The Double‐ITCZ Bias in CMIP3, CMIP5, and CMIP6 Models Based on Annual Mean Precipitation

<p>The double-Intertropical Convergence Zone (ITCZ) bias is one of the most outstanding problems in climate models. This study seeks to examine the double-ITCZ bias in the latest state-of-the-art fully coupled global climate models that participated in Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) in comparison to their previous generations (CMIP3 and CMIP5 models). To that end, we have analyzed the long-term annual mean tropical precipitation distributions and several precipitation bias indices that quantify the double-ITCZ biases in 75 climate models including 24 CMIP3 models, 25 CMIP3 models, and 26 CMIP6 models. We find that the double-ITCZ bias and its big inter-model spread persist in CMIP6 models but the double-ITCZ bias is slightly reduced from CMIP3 or CMIP5 models to CMIP6 models.</p>

The latest Coupled Model Intercomparison Project phase 6 (CMIP6) dataset was analyzed to examine the projected changes in temperature and precipitation over six South Asian countries during the twenty-first century. The CMIP6 model simulations reveal biases in annual mean temperature and precipitation over South Asia in the present climate. In the historical period, the median of the CMIP6 model ensemble systematically underestimates the annual mean temperature for all the South Asian countries, while a mixed behavior is shown in the case of precipitation. In the future climate, the CMIP6 models display higher sensitivity to greenhouse gas emissions over South Asia compared with the CMIP5 models. The multimodel ensemble from 27 CMIP6 models projects a continuous increase in the annual mean temperature over South Asia during the twenty-first century under three future scenarios. The projected temperature shows a large increase (over 6 °C under SSP5-8.5 scenario) over the northwestern parts of South Asia, comprising the complex Karakorum and Himalayan mountain ranges. Any large increase in the mean temperature over this region will most likely result in a faster rate of glacier melting. By the end of the twenty-first century, the annual mean temperature (uncertainty range) over South Asia is projected to increase by 1.2 (0.7–2.1) °C, 2.1 (1.5–3.3) °C, and 4.3 (3.2–6.6) °C under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively, relative to the present (1995–2014) climate. The warming over South Asia is also continuous on the seasonal time scale. The CMIP6 models projected higher warming in the winter season than in the summer over South Asia, which if verified will have repercussions for snow/ice accumulations as well as winter cropping patterns. The annual mean precipitation is also projected to increase over South Asia during the twenty-first century under all scenarios. The rate of change in the projected annual mean precipitation varies considerably between the South Asian countries. By the end of the twenty-first century, the country-averaged annual mean precipitation (uncertainty range) is projected to increase by 17.1 (2.2–49.1)% in Bangladesh, 18.9 (−4.9 to 72)% in Bhutan, 27.3 (5.3–160.5)% in India, 19.5 (−5.9 to 95.6)% in Nepal, 26.4 (6.4–159.7)% in Pakistan, and 25.1 (−8.5 to 61.0)% in Sri Lanka under the SSP5-8.5 scenario. The seasonal precipitation projections also shows large variability. The projected winter precipitation reveals a robust increase over the western Himalayas, with a corresponding decrease over the eastern Himalayas. On the other hand, the summer precipitation shows a robust increase over most of the South Asia region, with the largest increase over the arid region of southern Pakistan and adjacent areas of India, under the high-emission scenario. The results presented in this study give detailed insights into CMIP6 model performance over the South Asia region, which could be extended further to develop adaptation strategies, and may act as a guideline document for climate change related policymaking in the region.

The double Intertropical Convergence Zone (ITCZ) bias is an outstanding bias in many climate models. This work assesses the annual-mean double-ITCZ problem in the models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on several quantitative indices. Within the forty-six CMIP6 models, nine models from mainland China are evaluated as a group to verify the effort of model development from one perspective. The double-ITCZ bias and its large inter-model spread still exist in CMIP6 models. The overall performance of the models from Chinese mainland is similar with all CMIP6 models. It is found that the top-five models with relatively low double-ITCZ biases can effectively restrain the frequency of deep convection and related sea surface temperature (SST) bias in the southeastern Pacific dry subsidence region, which highlights the necessity of improving convective physics in climate models. Impacts of model resolution on the double-ITCZ problem are examined by comparing the high- and low-resolution groups in CMIP6 and High Resolution Model Intercomparison Project (HighResMIP) historical experiments, respectively. Increased resolution in atmospheric models is found to be able to reduce the positive precipitation bias over the tropical southern Atlantic, and improve the simulation of deep convection frequency and convective precipitation ratio there. However, the double-ITCZ bias over the Pacific is not improved significantly by increased resolution.

