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Results for “Coupled Model Intercomparison Project Phase 6 (CMIP6)”

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Coupled model intercomparison project phase 6 (CMIP6) high resolution model intercomparison project (HighResMIP) bias in extreme rainfall drives underestimation of amazonian precipitation

Abstract Extreme rainfall events drive the amount and spatial distribution of rainfall in the Amazon and are a key driver of forest dynamics across the basin. This study investigates how the 3-hourly predictions in the High Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project, CMIP6) represent extreme rainfall events at annual, seasonal, and sub-daily time scales. TRMM 3B42 (Tropical Rainfall Measuring Mission) 3 h data were used as observations. Our results showed that eleven out of seventeen HighResMIP models showed the observed association between rainfall and number of extreme events at the annual and seasonal scales. Two models captured the spatial pattern of number of extreme events at the seasonal and annual scales better (higher correlation) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals. Our results suggest that higher model resolution is a crucial factor for capturing extreme rainfall events in the Amazon, but it might not be the sole factor. Improving the representation of Amazon extreme rainfall events in HighResMIP models can help reduce model rainfall biases and uncertainties and enable more reliable assessments of the water cycle and forest dynamics in the Amazon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Documenting numerical experiments in support of the Coupled Model Intercomparison Project Phase 6 (CMIP6)

Abstract. Numerical simulation, and in particular simulation of the earth system, relies on contributions from diverse communities, from those who develop models to those involved in devising, executing, and analysing numerical experiments. Often these people work in different institutions and may be working with significant separation in time (particularly analysts, who may be working on data produced years earlier), and they typically communicate via published information (whether journal papers, technical notes, or websites). The complexity of the models, experiments, and methodologies, along with the diversity (and sometimes inexact nature) of information sources, can easily lead to misinterpretation of what was actually intended or done. In this paper we introduce a taxonomy of terms for more clearly defining numerical experiments, put it in the context of previous work on experimental ontologies, and describe how we have used it to document the experiments of the sixth phase for the Coupled Model Intercomparison Project (CMIP6). We describe how, through iteration with a range of CMIP6 stakeholders, we rationalized multiple sources of information and improved the clarity of experimental definitions. We demonstrate how this process has added value to CMIP6 itself by (a) helping those devising experiments to be clear about their goals and their implementation, (b) making it easier for those executing experiments to know what is intended, (c) exposing interrelationships between experiments, and (d) making it clearer for third parties (data users) to understand the CMIP6 experiments. We conclude with some lessons learnt and how these may be applied to future CMIP phases as well as other modelling campaigns.

Pascoe, Charlotte (ORCID:0000000223616778)↗
NASA NTRS2022

Climate Change Penalty and Benefit on Surface Ozone: A Global Perspective Based on CMIP6 Earth System Models

This work presents an analysis of the effect of climate change on surface ozone discussing the related penalties and benefits around the globe from the global modelling perspective based on simulations with five CMIP6 (Coupled Model Intercomparison Project Phase 6) Earth System Models. As part of AerChemMIP (Aerosol Chemistry Model Intercomparison Project) all models conducted simulation experiments considering future climate (ssp370SST) and present-day climate (ssp370pdSST) under the same future emissions trajectory (SSP3-7.0). A multi-model global average climate change benefit on surface ozone of −0.96 ± 0.07 ppbv °C−1 is calculated which is mainly linked to the dominating role of enhanced ozone destruction with higher water vapour abundances under a warmer climate. Over regions remote from pollution sources, there is a robust decline in mean surface ozone concentration on an annual basis as well as for boreal winter and summer varying spatially from −0.2 to −2 ppbv °C−1, with strongest decline over tropical oceanic regions. The implication is that over regions remote from pollution sources (except over the Arctic) there is a consistent climate change benefit for baseline ozone due to global warming. However, ozone increases over regions close to anthropogenic pollution sources or close to enhanced natural biogenic volatile organic compounds emission sources with a rate ranging regionally from 0.2 to 2 ppbv C−1, implying a regional surface ozone penalty due to global warming. Overall, the future climate change enhances the efficiency of precursor emissions to generate surface ozone in polluted regions and thus the magnitude of this effect depends on the regional emission changes considered in this study within the SSP3_7.0 scenario. The comparison of the climate change impact effect on surface ozone versus the combined effect of climate and emission changes indicates the dominant role of precursor emission changes in projecting surface ozone concentrations under future climate change scenarios.

