Paulo Ceppi

When anthropogenic CO ₂ emissions cease, global temperature does not stabilize immediately. The resulting global temperature change, the zero emissions commitment, is central to carbon budget assessments and understanding reversibility of global warming, yet its dependence on prior warming levels remains unclear. Here we show, using zero-emission simulations with the UK Earth System Model (UKESM1.2) across global warming levels of 1.5–5 K, that the zero emissions commitment stays near zero up to 2 K but becomes positive at higher warming levels, reaching 0.4–0.6 K after ~ 150 years. This dependence indicates that stabilizing or reversing warming becomes progressively more difficult as peak temperature rises. We identify the mechanism behind this behaviour: stronger warming from higher radiative disequilibrium and weakening carbon sinks, driven by saturated land carbon uptake and the reduced radiative impact of CO ₂ drawdown at higher background CO ₂ concentrations. Higher peak warming therefore commits the planet to additional warming, limiting temperature-overshoot reversibility.

The Quasi-Biennial Oscillation (QBO) is a dominant mode of stratospheric zonal wind variability. Observations indicate that the QBO influences tropical phenomena such as convection, precipitation, and the Madden–Julian Oscillation, yet climate models often fail to capture these relationships. This study examines the QBO's impact on high clouds, in particular tropical tropopause layer clouds, in CMIP6 historical simulations and CALIPSO observations. Building on recent findings that identified relevant cloud-controlling factors (CCFs) for high clouds, we apply CCF analysis to assess QBO-driven changes in high-cloud amount and interpret these changes in terms of contributions from controlling factors. Our results confirm that the QBO westerly (QBOW) phase is associated with reduced tropical mean high-cloud cover. CMIP6 models successfully capture the reduction in tropical high clouds associated with QBOW, but with a weaker magnitude and strong inter-model spread. Among the analysed CCFs, upper-tropospheric temperature, static stability at 150 hPa and relative humidity contribute most to this reduction in observations, and to the model bias. The substantial model bias and spread suggest that better constraints on high-cloud sensitivity to upper-tropospheric thermodynamics are needed to improve simulated QBO-related cloud responses. This framework may also help assess cloud responses to climate change, including the role of greenhouse gas–driven stratospheric cooling.

Diabatic processes are important contributors to cyclone intensification. However, precisely quantifying this contribution, and how it may change in a warming world, has remained a challenge. Previous frameworks use simplifying assumptions that constrain their applicability and limit their use to certain parts of the cyclone lifecycle. In this study, we develop a cyclone-centric Potential Vorticity (PV) framework to quantify the contribution from various processes to cyclonic PV intensification and to the maximum relative vorticity that cyclones attain. Applying this framework to cyclones tracked on model runs, we find that the PV intensification in the low-level cyclone is almost entirely associated with the in-situ PV generation from diabatic sources, both for summer and winter. The diabatic contribution to the maximum relative vorticity increases with cyclone strength, from about 35 % for a median cyclone to 85 % for the strongest cyclones in winter. With warming, low-level winter cyclones show a stronger increase in strength with warming than summer cyclones, which for the strongest cyclones can be attributed to winter cyclones being able to more easily utilise the increase in moisture due to their stronger vertical winds. For the strongest cyclones in both seasons, the vertical wind response with warming decreases the downward penetration of upper-level PV during cyclone intensification, consistent with the low-level cyclones being more diabatically driven. These results point to a "strong gets stronger" response of cyclones with warming, especially in winter, with important implications for extreme weather impacts of global warming.