The double Intertropical Convergence Zone (ITCZ) bias is an outstanding bias in many climate models. This work assesses the annual‐mean double‐ITCZ problem in the models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on several quantitative indices. Within the 46 CMIP6 models, 9 models from mainland China are evaluated as a group to verify the effort of model development from one perspective. The double‐ITCZ bias and its large intermodel spread still exist in CMIP6 models. The overall performance of the models from Chinese mainland is similar with all CMIP6 models. It is found that the top‐five models with relatively low double‐ITCZ biases can effectively restrain the frequency of deep convection and related sea surface temperature (SST) bias in the southeastern Pacific dry subsidence region, which highlights the necessity of improving convective physics in climate models. Impacts of model resolution on the double‐ITCZ problem are examined by comparing the high‐ and low‐resolution groups in CMIP6 and High Resolution Model Intercomparison Project (HighResMIP) historical experiments, respectively. Increased resolution in atmospheric models is found to be able to reduce the positive precipitation bias over the tropical southern Atlantic and improve the simulation of deep convection frequency and convective precipitation ratio there. However, the double‐ITCZ bias over the Pacific is not improved significantly by increased resolution.

An assessment is made of the modes of interannual variability in the seasonal mean summer and winter Southern Hemisphere (SH) 500-hPa geopotential height in the twentieth century in models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) dataset. Modes of variability of both the slow (signal) and intraseasonal (noise) components in the CMIP5 models are evaluated against those estimated from reanalysis data. There is general improvement in the leading modes of the slow (signal) component in CMIP5 models compared with the CMIP phase 3 (CMIP3) dataset. The largest improvement is in the spatial structures of the modes related to El Niño–Southern Oscillation variability in SH summer. An overall score metric is significantly higher for CMIP5 over CMIP3 in both seasons. The leading modes in the intraseasonal noise component are generally well reproduced in CMIP5 models, and there are few differences from CMIP3. A new total overall score metric is used to rank the CMIP5 models over both seasons. Weighting the seasons by the relative spread of overall scores is shown to be suitable for generating multimodel ensembles for further analysis of interannual variability. In multimodel ensembles, it is found that an ensemble of size 5 or 6 is sufficient in SH summer to reproduce well the dominant modes. In contrast, about 13 models are typically are required in SH winter. It is shown that it is necessary that the selected models individually reproduce well the leading modes of the slow component.

ABSTRACTA set of 27 global climate models from the Coupled Model Inter‐comparison Project Phase 5 (CMIP5) ensemble are assessed for their performance for the purpose of making future climate projection studies in the western tropical Pacific and differences to Coupled Model Inter‐comparison Project Phase 3 (CMIP3) are assessed. The CMIP5 models show some improvements upon CMIP3 in the simulation of the climate in the western tropical Pacific in the late 20th century. There are fewer CMIP5 models with very poor skill scores than in CMIP3 for some measures and a small group of the well‐performing models in CMIP5 have lower biases than in an equivalent group from CMIP3. These best‐performing models could be particularly informative for studying certain climate sensitivities and feedbacks in the region. There is evidence to reject one model as unsuitable for making regional climate projections in the region, and another two models unsuitable for analysis of the South Pacific Convergence Zone (SPCZ). However, while there have been improvements, many of the systematic model biases in the mean climate in CMIP3 are also present in the CMIP5 models. They are primarily related to the shape of the transition between the Indo‐Pacific warm pool and equatorial cold tongue, and the associated biases in the position and orientation of the SPCZ and Inter‐Tropical Convergence Zone, as well as in the spatial pattern, variability and teleconnections of the West Pacific monsoon, and the simulation of El Niño Southern Oscillation. Overall, the results show that careful interpretation and consideration of biases is required when using CMIP5 outputs for generating regional climate projections for the western tropical Pacific, particularly at the country scale, just as there was with CMIP3.