surface ozone↗
NASA NTRS2021

Future Sea Level Change Under CMIP5 and CMIP6 Scenarios From the Greenland and Antarctic Ice Sheets

Projections of the sea level contribution from the Greenland and Antarctic ice sheets (GrIS and AIS) rely on atmospheric and oceanic drivers obtained from climate models. The Earth System Models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) generally project greater future warming compared with the previous Coupled Model Intercomparison Project phase 5 (CMIP5) effort. Here we use four CMIP6 models and a selection of CMIP5 models to force multiple ice sheet models as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We find that the projected sea level contribution at 2100 from the ice sheet model ensemble under the CMIP6 scenarios falls within the CMIP5 range for the Antarctic ice sheet but is significantly increased for Greenland. Warmer atmosphere in CMIP6 models results in higher Greenland mass loss due to surface melt. For Antarctica, CMIP6 forcing is similar to CMIP5 and mass gain from increased snowfall counteracts increased loss due to ocean warming.

Antarctica↗
NASA NTRS2021

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the main set of future climate projections, based on concentration-driven simulations, within the Coupled Model Intercomparison Project phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century (2081–2100) encompassing the Tier 1 experiments based on the Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by close to 1.5 °C) reached at the upper end of the 5 %–95 % envelope of the highest scenario (SSP5-8.5). This is due to both the wider range of radiative forcing that the new scenarios cover and the higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensemble spreads, according to a set of initial condition ensemble simulations available under SSP3-7.0. These experiments suggest a tendency for internal variability to decrease along the course of the century in this scenario, a result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades around mid-century, as represented in SSP5-3.4OS, does not affect the end outcome of temperature and precipitation changes by 2100, which return to the same levels as those reached by the gradually increasing SSP4-3.4 (not erasing the possibility, however, that other aspects of the system may not be as easily reversible). Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level might be biased by the inclusion of models that have shown faster warming in the historical period than the observed. Those estimates show all scenarios reaching 1.5 °C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering between 20 and 27 years from present. The warming level of 2 °C of warming is reached as early as 2039 by the ensemble mean under SSP5-8.5 but as late as the mid-2060s under SSP1-2.6. The highest warming level considered (5 °C) is reached by the ensemble mean only under SSP5-8.5 and not until the mid-2090s.d-2090s.

Scenario Model Intercomparison Project (ScenarioMI↗
DOE OSTI2026

CONUS-wide Projected Flood Frequency and Uncertainty Estimates, Version 1.0

This dataset presents a large-ensemble of CONUS-wide projected flood frequency and uncertainty estimates across ~2.7 million NHDPlusV2 river reaches over the CONUS. The framework producing this dataset leverages a multi-model, uncertainty-aware modeling framework that allows evaluating shifts in flood frequences at the stream reach level across the CONUS. CONUS-wide ensemble streamflow projections generated from hydrologic simulations driven by downscaled and bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs are used to derive these flood frequency and uncertainty estimates over the period 1980 - 2099. A spatially consistent regional L-moment algorithm is applied across clusters defined by the US Hydrologic Unit Code Subregions (HUC4s and HUC8s) and NHDPlusV2 stream orders to estimate flood frequencies. The dataset also includes at-site based flood estimates that allow for the comparison between local and regional approach-based estimates, assess projected changes, and characterize their uncertainties. For more reliable estimation of rare flood frequencies such as 500 and 1000-year return periods, super-ensemble based estimates are also included in the dataset. This dataset is derived to support the "Impact-Informed Dam Safety Risk Assessment for Securing Hydropower Assests" project for the US Department of Energy (DOE) Hydropower and Hydrokinetic Office (H2O). For further details, refer to Kao et al. (2022), Ghimire et al. (2023), Ghimire et al. (2025), and Hosking and Wallis (1997).

