Regional climate imprints of recent historical changes in anthropogenic Near Term Climate Forcers

Earth Syst. Dynam., 16, 2161–2186, 2025 https://doi.org/10.5194/esd-16-2161-2025 © Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License. Research article Regional climate imprints of recent historical changes in anthropogenic Near Term Climate Forcers Alba Santos-Espeso1,2, María Gonçalves Ageitos1,2, Pablo Ortega1, Carlos Pérez García-Pando1,3, Markus G. Donat1,3, Margarida Samso Cabré1, and Saskia Loosveldt Tomas1 1Barcelona Supercomputing Center, Barcelona, Spain 2Universitat Politècnica de Catalunya, Barcelona, Spain 3ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain Correspondence: Alba Santos-Espeso ([email protected]) Received: 19 March 2025 – Discussion started: 17 April 2025 Revised: 17 October 2025 – Accepted: 21 November 2025 – Published: 5 December 2025 Abstract. Near-Term Climate Forcers (NTCFs) play a crucial role in shaping Earth’s climate, yet their effects are often overshadowed by long-lived greenhouse gases (GHGs) when addressing climate variability. This study explores the climatic impact of elevated non-methane NTCF concentrations from 1950 to 2014 using CMIP6-AerChemMIP simulations. We analyse data from four Earth System Models with interactive tropospheric chemistry and aerosol schemes, leveraging a twelve-member ensemble to ensure statistical robustness. Unlike single-species or idealised radiative forcing studies, our approach captures the combined effects of coemitted NTCF species. Our results show that the negative radiative forcing of aerosols dominates the overall NTCF impact, offsetting the warming effects of absorbing aerosols and tropospheric ozone. Multi-model mean analyses reveal three key regional climate responses: (1) a global cooling, amplified in the Arctic, where autumn temperatures decrease by up to 5°C, (2) a 38% increase in Labrador Sea ocean convection, and (3) changes in tropical precipitation, including a 0.6° southward displacement of the Intertropical Convergence Zone (ITCZ). This research addresses the mechanisms driving these climatic changes and underscores the importance of incorporating interactive NTCFs in climate projections. As inferred from their historical impact, future NTCF reductions could amplify regional responses to increasing GHG concentrations, thus requiring more ambitious mitigation strategies. 1 Introduction Understanding the intricate dynamics of Earth’s climate system and the influence of human activities is crucial for devising effective climate policies. A key message in the fight against global warming is the critical need to reduce anthropogenic atmospheric emissions. These emissions contribute to increased concentrations of species that directly or indirectly impact climate, broadly categorised into longlived greenhouse gases (GHGs) and near term climate forcers (NTCFs). While long-lived GHGs, such as carbon dioxide (CO2), are well-known for their persistent warming effects, NTCFs present a more complex and variable influence on the climate system. NTCFs are chemically and physically reactive compounds whose impact on climate occurs primarily within the first decade after their emission (Myhre et al., 2013). They include methane (CH4), tropospheric ozone, black carbon, organic carbon, sulphates, and other aerosols. Because of their short lifetimes, NTCFs, in particular aerosols, are heterogeneously distributed in the atmosphere, having the potential to affect climate variability both globally and regionally. While CH4is both a potent GHG and a NTCF due to its relatively short atmospheric lifetime compared to CO2, it behaves differently from other NTCFs as it is well-mixed throughout the atmosphere. In this study, we focus on non-methane NTCFs. The primary mechanism through which NTCFs influence climate is through modifications of the Earth’s radiative balPublished by Copernicus Publications on behalf of the European Geosciences Union. 2162 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis ance. Tropospheric ozone acts as a GHG, while aerosols impact radiation through both direct and indirect effects. Directly, aerosols alter radiative forcing by either absorbing or scattering sunlight: black carbon absorbs radiation, contributing to warming, whereas sulphates scatter radiation, leading to a cooling effect. Indirectly, aerosols influence cloud properties by acting as cloud condensation nuclei (CCN) and ice-nucleating particles (INP), enhancing cloud formation and altering cloud reflectivity, lifetime, and precipitation patterns. These aerosol-cloud interactions further modify radiative forcing, reinforcing the overall cooling effect of aerosols. (Szopa et al., 2021; Wall et al., 2022). Through their interaction with radiation, changes in the spatio-temporal distribution and composition of NTCFs lead to distinct climate responses. Several studies report NTCFs effects on atmospheric and oceanic circulations. Allen and Sherwood (2011), through sensitivity experiments with an atmospheric general circulation model, found nearly opposite responses in atmospheric circulation to radiation scattering or absorbing aerosols. While scattering aerosols reduce the Hadley cell width and displace the Intertropical Convergence Zone (ITCZ) southward, absorbing aerosols lead to a northward ITCZ shift. These responses are attributed in their study to interhemispheric temperature gradients arising from the spatially uneven distribution of the radiative forcing. Similar ITCZ behaviour has been reported as a response to the asymmetric distribution of dust (Evans et al., 2020) and volcanic aerosols (Pausata et al., 2020) between hemispheres. On the other hand, a multi-model analysis suggests that black carbon and tropospheric ozone, both contributing to tropospheric warming, are the most likely causes of the observed poleward shift of the tropical circulation in the Northern Hemisphere from 1979 to 1999 (Allen et al., 2012). In the ocean, aerosols affect the Labrador Sea convection and the Atlantic Meridional Overturning Circulation (AMOC). Studies based on CMIP5 and CMIP6 multi-model analyses suggest that increasing trends in global aerosol concentrations strengthened the