Knowledge about future global and regional warming is essential for effective adaptation planning and our current temperature projections are based on the output of global climate models (GCMs). Although GCMs agree on the direction of change, there are still significant discrepancies in the magnitude of the projected response1.  Here we develop a novel method2,3 for constraining uncertainty in future regional temperature projections based on the predictions of an observationally trained machine learning algorithm, Ridge-ERA5. Ridge-ERA5 - a Ridge regression model4- learns coefficients to represent observed relationships between daily temperature anomalies and a selection of thermodynamic and dynamical variables in the ECMWF Re-Analysis (ERA) 5 dataset5. Climate-invariance of the Ridge relationships is demonstrated in a perfect model framework: we train a set of 23 Ridge-CMIP models on historical data of the Coupled Model Intercomparison Project (CMIP) phase 66 and evaluate their predictions using future scenario data from the most extreme future emissions pathway, SSP 5-8.5.   Combining the historically constrained Ridge-ERA5 coefficients with normalised inputs from CMIP6 future climate change simulations forms the basis of a new methodology to derive observational constraints on regional climate change. For daily, regional (2°x2°), summer temperatures across the Northern Hemisphere, the Ridge-ERA5 observations-based constraint implies, for example, that a group of higher sensitivity CMIP6 models is inconsistent with observational evidence (including in Eastern, West & Central, and Northern Europe) potentially suggesting that the sensitivity of these models is indeed too high7,8. A key advantage of our new method is the ability to constrain regional projections at very high – daily – temporal resolution which includes extreme events such as heatwaves.    1) Brient, F. (2019) Reducing Uncertainties in Climate Projections with Emergent Constraints: Concepts, Examples and Prospects. Advances in Atmospheric Sciences 2020 37:1, 37(1), pp. 1–15.  2) Ceppi, P. and Nowack, P. (2021) Observational evidence that cloud feedback amplifies global warming. PNAS, 118(30).  3) Nowack, P. et al. An observational constraint on the uncertainty in stratospheric water vapour projections. (in review)  4) Hoerl, A. E. and Kennard, R. W. (1970) Ridge Regression: Applications to Nonorthogonal Problems. Technometrics, 12(1), pp. 69–82.   5) Hersbach, H. et al. (2020) The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), pp. 1999–2049.   6) Eyring, V. et al. (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), pp. 1937–1958.   7) Zelinka, M. D. et al. (2020) Causes of Higher Climate Sensitivity in CMIP6 Models. Geophysical Research Letters, 47(1).  8) Zhu, J., Poulsen, C. J. and Otto-Bliesner, B. L. (2020) High climate sensitivity in CMIP6 model not supported by paleoclimate. Nature Climate Change 2020 10:5, 10(5), pp. 378–379. 

Ensembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.

Abstract. Simulation of the carbon cycle in climate models is important due to its impact on climate change, but many weaknesses in its reproduction were found in previous models. Improvements in the representation of the land carbon cycle in Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) include the interactive treatment of both the carbon and nitrogen cycles, improved photosynthesis, and soil hydrology. To assess the impact of these model developments on aspects of the global carbon cycle, the Earth System Model Evaluation Tool (ESMValTool) is expanded to compare CO2-concentration- and CO2-emission-driven historical simulations from CMIP5 and CMIP6 to observational data sets. A particular focus is on the differences in models with and without an interactive terrestrial nitrogen cycle. Overestimations of photosynthesis (gross primary productivity (GPP)) in CMIP5 were largely resolved in CMIP6 for participating models with an interactive nitrogen cycle but remaining for models without one. This points to the importance of including nutrient limitation. Simulating the leaf area index (LAI) remains challenging, with a large model spread in both CMIP5 and CMIP6. In ESMs, the global mean land carbon uptake (net biome productivity (NBP)) is well reproduced in the CMIP5 and CMIP6 multi-model means. However, this is the result of an underestimation of NBP in the Northern Hemisphere, which is compensated by an overestimation in the Southern Hemisphere and the tropics. Carbon stocks remain a large uncertainty in the models. While vegetation carbon content is slightly better represented in CMIP6, the inter-model range of soil carbon content remains the same between CMIP5 and CMIP6. Overall, a slight improvement in the simulation of land carbon cycle parameters is found in CMIP6 compared to CMIP5, but with many biases remaining, further improvements of models in particular for LAI and NBP is required. Models from modeling groups participating in both CMIP phases generally perform similarly or better in their CMIP6 compared to their CMIP5 models. This improvement is not as significant in the multi-model means due to more new models in CMIP6, especially those using older versions of the Community Land Model (CLM). Emission-driven simulations perform just as well as the concentration-driven models, despite the added process realism. Due to this, we recommend that ESMs in future Coupled Model Intercomparison Project (CMIP) phases perform emission-driven simulations as the standard so that climate–carbon cycle feedbacks are fully active. The inclusion of the nitrogen limitation led to a large improvement in photosynthesis compared to models not including this process, suggesting the need to view the nitrogen cycle as a necessary part of all future carbon cycle models. Possible benefits when including further limiting nutrients such as phosphorus should also be considered.

