Ceppi, Paulo, Sarah Wilson Kemsley, Hendrik Andersen, Timothy Andrews, Ryan J Kramer, Peer Nowack, Casey J Wall, and Mark D Zelinka, March 2026: Emerging low-cloud feedback and adjustment in global satellite observations. Atmospheric Chemistry and Physics, 26(6), DOI:10.5194/acp-26-4153-20264153-4171. Abstract
From mid-2003 to mid-2024, a global decrease in low-cloud amount enhanced the absorption of solar radiation by 0.22±0.07 W m−2 per decade (±1σ range), accelerating the energy imbalance trend during that period (0.44 W m−2 per decade). Through controlling factor analysis, here we show that the low-cloud trend is due to a combination of cloud feedback and adjustments to greenhouse gases and aerosols (respectively 0.09±0.02, 0.05±0.03, and 0.03±0.03 W m−2 per decade), which jointly account for 74 % of the trend. The contribution of natural climate variability is weak but uncertain (0.01±0.08 W m−2 per decade), owing to a poorly constrained trend in boundary-layer inversion strength. Importantly, the observed low-cloud radiative trend lies well within the range of values simulated by contemporary global climate models under conditions close to present day. Any systematic model error in the representation of present-day global energy imbalance trends is thus likely to originate in processes unrelated to low clouds.
Dingley, Beth, James Anstey, Marta Abalos, Carsten Abraham, Tommi Bergman, Lisa Bock, Sonya Fiddes, Birgit Hassler, Ryan J Kramer, Fei Luo, Fiona M O'Connor, Petr Šácha, Isla Simpson, Laura J Wilcox, and Mark D Zelinka, April 2026: CMIP7 data request: Atmosphere priorities and opportunities. Geoscientific Model Development, 19(7), DOI:10.5194/gmd-19-2945-20262945–2984. Abstract
This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data unlocking key research avenues in atmospheric science and provides justification for the resources needed to produce this data. Topics within the CMIP7 Atmosphere Theme centre around processes and feedbacks in atmospheric science such as clouds, aerosols and atmospheric chemistry, atmospheric circulation, temperature variability and extremes, radiative forcings, and Earth system model evaluation. These topics are summarised in this paper as scientific “opportunities” which will be realised through CMIP7 experiments and Earth system model outputs. These opportunities were submitted by a thematic group of atmospheric science community representatives combined with an extended consultation process. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making, including supporting the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). As an author group, we also reflect on the process used to collate this data request and make recommendations to future CMIP governance on implementing a consultation on this scale in the future.
Radiative forcing from well-mixed greenhouse gases (WMGHGs) is a main driver of Earth’s energy imbalance and global surface climate change. It remains difficult to constrain, largely because its longwave (LW) instantaneous radiative forcing (IRF) component depends on atmospheric state and is subject to radiative parameterization error. The IRF measures the immediate change in radiative fluxes at the tropopause caused by perturbations in WMGHG concentrations. Here we show that increasing WMGHG concentrations have enhanced LW IRF by 3.69 ± 0.07 W m−2 (95% confidence interval) since 1850. We first use global line-by-line radiative transfer simulations to provide a global benchmark of LW IRF for the main WMGHGs under realistic, all-sky conditions. We then identify a robust linear relationship between LW IRF and outgoing longwave radiation (OLR), enabling state-dependent LW IRF to be directly inferred from regressions against satellite-observed OLR. Furthermore, LW IRF explains 91% of the inter-model spread in effective radiative forcing (ERF, which includes rapid atmospheric adjustments beyond the IRF) for CO2 (ref. 11) across Earth system models. Benchmarking model-simulated IRF using the regression technique reveals that most discrepancies originate from radiation parameterizations and correcting LW IRF biases would reduce uncertainty in CO2 ERF by 50%. Our results establish a simple and robust framework for quantifying state-dependent radiative forcing of WMGHGs, providing an observation-informed pathway for future climate assessments.
