CMIP5 Models Research Articles

Stronger Impact of Extreme Heat Event on Vegetation Temperature Sensitivity under Future Scenarios with High-Emission Intensity

Vegetation temperature sensitivity is a key indicator to understand the response of vegetation to temperature changes and predict potential shifts in ecosystem functions. However, under the context of global warming, the impact of future extreme heat events on vegetation temperature sensitivity remains poorly understood. Such research is crucial for predicting the dynamic changes in terrestrial ecosystem structure and function. To address this issue, we utilized historical (1850–2014) and future (2015–2100) simulation data derived from CMIP6 models to explore the spatiotemporal dynamics of vegetation temperature sensitivity under different carbon emission scenarios. Moreover, we employed correlation analysis to assess the impact of extreme heat events on vegetation temperature sensitivity. The results indicate that vegetation temperature sensitivity exhibited a declining trend in the historical period but yielded an increasing trend under the SSP245 and SSP585 scenarios. The increasing trend under the SSP245 scenario was less pronounced than that under the SSP585 scenario. By contrast, vegetation temperature sensitivity exhibited an upward trend until 2080 and it began to decline after 2080 under the SSP126 scenario. For all the three future scenarios, the regions with high vegetation temperature sensitivity were predominantly located in high latitudes of the Northern Hemisphere, the Tibetan Plateau, and tropical forests. In addition, the impact of extreme heat events on vegetation temperature sensitivity was intensified with increasing carbon emission intensity, particularly in the boreal forests and Siberian permafrost. These findings provide important insights and offer a theoretical basis and guidance to identify climatically sensitive areas under global climate change.

Intraseasonal shift in the wintertime North Atlantic jet structure projected by CMIP6 models

The projected winter changes of the North Atlantic eddy-driven jet (EDJ) under climate change conditions have been extensively analysed. Previous studies have reported a squeezed and elongated EDJ. However, other changes present large uncertainties, specifically those related to the intensity and latitude. Here, the projections of the EDJ in a multimodel ensemble of CMIP6 are scrutinised by using a multiparametric description of the EDJ. The multimodel mean projects non-stationary responses of the EDJ latitude through the winter, characterised by a poleward shift in early winter and equator migration in late winter. These intraseasonal shifts (rather than a genuine narrowing) explain the previously established squeezing of the EDJ and are linked to the future changes in different drivers: the 200 hPa meridional temperature gradient and Atlantic warming hole in early winter, and the stratospheric vortex in late winter. Model biases also influence EDJ projections, contributing to the poleward shift in early winter.

Evaluation of extreme precipitation events as simulated by CMIP6 models over Central Africa: Spatial patterns

Evaluation of extreme precipitation events as simulated by CMIP6 models over Central Africa: Spatial patterns

Observation-constrained climate change in China over 1850–2014 by aerosols in CMIP6 models

Observation-constrained climate change in China over 1850–2014 by aerosols in CMIP6 models

Capacity of a set of CMIP6 models to simulate Arctic sea ice drift

Abstract Evaluating CMIP6 model performance helps to improve the prediction of future changes in Arctic sea ice. We analyze the seasonal cycles, distribution, and evolution of sea ice in different regions from 1979 to 2014. We compare the output from selected CMIP6 models with reference data for sea ice motion. We also discuss the correlations between sea ice motion(SIM) and sea ice thickness (SIT) in reference data, and how CMIP6 models explain them. We select EC-Earth3, ACCESS-CM2, BCC-CSM2-MR, MPI-ESM1-2-HR, and NorESM2-LM for CMIP6 study. We compare outputs with reference data: Sea ice extent (SIE) from NSIDC; SIT from PIOMAS; and SIM from the IABP buoy data. Analytical techniques include Theil-Sen and Ordinary least squares (OLS) regression. Most selected CMIP6 models have seasonal cycles of SIM lagging behind IABP observations by 1-2 month and overestimate central Arctic SIM magnitude, with MPI-ESM1-2-HR having the highest discrepancy and NorESM2-LM lowest. The models show better simulation of SIM in the ice melting season than in the growing season. Models perform worse at capturing regional differences in SIM evolution and are overly conservative when simulating the increasing trend in ice motion, especially in coastal Arctic seas during summer. There is significant negative correlation between SIT and SIM in October.