Abstract. By regulating the global transport of heat, freshwater, and carbon, the Atlantic meridional overturning circulation (AMOC) serves as an important component of the climate system. During the late 20th and early 21st centuries, indirect observations and models suggest a weakening of the AMOC. Direct AMOC observations also suggest a weakening during the early 21st century but with substantial interannual variability. Long-term weakening of the AMOC has been associated with increasing greenhouse gases (GHGs), but some modeling studies suggest the build up of anthropogenic aerosols (AAs) may have offset part of the GHG-induced weakening. Here, we quantify 1900–2020 AMOC variations and assess the driving mechanisms in state-of-the-art climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6). The CMIP6 forcing (GHGs, anthropogenic and volcanic aerosols, solar variability, and land use and land change) multi-model mean shows negligible AMOC changes up to ∼ 1950, followed by robust AMOC strengthening during the second half of the 20th century (∼ 1950–1990) and weakening afterwards (1990–2020). These multi-decadal AMOC variations are related to changes in North Atlantic atmospheric circulation, including an altered sea level pressure gradient, storm track activity, surface winds, and heat fluxes, which drive changes in the subpolar North Atlantic surface density flux. To further investigate these AMOC relationships, we perform a regression analysis and decompose these North Atlantic climate responses into an anthropogenic aerosol-forced component and a subsequent AMOC-related feedback. Similar to previous studies, CMIP6 GHG simulations yield robust AMOC weakening, particularly during the second half of the 20th century. Changes in natural forcings, including solar variability and volcanic aerosols, yield negligible AMOC changes. In contrast, CMIP6 AA simulations yield robust AMOC strengthening (weakening) in response to increasing (decreasing) anthropogenic aerosols. Moreover, the CMIP6 all-forcing AMOC variations and atmospheric circulation responses also occur in the CMIP6 AA simulations, which suggests these are largely driven by changes in anthropogenic aerosol emissions. More specifically, our results suggest that AMOC multi-decadal variability is initiated by North Atlantic aerosol optical thickness perturbations to net surface shortwave radiation and sea surface temperature (and hence sea surface density), which in turn affect sea level pressure gradient and surface wind and – via latent and sensible heat fluxes – sea surface density flux through its thermal component. AMOC-related feedbacks act to reinforce this aerosol-forced AMOC response, largely due to changes in sea surface salinity (and hence sea surface density), with temperature-related (and cloud-related) feedbacks acting to mute the initial response. Although aspects of the CMIP6 all-forcing multi-model mean response resembles observations, notable differences exist. This includes CMIP6 AMOC strengthening from ∼ 1950 to 1990, when the indirect estimates suggest AMOC weakening. The CMIP6 multi-model mean also underestimates the observed increase in North Atlantic ocean heat content, and although the CMIP6 North Atlantic atmospheric circulation responses – particularly the overall patterns – are similar to observations, the simulated responses are weaker than those observed, implying they are only partially externally forced. The possible causes of these differences include internal climate variability, observational uncertainties, and model shortcomings, including excessive aerosol forcing. A handful of CMIP6 realizations yield AMOC evolution since 1900 similar to the indirect observations, implying the inferred AMOC weakening from 1950 to 1990 (and even from 1930 to 1990) may have a significant contribution from internal (i.e., unforced) climate variability. Nonetheless, CMIP6 models yield robust, externally forced AMOC changes, the bulk of which are due to anthropogenic aerosols.

Outputs from new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) promise improvement and enhancement of climate change projections information for Australia. Here we focus on three key aspects of CMIP6: what is new in these models, how the available CMIP6 models evaluate compared to CMIP5, and their projections of the future Australian climate compared to CMIP5 focussing on the highest emissions scenario. The CMIP6 ensemble has several new features of relevance to policymakers and others, for example, the integrated matrix of socioeconomic and concentration pathways. The CMIP6 models show incremental improvements in the simulation of the climate in the Australian region, including a reduced equatorial Pacific cold tongue bias, slightly improved rainfall teleconnections with large‐scale climate drivers, improved representation of atmosphere and ocean extreme heat events, as well as dynamic sea level. However, important regional biases remain, evident in the excessive rainfall over the Maritime Continent and rainfall pattern biases in the nearby tropical convergence zones. Projections of Australian temperature and rainfall from the available CMIP6 ensemble broadly agree with those from CMIP5, except for a group of CMIP6 models with higher climate sensitivity and greater warming and increase in some extremes after 2050. CMIP6 rainfall projections are similar to CMIP5, but the ensemble examined has a narrower range of rainfall change in austral summer in Northern Australia and austral winter in Southern Australia. Overall, future national projections are likely to be similar to previous versions but perhaps with some areas of improved confidence and clarity.