AMOC between 1850 and 1985 (Menary et al., 2020; Robson et al., 2022). And in the same line, future NTCFs reductions may enhance the projected AMOC weakening (Hassan et al., 2022). Recent research by Liu et al. (2024) found that Asian aerosol forcing has opposite effects on AMOC compared to those of emissions from Europe and North America. Their study, examining the AMOC slowdown from the mid-1990s as well as future projections, indicate that Asian aerosols hinder Labrador Sea convection, contributing to an AMOC slowdown. This result is particularly significant as Asia, despite recent declines, has become a primary region of anthropogenic aerosol emission, whereas until the 1990s, emissions were dominated by non-Asian sources. Building on this regional distinction, Cowan and Cai (2013) used a coupled atmosphereocean model and showed that non-Asian aerosols dominated the ocean response to global aerosol forcing during the 20th century, delaying the GHG-induced weakening of the meridional overturning circulation and, consistently, increasing the northward heat transport across the equatorial Atlantic. Another known hotspot for NTCF impacts is the Arctic. Black carbon and tropospheric ozone emissions contribute to Arctic surface warming, opposing the cooling effect of global tropospheric aerosols (Quinn et al., 2008; Sand et al., 2016). Krishnan et al. (2020) examined the mechanisms through which recent European aerosol reductions may have caused Arctic warming, giving great relevance to poleward heat transport changes. Using slab-ocean simulations to isolate atmospheric and ocean contributions, they found that Arctic warming is primarily driven by atmospheric turbulent fluxes and their interaction with sea ice, while ocean heat convergence produces a cooling effect. In contrast, Acosta Navarro et al. (2016) found that enhanced oceanic heat transport played a greater role, increasing Arctic energy intake and triggering sea ice responses. Regardless, of the source of the anomalies, the Arctic magnifies temperature changes through different active positive feedbacks, a phenomenon known as Arctic Amplification (Previdi et al., 2021). In the Arctic, temperature changes are predominantly confined to the lower troposphere due to strong surface-based processes and seasonal stratification, particularly during boreal autumn and winter. The lapse rate feedback in the Arctic is characterised by stronger temperature changes in the lower levels as compared to upper troposphere. The vertical temperature gradient modulates outgoing long-wave radiation, amplifying temperature variations (Boeke et al., 2021). Closely linked to this mechanism is the albedo feedback, where changes in sea ice extent regulate local energy intake during the light seasons, due to its higher albedo compared to the ocean surface. This enhances temperature variations, especially during darker seasons, when the ocean-atmosphere energy transfer occurs (Feldl et al., 2020). To better understand and account for these complex interactions, Earth System Models (ESMs) are essential tools for studying NTCF impacts on climate. By simulating the interplay between atmospheric, oceanic, and terrestrial components, these models provide valuable insights into climate sensitivities and feedback mechanisms. Collaborative initiatives such as the Coupled Model Intercomparison Project Phase 6 (CMIP6; Eyring et al., 2016) play a crucial role in advancing climate research by standardizing experimental frameworks, refining future scenarios, and enabling systematic model intercomparisons. Within CMIP6, historical simulations are a flagship set of experiments designed to evaluate ESM performance against observations and to investigate the role of external forcings in shaping the climate of the industrial era (1850–2014). These simulations incorporate estimates of past changes in relevant forcers, capturing human-induced changes in GHG concentrations and NTCFs. The Aerosols and Chemistry Model Intercomparison Project (AerChemMIP; Collins et al., 2017; Griffiths et al., 2025), endorsed by CMIP6, specifically targets NTCFs to quantify the climate and air quality impacts Earth Syst. Dynam., 16, 2161–2186, 2025 https://doi.org/10.5194/esd-16-2161-2025 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis 2163 of aerosols and chemically reactive gases through a range of dedicated simulations. Through the analysis of CMIP6-AerChemMIP simulation data, this study provides a comprehensive and quantitative assessment of NTCF impacts on the global climate system. Our multi-model analysis focuses on three main climate responses: pronounced Arctic cooling, increased Labrador Sea convection, and a southward displacement of the ITCZ. Section 2 describes our approach, Sect. 3 presents key findings, and Sect. 4 discusses their implications and potential directions for future research. 2 Methodology In the following subsections we describe the selection of model data, the statistical metrics applied, and key diagnostics used to assess NTCF impacts on specific aspects of climate such as ocean density and the ITCZ. All analyses were conducted using the Earth System Model Evaluation Tool (ESMValTool; Righi et al., 2020), an open-source tool that ensures consistent, traceable, and reproducible processing of multi-model climate data. 2.1 Model selection and experimental design For this study, we selected ESMs with interactive tropospheric chemistry and aerosols that contributed to two different CMIP6 experiments: historical and hist-piNTCF (Eyring et al., 2016; Collins et al., 2017). hist-piNTCF uses the same historical forcings as historical except for anthropogenic non-methane NTCFs emissions (aerosols, tropospheric ozone and their precursors), which are instead fixed at 1850 values. Therefore, hist-piNTCF omits the increased NTCF concentrations that are present in the historical experiment while maintaining other radiative forcers such as the well-mixed GHG (Hoesly et al., 2018). By comparing these two types of simulations, we can isolate the effects of NTCFs on historical climate variability. Using multiple ESMs enables us to assess the robustness and inter-model consistency of the NTCF signal while accounting for uncertainties