Ghimire, Ganesh [ORNL] (ORCID:0000000242843941)↗
DOE OSTI2021

Projected Changes in Temperature and Precipitation Over the United States, Central America, and the Caribbean in CMIP6 GCMs

The Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset is used to examine projected changes in temperature and precipitation over the United States (U.S.), Central America and the Caribbean. The changes are computed using an ensemble of 31 models for three future time slices (2021–2040, 2041–2060, and 2080–2099) relative to the reference period (1995–2014) under three Shared Socioeconomic Pathways (SSPs; SSP1-2.6, SSP2-4.5, and SSP5-8.5). The CMIP6 ensemble reproduces the observed annual cycle and distribution of mean annual temperature and precipitation with biases between – 0.93 and 1.27 °C and – 37.90 to 58.45%, respectively, for most of the region. However, modeled precipitation is too large over the western and Midwestern U.S. during winter and spring and over the North American monsoon region in summer, while too small over southern Central America. Temperature is projected to increase over the entire domain under all three SSPs, by as much as 6 °C under SSP5-8.5, and with more pronounced increases in the northern latitudes over the regions that receive snow in the present climate. Annual precipitation projections for the end of the twenty-first century have more uncertainty, as expected, and exhibit a meridional dipole-like pattern, with precipitation increasing by 10–30% over much of the U.S. and decreasing by 10–40% over Central America and the Caribbean, especially over the monsoon region. Seasonally, precipitation over the eastern and central subregions is projected to increase during winter and spring and decrease during summer and autumn. Over the monsoon region and Central America, precipitation is projected to decrease in all seasons except autumn. The analysis was repeated on a subset of 9 models with the best performance in the reference period; however, no significant difference was found, suggesting that model bias is not strongly influencing the projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models

Projection of the contribution of ice sheets to sea level change as part of the Coupled Model Intercomparison Project Phase 6 (CMIP6) takes the form of simulations from coupled ice sheet–climate models and stand-alone ice sheet models, overseen by the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). This paper describes the experimental setup for process-based sea level change projections to be performed with stand-alone Greenland and Antarctic ice sheet models in the context of ISMIP6. The ISMIP6 protocol relies on a suite of polar atmospheric and oceanic CMIP-based forcing for ice sheet models, in order to explore the uncertainty in projected sea level change due to future emissions scenarios, CMIP models, ice sheet models, and parameterizations for ice–ocean interactions. We describe here the approach taken for defining the suite of ISMIP6 stand-alone ice sheet simulations, document the experimental framework and implementation, and present an overview of the ISMIP6 forcing to be used by participating ice sheet modeling groups.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CMIP6-based Dynamically Downscaled Hydroclimate Projection over the Conterminous US

This dataset presents a suite of downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled dynamically using Regional Climate Model version 4 (RegCM4). Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-end (SSP585) emission scenario. This dataset is formulated to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details on this dataset, please refer to Kao et al. (2022) and Rastogi et al. (2022).

13 HYDRO ENERGY↗
DOE OSTI2025

CMIP6-based Multi-model Hydropower Projection over the Conterminous US, Version 1.1

This dataset presents a suite of hydropower projections for the conterminous United States (CONUS), derived from multiple downscaled and bias-corrected Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The CMIP6 GCMs are downscaled using either statistical (DBCCA) or dynamical (RegCM) approaches, based on two meteorological reference datasets (Daymet and Livneh). The resulting downscaled precipitation, temperature, and wind speed data are then used to drive two calibrated hydrologic models (VIC and PRMS), enabling simulations of projected future hydrologic responses across the CONUS. Simulated total runoff is subsequently employed to drive two hydropower models (WMP, now implemented as mosartwmpy-power, and WRES) to evaluate how climate change may affect future hydropower production for both federal and non-federal hydropower fleets. This dataset was developed to support the SECURE Water Act Section 9505 Assessment for the U.S. Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details, see Broman et al. (2024), Thurber et al. (2024), Kao et al. (2022), and Zhou et al. (2023).

