivity of these small water bodies with the surrounding terrestrial ecosystem makes them particularly vulnerable to growing land-use pressures and environmental change. The greatest pressure on the physical processes in these waters has been their extension and modification for agricultural and forestry drainage, resulting in highly modified discharge and temperature regimes that have implications for flood and drought control further downstream. The extensive length of the small stream network exposes rivers to a wide range of inputs, including nutrients, pesticides, heavy metals, sediment and emerging contaminants. Small water bodies have also been affected by invasions of non-native species, which along with the physical and chemical pressures, have affected most groups of organisms with consequent implications for the wider biodiversity within the catchment. Reducing the impacts and restoring the natural ecosystem function of these water bodies requires a three-tiered approach based on: restoration of channel hydromorphological dynamics; restoration and management of the riparian zone; and management of activities in the wider catchment that have both point-source and diffuse impacts. Such activities are expensive and so emphasis must be placed on integrated programmes that provide multiple benefits. Practical options need to be promoted through legislative regulation, financial incentives, markets for resource services and voluntary codes and actions.

Storylines: an alternative approach to representing uncertainty in physical aspects of climate change.

As climate change research becomes increasingly applied, the need for actionable information is growing rapidly. A key aspect of this requirement is the representation of uncertainties. The conventional approach to representing uncertainty in physical aspects of climate change is probabilistic, based on ensembles of climate model simulations. In the face of deep uncertainties, the known limitations of this approach are becoming increasingly apparent. An alternative is thus emerging which may be called a 'storyline' approach. We define a storyline as a physically self-consistent unfolding of past events, or of plausible future events or pathways. No a priori probability of the storyline is assessed; emphasis is placed instead on understanding the driving factors involved, and the plausibility of those factors. We introduce a typology of four reasons for using storylines to represent uncertainty in physical aspects of climate change: (i) improving risk awareness by framing risk in an event-oriented rather than a probabilistic manner, which corresponds more directly to how people perceive and respond to risk; (ii) strengthening decision-making by allowing one to work backward from a particular vulnerability or decision point, combining climate change information with other relevant factors to address compound risk and develop appropriate stress tests; (iii) providing a physical basis for partitioning uncertainty, thereby allowing the use of more credible regional models in a conditioned manner and (iv) exploring the boundaries of plausibility, thereby guarding against false precision and surprise. Storylines also offer a powerful way of linking physical with human aspects of climate change.