from structural model biases. Although additional AerChemMIP experiments (e.g., histpiAer) could help disentangle the effects of individual species, their limited availability across models would require reducing the ensemble size or breaking the consistency between experiment sets, compromising comparability. Focusing on NTCFs as a whole therefore provides a balanced perspective: it allows us to assess the combined effect of short-lived warming and cooling species–particularly relevant from a policy perspective–while leveraging a reasonably large ensemble of models. This approach complements and builds upon existing attribution studies that isolate the effects of individual forcers (e.g., Allen and Sherwood, 2011; Menary et al., 2020; Szopa et al., 2021; Zhang et al., 2021; Wu et al., 2024). We selected ESMs that provided at least three members for each experiment, i.e. the minimum requested by the AerChemMIP exercise. This requirement allows us to better constrain the forced signals by averaging out some of the internal variability that emerges spontaneously in each member due to the stochastic nature of the climate system (Tebaldi and Knutti, 2007). Considering all the previous points, the models included into the study are BCC-ESM1 (Wu et al., 2020), MRI-ESM2-0 (Yukimoto et al., 2019), UKESM1-0LL (Sellar et al., 2019), and EC-Earth3-AerChem (van Noije et al., 2021). These models represent a diverse set of contributions from different institutions, with varying ocean and atmospheric physical components, as well as atmospheric chemistry schemes (see Table A1). All four models include comprehensive gas-phase chemistry schemes that allow deriving tropospheric ozone concentrations. They also resolve key aerosol species, both anthropogenic and natural (i.e., dust and sea salt) taking into account their interactions with clouds and radiation. By representing these real life processes the models are able to capture possible feedbacks and indirect impacts of the applied forcings, which makes them suitable for the purpose of this study (Huijnen et al., 2010; Yukimoto et al., 2019; Mulcahy et al., 2020; Wu et al., 2020). The analysis focuses on the period from 1950 to 2014, during which the availability of satellite and higher-quality observational data improved confidence in the forcing estimates used in climate models, thereby enhancing the reliability of our results (Yang et al., 2016). Over this timeframe, atmospheric composition varied significantly. Global aerosol concentrations increased in the early decades, followed by a stabilisation from 1980s onward, with regional differences in anthropogenic emissions. While Europe and North America implemented aerosol reduction measures, Asian emissions continued to rise (Tørseth et al., 2012; Klimont et al., 2017; Aas et al., 2019), although, recent studies indicate that CMIP6 forcing datasets underestimate China’s reductions in anthropogenic aerosol emissions during 2006–2014 (Wang et al., 2021). This potential bias should be considered when interpreting our results. In contrast, GHG concentrations, including tropospheric ozone, showed a continuous increase throughout the study period (Bauer et al., 2020; Griffiths et al., 2021). These divergent trends are particularly relevant, as most aerosol species and GHGs exert opposing radiative effects, making their combined influence on climate a key aspect of our analysis. For completeness, we note that the emissions for each experiment are prescribed following the CMIP6 AerChemMIP protocol (Collins et al., 2017), and the time evolution of different NTCF species emissions is documented in Hoesly et al. (2018). Furthermore, a regional decomposition of the emissions can be found in Fig. 6.19 of Szopa et al. (2021). https://doi.org/10.5194/esd-16-2161-2025 Earth Syst. Dynam., 16, 2161–2186, 2025 2164 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis 2.2 Statistical analysis To assess the influence of NTCFs on key climate variables (e.g., temperature, precipitation, sea ice concentration), we analyse three main aspects: climatological mean differences, temporal variance changes, and annual value differences (Table 1). For each analysis, we first compute annual or seasonal means depending on the climate feature of interest. Atmospheric variables are interpolated onto a 2°×2° grid to facilitate inter-model comparison, while oceanic and sea ice variables are retained at their native resolution to preserve the integrity of their spatial discretization. For climatological means, we evaluate the direct difference between the two experiment ensembles (Eq. 1): 1X =Xhistorical −Xhist−piNTCF (1) where Xrepresents any given climate variable. The statistical confidence of the mean signal over the studied period is assessed using a paired-samples ttest at 95% significance level. This test accounts for potential model-dependent differences in mean states by pairing the samples and evaluating changes between experiments model by model. Changes in temporal variance can indicate alterations in physical processes or destabilisation of climate systems. To investigate this aspect, we study the standard deviations in time and compute the ratio between experiments (R). To facilitate interpretation, we express the variability changes due to NTCFs as a percentage (Eq. 2): R=σhistorical σhist−piNTCF Variability change (%) =(R−1)·100 (2) A positive percentage indicates increased variability due to NTCFs, while a negative percentage denotes reduced variability. Ensemble consistency is assessed based on the number of members agreeing on the sign of the response. Since the ensemble consists of twelve members, agreement in 10 out of 12 members indicates a ∼80% confidence, 11 out of 12 members a ∼90% confidence, and full agreement up to ∼100% confidence. For annual differences between experiments, we evaluate the means of 3 members per model and 12 members for the multi-model ensemble, which amount to relatively small sample sizes. To address this limitation, we employ a twosample bootstrap test with 5000 resamples and a 95 % significance level (Efron, 1979; Mudelsee and Alkio, 2007). This method generates new combinations of the historical and hist-piNTCF samples, preserving original sample sizes. For the multi-model mean, while the model contributions may vary across iterations we ensure it remains equal between samples. A difference between experiments is deemed statistically significant if zero falls outside the 95% confidence interval of the compiled 5000 resampled differences. 