Fridlind, Ann M., Gregory S Elsaesser, Marcus van Lier-Walqui, Grégory V Cesana, Elizabeth C Weatherhead, George Tselioudis, Gavin A Schmidt, Donifan Barahona, Brian Cairns, William D Collins, David B Considine, Lidia Cucurull, Larry DiGirolamo, Amber Emory, Otto Hasekamp, Shan He, and Ryan J Kramer, et al., March 2026: Towards a climate OSSE framework for satellite mission design. Bulletin of the American Meteorological Society, 107(3), DOI:10.1175/BAMS-D-24-0242.1. Abstract
The rich history of observing system simulation experiments (OSSEs) does not yet include a well-established framework for using climate models. The need for a climate OSSE is triggered by the need to quantify the value of a particular measurement for reducing the uncertainty in climate predictions, which differ from numerical weather predictions in that they depend on future atmospheric composition rather than the current state of the weather. However, both weather and climate modeling communities share a need for motivating major observing system investments. Here, we outline a new framework for climate OSSEs that leverages the use of machine learning to calibrate climate model physics against existing satellite data. We demonstrate its application using NASA’s GISS-E3 model to objectively quantify the value of potential future improvements in spaceborne measurements of Earth’s planetary boundary layer. A mature climate OSSE framework should be able to quantitatively compare the ability of proposed observing system architectures to answer a climate-related question, thus offering added value throughout the mission design process, which is subject to increasingly rapid advances in instrument and satellite technology. Technical considerations include selection of observational benchmarks and climate projection metrics, approaches to pinpoint the sources of model physics uncertainty that dominate uncertainty in projections, and the use of instrument simulators. Community and policy-making considerations include the potential to interface with an established culture of model intercomparison projects and a growing need to economically assess the value-driven efficiency of social spending on Earth observations.
Kramer, Ryan J., Chris Smith, and Timothy Andrews, May 2026: The Radiative Forcing Model Intercomparison Project (RFMIP2.0) for CMIP7. Geoscientific Model Development, 19(10), DOI:10.5194/gmd-19-4447-20264447-4466. Abstract
An external perturbation to the climate system from anthropogenic or natural activity first impacts the climate by inducing an alteration to Earth's energy budget, known as a radiative forcing. The characteristics of the radiative forcing, such as its global-mean magnitude and spatial pattern, determine the subsequent climate response. Therefore, understanding projections of climate change first requires diagnosing radiative forcing and evaluating its persistent uncertainty in Global Climate Models. As part of the Coupled Model Intercomparison Project phase 7 (CMIP7), the second iteration of the Radiative Forcing Model Intercomparison Project (RFMIP2.0) will enable the systematic characterization of effective radiative forcing and its components across state-of-the-art climate models, through a set of fixed-sea-surface-temperature timeslice and transient experiments. The protocol for RFMIP2.0, introduced here, will in part serve as a continuity and an expansion of core RFMIP experiments first introduced in CMIP6, some of which have now been incorporated into the overarching CMIP7 DECK and Assessment Fast Track protocols given their broad utility. This will allow for a consistent estimate for radiative forcing across multiple model generations, which is valuable for model evaluation and future development. RFMIP2.0 also includes new experiments that will address open questions about the definition of radiative forcing, such as its sensitivity to evolving surface conditions, and will further enhance an ever-growing swath of science applications that rely on an understanding of Earth's energy budget.
Accurate estimation of effective radiative forcing (ERF) is essential for understanding climate responses to external forcing. A common approach involves simulations with fixed sea surface temperatures (SSTs) while allowing land temperatures to evolve freely. However, this setup can introduce land-induced adjustments that influence ERF estimates. Using a state-of-the-art climate model, we investigate the influence of land surface temperature changes in shaping ERF under an abrupt quadrupling of carbon dioxide (CO2) (4xCO2). We conduct a series of experiments where land temperatures and soil moisture are either interactive or prescribed to their climatological values. We find that fixing land temperatures and soil moisture increases 4xCO2 ERF from 8.26 to 9.78 W m−2, primarily due to reduced longwave cooling and cloud changes. Additional idealized experiments demonstrate a monotonic decrease in 4xCO2 ERF but enhanced land precipitation with increasing land warming but fixed SSTs. These findings highlight the critical role of land processes in shaping radiative forcing estimates and provide insights into how land–sea warming contrasts influence convection, moisture transport, and precipitation patterns.