This chapter focused on Coupled Model Intercomparison Project, Phase 5 (CMIP5), and discussed various results. Starting from outlining very basic equations of global climate models (GCMs) as used in CMIP5 models, it described briefly about the aim and objective of the CMIP5 project. It is followed by discussion on various experiments, historical and RCP (Representative Concentration Pathway) situation. The global temperature generated using CMIP5 models was compared with observation. Indian Summer Monsoon and El Nino Southern Oscillation in CMIP5 models were analysed in the historical and RCP situation, and few areas of agreement and disagreement were discussed. Few results from the atmospheric version of CMIP5 models (AMIP5) and Phase 3 of CMIP5 experiments (CMIP3) were also presented. Some stratospheric features are shown well captured by models.

The South Pacific Convergence Zone (SPCZ) is poorly represented in global coupled simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5), with trademark biases such as the tendency to form a “double Intertropical convergence zone” and an equatorial cold tongue that extends too far westward. Such biases limit our confidence in projections of the future climate change for this region. In this study, we use a downscaling strategy based on a regional atmospheric general circulation model that accurately captures the SPCZ present-day climatology and interannual variability. More specifically, we investigate the sensitivity of the projected rainfall response to either just correcting present-day CMIP5 Sea Surface Temperature (SST) biases or correcting projected SST changes using an emergent constraint approach. While the equatorial western Pacific projected rainfall increase is robust in our experiments and CMIP5, correcting the projected CMIP5 SST changes yields a considerably larger reduction (~ 25%) than in CMIP5 simulations (~ + 3%) in the southwestern Pacific. Indeed, correcting the projected CMIP5 warming pattern yields stronger projected SST gradients, and more humidity convergence reduction under the SPCZ. Finally, our bias-corrected set of experiments yields an increase in equatorial rainfall and SPCZ variability in the future, but does not support the future increase in the frequency of zonal SPCZ events simulated by CMIP5 models. This study hence suggests that atmospheric downscaling studies should not only correct CMIP5 present-day SST biases but also projected SST changes to improve the reliability of their projections. Additional simulations with different physical parameterizations yield robust results.

The double intertropical convergence zone (ITCZ) bias still affects all the models that participate to CMIP5 (Coupled Model Intercomparison Project, phase 5). As an ensemble, general circulation models have improved little between CMIP3 and CMIP5 as far as the double ITCZ is concerned. The present study proposes a new process-oriented metrics that provides a robust statistical relationship between atmospheric processes and the double ITCZ bias, additionally to the existing relationship between the sea surface temperature (SST) and the double ITCZ bias. The SST contribution is examined using the THR-MLT index (Bellucci et al. in J Clim 5:1127–1145, 2010), which combines biases on the representation of local SSTs and the SST threshold leading to the onset of ascent in the double ITCZ region. As a metrics of a model’s bias in simulating the interaction between circulation and precipitation, we propose to use the Combined Precipitation Circulation Error (CPCE). It is computed as the quadratic error on the contribution of each vertical regime to the total precipitation over the tropical oceans. CPCE is a global measure of the circulation-precipitation coupling that characterizes the model physical parameterizations rather than the regional characteristics of the eastern Pacific. A linear regression analysis shows that most of the double ITCZ spread among CMIP5 coupled ocean–atmosphere models is attributed to SST biases, and that the precipitation large-scale dynamics relationship explains a significant fraction of the bias in these models, as well as in the atmosphere-only models.

Abstract. In response to increasing greenhouse gases, the subtropical edges of Earth's Hadley circulation shift poleward in global climate models. Recent studies have found that reanalysis trends in the Hadley cell edge over the past 30–40 years are within the range of trends simulated by Coupled Model Intercomparison Project Phase 5 (CMIP5) models and have documented seasonal and hemispheric asymmetries in these trends. In this study, we evaluate whether these conclusions hold for the newest generation of models (CMIP6). Overall, we find similar characteristics of Hadley cell expansion in CMIP5 and CMIP6 models. In both CMIP5 and CMIP6 models, the poleward shift of the Hadley cell edge in response to increasing greenhouse gases is 2–3 times larger in the Southern Hemisphere (SH), except during September–November. The trends from CMIP5 and CMIP6 models agree well with reanalyses, although prescribing observed coupled atmosphere–ocean variability allows the models to better capture reanalysis trends in the Northern Hemisphere (NH). We find two notable differences between CMIP5 and CMIP6 models. First, while both CMIP5 and CMIP6 models contract the NH summertime Hadley circulation equatorward (particularly over the Pacific sector), this contraction is larger in CMIP6 models due to their higher average climate sensitivity. Second, in recent decades, the poleward shift of the NH annual-mean Hadley cell edge is slightly larger in CMIP6 models. Increasing greenhouse gases drive similar trends in CMIP5 and CMIP6 models, so the larger recent NH trends in CMIP6 models point to the role of other forcings, such as aerosols.