2.3 Thermal and haline contributions to ocean density To evaluate the impact of NTCFs on ocean stratification, particularly in the Labrador Sea, we compute ocean density from potential temperature (thetao) and salinity (so) data using the polyTEOS10_bsq equation, a 55-term polynomial expression for density (Roquet et al., 2015). Additionally, to determine whether changes in stratification are driven by temperature or salinity variations, we calculate sigmaT and sigmaS, which represent the respective contributions of temperature and salinity changes to density (Bilbao et al., 2021). These values are derived using the thermal expansion (a) and haline contraction (b) coefficients, both computed as polynomial coefficients within the polyTEOS10_bsq framework (Eqs. 3 and 4): a= − ∂r ∂CT [kgm−3K−1](3) b=∂r ∂SA [kgm−3psu−1](4) where ris the density anomaly, CTis the conservative temperature and SAis the absolute salinity. In particular, we compute the potential density anomaly with reference pressure of 0dbar (sigma0). To facilitate interpretation, we normalise the data so that the normalised density anomaly (sigma) is expressed as the direct sum of sigmaT and sigmaS (Eqs. 5, 6 and 7): sigma =sigma0−DenRef (5) sigmaT = −a·(thetao−TempRef) (6) sigmaS =b·(so−SalRef) (7) where DenRef,TempRef and SalRef represent the vertical mean climatological values of ocean density, temperature, and salinity, respectively. 2.4 ITCZ characterisation A key objective of this study is to assess changes in equatorial precipitation resulting from the presence of NTCFs. Following methodologies similar to Frierson and Hwang (2012) and Donohoe et al. (2019), we analyse the full precipitation distribution rather than focusing solely on the latitude of maximum precipitation, as is commonly done. This approach allows us to capture not only latitudinal displacements of the ITCZ but also any potential impacts on the equatorial rainfall amount. To evaluate tropical precipitation changes, we employ two ITCZ-related indices. First, we calculate the zonal mean precipitation from 20°S to 20°N. Then, we determine the coordinates of the precipitation centroid (PCENT), defined as the point that delineates regions of equal weight in the precipitation distribution. By comparing PCENT coordinates between the historical and hist-piNTCF ensembles, we quantify the effects of historical NTCFs on the ITCZ latitude (1lat) and Earth Syst. Dynam., 16, 2161–2186, 2025 https://doi.org/10.5194/esd-16-2161-2025 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis 2165 Table 1. Statistical methods used to quantify and evaluate the confidence of NTCF-induced changes in climate. Signal Confidence evaluation Climatological mean difference two-sample ttest (95% significance) Temporal variance ratio ensemble agreement (80%–100% confidence) Annual difference two-sample bootstrap test (95% significance) equatorial precipitation amount (1pr). The indexes are defined as follows (Eqs. 8 and 9): 1lat =lat(PCENThistorical)−lat(PCENThist−piNTCF) (8) 1pr =pr(PCENThistorical)−pr(PCENThist−piNTCF) (9) This refined approach provides a more comprehensive assessment of ITCZ shifts and their implications for tropical precipitation patterns. 3 Results and discussion 3.1 Global signals To assess climatic responses to NTCFs, we compare the multi-model ensemble means of the historical and histpiNTCF experiments. We analyse the mean state differences and the variance changes in key variables, namely surface air temperature (tas) and precipitation (pr), which together provide an overall view of the main physical responses. Our results reveal three prominent climatic signals (Fig. 1). Firstly, higher concentrations of NTCFs induce a global cooling effect, most pronounced in the Arctic (Fig. 1b). This is consistent with enhanced regional cooling by aerosols (Lewinschal et al., 2019; Westervelt et al., 2020; Szopa et al., 2021), counteracting the warming effects of tropospheric ozone in this region (Sand et al., 2016). The Arctic response is likely magnified by Polar Amplification mechanisms (Previdi et al., 2021), further examined in Sect. 3.2. Secondly, we detect an increase in tas variability over the Labrador and Norwegian Seas, key regions of deep water formation (Fig. 1d). This variance increase concentrates on multidecadal scales (not shown), consistent with the characteristic timescales of North Atlantic ocean circulation and convection. In fact, similar connections between aerosol forcing and enhanced ocean convection have been identified in previous studies (Delworth and Dixon, 2006; Iwi et al., 2012). The detected NTCF signal is explored further in Sect. 3.3. Lastly, in the Tropics, historical NTCFs induce a notable decrease in precipitation north of the equator and an increase to the south, with no clear changes in precipitation variance detected (Fig. 1f, h). This pattern is consistent with a southward displacement of the ITCZ, a phenomenon observed in response to aerosol increases in previous studies (Pausata et al., 2020; Zhao and Suzuki, 2021), despite potential opposing influences from tropospheric ozone (Allen et al., 2012). This response is discussed in detail in Sect. 3.4. 