The change in Earth's energy budget caused by anthropogenic aerosols interacting with clouds is the most uncertain contributor to the historical energy budget trend, with important implications for future climate projections. Recent studies estimating the effective radiative forcing from aerosol-cloud interactions (ERF_aci) using satellite observations and a Cloud-Controlling Factor (CCF) analysis have produced a large spread of results, ranging from approximately -0.3 to -1.5 Wm^-2. This spread is comparable to the full IPCC AR6 uncertainty range, reflecting the use of different datasets and methodological choices across studies, often without a systematic basis for selecting among them. Here we develop a unified framework to rigorously evaluate these methodological choices across multiple reanalysis datasets, using both climate model simulations and observed regional aerosol trends as independent validation tests. Applying model based bias-correction to the best configuration yields a best-estimate global ERF_aci of -0.84 Wm^-2 (66% confidence interval: -1.21 to -0.47 Wm^-2) and an implied Equilibrium Climate Sensitivity of 3.33K (66% confidence interval: 2.65 to 4.22K), both consistent with IPCC AR6 and WCRP 2019 assessments but different from previous CCF-based estimates.

Record-breaking extreme rainfall events, such as those observed in Southeast Africa (SEAF) in February 2023, underscore the urgent need to quantify anthropogenic impacts on extreme weather risks. This study presents an attribution and projection analysis of February extreme rainfall in SEAF by evaluating probabilistic risk changes across multiple Earth System Model (ESM) ensembles and numerical weather prediction (NWP) experiments. While NWP models reliably capture the magnitude and frequency of these extremes, ESMs generally exhibit a wet bias, necessitating the use of relative thresholds. Our attribution analysis reveals that greenhouse gas (GHG) forcing significantly increases the risk of extreme events (e.g. once-in-70-year and once-in-150-year events, comparable to the precipitation intensities of 2000 and 2023). However, this risk increase remains less certain under all-forcing simulations, as ESMs show substantial disagreement regarding aerosol (AER) influences. Future projections under various shared socioeconomic pathways indicate that a higher GHG emission exacerbates these risks, highlighting the critical importance of aggressive mitigation efforts. Furthermore, shifts in generalized extreme value (GEV) parameters demonstrate how forcing-driven climate changes may deform the risk probability distribution, with a higher emission of GHG potentially altering both the climatological mean and interannual variability of SEAF extreme rainfall. Our findings are supported by both ESM and NWP results (particularly short-lead forecasts), with the latter exhibiting lower uncertainty due to constraints on synoptic systems and internal oceanic variability. We conclude that GHG forcing likely elevates the risk of 2023-like extreme events in SEAF and discuss the remaining sources of uncertainty in these projections.

Diabatic processes are important contributors to cyclone intensification. However, precisely quantifying this contribution, and how it may change in a warming world, has remained a challenge. Previous frameworks use simplifying assumptions that constrain their applicability and limit their use to certain parts of the cyclone lifecycle. In this study, we develop a cyclone-centric Potential Vorticity (PV) framework to quantify the contribution from various processes to cyclonic PV intensification and to the maximum relative vorticity that cyclones attain. Applying this framework to cyclones tracked on model runs, we find that the PV intensification in the low-level cyclone is almost entirely associated with the in-situ PV generation from diabatic sources, both for summer and winter. The diabatic contribution to the maximum relative vorticity increases with cyclone strength, from about 35 % for a median cyclone to 85 % for the strongest cyclones in winter. With warming, low-level winter cyclones show a stronger increase in strength with warming than summer cyclones, which for the strongest cyclones can be attributed to winter cyclones being able to more easily utilise the increase in moisture due to their stronger vertical winds. For the strongest cyclones in both seasons, the vertical wind response with warming decreases the downward penetration of upper-level PV during cyclone intensification, consistent with the low-level cyclones being more diabatically driven. These results point to a "strong gets stronger" response of cyclones with warming, especially in winter, with important implications for extreme weather impacts of global warming.

Cloud processes constitute one of the key uncertainties for climate change projections. The fourth iteration of the Cloud Feedback Model Intercomparison Project, CFMIP4, contributes to the Coupled Model Intercomparison Project phase 7 (CMIP7), by providing a set of global climate model experiments aiming to enhance our understanding of clouds, circulation and climate sensitivity, thereby informing improved projections of future climate change. CFMIP4 targets four knowledge gaps: (1) Physical mechanisms of cloud feedback and adjustment; (2) Dependence of cloud feedback and adjustment on climate base state and on the nature of the forcing; (3) Coupled mechanisms of the sea-surface temperature “pattern effect”; and (4) Coupling of clouds with circulation and precipitation. CFMIP4 contributes four CMIP7 Assessment Fast Track experiments that are central to the quantification of climate feedback and sensitivity in past, present and future climates, essential for process understanding and model evaluation. Furthermore, CFMIP4 supports the joint analysis of models and observations through a data request that includes process and satellite simulator output.