Voisin, Nathalie [Pacific Northwest National Labor↗
DOE OSTI2024

Forcing data (CESM2/CMIP6) for projection of drought impacts (2015-2100) at the K34 site in Manaus, Brazil

Historical and projected output data variables extracted and derived from Community Earth System Model 2 (CESM2) runs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) archive. CESM2 is a fully coupled Earth system model used in simulations of Earth's past, present, and future climates (Danabasoglu et al., 2020). Variables in this dataset are in six-hourly resolution, and include air temperature (both in K and ℃), specific humidity (kg kg-1), air pressure (Pa), relative humidity (%), and vapor pressure deficit (kPa). CESM2 was the only CMIP6 model that provided VPD at the high temporal resolution required for this analysis. Data are included in .csv files, and the text file CESM2-CMIP6_forcing_K34-Manaus_headers.txt provides descriptions of data file headers.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2024

Evaluation of CMIP6 Model Simulations of PM 2.5 and its Components Over China

Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter (PM 2.5 ) as major climate forcers. Yet the model performance for PM 2.5 components remains little evaluated due in part to a lack of observational data. Here, we evaluate near-surface concentrations of PM 2.5 and its five main components over China as simulated by 14 CMIP6 models, including organic carbon (OC; available in 14 models), black carbon (BC; 14 models), sulfate (14 models), nitrate (4 models), and ammonium (5 models). For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM2.5 and from 2469 measurement records in the literature for PM 2.5 components. Seven models output total PM 2.5 concentrations, and they all underestimate the observed total PM 2.5 over eastern China, with GFDL-ESM4 (−1.5 %) and MPI-ESM-1-2-HAM (−1.1 %) exhibiting the smallest biases averaged over the whole country. The other seven models, for which we recalculate total PM 2.5 from the available component output, underestimate the total PM 2.5 concentrations partly because of the missing model representations of nitrate and ammonium. Concentrations of the five individual components are underestimated in almost all models, except that sulfate is overestimated in MPI-ESM-1-2-HAM by 12.6 % and in MRI-ESM2-0 by 24.5 %. The underestimation is the largest for OC (by −71.2 % to −37.8 % across the 14 models) and the smallest for BC (−47.9 % to −12.1 %). The multi-model mean (MMM) reproduces the observed spatial pattern for OC (R = 0.51), sulfate (R = 0.57), nitrate (R = 0.70) and ammonium (R = 0.74) fairly well, yet the agreement is poorer for BC (R = 0.39). The varying performances of ESMs on total PM 2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing.

CMIP6↗
NASA NTRS2019

initMIP-Antarctica: an Ice Sheet Model Initialization Experiment of ISMIP6

Ice sheet numerical modeling is an important tool to estimate the dynamic contribution of the Antarctic ice sheet to sea level rise over the coming centuries. The influence of initial conditions on ice sheet model simulations, however, is still unclear. To better understand this influence, an initial state intercomparison exercise (initMIP) has been developed to compare, evaluate, and improve initialization procedures and estimate their impact on century-scale simulations. initMIP is the first set of experiments of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6), which is the primary Coupled Model Intercomparison Project Phase 6 (CMIP6) activity focusing on the Greenland and Antarctic ice sheets. Following initMIP-Greenland, initMIP-Antarctica has been designed to explore uncertainties associated with model initialization and spin-up and to evaluate the impact of changes in external forcings. Starting from the state of the Antarctic ice sheet at the end of the initialization procedure, three forward experiments are each run for 100 years: a control run, a run with a surface mass balance anomaly, and a run with a basal melting anomaly beneath floating ice. This study presents the results of initMIP-Antarctica from 25 simulations performed by 16 international modeling groups. The submitted results use different initial conditions and initialization methods, as well as ice flow model parameters and reference external forcings. We find a good agreement among model responses to the surface mass balance anomaly but large variations in responses to the basal melting anomaly. These variations can be attributed to differences in the extent of ice shelves and their upstream tributaries, the numerical treatment of grounding line, and the initial ocean conditions applied, suggesting that ongoing efforts to better represent ice shelves in continental-scale models should continue.