3.2 NTCFs impact on Arctic temperature Delving into the Arctic tas signal (Fig. 1b), we observe that the cooling in the historical ensemble, compared to histpiNTCF, is most pronounced at the lowest levels of the atmosphere between 70 and 90°N (Fig. 2). Regarding the season, the cooling peaks in boreal autumn (up to −5°C difference), while in summer, the strongest anomalies shift towards lower latitudes (Fig. B1). This temperature behaviour aligns with Arctic Amplification (AA), with the strongest temperature changes occurring near the surface, and seasonal feedbacks causing greater amplification in autumn and winter (Previdi et al., 2021). This cooling is consistent with the expected influence of higher aerosols concentrations in the historical ensemble, further amplified through sea ice–associated feedbacks. The temporal evolution of the global and Arctic temperature responses (Fig. 3) reveals two distinct phases: from 1950 to the 1980s, the historical ensemble shows a cooling trend, whereas the hist-piNTCF ensemble experiences a slow temperature increase; after the 1980s, both ensembles show similar warming trends. This trend change is particularly evident in the differences between ensembles (Fig. 3b, d), which closely follow historical aerosol concentration trends (Fig. B2b). In fact, a strong anti-correlation (r= −0.86) highlights the coupling between the od550aer and tas global signals across the ensemble members available for both variables (see Fig. B2 caption). Zhang et al. (2021) explicitly attribute the “pothole-shaped” temperature evolution seen in historical experiments between 1960 and 1990 (Fig. 3a, c) to aerosols, arguing that excessive sulphate loading caused CMIP6 models to overestimate the aerosol-induced cooling anomaly, which is absent in aerosol-free experiments. The continued increase in ozone and other greenhouse gases (GHGs) provides a plausible explanation for the offset of the NTCF-induced cooling trend after 1980. To quantify the AA attributable to NTCFs, we compute the Arctic Amplification Factor (AFF; Wu et al., 2024) (Eq. 10): AAF =m(1Tarctic) m(1Tglobal)(10) where 1Tirepresents the temperature difference between the historical and hist-piNTCF ensembles in the different regions (Fig. 3b, d), and mrepresents the slope of these signals (linear trends). For the period 1950–1980, the AAF of NTCFs is 3.87 for the multi-model mean (see Table A2 for individual model values), indicating that Arctic cooling due to https://doi.org/10.5194/esd-16-2161-2025 Earth Syst. Dynam., 16, 2161–2186, 2025 2166 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis Figure 1. Impact of historical NTCFs on annual surface air temperature (tas;a, b, c, d) and precipitation (pr;e, f, g, h) as derived from the comparison of historical and hist-piNTCF CMIP6 simulations over the period 1950–2014. (a, e) Climatology for the multi-model historical mean and (b, f) difference in climatologies between the multi-model historical and hist-piNTCF ensemble means. (c, g) Standard deviation in time for the multi-model historical ensemble mean and (d, h) temporal variance ratio between the historical ensemble mean and its histpiNTCF counterpart (expressed as percentage change). Global mean values for each magnitude are shown in gray in the title. The historical and hist-piNTCF ensembles analysed are comprised of 4 models (BCC-ESM1, MRI-ESM2-0, UKESM1-0-LL and EC-Earth3-AerChem) with 3 members each. Stippling is applied to significant values according to a paired sample ttest with a 95% confidence (b, f) and different percentages of ensemble members coinciding in the sign of the response (d, h). NTCFs was nearly four times stronger than the global average. This aligns with a previous quantification of 3.87±0.48 for anthropogenic aerosol forcing during a comparable time period (Wu et al., 2024), highlighting the dominant role of aerosol forcing amongst the different NTCFs species. After the 1980s, however, this forcing diminishes in significance, as GHG driven warming becomes the primary driver of both Arctic and global temperature trends. A recent study (Wu et al., 2024) has found that AA due to anthropogenic aerosols exceeds that induced by GHGs because of stronger feedback sensitivity to aerosol cooling. In particular, the study suggests sea ice-related feedbacks to be Earth Syst. Dynam., 16, 2161–2186, 2025 https://doi.org/10.5194/esd-16-2161-2025 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis 2167 Figure 2. Impact of historical NTCFs on annual zonal mean air temperature (ta) over the period 1950–2014. (a) Climatology for the multimodel historical ensemble mean and (b) difference between the historical and hist-piNTCF ensemble means. The ensembles analysed are comprised of 4 models (BCC-ESM1, MRI-ESM2-0, UKESM1-0-LL and EC-Earth3-AerChem) with 3 members each. Stippling is applied to significant values according to a paired sample ttest with a 95 % confidence. Figure 3. Impact of NTCFs on the Arctic (70–90° N; a, b) and global (c, d) surface air temperature (tas), as derived from CMIP6 simulations over the period 1950–2014. (a, c) Annual means from historical (solid) and hist-piNTCF (dashed) CMIP6 experiments (b, d) and their difference (symbols). In panels (b),(d) solid lines represent the linear trends for the period 1950–1980 and filled symbols indicate significant values based on a bootstrapping significance test with 95 % confidence (see Sect. 2.2). Colours and shapes represent data from individual models (BCC-ESM1: blue triangles, EC-Earth3-AerChem: green squares, MRI-ESM2-0: yellow diamonds, UKESM1-0-LL: pink crosses) and black circles show the multi-model mean. For each experiment and model we consider the mean of 3 members. https://doi.org/10.5194/esd-16-2161-2025 Earth Syst. Dynam., 16, 2161–2186, 2025 2168 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis more effective in response to aerosols. Indeed, the NTCFinduced cooling in Fig. 1b aligns with an increase in sea ice concentration (siconc). Examining boreal autumn data – when sea ice retreat peaks (Deser et al., 2010) – reveals a consistent increase in sea ice extent in the historical ensemble relative to hist-piNTCF across multiple models (Fig. B3). The strongest increase occurs in the Barents Sea, a region known for its high sensitivity to external forcing and large sea ice internal variability (Rieke et al., 2023; Siew et al., 2024), and spatially aligns with the most pronounced temperature decreases (Fig. 1b). While this co-variability is consistent with the operation of local positive sea ice-related feedback mechanisms, the observed changes likely reflect the combined influence of several processes governing the cooling and its amplification (Previdi et al., 2021). Additionally, Arctic sea ice expansion may contribute to the tropical cooling signal observed in Fig. 2. This vertical structure, showing amplified temperature anomalies in the tropical upper troposphere, is consistent with the