Climate processes

Abstract:

Abstract:

Objective: To analyse associations between feel-like temperatures measured with the universal thermal climate index (UTCI) and injury rates during international athletic championships.

Methods: During 10 international outdoor athletic championships from 2007 to 2022, in-competition injuries were collected by medical teams and local organising committees. UTCI was extracted hourly from a global reanalysis of observed atmospheric conditions during each championship. We performed Poisson regressions with incidence rates (number of injuries per 1000 athlete starts) as outcomes and UTCI as a predictive variable adjusted for sex, for all and time-loss injuries, for different injured tissue types (ie, muscle, tendon, ligament, articular, bone and skin) and specific discipline (ie, sprints, hurdles, jumps, throws, middle distance, long distance, marathon and race walking).

Results: A total of 1203 in-competition injuries were reported for 29 579 athlete starts. For all in-competition injuries (ie, all injured tissue types and all disciplines), higher UTCI was associated with lower incidence rates for time-loss injuries (IRR=0.98, 95% CI 0.97 to 0.98) but not for all injuries (IRR=1.00, 95% CI 1.00 to 1.01). Based on injured tissue type with all disciplines included, higher UTCI was associated with lower incidence rates for all (IRR=0.97, 95% CI 0.97 to 0.98) and time-loss (IRR=0.96, 95% CI 0.96 to 0.96) muscle injuries. Based on the specific discipline, higher UTCI was associated with lower incidence rates for all and time-loss muscle injuries for sprints (IRR=0.95, 95% CI 0.95 to 0.96, and IRR=0.94, 95% CI 0.93 to 0.94, respectively), hurdles (IRR=0.97, 95% CI 0.96 to 97, and IRR=0.95, 95% CI 0.94 to 0.96, respectively) and throws (IRR=0.97, 95% CI 0.97 to 0.98).

Conclusions: Higher feel-like temperatures were associated with a decreased risk of time-loss and muscle injuries, particularly in sprints, hurdles and throws. Although the precise mechanism for lower injury rates with higher feel-like temperatures requires further investigation, adapting preparations such as warm-up or clothing to forecasted weather conditions may be of benefit.

Abstract:

Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.

Abstract:

Ships brighten low marine clouds from emissions of sulfur and aerosols, resulting in visible “ship tracks”. In 2020, new shipping regulations mandated an ∼80% reduction in the allowed fuel sulfur content. Recent observations indicate that visible ship tracks have decreased. Model simulations indicate that since 2020 shipping regulations have induced a net radiative forcing of +0.12 Wm−2. Analysis of recent temperature anomalies indicates Northern Hemisphere surface temperature anomalies in 2022–2023 are correlated with observed cloud radiative forcing and the cloud radiative forcing is spatially correlated with the simulated radiative forcing from the 2020 shipping emission changes. Shipping emissions changes could be accelerating global warming. To better constrain these estimates, better access to ship position data and understanding of ship aerosol emissions are needed. Understanding the risks and benefits of emissions reductions and the difficultly in robust attribution highlights the large uncertainty in attributing proposed deliberate climate intervention.