Assessing the Annual-Scale Insolation–Temperature Relationship over Northern Hemisphere in CMIP6 Models and Its Implication for Orbital-Scale Simulation

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.

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.

Evaluation and future projections of wind energy resources over the Northern Hemisphere in CMIP5 and CMIP6 models

This is the first study to show the global Cut-off Low (COL) activity in 46 models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6). The COL historical simulations for the period 1979–2005 obtained from the CMIP5 and CMIP6 models and their ensembles are compared with the ERA5 reanalysis using an objective feature-tracking algorithm. The results show that the CMIP6 models simulate the spatial distribution of COLs more realistically than the CMIP5 models. Some improvements include reduced equatorward bias and underestimation over regions of high COL density. Reduced biases in CMIP6 are mainly attributed to the improved representation of the zonal wind due to the poleward shift of the subtropical jet streams. The CMIP5 models systematically underestimate the COL intensity as measured by the T42 vorticity at 250 hPa. In CMIP6, the intensity is still underestimated in summer, but overestimated in winter in part due to increased westerlies. The overestimation is enhanced by the finer spatial resolution models that identify more of the strong systems compared to coarser resolution models. Other aspects of COLs such as their temporal and lifetime distributions are modestly improved in CMIP6 compared to CMIP5. Finally, the predictive skill of climate models is evaluated using five variables and the Taylor diagram. We find that 15 out of the 20 (75%) best coupled models belong to CMIP6, and this highlights the overall improvement compared to its predecessor CMIP5. Despite this, the use of the multi-model ensemble average seems to be better in simulating COLs than individual models.

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.

Abstract. Poor air quality is currently responsible for large impacts on human health across the world. In addition, the air pollutants ozone (O3) and particulate matter less than 2.5 µm in diameter (PM2.5) are also radiatively active in the atmosphere and can influence Earth's climate. It is important to understand the effect of air quality and climate mitigation measures over the historical period and in different future scenarios to ascertain any impacts from air pollutants on both climate and human health. The Coupled Model Intercomparison Project Phase 6 (CMIP6) presents an opportunity to analyse the change in air pollutants simulated by the current generation of climate and Earth system models that include a representation of chemistry and aerosols (particulate matter). The shared socio-economic pathways (SSPs) used within CMIP6 encompass a wide range of trajectories in precursor emissions and climate change, allowing for an improved analysis of future changes to air pollutants. Firstly, we conduct an evaluation of the available CMIP6 models against surface observations of O3 and PM2.5. CMIP6 models consistently overestimate observed surface O3 concentrations across most regions and in most seasons by up to 16 ppb, with a large diversity in simulated values over Northern Hemisphere continental regions. Conversely, observed surface PM2.5 concentrations are consistently underestimated in CMIP6 models by up to 10 µg m−3, particularly for the Northern Hemisphere winter months, with the largest model diversity near natural emission source regions. The biases in CMIP6 models when compared to observations of O3 and PM2.5 are similar to those found in previous studies. Over the historical period (1850–2014) large increases in both surface O3 and PM2.5 are simulated by the CMIP6 models across all regions, particularly over the mid to late 20th century, when anthropogenic emissions increase markedly. Large regional historical changes are simulated for both pollutants across East and South Asia with an annual mean increase of up to 40 ppb for O3 and 12 µg m−3 for PM2.5. In future scenarios containing strong air quality and climate mitigation measures (ssp126), annual mean concentrations of air pollutants are substantially reduced across all regions by up to 15 ppb for O3 and 12 µg m−3 for PM2.5. However, for scenarios that encompass weak action on mitigating climate and reducing air pollutant emissions (ssp370), annual mean increases in both surface O3 (up 10 ppb) and PM2.5 (up to 8 µg m−3) are simulated across most regions, although, for regions like North America and Europe small reductions in PM2.5 are simulated due to the regional reduction in precursor emissions in this scenario. A comparison of simulated regional changes in both surface O3 and PM2.5 from individual CMIP6 models highlights important regional differences due to the simulated interaction of aerosols, chemistry, climate and natural emission sources within models. The projection of regional air pollutant concentrations from the latest climate and Earth system models used within CMIP6 shows that the particular future trajectory of climate and air quality mitigation measures could have important consequences for regional air quality, human health and near-term climate. Differences between individual models emphasise the importance of understanding how future Earth system feedbacks influence natural emission sources, e.g. response of biogenic emissions under climate change.