operation of moist-adiabatic lapse rate adjustments (Colman and Soden, 2021). The pattern also aligns with the response to Arctic sea ice loss reported by England et al. (2020). They link Arctic sea ice loss to a slowdown in subtropical meridional ocean circulation, reducing equatorial upwelling and warming the tropical atmosphere, suggesting that the enhanced Arctic sea ice extent in the historical ensemble (Fig. B3) may further contribute to the observed cooling. Overall, our results show that NTCFs produced Arctic cooling with strong amplification between 1950 and 1980. The observed increase in sea ice extent spatially aligns with the temperature response, which could suggest the operation of sea ice-related feedback mechanisms. Other processes that have been previously invoked to explain Arctic Amplification, such as changes in atmospheric and oceanic poleward energy transports (Iwi et al., 2012; Robson et al., 2022; Needham and Randall, 2023), cloud and water vapour feedback (Goosse et al., 2018), and lapse-rate feedback (Pithan and Mauritsen, 2014) may have contributed to the pronounced regional temperature changes. Quantifying their relative influence, however, lies beyond the scope of this analysis. 3.3 NTCFs impact on Labrador Sea convection Our results suggest that historical NTCFs enhanced surface temperature variability in key regions of deep water formation of the subpolar North Atlantic (SPNA; Fig. 1d). To better understand this surface signal, we assess changes in the mixed layer depth (mlotst), a widely used proxy for oceanic convection. The analysis shows that higher historical NTCF concentrations led to increased convection in the Labrador Sea across all models considered (Fig. 4). Additionally, a pronounced deepening of convection is observed in the Greenland Sea in all models except BCC-ESM1. The months of February, March and April are the focus of this analysis, as they correspond to the peak convection season in the Labrador Sea (Fig. B4). Notably, EC-Earth3-AerChem displays a unique behaviour, with two out of three historical ensemble members showing episodes of collapsed convection in the Labrador Sea (Fig. B5), a phenomenon absent in the hist-piNTCF members. This behaviour is consistent with known Labrador Sea convection shutdowns in EC-Earth3-models that can persist for extended periods (Bilbao et al., 2021; Döscher et al., 2022). Meccia et al. (2023) attributes these episodes to a multi-centennial oscillation triggered by the accumulation of salinity anomalies in the Arctic that, when released into the North Atlantic, affect water column stability and therefore convection. Importantly, they find that future scenarios with warmer climates lack sufficient sea ice to trigger the collapsing mechanism, potentially explaining its absence on histpiNTCF members. Due to the strong dependency of the collapse episodes on internal variability, the specific response of convection to anthropogenic NTCFs is not correctly reflected in Fig. 4d and is likely underestimated. Consequently, the following analyses consider separately the EC-Earth3AerChem member that maintains active convection (denoted by thin lines). The temporal evolution of mlotst in the Labrador Sea (Fig. 5) provides further insights. Despite differences in their mean states, all models show comparable and significant responses to NTCFs. The hist-piNTCF experiments show a decrease in convection, in line with the expected response to rising GHG concentrations. In contrast, all historical experiments show stable or increasing mlotst values except for MRI-ESM2-0 (Fig. 5a). This model reports increasing convection until the 1980s after which convection declines, aligning with a first period of increasing global aerosol concentrations followed by a second period with stabilised aerosol concentrations and stronger GHG forcing (Fig. B2). This suggests NTCFs counteracted, or at least mitigated, the GHG-driven decline in convection. The difference signal (Fig. 5b) shows a persistent enhancement of convection with decadal oscillations that are not in phase across models. To quantify the convection increase in response to NTCFs, we define the Labrador Sea Convection Response (LSCR) using a linear approximation (Eq. 11): LSCR(%) =mN LSCclim ×100 (11) where LSCclim represents the mean Labrador Sea mlotst during the first decade in hist-piNTCF,mdenotes the slope of the historical minus hist-piNTCF difference (Fig. 5b), and N equals 65 years. The multi-model mean suggests a 38% increase in Labrador Sea convection due to NTCFs from 1950 to 2014 (individual model LSCR values are provided in Table A3). To better understand the reasons for the consistent model response in mixed layer depth, we study the vertical profiles of potential temperature (thetao), salinity (so), and potential Earth Syst. Dynam., 16, 2161–2186, 2025 https://doi.org/10.5194/esd-16-2161-2025 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis 2169 Figure 4. Impact of historical NTCFs on ocean mixed layer thickness defined by sigma T (mlotst) in the months of February, March and April (FMA), as derived from the comparison of historical and hist-piNTCF CMIP6 simulations over the period 1950–2014. (a, c, e, g) FMA climatology for the historical experiment and (b, d, f, h) difference in climatologies between the historical and hist-piNTCF ensembles. For each experiment and model we consider the mean of 3 members. Stippling is applied to significant values according to a two independent samples ttest with a 95% confidence (b, d, f, h). The black box limits the Labrador Sea area (60, 45°W; 50, 65° N). density (sigma0; see Sect. 2.3) in the Labrador Sea (Fig. 6). Compared to hist-piNTCF, the historical ensemble exhibits colder and saltier near-surface conditions. Both contributing to higher surface density, these factors are linked to weaker local stratification and therefore intensified convection (as observed in Fig. 4). The saltier surface conditions may result from a positive feedback: stronger convection, initially driven by surface cooling, brings saltier subsurface waters to the surface, further increasing surface density and reinforcing deep convection. Although our analysis based on monthly model outputs