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Mammals have dominated the Earth for the last ~55 Myr. Mammals have shown remarkable adaptation and resilience to climate change. However, it is unknown how long the Earth will be able to continue to sustain mammalian life. Estimates suggest the ultimate demise of all life will be in a ‘Venusian’ style runaway greenhouse climate ~1.5 billion years where increasing solar luminosity (L☉) will raise temperature beyond that able to sustain life. However, conditions may develop sooner that will render the Earth naturally inhospitable to mammals. In ~250 million years all the continents of the world come together to form the Earth's fourth supercontinent, Pangea-Ultima. A natural consequence of the creation and decay of Pangea-Ultima will be extremes in pCO2, both low (silicate weathering) and high (volcanic degassing). Here we show that variations in pCO2, increased solar luminosity (~2% greater than now), and extreme continentality will lead to extreme climate states that are inhospitable to mammalian life. We assess the impact of these climate states on mammalian physiological limits using dry-bulb, wet-bulb, and Humidex stress indicators as well as planetary habitability index. Although low pCO2 states will increase habitability, snowball Earth conditions may occur if the silicate weathering-pCO2 burial feedback becomes too strong (resulting in low pCO2 values

Climate policies such as the Paris Agreement are framed in terms of global warming levels. Based on past warming and past CO2 emissions, the amount of future cumulative CO2 emissions allowed to keep global warming at or below a global warming level can be estimated. Yet, global warming scenarios in the successive Coupled Model Intercomparison Projects are framed in terms of prescribed atmospheric CO2 concentration or emissions, yielding a wide range of warming levels per CO2 pathway in response to the different transient climate responses to cumulative emissions in the coupled climate models. Based on these scenarios and the latest model projections, the IPCC Sixth Assessment Report assessed climatic impacts of different warming levels. These impacts are thus evaluated in simulations where the warming targets are passed transiently, at different points in time, and not stabilized, as opposed to how climate agreements are framed.Here, we propose a new Model Intercomparison Project AERA-MIP building on an adaptive approach - the Adaptive Emissions Reduction Approach - that successively calculates the compatible emissions to stabilize global warming at the required temperature target. Earth System Models (ESMs) are run forward in emission-driven mode, with prescribed, model-specific emissions successively calculated every five years, so that all models reach the same warming target and thereafter stabilize at this warming level. The warming uncertainty is thus side-stepped, while different emissions pathways emerge out of the variety of participating ESMs. The approach is based on the TCRE framework and successively adapting for any changes in the Earth System that might affect global mean surface temperature, including the zero emissions commitment as emissions approach zero.Simulations of the first participating modelling centers already reveal a panel of emissions pathways that successfully stabilize global warming at 1.5ºC and 2ºC. This includes the decline rate from peak emissions, the timing of having to reach net-zero emissions, and the magnitude of negative emissions needed to stabilize the climate. These different emissions pathways result in a range of atmospheric CO2 concentration evolution (350 to 450 ppm at year 2100 in the 1.5°C stabilization scenario) and distribution of anthropogenic carbon in the Earth System components. Unlike concentration-driven projections, these AERA simulations provide an uncertainty range for impacts that are directly affected by atmospheric CO2 concentration such as ocean acidification. The project also includes temporary temperature overshoot simulations using the AERA approach.