The Quasi-Biennial Oscillation (QBO) is a dominant mode of stratospheric zonal wind variability. Observations indicate that the QBO influences tropical phenomena such as convection, precipitation, and the Madden–Julian Oscillation, yet climate models often fail to capture these relationships. This study examines the QBO’s impact on high clouds, in particular tropical tropopause layer clouds, in CMIP6 historical simulations and CALIPSO observations. Building on recent findings that identified relevant cloud-controlling factors (CCFs) for high clouds, we apply CCF analysis to assess QBO-driven changes in high-cloud amount and interpret these changes in terms of contributions from controlling factors. Our results confirm that the QBO westerly (QBOW) phase is associated with reduced tropical mean high-cloud cover. CMIP6 models successfully capture the reduction in tropical high clouds associated with QBOW, but with a weaker magnitude and strong inter-model spread. Among the analysed CCFs, upper-tropospheric temperature, static stability at 150 hPa and relative humidity contribute most to this reduction in observations, and to the model bias. The substantial model bias and spread suggest that better constraints on high-cloud sensitivity to upper-tropospheric thermodynamics are needed to improve simulated QBO-related cloud responses. This framework may also help assess cloud responses to climate change, including the role of greenhouse gas–driven stratospheric cooling.

Diabatic processes are important contributors to cyclone intensification. However, precisely quantifying this contribution, and how it may change in a warming world, has remained a challenge. Previous frameworks use simplifying assumptions that constrain their applicability and limit their use to certain parts of the cyclone lifecycle. In this study, we develop a cyclone-centric Potential Vorticity (PV) framework to quantify the contribution from various processes to cyclonic PV intensification and to the maximum relative vorticity that cyclones attain. Applying this framework to cyclones tracked on model runs, we find that the PV intensification in the low-level cyclone is almost entirely associated with the in-situ PV generation from diabatic sources, both for summer and winter. The diabatic contribution to the maximum relative vorticity increases with cyclone strength, from about 35 % for a median cyclone to 85 % for the strongest cyclones in winter. With warming, low-level winter cyclones show a stronger increase in strength with warming than summer cyclones, which for the strongest cyclones can be attributed to winter cyclones being able to more easily utilise the increase in moisture due to their stronger vertical winds. For the strongest cyclones in both seasons, the vertical wind response with warming decreases the downward penetration of upper-level PV during cyclone intensification, consistent with the low-level cyclones being more diabatically driven. These results point to a "strong gets stronger" response of cyclones with warming, especially in winter, with important implications for extreme weather impacts of global warming.

The change in Earth's energy budget caused by anthropogenic aerosols interacting with clouds is the most uncertain contributor to the historical energy budget trend, with important implications for future climate projections. Recent studies estimating the effective radiative forcing from aerosol-cloud interactions (ERF_aci) using satellite observations and a Cloud-Controlling Factor (CCF) analysis have produced a large spread of results, ranging from approximately -0.3 to -1.5 Wm^-2. This spread is comparable to the full IPCC AR6 uncertainty range, reflecting the use of different datasets and methodological choices across studies, often without a systematic basis for selecting among them. Here we develop a unified framework to rigorously evaluate these methodological choices across multiple reanalysis datasets, using both climate model simulations and observed regional aerosol trends as independent validation tests. Applying model based bias-correction to the best configuration yields a best-estimate global ERF_aci of -0.84 Wm^-2 (66% confidence interval: -1.21 to -0.47 Wm^-2) and an implied Equilibrium Climate Sensitivity of 3.33K (66% confidence interval: 2.65 to 4.22K), both consistent with IPCC AR6 and WCRP 2019 assessments but different from previous CCF-based estimates.