Ambient carbon monoxide and daily mortality: a global time-series study in 337 cities

BACKGROUND: Epidemiological evidence on short-term association between ambient carbon monoxide (CO) and mortality is inconclusive and limited to single cities, regions, or countries. Generalisation of results from previous studies is hindered by potential publication bias and different modelling approaches. We therefore assessed the association between short-term exposure to ambient CO and daily mortality in a multicity, multicountry setting. METHODS: We collected daily data on air pollution, meteorology, and total mortality from 337 cities in 18 countries or regions, covering various periods from 1979 to 2016. All included cities had at least 2 years of both CO and mortality data. We estimated city-specific associations using confounder-adjusted generalised additive models with a quasi-Poisson distribution, and then pooled the estimates, accounting for their statistical uncertainty, using a random-effects multilevel meta-analytical model. We also assessed the overall shape of the exposure-response curve and evaluated the possibility of a threshold below which health is not affected. FINDINGS: Overall, a 1 mg/m3 increase in the average CO concentration of the previous day was associated with a 0·91% (95% CI 0·32-1·50) increase in daily total mortality. The pooled exposure-response curve showed a continuously elevated mortality risk with increasing CO concentrations, suggesting no threshold. The exposure-response curve was steeper at daily CO levels lower than 1 mg/m3, indicating greater risk of mortality per increment in CO exposure, and persisted at daily concentrations as low as 0·6 mg/m3 or less. The association remained similar after adjustment for ozone but was attenuated after adjustment for particulate matter or sulphur dioxide, or even reduced to null after adjustment for nitrogen dioxide. INTERPRETATION: This international study is by far the largest epidemiological investigation on short-term CO-related mortality. We found significant associations between ambient CO and daily mortality, even at levels well below current air quality guidelines. Further studies are warranted to disentangle its independent effect from other traffic-related pollutants. FUNDING: EU Horizon 2020, UK Medical Research Council, and Natural Environment Research Council