We implement the Wet Bulb Globe Temperature (WBGT), a standardized heat stress metric, into the Community Land Model (CLM5), the land surface component of the Community Earth System Model (CESM2). This includes the notoriously difficult calculation related to measuring human heat stress: radiation. Following the International Organization for Standardization (ISO) 7243, physical representations of the instruments, a globe thermometer and natural wet bulb thermometer, simulate where humans work and live in non-urban environments. By using ISO 7243 within CLM5, we create a common framework within Earth system models to calculate the impact of radiation on temperature-moisture covariance. We demonstrate the capabilities of the WBGT using a default configuration of CLM5. We output 4x daily temporal resolutions globally, showing the advantage of simulating the WBGT within each environment. The WBGT outdoor and indoor calibration is simulated in an averaged grid cell, above the vegetation canopy, below the vegetation canopy, and bare ground environments. We examine the 1995 Chicago Heatwave, specifically the rural regions impacted by the heatwave, and demonstrate that the grid cell average calculated at the CLM5 30-minute time step is a poor representation of human environments and can differ by multiple degrees. In high heat stress environments following ISO 7243, a 0.5C change in WBGT can lead to a >10% reduction in labor capacity. This difference in temperature and labor capacity shows that assumptions about calculating a non-linear algorithm — even utilizing high temporal frequency grid cell averages that drive non-linear labor capacity impact models — is a flawed approach that can grossly over or underestimate the impact of heat stress on future climate change projections. To accurately assess the direct exposure, risk, and damage of climate change on people, it is critical to implement diagnostics directly into Earth system models.

Extreme weather events are major causes of loss of life and damage infrastructure worldwide. High temperatures cause heat stress on humans, livestock, crops and infrastructure. Heat stress exposure is projected to increase with ongoing climate change. Extremes of temperature are common in Africa and infrastructure is often incapable of providing adequate cooling. We show how easily accessible cooling technology, such as evaporative coolers, prevent heat stress in historic timescales but are unsuitable as a solution under climate change. As temperatures increase, powered cooling, such as air conditioning, is necessary to prevent overheating. This will, in turn, increase demand on already stretched infrastructure. We use high temporal resolution climate model data to estimate the demand for cooling according to two metrics, firstly the apparent temperature and secondly the discomfort index. For each grid cell we calculate the heat stress value and the amount of cooling required to turn a heat stress event into a non heat stress event. We show the increase in demand for cooling in Africa is non uniform and that equatorial countries are exposed to higher heat stress than higher latitude countries. We further show that evaporative coolers are less effective in tropical regions than in the extra tropics. Finally, we show that neither low nor high efficiency coolers are sufficient to return Africa to current levels of heat stress under climate change.

Changes between icehouse and greenhouse states are known to be the result from non-linear climate responses. However, the magnitudes of these responses are not well constrained. Recent work shows that climate models, specifically the Community Earth System Model version 1 (CESM1), have improved substantially in their capacity to quantify the Last Glacial Maximum (LGM) state. Given that CESM1 can reproduce the LGM well, we consider the combined impacts of estimated ice sheet heights, Quaternary orbits, and greenhouse gas changes for a range of Quaternary climate states. To that end, we conducted two sets of experiments: first, a series of sensitivity experiments on the Preindustrial climate and second, experiments on Quaternary glacial states.In the first set of the experiments, we show how CESM1 quantifies the impacts of ice height, orbit, and greenhouse gas changes by considering each component incrementally. Then we demonstrate that they combine through non-linear impacts. The analysis is based on seven sensitivity experiments: 1) Late Holocene orbit, 2) Representative Concentration Pathway 8.5 (RCP85) greenhouse gases, 3) LGM orbit, 4) LGM greenhouse gasses, and 5) Greenland icesheet height changes, 6) LGM orbit with Greenland icesheet height changes, and 7) LGM orbit with LGM greenhouse gases and Greenland icesheet height changes. We show that adding individually these component changes do not linearly combine to match the simulations with combined changes.These non-linear effects guide the second set of experiments, because non-linear systems are predictable due to state dependent outcomes. We use of 4 glacial ice sheet height differences and 4 glacial maximum orbital states (LGM, and Marine Isotopic Stage 4,6, and 8), for a total of 16 sensitivity experiments. These orbits are known glacial maximal states, and the 4 ice sheet heights are within the range of estimated ice volumes. We analyze these simulations in two ways, 1) the explicit effect of changes in orbit while holding the ice sheet constant, and 2) the explicit effect of changes in ice sheet height, while holding the orbit constant.Our results show that ice sheet heights dominate the changes in climate system, regardless of orbit. But, there are subtle regional effects that orbit has that are not explained by ice sheet height changes. For example, higher ice sheets induce a global temperature increase, but regionally within Europe, there are non-linear changes in warming or cooling that are unexplained by the ice sheets. As the ice sheet height is lowered, the changes in Europe do not linearly change, and are dependent on the orbit configuration.These results show that there are specific pathways for climate that occur due to the combination of icesheet height and orbit, and theoretically imply a constraint on the real climate state. In a linear system, these 16 states would represent the variability of the Quaternary, but as this is a non-linear system only 1 state is physical for a given orbit. As proxy data spatial and temporal resolution improves for the Quaternary, combined with these modeled climates, we expect substantial constraints on the available realistic climate states.