DeSouza-Machado, Sergio, L Larrabee Strow, and Ryan J Kramer, August 2025: Geophysical trends inferred from 20 years of AIRS infrared global observations. JGR Atmospheres, 130(15), DOI:10.1029/2025JD043501. Abstract
Daily spectral radiance observations by NASA's Atmospheric Infrared Sounder contain detailed information about surface and atmospheric temperature and water vapor. We obtain climate geophysical trends from 20 years (2002/09–2022/08) of Atmospheric Infrared Sounder (AIRS) observations using a novel method operating mostly in radiance space. The observations are binned into 3 x 5 degree tiles using 16 day intervals, after which nominally clear scenes are selected for each tile to construct the spectral radiance time series. Deseasonalized spectral trends are then obtained, which are inverted using a physical retrieval to obtain geophysical trends. This approach is distinct from traditional use of radiances whereby trends are generated after operational retrievals or assimilation into Reanalysis models. Our approach rigorously ties the derived geophysical trends to the observed radiance trends, using far fewer computational resources and time. The retrieved trends are compared to trends derived from ERA5 and MERRA2 reanalysis geophysical fields, and NASA Level3 AIRS v7 and CLIMCAPS v2 data. Our retrieved surface temperature trends agree quite well with ERA5, CLIMCAPS, and the GISS surface climatology trends. Atmospheric temperature profile trends exhibit some variability among all these data sets, especially in the polar stratosphere. Water vapor profile trends are nominally similar among the data sets except for the AIRS v7 which exhibits drying trends in the mid troposphere. Spectral closure between observed trends and those computed by running the reanalysis and AIRS L3 monthly retrieval products through a radiative transfer code are discussed, with the major differences arising in the water vapor sounding region.
Fan, Chongxing, David J Paynter, Ryan J Kramer, and Pu Lin, September 2025: Sensitivity of Earth's radiation budget to lower boundary condition data sets in historical climate simulations. Geophysical Research Letters, 52(17), DOI:10.1029/2025GL115914. Abstract
Atmospheric general circulation models are important tools to study climate change. A routine vindication for these models is to compare simulations with prescribed historical sea surface temperature (SST) and sea ice (SI) fraction against observations. Here, we report significant differences in the Earth's radiation budget in an Atmospheric general circulation model prescribed by different SST/SI data sets. This is caused by the distinct trends across different SST/SI data sets over the past decades despite all being based on observations. In particular, one data set reports a double rate of warming and an abrupt increase in SI coverage compared to the others. When using this data set as the boundary condition, the simulation predicts Earth's energy loss, in contrast to the observed energy gain. Our study calls for awareness of the impact of SST/SI data set uncertainty on radiation trends, especially for multimodel comparison projects, including CERESMIP and CMIP7.
Fujiwara, Masatomo, Bomin Sun, Anthony Reale, Domenico Cimini, Salvatore Larosa, Lori Borg, Christoph von Rohden, Michael Sommer, Ruud Dirksen, Marion Maturilli, Holger Vömel, Rigel Kivi, Bruce Ingleby, and Ryan J Kramer, et al., July 2025: Justification for high-ascent attainment for balloon radiosonde soundings at GRUAN and other sites. Atmospheric Measurement Techniques, 18(3), DOI:10.5194/amt-18-2919-20252919-2955. Abstract
We assess and illustrate the benefits of high-altitude attainment of balloon-borne radiosonde soundings, up to and beyond 10 hPa level compared to, for example, 30 hPa, at operational stations and at sites of the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN). We first discuss technical challenges and the possible solutions for balloon soundings at these higher altitudes. Then, we assess the role of high-ascent radiosonde measurements in climate monitoring and various process studies, contributions to satellite calibration and validation, and impacts on numerical weather prediction systems. The analysis herein shows that the extra costs and technical challenges involved in consistent attainment of high ascents are more than outweighed by the benefits for a broad variety of real-time and delayed-mode applications. Consistent attainment of high ascents should therefore be pursued across the GRUAN network and the broader observational network.
