Assessment of Modes of Interannual Variability of Southern Hemisphere Atmospheric Circulation in CMIP5 Models

An assessment is made of the modes of interannual variability in the seasonal mean summer and winter Southern Hemisphere 500 hPa geopotential height in the twentieth century in models from the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset. The analysis is done for both the intraseasonal and slow components of the geopotential height. When the CMIP3 models are assessed against reanalysis data, the spatial structure and variance of the leading modes in the intraseasonal component are generally well reproduced. There are systematic differences between the models in their reproduction of the leading modes in the slow component. An overall score using the leading modes in the slow component allows a categorisation of CMIP3 model performance. Using an ensemble from four models that suitably reproduce the twentieth century modes, modes of variability in the slow-internal and slow-external components are estimated. The leading mode of the slow-external component is shown to be related to observed changes in greenhouse gas concentrations. In this ensemble, there is little change in the leading modes in the intraseasonal component in the twenty-first century. Larger changes in variance, and subtle changes in regional-scale structure, are found for the leading modes in the slow-internal component. These are related to changes in the slowly varying dynamics of the Southern Annular Mode and the El Nino-Southern Oscillation. By far the biggest change is in the leading mode of the slow-external component. The spatial structure becomes uniform in the twenty-first century, and the variance increases with increasing greenhouse gas concentrations.

The atmospheric circulation acts as a bridge between large-scale sources of climate variability, and climate variability on regional scales. Here a statistical method is applied to monthly mean Southern Hemisphere 500hPa geopotential height to separate the interannual variability of the seasonal mean into intraseasonal and slowly varying (time scales of a season or longer) components. Intraseasonal and slow modes of variability are estimated from realisations of models from the Coupled Model Intercomparison Project Phase 3 (CMIP3) twentieth century coupled climate simulation (20c3m) and are evaluated against those estimated from reanalysis data. The intraseasonal modes of variability are generally well reproduced across all CMIP3 20c3m models for both Southern Hemisphere summer and winter. The slow modes are in general less well reproduced than the intraseasonal modes, and there are larger differences between realisations than for the intraseasonal modes. New diagnostics are proposed to evaluate model variability. It is found that differences between realisations from each model are generally less than inter-model differences. Differences between model-mean diagnostics are found. The results obtained are applicable to assessing the reliability of changes in atmospheric circulation variability in CMIP3 models and for their suitability for further studies of regional climate variability.

The double‐intertropical convergence zone (ITCZ) bias is one of the most outstanding errors in all previous generations of climate models. Here, the annual double‐ITCZ bias and the associated precipitation bias in the latest climate models for Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) are examined in comparison to their previous generations (CMIP Phase 3 [CMIP3] and CMIP Phase 5 [CMIP5]). All three generations of CMIP models share similar systematic annual multi‐model ensemble mean precipitation errors in the tropics. The notorious double‐ITCZ bias and its big inter‐model spread persist in CMIP3, CMIP5, and CMIP6 models. Based on several tropical precipitation bias indices, the double‐ITCZ bias is slightly reduced from CMIP3 or CMIP5 to CMIP6. In addition, the annual equatorial Pacific cold tongue persists in all three generations of CMIP models, but its inter‐model spread is reduced from CMIP3 to CMIP5 and from CMIP5 to CMIP6.

In this paper the model outputs from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) are used to examine the climatology and interannual variability of the East Asian winter monsoon (EAWM). The multimodel ensemble (MME) is able to reproduce reasonably well the circulation features of the EAWM. The simulated surface air temperature still suffers from a cold bias over East Asia, but this bias is reduced compared with CMIP phase 3 models. The intermodel spread is relatively small for the large-scale circulations, but is large for the lower-tropospheric meridional wind and precipitation along the East Asian coast. The interannual variability of the EAWM-related circulations can be captured by most of the models. A general bias is that the simulated variability is slightly weaker than in the observations. Based on a selected dynamic EAWM index, the patterns of the EAWM-related anomalies are well reproduced in MME although the simulated anomalies are slightly weaker than the observations. One general bias is that the northeasterly anomalies over East Asia cannot be captured to the south of 30°N. This bias may arise both from the inadequacies of the EAWM index and from the ability of models to capture the EAWM-related tropical–extratropical interactions. The ENSO–EAWM relationship is then evaluated and about half of the models can successfully capture the observed ENSO–EAWM relationship, including the significant negative correlation between Niño-3.4 and EAWM indices and the anomalous anticyclone (or cyclone) over the northwestern Pacific. The success of these models is attributed to the reasonable simulation of both ENSO’s spatial structure and its strength of interannual variability.

In this paper, the performances of 12 CMIP5 (Coupled Model Intercomparison Project phase 5) models for simulating the climatology and interannual variability of the East Asian trough (EAT) are assessed using the National Centers for Environmental Prediction (NCEP) reanalysis data and the outputs of the CMIP5 models. The multimodel ensemble (MME) successfully reproduces the spatial pattern and spatial variations in the climatology and interannual variability of the EAT but the intensity and interannual variability of EAT are weaker than in the observations. The biases in intensity (interannual variability) are larger over the southern (northern) part of the EAT than over the northern (southern) part. The intermodel spreads are small for the EAT intensity but are large for its location in terms of both latitude and longitude. The simulated EAT in the MME is about 3° E and 1.5° S of that observed. All 12 CMIP5 models reproduce the first empirical orthogonal function (EOF) mode of EAT activity; however, its intensity and location are only successfully captured in half of the models and its linear weakening trend is simulated in ten models. The second EOF mode of EAT activity and its linear strengthening trend are successfully reproduced in eight models.