Fluctuating temperature modifies heat-mortality association around the globe

Studies have investigated the effects of heat and temperature variability (TV) on mortality. However, few assessed whether TV modifies the heat-mortality association. Data on daily temperature and mortality in the warm season were collected from 717 locations across 36 countries. TV was calculated as the standard deviation of the average of the same and previous days’ minimum and maximum temperatures. We used location-specific quasi-Poisson regression models with an interaction term between the cross-basis term for mean temperature and quartiles of TV to obtain heat-mortality associations under each quartile of TV, and then pooled estimates at the country, regional, and global levels. Results show the increased risk in heat-related mortality with increments in TV, accounting for 0.70% (95% confidence interval [CI]: −0.33 to 1.69), 1.34% (95% CI: −0.14 to 2.73), 1.99% (95% CI: 0.29–3.57), and 2.73% (95% CI: 0.76–4.50) of total deaths for Q1–Q4 (first quartile–fourth quartile) of TV. The modification effects of TV varied geographically. Central Europe had the highest attributable fractions (AFs), corresponding to 7.68% (95% CI: 5.25–9.89) of total deaths for Q4 of TV, while the lowest AFs were observed in North America, with the values for Q4 of 1.74% (95% CI: −0.09 to 3.39). TV had a significant modification effect on the heat-mortality association, causing a higher heat-related mortality burden with increments of TV. Implementing targeted strategies against heat exposure and fluctuant temperatures simultaneously would benefit public health. © 2022 The Author(s)Funding text 1: This study was supported by the Australian Research Council (DP210102076) and the Australian National Health and Medical Research Council (APP2000581). Y.W and B.W. were supported by the China Scholarship Council (nos. 202006010044 and 202006010043); S.L. was supported by an Emerging Leader Fellowship of the Australian National Health and Medical Research Council (no. APP2009866); Y.G. was supported by Career Development Fellowship (no. APP1163693) and Leader Fellowship (no. APP2008813) of the Australian National Health and Medical Research Council; J.K. and A.U. were supported by the Czech Science Foundation (project no. 20–28560S); N.S. was supported by the National Institute of Environmental Health Sciences-funded HERCULES Center (no. P30ES019776); Y.H. was supported by the Environment Research and Technology Development Fund (JPMEERF15S11412) of the Environmental Restoration and Conservation Agency; M.d.S.Z.S.C. and P.H.N.S. were supported by the São Paulo Research Foundation (FAPESP); H.O. and E.I. were supported by the Estonian Ministry of Education and Research (IUT34–17); J.M. was supported by a fellowship of Fundação para a Ciência e a Tecnlogia (SFRH/BPD/115112/2016); A.G. and F.S. were supported by the Medical Research Council UK (grant ID MR/R013349/1), the Natural Environment Research Council UK (grant ID NE/R009384/1), and the EU's Horizon 2020 project, Exhaustion (grant ID 820655); A.S. and F.d.D. were supported by the EU's Horizon 2020 project, Exhaustion (grant ID 820655); V.H. was supported by the Spanish Ministry of Economy, Industry and Competitiveness (grant ID PCIN-2017–046); and A.T. by MCIN/AEI/10.13039/501100011033 (grant CEX2018-000794-S). Statistics South Africa kindly provided the mortality data, but had no other role in the study. Y.G. A.G. M.H. and B. Armstrong set up the collaborative network. Y.G. S.L. and Y.W. designed the study. Y.G. S.L. and A.G. developed the statistical methods. Y.W. B.W. S.L. and Y.G. took the lead in drafting the manuscript and interpreting the results. Y.W. B.W. Y.G. A.G. S.T. A.O. A.U. A.S. A.E. A.M.V.-C. A. Zanobetti, A.A. A. Zeka, A.T. B. Alahmad, B. Armstrong, B.F. C.Í. C. Ameling, C.D.l.C.V. C. Åström, D.H. D.V.D. D.R. E.I. E.L. F.M. F.A. F.D. F.S. G.C.-E. H. Kan, H.O. H. Kim, I.-H.H. J.K. J.M. J.S. K.K. M.H.-D. M.S.R. M.H. M.P. M.d.S.Z.S.C. N.S. P.M. P.G. P.H.N.S. R.A. S.O. T.N.D. V.C. V.H. W.L. X.S. Y.H. M.L.B. and S.L. provided the data and contributed to the interpretation of the results and the submitted version of the manuscript. Y.G. S.L. and Y.W. accessed and verified the data. All of the authors had full access to all of the data in the study and had final responsibility for the decision to submit for publication. The authors declare no competing interests.; Funding text 2: This study was supported by the Australian Research Council ( DP210102076 ) and the Australian National Health and Medical Research Council ( APP2000581 ). Y.W and B.W. were supported by the China Scholarship Council (nos. 202006010044 and 202006010043 ); S.L. was supported by an Emerging Leader Fellowship of the Australian National Health and Medical Research Council (no. APP2009866 ); Y.G. was supported by Career Development Fellowship (no. APP1163693) and Leader Fellowship (no. APP2008813) of the Australian National Health and Medical Research Council ; J.K. and A.U. were supported by the Czech Science Foundation (project no. 20–28560S ); N.S. was supported by the National Institute of Environmental Health Sciences -funded HERCULES Center (no. P30ES019776 ); Y.H. was supported by the Environment Research and Technology Development Fund ( JPMEERF15S11412 ) of the Environmental Restoration and Conservation Agency; M.d.S.Z.S.C. and P.H.N.S. were supported by the São Paulo Research Foundation (FAPESP); H.O. and E.I. were supported by the Estonian Ministry of Education and Research ( IUT34–17 ); J.M. was supported by a fellowship of Fundação para a Ciência e a Tecnlogia ( SFRH/BPD/115112/2016 ); A.G. and F.S. were supported by the Medical Research Council UK (grant ID MR/R013349/1 ), the Natural Environment Research Council UK (grant ID NE/R009384/1 ), and the EU’s Horizon 2020 project, Exhaustion (grant ID 820655 ); A.S. and F.d.D. were supported by the EU’s Horizon 2020 project, Exhaustion (grant ID 820655 ); V.H. was supported by the Spanish Ministry of Economy, Industry and Competitiveness (grant ID PCIN-2017–046 ); and A.T. by MCIN/AEI/10.13039/501100011033 (grant CEX2018-000794-S). Statistics South Africa kindly provided the mortality data, but had no other role in the study

Comparison of weather station and climate reanalysis data for modelling temperature-related mortality

Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk. © 2022, The Author(s).The original version of this Article contained an error in Affiliation 25, which was incorrectly given as ‘Faculty of Medicine ArqFuturo INSPER, University of São Paulo, São Paulo, Brazil’. The correct affiliation is listed below. Faculty of Medicine, University of São Paulo, São Paulo, Brazil The original Article has been corrected. © The Author(s) 2022.The study was primarily supported by Grants from the European Commission’s Joint Research Centre Seville (Research Contract ID: JRC/SVQ/2020/MVP/1654), Medical Research Council-UK (Grant ID: MR/R013349/1), Natural Environment Research Council UK (Grant ID: NE/R009384/1), European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655). The following individual Grants also supported this work: J.K and A.U were supported by the Czech Science Foundation, project 20-28560S. A.T was supported by MCIN/AEI/10.13039/501100011033, Grant CEX2018-000794-S. V.H was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant agreement No 101032087. This work was generated using Copernicus Climate Change Service (C3S) information [1985–2019]

Global, regional, and national burden of mortality associated with short-term temperature variability from 2000-19: a three-stage modelling study

BACKGROUND: Increased mortality risk is associated with short-term temperature variability. However, to our knowledge, there has been no comprehensive assessment of the temperature variability-related mortality burden worldwide. In this study, using data from the MCC Collaborative Research Network, we first explored the association between temperature variability and mortality across 43 countries or regions. Then, to provide a more comprehensive picture of the global burden of mortality associated with temperature variability, global gridded temperature data with a resolution of 0.5 degrees x 0.5 degrees were used to assess the temperature variability-related mortality burden at the global, regional, and national levels. Furthermore, temporal trends in temperature variability-related mortality burden were also explored from 2000-19. METHODS: In this modelling study, we applied a three-stage meta-analytical approach to assess the global temperature variability-related mortality burden at a spatial resolution of 0.5 degrees x 0.5 degrees from 2000-19. Temperature variability was calculated as the SD of the average of the same and previous days' minimum and maximum temperatures. We first obtained location-specific temperature variability related-mortality associations based on a daily time series of 750 locations from the Multi-country Multi-city Collaborative Research Network. We subsequently constructed a multivariable meta-regression model with five predictors to estimate grid-specific temperature variability related-mortality associations across the globe. Finally, percentage excess in mortality and excess mortality rate were calculated to quantify the temperature variability-related mortality burden and to further explore its temporal trend over two decades. FINDINGS: An increasing trend in temperature variability was identified at the global level from 2000 to 2019. Globally, 1 753 392 deaths (95% CI 1 159 901-2 357 718) were associated with temperature variability per year, accounting for 3.4% (2.2-4.6) of all deaths. Most of Asia, Australia, and New Zealand were observed to have a higher percentage excess in mortality than the global mean. Globally, the percentage excess in mortality increased by about 4.6% (3.7-5.3) per decade. The largest increase occurred in Australia and New Zealand (7.3%, 95% CI 4.3-10.4), followed by Europe (4.4%, 2.2-5.6) and Africa (3.3, 1.9-4.6). INTERPRETATION: Globally, a substantial mortality burden was associated with temperature variability, showing geographical heterogeneity and a slightly increasing temporal trend. Our findings could assist in raising public awareness and improving the understanding of the health impacts of temperature variability. FUNDING: Australian Research Council, Australian National Health & Medical Research Council