We evaluate cloud simulations using satellite simulators against multiple observational data sets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a fully two-moment Morrison-Gettelman cloud microphysical parameterization with prognostic precipitation (MG2) is applied, denoted as AM4-MG2. The modeled cloud spatial distributions, vertical profiles, phase partitioning, cloud-to-precipitation transitions, and radiative effects compare reasonably well with satellite observations. Model biases include the under-prediction of total and low-level clouds, especially optically thin/intermediate clouds with cloud optical depth of less than 23, but the over-prediction of thick clouds, indicating “too few, too bright” biases. These biases counteract each other, and give rise to reasonable estimates of cloud radiative effects. The underestimate of low-level clouds is associated with too early and too frequent drizzle/precipitation formation. The precipitation bias is improved in AM4-MG2, where the autoconversion scheme initiates the precipitation more realistically. There also exist discrepancies between models and observations for midlevel and high-level clouds. Additional biases include the underestimate of liquid cloud fraction and the overestimate of ice cloud fraction.
Janoski, Tyler P., Ivan Mitevski, Ryan J Kramer, Michael Previdi, and Lorenzo M Polvani, May 2025: ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks. Geoscientific Model Development, 18(10), DOI:10.5194/gmd-18-3065-20253065-3079. Abstract
Climate feedbacks are a significant source of uncertainty in future climate projections and need to be quantified accurately and robustly. The radiative kernel method is commonly used to efficiently compute individual climate feedbacks from climate model or reanalysis output. Despite its popularity, it suffers from complications, including difficult-to-locate radiative kernels, inconsistent kernel properties, and a lack of standardized assumptions in radiative feedback calculations, limiting the robustness and reproducibility of climate feedback computations. We designed the ClimKern project to address these issues with a kernel repository and a separate but complementary Python package of the same name. We selected 11 sets of radiative kernels and gave them a common nomenclature and data structure. The ClimKern Python package provides easy access to the kernel repository and functions to compute feedbacks, sometimes with a single line of code. ClimKern functions contain helpful optional parameters while maintaining standard practices between calculations.
After documenting the kernels and ClimKern package, we test it with sample climate model output from an abrupt 2×CO2 experiment to explore the sensitivity of feedback calculations to kernel choice. Interkernel spread exhibits considerable spatial heterogeneity, with the greatest spread in the surface albedo and cloud feedbacks occurring in the Arctic and Southern Ocean. In the global mean, the Planck and surface albedo feedbacks show the greatest interkernel variability. Our results highlight the importance of using multiple radiative kernels and standardizing feedback calculations in climate feedback, sensitivity, and polar amplification studies. As ClimKern continues to evolve, we hope others will contribute to its development to make it an even greater tool for the radiative feedback community.