The frequency and intensity of extreme thermal stress conditions during summer are expected to increase due to climate change. This study examines sixteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that have been bias-adjusted using the quantile delta mapping method. These models provide Universal Thermal Climate Index (UTCI) for summer seasons between 1979 and 2010, which are regridded to a similar spatial grid as ERA5-HEAT (available at 0.25° × 0.25° spatial resolution) using bilinear interpolation. The evaluation compares the summertime climatology and trends of the CMIP6 multi-model ensemble (MME) mean UTCI with ERA5 data, focusing on a regional hotspot in northwest India (NWI). The Pattern Correlation Coefficient (between CMIP6 models and ERA5) values exceeding 0.9 were employed to derive the MME mean of UTCI, which was subsequently used to analyze the climatology and trends of UTCI in the CMIP6 models.The spatial climatological mean of CMIP6 MME UTCI demonstrates significant thermal stress over the NWI region, similar to ERA5. Both ERA5 and CMIP6 MME UTCI show a rising trend in thermal stress conditions over NWI. The temporal variation analysis reveals that NWI experiences higher thermal stress during the summer compared to the rest of India. The number of thermal stress days is also increasing in NWI and major Indian cities according to ERA5 and CMIP6 MME. Future climate projections under different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) indicate an increasing trend in thermal discomfort conditions throughout the twenty-first century. The projected rates of increase are approximately 0.09 °C per decade, 0.26 °C per decade, and 0.56 °C per decade, respectively. Assessing the near (2022–2059) and far (2060–2100) future, all three scenarios suggest a rise in intense heat stress days (UTCI > 38 °C) in NWI. Notably, the CMIP6 models predict that NWI could reach deadly levels of heat stress under the high-emission (SSP5-8.5) scenario. The findings underscore the urgency of addressing climate change and its potential impacts on human well-being and socio-economic sectors.

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.

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.

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.

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.

Abstract. Anthropogenic climate change is projected to lead to ocean warming, acidification, deoxygenation, reductions in near-surface nutrients, and changes to primary production, all of which are expected to affect marine ecosystems. Here we assess projections of these drivers of environmental change over the twenty-first century from Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) that were forced under the CMIP6 Shared Socioeconomic Pathways (SSPs). Projections are compared to those from the previous generation (CMIP5) forced under the Representative Concentration Pathways (RCPs). A total of 10 CMIP5 and 13 CMIP6 models are used in the two multi-model ensembles. Under the high-emission scenario SSP5-8.5, the multi-model global mean change (2080–2099 mean values relative to 1870–1899) ± the inter-model SD in sea surface temperature, surface pH, subsurface (100–600 m) oxygen concentration, euphotic (0–100 m) nitrate concentration, and depth-integrated primary production is +3.47±0.78 ∘C, -0.44±0.005, -13.27±5.28, -1.06±0.45 mmol m−3 and -2.99±9.11 %, respectively. Under the low-emission, high-mitigation scenario SSP1-2.6, the corresponding global changes are +1.42±0.32 ∘C, -0.16±0.002, -6.36±2.92, -0.52±0.23 mmol m−3, and -0.56±4.12 %. Projected exposure of the marine ecosystem to these drivers of ocean change depends largely on the extent of future emissions, consistent with previous studies. The ESMs in CMIP6 generally project greater warming, acidification, deoxygenation, and nitrate reductions but lesser primary production declines than those from CMIP5 under comparable radiative forcing. The increased projected ocean warming results from a general increase in the climate sensitivity of CMIP6 models relative to those of CMIP5. This enhanced warming increases upper-ocean stratification in CMIP6 projections, which contributes to greater reductions in upper-ocean nitrate and subsurface oxygen ventilation. The greater surface acidification in CMIP6 is primarily a consequence of the SSPs having higher associated atmospheric CO2 concentrations than their RCP analogues for the same radiative forcing. We find no consistent reduction in inter-model uncertainties, and even an increase in net primary production inter-model uncertainties in CMIP6, as compared to CMIP5.

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.

The Southern Hemisphere (SH) eddy-driven jet stream has been shown to move poleward in climate models in response to greenhouse gas forcing, but the magnitude of the shift is uncertain. Here we address the fact that the latest Coupled Model Intercomparison Project phase 6 (CMIP6) models simulate, on average, a smaller jet shift in response to an abrupt quadrupling in CO2 than the predecessor models (Coupled Model Intercomparison Project phase 5 (CMIP5)), despite producing larger global average surface warming. We focus on the response in the first decade when the majority of the long-term jet shift occurs and when the difference between CMIP5 and CMIP6 models emerges. We hypothesise the smaller poleward jet shift is related to the weaker increase in the meridional sea surface temperature (SST) gradient across the southern extratropics in CMIP6 models. We impose the multi-model mean SST patterns alongside a quadrupling in CO2 in an intermediate complexity general circulation model (IGCM4) and show that many of the regional and seasonal differences in lower tropospheric zonal winds between CMIP5 and CMIP6 models are reproduced by prescribing the SST patterns. The main exception is in austral summer when the imposed SST patterns and CO2 increase in IGCM4 produce weaker differences in zonal wind response compared to those simulated by CMIP5/6 models. Further IGCM4 experiments that prescribe only SH extratropical SSTs simulate a weaker jet shift for CMIP6 SSTs than for CMIP5, comparable to the full experiment. The results demonstrate that SH SST patterns are an important source of uncertainty for the shift of the midlatitude circulation in response to CO2 forcing. The study also provides an alternative explanation than was proposed by Curtis et al (2020 Environ. Res. Lett. 15 64011), who suggested model improvements in jet biases could account for the smaller jet shift in CMIP6 models in the extended austral winter season.