Seasonality of mortality under climate change: a multicountry projection study

Data sharing: All data used in our study were obtained from the MCC Collaborative Research Network under a data-sharing agreement and cannot be made publicly available. Researchers can refer to collaborators of the Network, who are listed as coauthors of this Article (primary contact: Antonio Gasparrini, [email protected]), for information on accessing the data for each country. The R code is available on request, and a reproducible example is publicly available on the personal GitHub website of the first author (https://github.com/LinaMadaniyazi).For more on the MCC see https://mccstudy.lshtm.ac.uk/Supplementary Material is available online at: https://www.sciencedirect.com/science/article/pii/S2542519623002693#sec1 .Background: Climate change can directly impact temperature-related excess deaths and might subsequently change the seasonal variation in mortality. In this study, we aimed to provide a systematic and comprehensive assessment of potential future changes in the seasonal variation, or seasonality, of mortality across different climate zones. Methods: In this modelling study, we collected daily time series of mean temperature and mortality (all causes or non-external causes only) via the Multi-Country Multi-City Collaborative (MCC) Research Network. These data were collected during overlapping periods, spanning from Jan 1, 1969 to Dec 31, 2020. We projected daily mortality from Jan 1, 2000 to Dec 31, 2099, under four climate change scenarios corresponding to increasing emissions (Shared Socioeconomic Pathways [SSP] scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We compared the seasonality in projected mortality between decades by its shape, timings (the day-of-year) of minimum (trough) and maximum (peak) mortality, and sizes (peak-to-trough ratio and attributable fraction). Attributable fraction was used to measure the burden of seasonality of mortality. The results were summarised by climate zones. Findings: The MCC dataset included 126 809 537 deaths from 707 locations within 43 countries or areas. After excluding the only two polar locations (both high-altitude locations in Peru) from climatic zone assessments, we analysed 126 766 164 deaths in 705 locations aggregated in four climate zones (tropical, arid, temperate, and continental). From the 2000s to the 2090s, our projections showed an increase in mortality during the warm seasons and a decrease in mortality during the cold seasons, albeit with mortality remaining high during the cold seasons, under all four SSP scenarios in the arid, temperate, and continental zones. The magnitude of this changing pattern was more pronounced under the high-emission scenarios (SSP3-7.0 and SSP5-8.5), substantially altering the shape of seasonality of mortality and, under the highest emission scenario (SSP5-8.5), shifting the mortality peak from cold seasons to warm seasons in arid, temperate, and continental zones, and increasing the size of seasonality in all zones except the arid zone by the end of the century. In the 2090s compared with the 2000s, the change in peak-to-trough ratio (relative scale) ranged from 0·96 to 1·11, and the change in attributable fraction ranged from 0·002% to 0·06% under the SSP5-8.5 (highest emission) scenario. Interpretation: A warming climate can substantially change the seasonality of mortality in the future. Our projections suggest that health-care systems should consider preparing for a potentially increased demand during warm seasons and sustained high demand during cold seasons, particularly in regions characterised by arid, temperate, and continental climates.This study was primarily supported by the Environment Research and Technology Development Fund (grant number JPMEERF20231007) of the Environmental Restoration and Conservation Agency, provided by the Ministry of the Environment of Japan. MH was supported by the Japan Science and Technology Agency as part of the Strategic International Collaborative Research Program (grant number JPMJSC20E4). AG was supported by the UK Medical Research Council (grant number MR/V034162/1) and the EU's Horizon 2020 research project Exhaustion (grant number 820655). AU and JK were supported by the Czech Science Foundation (project 22–24920S). JJKJ was supported by the Academy of Finland (grant number 310372; Global Health Risks Related to Atmospheric Composition and Weather Consortium). FS was supported by the Italian Ministry of University and Research, Department of Excellence project 2023–2027, Rethinking Data Science—Department of Statistics, Computer Science and Applications—University of Florence

Global, regional, and national burden of mortality associated with cold spells during 2000–19: a three-stage modelling study