Mauritsen, Thorsten, Yoko Tsushima, Benoit Meyssignac, Norman G Loeb, Maria Hakuba, Peter Pilewskie, Jason N S Cole, Kentaroh Suzuki, Thomas P Ackerman, Richard P Allan, Timothy Andrews, Frida A M Bender, Jonah Bloch-Johnson, Alejandro Bodas-Salcedo, Anca Brookshaw, Paulo Ceppi, Nicolas Clerbaux, Andrew E Dessler, Aaron Donohoe, Jean-Louis Dufresne, Veronika Eyring, Kirsten L Findell, Andrew Gettelman, Jake J Gristey, Ed Hawkins, Patrick Heimbach, Helene T Hewitt, Nadir Jeevanjee, Colin Jones, Sarah M Kang, Selji Kato, Jennifer E Kay, Stephen A Klein, Reto Knutti, Ryan J Kramer, June-Yi Lee, Daniel T McCoy, Brian Medeiros, Linda Megner, Angshuman Modak, Tomoo Ogura, Matthew D Palmer, and David J Paynter, et al., June 2025: Earth's energy imbalance more than doubled in recent decades. AGU Advances, 6(3), DOI:10.1029/2024AV001636. Abstract
Global warming results from anthropogenic greenhouse gas emissions which upset the delicate balance between the incoming sunlight, and the reflected and emitted radiation from Earth. The imbalance leads to energy accumulation in the atmosphere, oceans and land, and melting of the cryosphere, resulting in increasing temperatures, rising sea levels, and more extreme weather around the globe. Despite the fundamental role of the energy imbalance in regulating the climate system, as known to humanity for more than two centuries, our capacity to observe it is rapidly deteriorating as satellites are being decommissioned.
Park, Chanyoung, Brian J Soden, Ryan J Kramer, Tristan L'Ecuyer, and Haozhe He, July 2025: Observational constraints suggest a smaller effective radiative forcing from aerosol–cloud interactions. Atmospheric Chemistry and Physics, 25(13), DOI:10.5194/acp-25-7299-20257299-7313. Abstract
The effective radiative forcing due to aerosol–cloud interactions (ERFaci) is difficult to quantify, leading to large uncertainties in model projections of historical forcing and climate sensitivity. In this study, satellite observations and reanalysis data are used to examine the low-level cloud radiative responses to aerosols. While some studies assume that the activation rate of cloud droplet number concentration (Nd) in response to variations in sulfate mass concentration (SO4^2-) has a one-to-one relationship, we find this assumption to be incorrect. Our analysis estimates a global mean activation rate of 0.35 ± 0.17 (90 % confidence) and demonstrates that explicitly accounting for the activation rate is crucial for accurate ERFaci estimation. This is corroborated through a “perfect-model” cross-validation using state-of-the-art climate models. Our results suggest a smaller and less uncertain value of the global ERFaci (−0.32 ± 0.21 W m−2 for SO4^2- , 90 % confidence) than recent climate assessments (e.g., −0.93 ± 0.7 W m−2, 90 % confidence), indicating that ERFaci may be less impactful than previously thought. Our results are also consistent with observationally constrained estimates of total cloud feedback and recent estimates that models with weaker ERFaci better match the observed hemispheric warming asymmetry over the historical period.
Allen, Robert J., Xueying Zhao, Cynthia A Randles, Ryan J Kramer, Bjørn H Samset, and Christopher J Smith, October 2024: Present-day methane shortwave absorption mutes surface warming relative to preindustrial conditions. Atmospheric Chemistry and Physics, 24(19), DOI:10.5194/acp-24-11207-2024. Abstract
Recent analyses show the importance of methane shortwave absorption, which many climate models lack. In particular, Allen et al. (2023) used idealized climate model simulations to show that methane shortwave absorption mutes up to 30 % of the surface warming and 60 % of the precipitation increase associated with its longwave radiative effects. Here, we explicitly quantify the radiative and climate impacts due to shortwave absorption of the present-day methane perturbation. Our results corroborate the hypothesis that present-day methane shortwave absorption mutes the warming effects of longwave absorption. For example, the global mean cooling in response to the present-day methane shortwave absorption is K, which offsets 28 % (7 %–55 %) of the surface warming associated with present-day methane longwave radiative effects. The precipitation increase associated with the longwave radiative effects of the present-day methane perturbation (0.012±0.006 mm d−1) is also muted by shortwave absorption but not significantly so ( mm d−1). The unique responses to methane shortwave absorption are related to its negative top-of-the-atmosphere effective radiative forcing but positive atmospheric heating and in part to methane's distinctive vertical atmospheric solar heating profile. We also find that the present-day methane shortwave radiative effects, relative to its longwave radiative effects, are about 5 times larger than those under idealized carbon dioxide perturbations. Additional analyses show consistent but non-significant differences between the longwave versus shortwave radiative effects for both methane and carbon dioxide, including a stronger (negative) climate feedback when shortwave radiative effects are included (particularly for methane). We conclude by reiterating that methane remains a potent greenhouse gas.