ice sheet model↗
NASA NTRSDate not supplied

Better Constraining Supercooled Clouds Could Reduce Projected Warming Spread

The increase of climate sensitivity to rising greenhouse gas concentrations in the coupled model intercomparison project phase 6 (CMIP6) Earth system models (ESMs) compared to CMIP5 ESMs is primarily attributed to a larger extratropical cloud response to climate change, referred to as cloud feedback. The ratio of supercooled liquid cloud water relative to all cloud water, termed liquid phase ratio (LPR), which has also notably increased in many recent ESMs, is thought to be a primary driver of the extratropical cloud feedback increase. Unlike the preponderance of previous studies that compare native model LPR directly with observations, here we evaluate LPR over three ESM generations against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) using an instrument simulator approach, which mimics instrument limitations and uses consistent cloud definitions and resolutions. We find that current coupled model intercomparison project (CMIP) ESMs collectively simulate greater LPR than previous CMIP generations and overestimate LPR compared to observations, contrary to previous findings, likely driven by past inconsistent comparisons of ESM outputs with CALIPSO observations. We further show that greater LPR in ESM present-day climate is unexpectedly correlated with a smaller extratropical cloud feedback, attributable to a decrease of cloud amount exceeding the increase of cloud optical depth. Finally, our results suggest that improving constraints on model LPR using our evaluation framework would likely reduce the spread in CMIP6 climate sensitivities, owing to its effect on extratropical cloud feedback from supercooled clouds.

greenhouse gas concentrations↗
DOE OSTI2021

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Abstract. The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the main set of future climate projections, based on concentration-driven simulations, within the Coupled Model Intercomparison Project phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century (2081–2100) encompassing the Tier 1 experiments based on the Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by close to 1.5 ∘C) reached at the upper end of the 5 %–95 % envelope of the highest scenario (SSP5-8.5). This is due to both the wider range of radiative forcing that the new scenarios cover and the higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensemble spreads, according to a set of initial condition ensemble simulations available under SSP3-7.0. These experiments suggest a tendency for internal variability to decrease along the course of the century in this scenario, a result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades around mid-century, as represented in SSP5-3.4OS, does not affect the end outcome of temperature and precipitation changes by 2100, which return to the same levels as those reached by the gradually increasing SSP4-3.4 (not erasing the possibility, however, that other aspects of the system may not be as easily reversible). Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level might be biased by the inclusion of models that have shown faster warming in the historical period than the observed. Those estimates show all scenarios reaching 1.5 ∘C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering between 20 and 27 years from present. The warming level of 2 ∘C of warming is reached as early as 2039 by the ensemble mean under SSP5-8.5 but as late as the mid-2060s under SSP1-2.6. The highest warming level considered (5 ∘C) is reached by the ensemble mean only under SSP5-8.5 and not until the mid-2090s.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model

The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990–2014) and future (2015–2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60°N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO2−4), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO2−4 burdens decrease significantly in all simulations by 10 %–60 % following the reductions of 7 %–78 % in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030–2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol–radiation interactions (RFARI) of −0.39 ± 0.01 W/sq. m, which is −0.08 W/sq. m larger than the 1990–2010 mean forcing (−0.32 W/sq. m), of which −0.24 ± 0.01 W/sq. m was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of −0.35 to −0.40 W/sq. m for the same period, which is −0.01 to −0.06 W/sq. m larger than the 1990–2010 mean forcing of −0.35 W/sq. m. The scenarios with little to no mitigation (worst-case scenarios) led to very small changes in the RFARI, while scenarios with medium to large emission mitigations led to increases in the negative RFARI, mainly due to the decrease in the positive BC forcing and the decrease in the negative SO2−4 forcing. The anthropogenic aerosols accounted for −0.24 to −0.26 W/sq. m of the net RFARI in 2030–2050 period, in Eclipse and CMIP6 ensembles, respectively. Finally, all simulations showed an increase in the Arctic surface air temperatures throughout the simulation period. By 2050, surface air temperatures are projected to increase by 2.4 to 2.6 °C in the Eclipse ensemble and 1.9 to 2.6 °C in the CMIP6 ensemble, compared to the 1990–2010 mean. Overall, results show that even the scenarios with largest emission reductions leads to similar impact on the future Arctic surface air temperatures and sea-ice extent compared to scenarios with smaller emission reductions, implying reductions of greenhouse emissions are still necessary to mitigate climate change.