Data sharing: All used data were obtained from the Multi-Country Multi-City (MCC) Collaborative Research Network (https://mccstudy.lshtm.ac.uk/) under a data sharing agreement and cannot be made publicly available. Researchers can refer to MCC participants, who are listed as coauthors of our study, for information on accessing the data for each country.Supplementary Material is available online at: https://www.sciencedirect.com/science/article/pii/S2542519623002772#sec1 .Background: Exposure to cold spells is associated with mortality. However, little is known about the global mortality burden of cold spells. Methods: A three-stage meta-analytical method was used to estimate the global mortality burden associated with cold spells by means of a time series dataset of 1960 locations across 59 countries (or regions). First, we fitted the location-specific, cold spell-related mortality associations using a quasi-Poisson regression with a distributed lag non-linear model with a lag period of up to 21 days. Second, we built a multivariate meta-regression model between location-specific associations and seven predictors. Finally, we predicted the global grid-specific cold spell-related mortality associations during 2000–19 using the fitted meta-regression model and the yearly grid-specific meta-predictors. We calculated the annual excess deaths, excess death ratio (excess deaths per 1000 deaths), and excess death rate (excess deaths per 100 000 population) due to cold spells for each grid across the world. Findings: Globally, 205 932 (95% empirical CI [eCI] 162 692–250 337) excess deaths, representing 3·81 (95% eCI 2·93–4·71) excess deaths per 1000 deaths (excess death ratio), and 3·03 (2·33–3·75) excess deaths per 100 000 population (excess death rate) were associated with cold spells per year between 2000 and 2019. The annual average global excess death ratio in 2016–19 increased by 0·12 percentage points and the excess death rate in 2016–19 increased by 0·18 percentage points, compared with those in 2000–03. The mortality burden varied geographically. The excess death ratio and rate were highest in Europe, whereas these indicators were lowest in Africa. Temperate climates had higher excess death ratio and rate associated with cold spells than other climate zones. Interpretation: Cold spells are associated with substantial mortality burden around the world with geographically varying patterns. Although the number of cold spells has on average been decreasing since year 2000, the public health threat of cold spells remains substantial. The findings indicate an urgency of taking local and regional measures to protect the public from the mortality burdens of cold spells.Australian Research Council, Australian National Health and Medical Research Council, EU's Horizon 2020 Project Exhaustion. This study was supported by the Australian Research Council (DP210102076), the Australian National Health and Medical Research Council (APP2000581) and the EU's Horizon 2020 Project Exhaustion (Grant ID: 820655). YGa and WH were supported by the China Scholarship Council (number 202008110182 and number 202006380055). YGu was supported by the Leader Fellowship (number APP2008813) of the Australian National Health and Medical Research Council. QZ was supported by the Natural Science Foundation of Shandong Province in China (grant ZR2021QH318) and the Shandong Excellent Young Scientists Fund Program (Overseas) (grant 2022HWYQ-055). AG was supported by the European Union's Horizon 2020 Project Exhaustion (Grant ID: 820655). JK and AU were supported by the Czech Science Foundation (project 22-24920S). VH was supported by the European Union's Horizon 2020 Research and Innovation Programme (Marie Skłodowska-Curie Grant Agreement Number 101032087), and SL was supported by an Emerging Leader Fellowship of the Australian National Health and Medical Research Council (number APP2009866)

Comparison for the effects of different components of temperature variability on mortality: A multi-country time-series study

