Wet-bulb globe temperature (WBGT)–a standard measure for workplace heat stress regulation–incorporates the complex, nonlinear interaction among temperature, humidity, wind and radiation. This complexity requires WBGT to be calculated iteratively following the recommended approach developed by Liljegren and colleagues. The need for iteration has limited the wide application of Liljegren’s approach, and stimulated various simplified WBGT approximations that do not require iteration but are potentially seriously biased. By carefully examining the self-nonlinearities in Liljegren’s model, we develop a zero-iteration analytic approximation of WBGT while maintaining sufficient accuracy and the physical basis of the original model. The new approximation slightly deviates from Liljegren’s full model—by less than 1oC in 99\% cases over 93\% of global land area. The annual mean and 75-99\% percentiles of WBGT are also well represented with biases within ±0.5oC globally. This approximation is clearly more accurate than other commonly used WBGT approximations. Physical intuition can be developed on the processes controlling WBGT variations from an energy balance perspective. This may provide a basis for applying WBGT to understanding the physical control of heat stress.

Because of the climatological prevalence of hot, humid conditions, moist heat extremes are a significant challenge to the health and wellbeing of the people in India. While research has demonstrated the importance of summer monsoon to moist heat in India, impact of monsoon-break and warm spells in modulating extreme moist heat regionally has not been fully investigated. Here we investigate moist heat extremes, as measured by the Wet-Bulb Globe Temperature (WBGT) metric, specifically during monsoon and monsoon-break periods and find that they pose a major threat to physical labor and health relative to other seasons. During the 1951-2020 break period, an increasing trend in areas exposed (~42.76 million km2), representing at least 670 million people, were exposed to extreme and detrimental WBGT values >31°C. Our results imply that future studies on extreme moist heat must pay close attention to the variation of weather systems on synoptic to subseasonal time scales that are superimposed on the seasonal monsoon migration.

Heat-lows are low pressure systems that have been qualitatively compared to dry tropical cyclones (TCs) given their low-level cyclonic circulation which is co-located with strong surface sensible heat fluxes. This work examines the climatology of West African heat-lows and their relationship to TCs by extending the concept of potential intensity (PI) over land. Using ERA5 reanalysis, we calculate PI over land from sensible heat flux and land surface roughness and orography data and use pre-existing data of tracked dry low-pressure systems to create composite West African heat-lows during Summer. PI spatial distribution correlates well with that of heat-low tracks and closely matches the peak heat-low wind speeds. The asymmetric and baroclinic structure of heat-low composites and the lack of marked diurnal cycles suggest that West African heat-lows are more likely analogous to TC-like baroclinic vortices such as medicanes or TCs during extratropical transition.

The early Eocene (~56-48 million years ago) is characterised by high CO2 estimates (1200-2500 ppmv) and elevated global temperatures (~10 to 16°C higher than modern). However, the response of the hydrological cycle during the early Eocene is poorly constrained, especially in regions with sparse data coverage (e.g. Africa). Here we present a study of African hydroclimate during the early Eocene, as simulated by an ensemble of state-of-the-art climate models in the Deep-time Model Intercomparison Project (DeepMIP). A comparison between the DeepMIP pre-industrial simulations and modern observations suggests that model biases are model- and geographically dependent, however these biases are reduced in the model ensemble mean. A comparison between the Eocene simulations and the pre-industrial suggests that there is no obvious wetting or drying trend as the CO2 increases. The results suggest that changes to the land sea mask (relative to modern) in the models may be responsible for the simulated increases in precipitation to the north of Eocene Africa, whereas it is likely that changes in vegetation in the models are responsible for the simulated region of drying over equatorial Eocene Africa. There is an increase in precipitation over equatorial and West Africa and associated drying over northern Africa as CO2 rises. There are also important dynamical changes, with evidence that anticyclonic low-level circulation is replaced by increased south-westerly flow at high CO2 levels. Lastly, a model-data comparison using newly-compiled quantitative climate estimates from palaeobotanical proxy data suggests a marginally better fit with the reconstructions at lower levels of CO2.

The Miocene epoch, spanning 23.03-5.33Ma, was a dynamic climate of sustained, polar amplified warmth. Miocene atmospheric CO2 concentrations are typically reconstructed between 300-600ppm and were potentially higher during the Miocene Climatic Optimum (16.75-14.5Ma). With surface temperature reconstructions pointing to substantial midlatitude and polar warmth, it is unclear what processes maintained the much weaker-than-modern equator-to-pole temperature difference. Here we synthesize several Miocene climate modeling efforts together with available terrestrial and ocean surface temperature reconstructions. We evaluate the range of model-data agreement, highlight robust mechanisms operating across Miocene modelling efforts, and regions where differences across experiments result in a large spread in warming responses. Prescribed CO2 is the primary factor controlling global warming across the ensemble. On average, elements other than CO2, such as Miocene paleogeography and ice sheets, raise global mean temperature by ~ 2℃, with the spread in warming under a given CO2 concentration (due to a combination of the spread in imposed boundary conditions and climate feedback strengths) equivalent to ~1.2 times a CO2 doubling. This study uses an ensemble of opportunity: models, boundary conditions, and reference datasets represent the state-of-art for the Miocene, but are inhomogeneous and not ideal for a formal intermodel comparison effort. Acknowledging this caveat, this study is nevertheless the first Miocene multi-model, multi-proxy comparison attempted so far. This study serves to take stock of the current progress towards simulating Miocene warmth while isolating remaining challenges that may be well served by community-led efforts to coordinate modelling and data activities within a common analysis framework.

Wet bulb globe temperature (WBGT) is a widely applied heat stress index. However, most applications of WBGT within the heat stress impacts literature do not use WBGT at all, but one of the ad hoc approximations, typically the simplified WBGT (sWBGT) or the environmental stress index (ESI). Surprisingly little is known about how well these approximations work for the global climate and climate change settings that they are being applied to. Here we assess the bias distribution as a function of temperature, humidity, wind speed and radiative conditions of both sWBGT and ESI relative to a well-validated, explicit physical model for WBGT developed by Liljegren, within an idealized context and the more realistic setting of ERA5 reanalysis data. sWBGT greatly overestimates heat stress in hot-humid areas. ESI has much smaller biases in the range of standard climatological conditions. However, both metrics may substantially underestimate extreme heat especially over subtropical dry regions. These systematic biases demonstrate that sWBGT-derived estimates of heat stress and its health and labor consequences are significantly overestimated over much of the world today. We recommend discontinuing the use of sWBGT. ESI may be acceptable for assessing average heat stress or integrated impact over a long period like a year, but less suitable for health applications, extreme heat stress analysis, or as an operational index for heat warning, heatwave forecasting or guiding activity modification at workplace. Nevertheless, Liljegren’s approach should be preferred over these ad hoc approximations and we provide a Python implementation to encourage its widespread use.