Aerosols↗
NASA NTRS2020

Historical and Future Changes in Air Pollutants from CMIP6 Models

Poor air quality is currently responsible for large impacts on human health across the world. In addition, the air pollutants ozone (O3) and particulate matter less than 2.5 µm in diameter (PM2.5) are also radiatively active in the atmosphere and can influence Earth's climate. It is important to understand the effect of air quality and climate mitigation measures over the historical period and in different future scenarios to ascertain any impacts from air pollutants on both climate and human health. The Coupled Model Intercomparison Project Phase 6 (CMIP6) presents an opportunity to analyse the change in air pollutants simulated by the current generation of climate and Earth system models that include a representation of chemistry and aerosols (particulate matter). The shared socio-economic pathways (SSPs) used within CMIP6 encompass a wide range of trajectories in precursor emissions and climate change, allowing for an improved analysis of future changes to air pollutants. Firstly, we conduct an evaluation of the available CMIP6 models against surface observations of O3 and PM2.5. CMIP6 models consistently overestimate observed surface O3 concentrations across most regions and in most seasons by up to 16 ppb, with a large diversity in simulated values over Northern Hemisphere continental regions. Conversely, observed surface PM2.5 concentrations are consistently underestimated in CMIP6 models by up to 10 µg m−3, particularly for the Northern Hemisphere winter months, with the largest model diversity near natural emission source regions. The biases in CMIP6 models when compared to observations of O3 and PM2.5 are similar to those found in previous studies. Over the historical period (1850–2014) large increases in both surface O3 and PM2.5 are simulated by the CMIP6 models across all regions, particularly over the mid to late 20th century, when anthropogenic emissions increase markedly. Large regional historical changes are simulated for both pollutants across East and South Asia with an annual mean increase of up to 40 ppb for O3 and 12 µg m−3 for PM2.5. In future scenarios containing strong air quality and climate mitigation measures (ssp126), annual mean concentrations of air pollutants are substantially reduced across all regions by up to 15 ppb for O3 and 12 µg m−3 for PM2.5. However, for scenarios that encompass weak action on mitigating climate and reducing air pollutant emissions (ssp370), annual mean increases in both surface O3 (up 10 ppb) and PM2.5 (up to 8 µg m−3) are simulated across most regions, although, for regions like North America and Europe small reductions in PM2.5 are simulated due to the regional reduction in precursor emissions in this scenario. A comparison of simulated regional changes in both surface O3 and PM2.5 from individual CMIP6 models highlights important regional differences due to the simulated interaction of aerosols, chemistry, climate and natural emission sources within models. The projection of regional air pollutant concentrations from the latest climate and Earth system models used within CMIP6 shows that the particular future trajectory of climate and air quality mitigation measures could have important consequences for regional air quality, human health and near-term climate. Differences between individual models emphasise the importance of understanding how future Earth system feedbacks influence natural emission sources, e.g. response of biogenic emissions under climate change.

Air pollutants↗
DOE OSTI2025

Data and code for Daily and Multi-Day Extreme Rainfall Analysis Under Future Climates Using Stochastic Storm Transposition and NEX-GDDP-CMIP6 Over CONUS

This data package provides inputs, codes, and outputs for a comprehensive analysis of projected changes in extreme precipitation across 10 regions of the continental United States, using 34 downscaled Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections, Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) dataset. These models are part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a coordinated climate modeling framework widely used to assess climate change impacts. The analysis applies a stochastic storm transposition method to quantify changes in extreme rainfall under two Shared Socioeconomic Pathway (SSP) climate scenarios—SSP2-4.5 (moderate emissions) and SSP5-8.5 (high emissions)—compared to historical conditions (1995–2014 vs. 2081–2100). The dataset includes rainfall depth estimates for extreme events with return periods from 2 to 500 years across multiple storm durations (1, 3, and 5 days) for each of the 10 U.S. regions. Weighted ensemble statistics are derived from individual ESM performance against historical precipitation patterns, enabling robust uncertainty quantification through both sign-based and permutation-test-based model agreement assessments. Key analyses address: (1) relative changes in extreme precipitation for each climate scenario, (2) differences between SSP scenarios (SSP5-8.5 vs. SSP2-4.5), (3) contrasts between rare and frequent events, and (4) variations between multi-day and daily storm durations. The workflow produces ensemble statistics—median, 5th, 25th, 75th, and 95th percentiles—along with model agreement metrics that identify regions and event types with robust climate change signals. The dataset includes: processed rainfall depth outputs (netCDF format) from the RainyDay Python package, ESM weights from historical performance evaluation using DayMet observations, ensemble statistics across all storm dimensions, and figures summarizing key findings.

54 ENVIRONMENTAL SCIENCES↗