Fiedler, Stephanie, Vaishali Naik, Fiona M O'Connor, Christopher J Smith, Paul T Griffiths, and Ryan J Kramer, et al., March 2024: Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP. Geoscientific Model Development, 17(6), DOI:10.5194/gmd-17-2387-20242387–2417. Abstract
The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood, and uncertainty in climate model results persists, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. We synthesize current challenges and emphasize opportunities for advancing our understanding of the interactions between atmospheric composition, air quality, and climate change, as well as for quantifying model diversity. Our perspective is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specializations across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation–response paradigm through multi-model ensembles of Earth system models of varying complexity. We discuss the challenges of gaining insights from Earth system models that face computational and process representation limits and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible and machine learning approaches where they are needed, e.g., for faster and better subgrid-scale parameterizations and pattern recognition in big data. New model constraints can arise from augmented observational products that leverage multiple datasets with machine learning approaches. Future MIPs can develop smart experiment protocols that strive towards an optimal trade-off between the resolution, complexity, and number of simulations and their length and, thereby, help to advance the understanding of climate change and its impacts.
When evaluating the effect of carbon dioxide (CO2) changes on Earth’s climate, it is widely assumed that instantaneous radiative forcing from a doubling of a given CO2 concentration (IRF2×CO2) is constant and that variances in climate sensitivity arise from differences in radiative feedbacks or dependence of these feedbacks on the climatological base state. Here, we show that the IRF2×CO2 is not constant, but rather depends on the climatological base state, increasing by about 25% for every doubling of CO2, and has increased by about 10% since the preindustrial era primarily due to the cooling within the upper stratosphere, implying a proportionate increase in climate sensitivity. This base-state dependence also explains about half of the intermodel spread in IRF2×CO2, a problem that has persisted among climate models for nearly three decades.
Xiang, Baoqiang, Shang-Ping Xie, Sarah M Kang, and Ryan J Kramer, June 2023: An emerging Asian aerosol dipole pattern reshapes the Asian summer monsoon and exacerbates northern hemisphere warming. npj Climate and Atmospheric Science, 6, 77, DOI:10.1038/s41612-023-00400-8. Abstract
Since the early 2010s, anthropogenic aerosols have started decreasing in East Asia (EA) while have continued to increase in South Asia (SA). Yet the climate impacts of this Asian aerosol dipole (AAD) pattern remain largely unknown. Using a state-of-the-art climate model, we demonstrate that the climate response is distinctly different between the SA aerosol increases and EA aerosol decreases. The SA aerosol increases lead to ~2.7 times stronger land summer precipitation change within the forced regions than the EA aerosol decreases. Contrastingly, the SA aerosol increases, within the tropical monsoon regime, produce weak and tropically confined responses, while the EA aerosol decreases yield a pronounced northern hemisphere warming aided by extratropical mean westerly and positive air-sea feedbacks over the western North Pacific. By scaling the observed instantaneous shortwave radiative forcing, we reveal that the recent AAD induces a pronounced northern hemisphere extratropical (beyond 30°N) warming (0.024 ± 0.010 °C decade−1), particularly over Europe (0.049 ± 0.009 °C decade−1). These findings highlight the importance of the pattern effect of forcings in driving global climate and have important implications for decadal prediction.