Data availability: The authors do not have permission to share data.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0160412024002988?via%3Dihub#s0095 .Background: Temperature variability (TV) is associated with increased mortality risk. However, it is still unknown whether intra-day or inter-day TV has a higher effect. Objectives: We aimed to assess the association of intra-day TV and inter-day TV with all-cause, cardiovascular, and respiratory mortality. Methods: We collected data on total, cardiovascular, and respiratory mortality and meteorology from 758 locations in 47 countries or regions from 1972 to 2020. We defined inter-day TV as the standard deviation (SD) of daily mean temperatures across the lag interval, and intra-day TV as the average SD of minimum and maximum temperatures on each day. In the first stage, inter-day and intra-day TVs were modelled simultaneously in the quasi-Poisson time-series model for each location. In the second stage, a multi-level analysis was used to pool the location-specific estimates. Results: Overall, the mortality risk due to each interquartile range [IQR] increase was higher for intra-day TV than for inter-day TV. The risk increased by 0.59% (95% confidence interval [CI]: 0.53, 0.65) for all-cause mortality, 0.64% (95% CI: 0.56, 0.73) for cardiovascular mortality, and 0.65% (95% CI: 0.49, 0.80) for respiratory mortality per IQR increase in intra-day TV0–7 (0.9 °C). An IQR increase in inter-day TV0–7 (1.6 °C) was associated with 0.22% (95% CI: 0.18, 0.26) increase in all-cause mortality, 0.44% (95% CI: 0.37, 0.50) increase in cardiovascular mortality, and 0.31% (95% CI: 0.21, 0.41) increase in respiratory mortality. The proportion of all-cause deaths attributable to intra-day TV0–7 and inter-day TV0–7 was 1.45% and 0.35%, respectively. The mortality risks varied by lag interval, climate area, season, and climate type. Conclusions: Our results indicated that intra-day TV may explain the main part of the mortality risk related to TV and suggested that comprehensive evaluations should be proposed in more countries to help protect human health.This study was supported by the Australian Research Council (DP210102076) and the Australian National Health and Medical Research Council (GNT2000581). BW by China Scholarship Council (number 202006010043); WY by China Scholarship Council (number 202006010044); SL by an Emerging Leader Fellowship of the Australian National Health and Medical Research Council (number GNT2009866); JK and AU by the Czech Science Foundation (project number 20–28560S); NS by the National Institute of Environmental Health Sciences-funded HERCULES Center (P30ES019776); S-CP and YLG by the Ministry of Science and Technology (Taiwan; MOST 109–2621-M-002–021); YH by the Environment Research and Technology Development Fund (JPMEERF15S11412) of the Environmental Restoration and Conservation Agency; MdSZSC and PHNS by the São Paulo Research Foundation (FAPESP); ST by the Science and Technology Commission of Shanghai Municipality (grant number 18411951600); HO and EI by the Estonian Ministry of Education and Research (IUT34–17); JM by a fellowship of Fundação para a Ciência e a Tecnlogia (SFRH/BPD/115112/2016); AG by the Medical Research Council UK (grant IDs: MR/V034162/1 and MR/R013349/1), the Natural Environment Research Council UK (grant ID: NE/R009384/1), and the EU's Horizon 2020 project, Exhaustion (grant ID: 820655); AS, SR, and FdD by the EU's Horizon 2020 project, Exhaustion (grant ID 820655); VH by the Spanish Ministry of Economy, Industry and Competitiveness (grant ID PCIN-2017–046); AT by MCIN/AEI/10.13039/501100011033 (grant CEX2018-000794-S); YG by Career Development Fellowship (number GNT1163693) and Leader Fellowship (number GNT2008813) of the Australian National Health and Medical Research Council; Statistics South Africa kindly provided the mortality data, but had no other role in the study. This Article is published in memory of Simona Fratianni who helped to contribute the data for Romania

Impact of population aging on future temperature-related mortality at different global warming levels

Data availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Data were collected within the MCC Collaborative Research Network under a data sharing agreement and cannot be made publicly available.Code availability: A sample of the analysis code is available from https://github.com/CHENlab-Yale/MCC_ProjAging_Temp .Supplementary information is available online at: https://link-springer-com.ezproxytest.brunel.ac.uk/article/10.1038/s41467-024-45901-z#Sec15 .Older adults are generally amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with global warming, the influence of population aging on these trends remains unclear. Here we show that at 1.5 °C, 2 °C, and 3 °C of global warming, heat-related mortality in 800 locations across 50 countries/areas will increase by 0.5%, 1.0%, and 2.5%, respectively; among which 1 in 5 to 1 in 4 heat-related deaths can be attributed to population aging. Despite a projected decrease in cold-related mortality due to progressive warming alone, population aging will mostly counteract this trend, leading to a net increase in cold-related mortality by 0.1%–0.4% at 1.5–3 °C global warming. Our findings indicate that population aging constitutes a crucial driver for future heat- and cold-related deaths, with increasing mortality burden for both heat and cold due to the aging population.We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modeling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. K.C. was supported by the Yale Planetary Solutions Project seed grant. A.G., A.S., and S.R. were supported by the European Union’s Horizon 2020 Project Exhaustion grant (820655). A.G. was also supported by the Medical Research Council UK grant (MR/V034162/1). J.M. received funding from the Fundação para a Ciência e a Tecnlogia Grant (SFRH/BPD/115112/2016). A.T. was supported by the MCIN/AEI/10.13039/501100011033 grant (CEX2018-000794-S). A.U. and J.K. were supported by the Czech Science Foundation (22-24920S). F.S. was supported by the Italian Ministry of University and Research (MUR), Department of Excellence project 2023-2027 ReDS ‘Rethinking Data Science’ - Department of Statistics, Computer Science and Applications - University of Florence. MNM. was supported by the European Commission (H2020-MSCA-IF-2020) under REA grant agreement no. 101022870. A.V.C. acknowledges the support of the Swiss National Foundation (TMSGI3_211626). V.H. received funding from the European Union’s Horizon 2020 research and innovation program (Marie Skłodowska-Curie Grant Agreement No.: 101032087)