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.
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.