Thornhill, Gillian D., William J Collins, Ryan J Kramer, Dirk Olivié, Ragnhild B Skeie, Fiona M O'Connor, N Luke Abraham, Ramiro Checa-Garcia, Susanne E Bauer, Makoto Deushi, Louisa K Emmons, Piers M Forster, Larry W Horowitz, Ben Johnson, James Keeble, Jean-Francois Lamarque, Martine Michou, Michael J Mills, Jane P Mulcahy, Gunnar Myhre, Pierre Nabat, and Vaishali Naik, et al., January 2021: Effective radiative forcing from emissions of reactive gases and aerosols – a multi-model comparison. Atmospheric Chemistry and Physics, 21(2), DOI:10.5194/acp-21-853-2021853-874. Abstract
This paper quantifies the pre-industrial (1850) to present-day (2014) effective radiative forcing (ERF) of anthropogenic emissions of NOX, volatile organic compounds (VOCs; including CO), SO2, NH3, black carbon, organic carbon, and concentrations of methane, N2O and ozone-depleting halocarbons, using CMIP6 models. Concentration and emission changes of reactive species can cause multiple changes in the composition of radiatively active species: tropospheric ozone, stratospheric ozone, stratospheric water vapour, secondary inorganic and organic aerosol, and methane. Where possible we break down the ERFs from each emitted species into the contributions from the composition changes. The ERFs are calculated for each of the models that participated in the AerChemMIP experiments as part of the CMIP6 project, where the relevant model output was available.
The 1850 to 2014 multi-model mean ERFs (± standard deviations) are −1.03 ± 0.37 W m−2 for SO2 emissions, −0.25 ± 0.09 W m−2 for organic carbon (OC), 0.15 ± 0.17 W m−2 for black carbon (BC) and −0.07 ± 0.01 W m−2 for NH3. For the combined aerosols (in the piClim-aer experiment) it is −1.01 ± 0.25 W m−2. The multi-model means for the reactive well-mixed greenhouse gases (including any effects on ozone and aerosol chemistry) are 0.67 ± 0.17 W m−2 for methane (CH4), 0.26 ± 0.07 W m−2 for nitrous oxide (N2O) and 0.12 ± 0.2 W m−2 for ozone-depleting halocarbons (HC). Emissions of the ozone precursors nitrogen oxides (NOx), volatile organic compounds and both together (O3) lead to ERFs of 0.14 ± 0.13, 0.09 ± 0.14 and 0.20 ± 0.07 W m−2 respectively. The differences in ERFs calculated for the different models reflect differences in the complexity of their aerosol and chemistry schemes, especially in the case of methane where tropospheric chemistry captures increased forcing from ozone production.
Skeie, Ragnhild B., Gunnar Myhre, Øivind Hodnebrog, Philip Cameron-Smith, Makoto Deushi, Michaela I Hegglin, Larry W Horowitz, Ryan J Kramer, Martine Michou, Michael J Mills, Dirk Olivié, Fiona M O'Connor, and David J Paynter, et al., August 2020: Historical total ozone radiative forcing derived from CMIP6 simulations. npj Climate and Atmospheric Science, 3, 32, DOI:https://doi.org/10.1038/s41612-020-00131-0. Abstract
Radiative forcing (RF) time series for total ozone from 1850 up to the present day are calculated based on historical simulations of ozone from 10 climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). In addition, RF is calculated for ozone fields prepared as an input for CMIP6 models without chemistry schemes and from a chemical transport model simulation. A radiative kernel for ozone is constructed and used to derive the RF. The ozone RF in 2010 (2005–2014) relative to 1850 is 0.35 W m−2 [0.08–0.61] (5–95% uncertainty range) based on models with both tropospheric and stratospheric chemistry. One of these models has a negative present-day total ozone RF. Excluding this model, the present-day ozone RF increases to 0.39 W m−2 [0.27–0.51] (5–95% uncertainty range). The rest of the models have RF close to or stronger than the RF time series assessed by the Intergovernmental Panel on Climate Change in the fifth assessment report with the primary driver likely being the new precursor emissions used in CMIP6. The rapid adjustments beyond stratospheric temperature are estimated to be weak and thus the RF is a good measure of effective radiative forcing.