The present research is aimed at evaluating the climate coupled models with respect to the observational datasets for the investigation of the characteristics of the Indian summer monsoon (ISM). The monthly averaged historical simulations from 12 coupled climate models that participated in Coupled Model Intercomparison Project Phase 6 (CMIP6) are compared with the ground based observational data, satellite and reanalysis datasets for the study period of 1980–2014. This study uses these high‐resolution models to investigate monsoon features, onset and withdrawal dates, primarily focusing on individual model performances rather than highly documented multi‐model mean performance. Performance evaluation of the models suggests that sea surface temperature (SST) simulations by the models are in better agreement for the Arabian Sea than the Bay of Bengal with respect to the ERA5 reanalysis data sets. Further, inter‐model differences amongst the CMIP6 models in estimating the spatial distribution of various monsoon system variables during pre‐monsoon and monsoon seasons are noted which may be attributed to the different model components and varying physics configuration, nonetheless, models like NESM3 and INM‐CN5 are able to reproduce ISM pattern reasonably well. The annual precipitation cycle demonstrates a good agreement between most climate models and IMD data. Evolution of tropospheric temperature gradient (ΔTT) estimated from the CMIP6 models mimics the temporal pattern of the annual rainfall and therefore, is used to estimate the onset and withdrawal dates from CMIP6 models; however, high variability is noted amongst the CMIP6 models in retrieving the onset and withdrawal dates when compared with the IMD observations. Most of the models show a shorter rainy season except NorESM2‐MM. Overall our results suggest that the CMIP6 models can be used for the seasonal mean evaluation of monsoon system parameters and process‐based studies to improve our present understanding of the ISM system.

Evaluation of CMIP climate model hydrological output for the Mississippi River Basin using GRACE satellite observations

Recent research demonstrated the existence of a combination mode (C-mode) originating from the atmospheric nonlinear interaction between the El Niño-Southern Oscillation (ENSO) and the Pacific warm pool annual cycle. In this paper, we show that the majority of coupled climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are able to reproduce the observed spatial pattern of the C-mode in terms of surface wind anomalies reasonably well, and about half of the coupled models are able to reproduce spectral power at the combination tone periodicities of about 10 and/or 15 months. Compared to the CMIP5 historical simulations, the CMIP5 Atmospheric Model Intercomparison Project (AMIP) simulations can generally exhibit a more realistic simulation of the C-mode due to prescribed lower boundary forcing. Overall, the multi-model ensemble average of the CMIP5 models tends to capture the C-mode better than the individual models. Furthermore, the models with better performance in simulating the ENSO mode tend to also exhibit a more realistic C-mode with respect to its spatial pattern and amplitude, in both the CMIP5 historical and AMIP simulations. This study shows that the CMIP5 models are able to simulate the proposed combination mode mechanism to some degree, resulting from their reasonable performance in representing the ENSO mode. It is suggested that the main ENSO periods in the current climate models needs to be further improved for making the C-mode better.

Köppen-Geiger climate classification is a valuable tool to define climate zones based on the annual cycles of temperature and precipitation. In this study, we use the high-emission scenario global climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and phase 5 (CMIP5) along with observations and apply the Köppen-Geiger climate classification. We aim to address the ecological consequences of climate change and compare the two generations of models. Compared to their predecessors, CMIP6 models show slightly improved performance in representing the reference period (1976-2005) observed climate zones. CMIP6 models project a 42-48% change in climate zones by the end of the century, depending on which ensemble subset is used. The projected change rates based on CMIP6 are above the global average for Europe (81-88%) and North America (57-66%). The reductions in the areas of cold and polar climate zones are more pronounced in CMIP6 models compared to CMIP5. Using an ensemble subset of CMIP6 models that are consistent with the latest evidence for equilibrium climate sensitivity limits the changes in climate zones, and their results converge towards the results based on CMIP5. CMIP6 models also project a greater rate of climate zone change throughout the century than CMIP5. The greater change rate observed in CMIP6 is essentially dominated by the stronger projected warming rates of these models, whose plausibility is a matter of concern.