does not allow us to clearly separate the driving signals of deep convection from the resulting response, a similar feedback mechanism has been identified in idealised frameworks (Lenderink and Haarsma, 1994), suggesting that this process is plausible in regions such as the Labrador Sea where subsurface waters are climatologically saltier (Fig. 6b). This https://doi.org/10.5194/esd-16-2161-2025 Earth Syst. Dynam., 16, 2161–2186, 2025 2176 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis delve into the mechanisms driving climatic changes and emphasise the importance of incorporating interactive NTCFs in climate projections. While anthropogenic NTCFs will likely decline in the future, natural aerosols’ evolution remains uncertain. Hence it is essential to understand the feedbacks between natural and anthropogenic species, as well as their evolving contributions under changing climatic conditions, and to accurately quantify their effects to shape effective GHG mitigation strategies. Appendix A: Supplementary tables Table A1. Ensemble members and components of the models used in the study. model BCC-ESM1 EC-Earth3-AerChem MRI-ESM2-0 UKESM1-0-LL members r(1:3)i1p1f1 r(1,3,4)i1p1f1 r(1,3,5)i1p1f1 r(1:3)i1p1f1 atmosphere BCC-AGCM3-Chem IFS MRI-AGCM3.5 MetUM-HadGEM3GA7.1 resolution (lat×lon ×lev) 2.8° ×2.8°×26L 0.7° ×0.7°×91L 1.125° ×1.125°×80L 1.25° ×1.875°×85L top level 2.914hPa 0.01hPa 0.01hPa 85km aerosols & reactive gases Same as atmosphere TM5-mp 3.0 aerosols: MASINGAR mk-2r4c 0.938°×1.875° ×80L UKCA-StratTrop resolution (lat×lon ×lev) 2° ×3°×34L gases: MRI-CCM2.1 1.4°×2.8° ×80L 1.25°×1.875° ×85L ocean MOM4-L40 NEMO 3.6 MRI.COM4.4 (+bgchem) NEMO-HadGEM3GO6.0 + MEDUSA2(bgchem) resolution 1/3°×1° ×40L (30°S–30° N) 1/3°×1° ×75L (30°S–30° N) 0.3°×1° ×61L (10°S–10° N) 1/3°×1° ×75L (30°S–30° N) (lat×lon ×lev) 1° ×1°×40L (rest) 1°×1° ×75L (rest) 0.5°×1°×61L (rest) 1°×1° ×75L (rest) sea ice SIS2 LIM3 Same as ocean CICE-HadGEM3-GSI8 land BCC-AVIM2.0 HTESSEL HAL 1.0 JULES-ES-1.0 Table A2. Arctic Amplification Factor (AAF; Wu et al., 2024) attributed to Near-Term Climate Forcers (NTCFs) as derived from the comparison of historical and hist-piNTCF CMIP6 simulations over 1950–1980. Values shown for individual models and their ensemble mean. Model AAF pre80s BCC-ESM1 3.85 EC-Earth3-AerChem 4.48 MRI-ESM2-0 3.12 UKESM1-0-LL 4.10 Ensemble Mean 3.87 Table A3. Labrador Sea Convection Response (LSCR; percentage change in mixed layer depth) attributed NTCFs as derived from the comparison of historical and hist-piNTCF CMIP6 simulations over 1950–2014. Values shown for individual models and their ensemble mean. Model LSCR (%) BCC-ESM1 24.01 EC-Earth3-AerChem 59.66 MRI-ESM2-0 25.98 UKESM1-0-LL 194.23 Ensemble Mean 37.82 Earth Syst. Dynam., 16, 2161–2186, 2025 https://doi.org/10.5194/esd-16-2161-2025 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis 2177 Table A4. ITCZ response to NTCFs quantified by latitude displacement (1lat) and precipitation change (1pr) as derived from the comparison of historical and hist-piNTCF CMIP6 simulations over 1950–2014. Values shown for individual models and their ensemble mean. Model 1lat(°) 1pr(%) BCC-ESM1 −0.4−2.5 EC-Earth3-AerChem −1.1−1.8 MRI-ESM2-0 −0.4−1.8 UKESM1-0-LL −0.6−1.9 Ensemble Mean −0.6−2.0 Appendix B: Supplementary figures B1 NTCFs impact on Arctic temperature Figure B1. Impact of historical NTCF forcings on seasonal zonal surface air temperature (tas) during the period 1951–2014. (a) Seasonal climatology (DJF: blue, MAM: green, JJA: yellow, SON: pink) for the multi-model historical mean and (b) difference in climatologies between the multi-model historical and hist-piNTCF ensemble means. The ensembles analysed are comprised of 4 models (BCC-ESM1, MRI-ESM2-0, UKESM1-0-LL and EC-Earth3-AerChem) with 3 members each. https://doi.org/10.5194/esd-16-2161-2025 Earth Syst. Dynam., 16, 2161–2186, 2025 2178 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis Figure B2. Annual global mean evolution of the (a) ozone concentration (o3) at 500 hPa and (b) aerosol optical depth at 550nm (od550aer) for the historical (solid lines) and hist-piNTCF (dashed lines) CMIP6 simulations over the period 1950–2014. Colours represent data from individual models (BCC-ESM1: blue, EC-Earth3-AerChem: green, MRI-ESM2-0: yellow, UKESM1-0-LL: pink). Each model mean is obtained from 3 different members but for UKESM1-0-LL od550aer data, which only has 2 members available, and BCC-ESM1 with no data available for this variable. Note that MRI-ESM2-0 resolves stratospheric chemistry and its effect is included in the od550aer. As a result, peaks following major volcanic eruptions are present. Regardless, all models in this study account for the radiative effects of volcanic aerosols either explicitly or through prescribed datasets or parametrisations. Figure B3. Impact of historical NTCFs on mean sea ice concentration (siconc) in boreal autumn (September, October and November) during the period 1951–2014 for different CMIP6 models: (a) BCC-ESM1, (b) MRI-ESM2-0, (c) UKESM1-0-LL). The colours represent the siconc climatology for the historical experiment and the contours the sea ice edge (siconc =15%) for the experiments historical (solid red) and hist-piNTCF (dashed black). Values of sea ice extent change (1SIE, shown in gray above each panel) denote the percentage difference in total area with siconc ≥15% (sea ice extent) between historical and hist-piNTCF experiments, relative to the hist-piNTCF extent. For each experiment and model we consider the mean of 3 members but for BCC-ESM1 hist-piNTCF, with data available only for 1 member. Note that EC-Earth3-AerChem is not shown as siconc data were not available for the hist-piNTCF experiment. Earth Syst. Dynam., 16, 2161–2186, 2025 https://doi.org/10.5194/esd-16-2161-2025 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis 2179 B2 NTCFs impact on Labrador Sea convection Figure B4. Seasonal cycle of the Labrador Sea ocean mixed layer thickness defined by sigma T (mlotst), as derived from the historical (solid lines) and hist-piNTCF (dashed lines) CMIP6 simulations over the period 1950–2014. Colours represent data from individual models (BCC-ESM1: blue, EC-Earth3-AerChem: green, MRI-ESM2-0: yellow, UKESM1-0-LL: pink). Each model mean (thick lines) is obtained from 3 different members. The EC-Earth3-Aerchem model member r1i1pif1 is represented as well (thin lines). The Labrador Sea area is defined as: (60, 45°W; 50, 65°N). Figure B5. Comparison of the Labrador Sea ocean mixed layer thickness defined by sigma T (mlotst) for the different members of the model EC-Earth3-Aerchem. Data is obtained from the CMIP6 simulations: (a) historical and (b) hist-piNTCF. The period of study considers the months of February, March and April (FMA) between 1950–2014. The Labrador Sea area is defined as: (60, 45°W; 50, 65° N). https://doi.org/10.5194/esd-16-2161-2025 Earth Syst. Dynam., 16, 2161–2186, 2025 2180 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis Figure B6. Impact of NTCFs on the contributions of temperature and salinity to density changes in the Labrador Sea (60, 45°W; 50, 65° N), based on the comparison of historical and hist-piNTCF CMIP6 simulations over the period 1950–2014. (a, b, c) Monthly climatology for the historical experiment, and (d, e, f) mean difference between historical and hist-piNTCF. The variables analysed are: (a, d) potential density anomalies (sigma)(b, e) temperature’s contribution to density (sigmaT) and (c, f) salinity’s contribution to density (sigmaS). Details on the computation of these magnitudes are provided in Sect. 2.3. To enhance clarity only data from every second month is shown. Colours represent the state of climatological convection according to Fig. B4: active (Oct-May; blue) and nonactive (June–September; red). The ensembles analysed are comprised of 4 models (BCC-ESM1, MRI-ESM2-0, UKESM1-0-LL and EC-Earth3-AerChem), with 3 members each but for EC-Earth3-AerChem, which only has one member without suppressed convection. Earth Syst. Dynam., 16, 2161–2186, 2025 https://doi.org/10.5194/esd-16-2161-2025 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis 2181 B3 NTCFs impact on tropical precipitation Figure B7. Annual mean evolution of the (a) netR_HD and (b) od550aer_HD indexes for the historical (solid lines) and hist-piNTCF (dashed lines) CMIP6 simulations over the period 1950–2014. Colours represent data from individual models (BCC-ESM1: blue, EC-Earth3AerChem: green, MRI-ESM2-0: yellow, UKESM1-0-LL: pink). Each model mean is obtained from 3 different members but for UKESM10-LL od550aer data, which only has 2 members available, and BCC-ESM1 with no data available for this variable. Note that MRI-ESM2-0 resolves stratospheric chemistry and its effect is included in the od550aer. As a result, peaks following major volcanic eruptions are present. Regardless, all models in this study account for the radiative effects of volcanic aerosols either explicitly or through prescribed datasets or parametrisations. Figure B8. Impact of historical NTCF forcings on (a) net radiation at the top of the atmosphere (netR), (b) cloud forcing (all-sky minus clear-sky netR) and (c) cloud cover (clt), as derived from the comparison of historical and hist-piNTCF CMIP6 simulations over the period 1950–2014. The panels show the difference in climatologies between the multi-model historical and hist-piNTCF ensemble means. Global mean values for each magnitude are shown in gray in the titles. Both ensembles are comprised of four models (BCC-ESM1, MRI-ESM20, UKESM1-0-LL, and EC-Earth3-AerChem), with three members each. Stippling colours indicate whether the signal of the represented variable is significant (grey), both variables are significant and have the same sign (black) or both variables are significant and have opposing sign (white). Significance is determined through a paired sample ttest with 95% confidence. https://doi.org/10.5194/esd-16-2161-2025 Earth Syst. Dynam., 16, 2161–2186, 2025 2182 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis Figure B9. Relationship between the net radiation hemispheric difference index (netR_HD) and the ITCZ precipitation amount index in two multi-model ensembles of historical and hist-piNTCF CMIP6 simulations. Decadal means of the indexes over the period 1950–2009 are represented by red dots (historical) and blue squares (hist-piNTCF), with lighter shades indicating more recent decades. Data from 3 members is plotted for each experiment and model: (a) BCC-ESM1, (b) EC-Earth3-AerChem (c) MRI-ESM2-0, (d) UKESM1-0-LL). The orange line denotes the linear fit across both ensembles. Code and data availability. The analyses developed in this study use CMIP6 data, publicly available on the Earth System Grid Federation (ESGF) portal. The code used to preprocess this data and produce the plots within the article is available at https://doi.org/10.5281/zenodo.17698021 (Santos-Espeso, 2025). Author contributions. ASE formal analysis, visualisation and writing. MGA and PO conceptualisation, supervision and writing review. MSC data curation. SLT software. CPG and MGD writing review. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher. Acknowledgements. We extend our gratitude to Eneko MartinMartinez, Aude Carréric and Roberto Bilbao, for their constructive feedback on the analyses performed. We thank the in-house technical support group easing our everyday work. We also appreciate the ESMValTool development team for their work and tool support. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providEarth Syst. Dynam., 16, 2161–2186, 2025 https://doi.org/10.5194/esd-16-2161-2025 A. Santos-Espeso et al.: NTCFs impacts on climate CMIP6 analysis 2183 ing access, and the multiple funding agencies who support CMIP6 and ESGF. Financial support. This research has been supported by the HORIZON EUROPE Climate, Energy and Mobility (grant nos. 101056783 and 101137680), the AXA Research Fund (grant no. AX000200), and the Agència de Gestió d’Ajuts Universitaris i de Recerca (grant nos. 2021 SGR 00786 and 2021 SGR 01550). Review statement. This paper was edited by Gabriele Messori and reviewed by two anonymous referees. References Aas, W., Mortier, A., Bowersox, V., Cherian, R., Faluvegi, G., Fagerli, H., Hand, J., Klimont, Z., Galy-Lacaux, C., Lehmann, C. M. B., Myhre, C. L., Myhre, G., Olivié, D., Sato, K., Quaas, J., Rao, P. S. 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