Glacier modelling of the Alpine region during past ice ages has received increasing attention in the recent years. Considering the complexity of the Alpine topography, high spatial resolution climate information is required for running glacier models over the area. However, continuous climatic signal at resolution higher than 10 km and covering several hundred thousand of years cannot be directly derived using dynamical climate models. Alternative strategies must be considered.Here, a climate emulator providing monthly temperature and precipitation over the Alpine region, with a horizontal resolution of 2x2 km and covering the last 400’000 years at intervals of 100 years, is presented. The emulatorcombines a dynamical modelling chain of Earth System Models (ESMs) and a Regional Climate Model (RCM) with different statistical modelling methods. The dynamical modelling chain delivers climate information at highest accuracy based upon physical prognostic equations for specific time slices. The subsequent statistical modelling uses these time slices as physically consistent boundaries and estimates the climate conditions in between them, thus generatinga long-term climate evolution.A total of 19 climate model experiments are conducted for different time-slices of the considered study period, including also sensitivity tests with changes in the ice sheet height of the Northern Hemisphere and land cover type. Starting from a simple linear regression, a series of different statistical approaches is tested for building the emulator. An evaluation of the different versions is then conducted against one of the RCM time-slice experiments, left out in turns from the training set of the statistical model. Results show robust skills of the emulator in the representation of temperature, whose changes are mainly driven by smooth variations in the seasonal pattern of insolation at different time-steps, already using a simple linear regression. For precipitation, non-linearities associated to changes in the large-scale atmospheric circulation seem to dominate, making the use of more complex statistical approaches more appropriate. Additional evaluation tests conducted using glacier modelling driven by the outputs of the developed emulator confirm its potential for reconstructing the ice extent over the Alpine region during the last ice ages.

Extreme high temperatures are associated with elevated human mortality risks. This is evidenced by a typically U- or J-shaped relationship between daily temperature and mortality found for most places in the world where data exist. However, high temperature is not the only contributor to heat stress. Humidity is also an important factor because it affects evaporation of sweat, which is crucial for cooling the human body in hot environments. Although various heat stress metrics, many of which are a combination of atmospheric temperature and humidity based on different physiological assumptions, have been developed to estimate heat stress, the relationship between these metrics and mortality remains unclear.In this study, the relationships between seven heat stress metrics — wet bulb temperature, apparent temperature, discomfort index and swamp cooler temperatures at four different efficiencies [1] — and mortality are systematically assessed using well-established Distributed Lag Non-linear Models (DLNMs) [2]. The predictive powers of these metrics, as well as that of daily mean temperature, are compared for the summer season at global locations in 39 countries, where sufficient meteorological and health data are available [3]. The results of this study provide new information as to which of these metrics are most indicative of summer mortality in different locations, and whether the ‘best-fit’ heat stress metric for a location gives a substantially different mortality estimate compared to the commonly used daily mean temperature. These results have important implications for heat-health impact monitoring, developing national and international heat-health action plans, as well as for projecting future heat-related mortality under different climate change scenarios.References:[1] Buzan, J. R. et al.: Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5. Geosci. Model Dev., 8, 151–170, 2015.[2] Gasparrini and Armstrong: Reducing and meta-analysing estimates from distributed lag non-linear models. BMC Medical Research Methodology, 13:1, 2013.[3] Vicedo-Cabrera, A. M. et al.: The burden of heat-related mortality attributable to recent human-induced climate change. Nature Climate Change, 11, 492–500, 2021.