<p>A plausible simulation of the global energy balance is a first-order requirement for a credible climate model. In the present study I investigate the representation of the global energy balance in 40 state-of-the-art global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). In the CMIP6 multi-model mean, the magnitudes of the energy balance components are often in better agreement with recent reference estimates compared to earlier model generations  such as CMIP5 on a global mean basis. However, the inter-model spread in the representation of many of the components remains substantial, often on the order of 10-20 Wm<sup>-2</sup> globally,  except for aspects of the shortwave clear-sky budgets, which are now more consistently simulated by the CMIP6 models. The substantial inter-model spread in the simulated global mean latent heat fluxes in the CMIP6 models, exceeding 20% (18 Wm<sup>-2</sup>),  further implies also large discrepancies in their representation of the global water balance. From a historic perspective of model development over the past decades, the largest adjustments in the magnitudes of the simulated present-day global mean energy balance components occurred in the shortwave atmospheric clear-sky absorption and the surface downward longwave radiation. Both components were gradually adjusted upwards over several model generations, on the order of 10 Wm<sup>-2</sup>, to reach 73 and 344 Wm<sup>-2</sup>, respectively in the CMIP6 multi-model means. Thereby, CMIP6 has become the first model generation that largely remediates long-standing model deficiencies related to an overestimation in surface downward shortwave and compensational underestimation in downward longwave radiation in its multi-model mean (Wild 2020).</p><p>Published in: Wild, M., 2020: The global energy balance as represented in CMIP6 climate models. Clim Dyn <strong>55, </strong>553–577. https://doi.org/10.1007/s00382-020-05282-7</p><p> </p>

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. 

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.

As the major renewable energy, wind can greatly reduce carbon emissions. Following the “carbon neutral” strategy, wind power could help to achieve the realization of energy transformation and green development. Based on ERA5 reanalysis data and the multi-ensemble historical and scenario simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a variety of statistical analyses are used to evaluate the performance of CMIP6 simulating the wind speed in China. The conclusions are as follows: spatial patterns of the nine CMIP6 models are similar with ERA5, but BCC-CSM2-MR and MRI-ESM2-0 highly overestimate the wind speed in northwest China. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM behave better than the other six CMIP6 models in four specific regions are chosen for detailed study. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM tend to simulate a larger wind speed than ERA5 except the yearly averaged wind speed in region II and region IV. CESM2-WACCM and NorESM2-MM simulate a large monthly mean wind speed, but the value is relatively close with ERA5 in the summer. HadGEM3-GC31-MM overestimates wind speed in region I and region II from April to October, but gets closer with ERA during winter. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM simulate an increasing trend in Tibetan Plateau and Xinjiang in the next 100 years, while NorESM2-MM projects rising wind speed in the eastern part of Inner Mongolia, and HadGEM3-GC31-MM simulates increasing wind speed in the northeast and central China. The future wind speed in three models is projected to decline in region I, and the value of HadGEM3-GC31-MM is much larger. In region II, wind speed simulated by three models is projected to decrease, but the wind speed from HadGEM3-GC31-MM in region III and modeled wind speed in region IV from NorESM2-MM would climb with the slope equal to 0.0001 and 0.0012, respectively. This study indicates that the CMIP6 models have certain limitations to perform realistic wind changes, but CMIP6 could provide available reference for the projection of wind in specific areas.

A plausible simulation of the global energy balance is a first-order requirement for a credible climate model. Here I investigate the representation of the global energy balance in 40 state-of-the-art global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). In the CMIP6 multi-model mean, the magnitudes of the energy balance components are often in better agreement with recent reference estimates compared to earlier model generations on a global mean basis. However, the inter-model spread in the representation of many of the components remains substantial, often on the order of 10–20 Wm−2 globally, except for aspects of the shortwave clear-sky budgets, which are now more consistently simulated by the CMIP6 models. The substantial inter-model spread in the simulated global mean latent heat fluxes in the CMIP6 models, exceeding 20% (18 Wm−2), further implies also large discrepancies in their representation of the global water balance. From a historic perspective of model development over the past decades, the largest adjustments in the magnitudes of the simulated present-day global mean energy balance components occurred in the shortwave atmospheric clear-sky absorption and the surface downward longwave radiation. Both components were gradually adjusted upwards over several model generations, on the order of 10 Wm−2, to reach 73 and 344 Wm−2, respectively in the CMIP6 multi-model means. Thereby, CMIP6 has become the first model generation that largely remediates long-standing model deficiencies related to an overestimation in surface downward shortwave and compensational underestimation in downward longwave radiation in its multi-model mean.