Indian Summer Monsoon Precipitation Dominates the Reproduction of Circumglobal Teleconnection Pattern: A Comparison of CMIP5 and CMIP6 Models

Abstract The zonal wavenumber-5 circumglobal teleconnection (CGT) pattern is one of the most critical atmospheric teleconnection patterns during boreal summer over the Northern Hemisphere (NH). CGT can exert significant climatic impact across NH including Europe, East Asia, and North America, but how reliable coupled climate models simulate the characteristics of CGT is poorly understood. Here, 20 coupled models with their respective versions in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and CMIP6 are selected to evaluate their performance on CGT simulation. We find that while both CMIP5 and CMIP6 models are able to capture the basic features of CGT in multimodel mean (MMM), there are large intermodel discrepancies in the simulation of CGT pattern among CMIP5 and CMIP6 models. High-skill models exhibit strong action center over west-central Asia, coinciding with the pattern derived from reanalysis, while the corresponding action center in low-skill models is weaker. Further analyses demonstrate that high-skill models are capable of simulating more realistic Indian summer monsoon (ISM) precipitation anomalies related to CGT. The resultant anomalous upper-tropospheric divergence over west-central Asia, acting as a Rossby wave source, can therefore excite the abovementioned action center. This high- and low-skill model difference on CGT–ISM relationship is consistent in both CMIP5 and CMIP6. It is also found that high-skill models tend to simulate more realistic CGT–ENSO relationship. The relationship between simulation skills of CGT–ENSO correlation and CGT spatial pattern is attributed to the remote impact of ENSO on CGT wave train through affecting ISM precipitation anomalies.

Evaluation of atmospheric moisture transport to the Tibetan Plateau from 33 CMIP6 models

Atmospheric moisture transport is pivotal in regulating water resources over the Tibetan Plateau (TP). With the growing concerns about climate change, understanding the evolution of atmospheric moisture transport over the TP has become increasingly critical. however, the spatiotemporal distinctions of this transport remain poorly understood in the CMIP6 models. Here, we conducted a comprehensive evaluation of simulated historical atmospheric moisture transport from 33 CMIP6 models, utilizing a novel methodology that assesses the accuracy of model simulations in replicating regional atmospheric moisture transport over the TP. Our results indicate that the CMIP6 models generally succeed in reproducing the broad spatial patterns of atmospheric moisture transport. Nonetheless, substantial errors occur during the monsoon period, primarily attributable to inaccuracies in the location, movement, and intensity of the simulated Indian summer monsoon. The coarser resolution and poor representation of physical processes are potential reasons for errors in atmospheric moisture transport simulation over the TP. The Failure to simulate the terrain blocking on atmospheric moisture transport exacerbates these deficiencies, leading to significant discrepancies. Of the 33 CMIP6 models we investigated, over one-third displayed serious deficiencies in this regard. While coarser resolution and orographic gravity waves are plausible factors, they do not fully account for all the results obtained in this study. Insufficiently detailed or inaccurate topographic data used in the models may also contribute to this deficiency. This study highlights the necessity of using rigorously evaluated models to develop effective regional adaptation strategies over the Tibetan Plateau.

Evaluation and comparison of the performances of the CMIP5 and CMIP6 models in reproducing extreme rainfall in the Upper Blue Nile basin of Ethiopia

Evaluation and comparison of the performances of the CMIP5 and CMIP6 models in reproducing extreme rainfall in the Upper Blue Nile basin of Ethiopia

Projection of Future Climatic Variables based on CMIP5 and CMIP6 Models in the Gedarchay Catchment (West Azarbaijan)

Projection of Future Climatic Variables based on CMIP5 and CMIP6 Models in the Gedarchay Catchment (West Azarbaijan)

Future Global River Ice in CMIP6 Models under Climate Change

Abstract River ice changes due to climate change significantly impact river hydrology, economies, and societies. This study employed the CMIP6 data and a river ice model to predict global river ice changes in response to climate change. Results indicate significant declines in global river ice due to global warming. With each 1°C increase in surface air temperature (SAT) in the future, river ice extent of ice-affected rivers decrease by 2.11 percentage points, and ice duration shorten by 8.4 days. Under the shared socioeconomic pathways 2-4.5 (SSP2-4.5) scenario, the long-term mean SAT is 1.2°–2.5°C higher than in the near term. This leads to a 1.9–4.4-percentage-point decrease in global river ice extent, a 6.8–15.1-day decrease in river ice duration, and ice-free rivers increasing by up to 4.02%. The SSP5-8.5 scenario predicts a warmer long-term mean SAT, leading to greater reductions in river ice. Geographically, river ice loss is most notable in North America, Europe, Siberia, and the Tibetan Plateau (TIB), particularly in peninsular, coastal, and mountainous regions due to the combined effects of initial temperatures and temperature increases. Overall, the eastern Europe (EEU) shows the greatest loss of river ice on average. Monthly analyses show the most substantial decreases from October to June, indicating pronounced seasonal variability. This study provides valuable insights for addressing challenges related to river ice changes in high-latitude and high-elevation regions. Significance Statement River ice has a significant impact on various aspects, including hydrology, ecology, and the economy. The ongoing global warming phenomenon has resulted in a decline in river ice. This ice acts as a barrier, affecting river gas exchange and influencing the metabolism of the river, which is crucial for regulating greenhouse gas (GHG) emissions. The primary objective of this research is to examine the response of river ice to future climate change. The outcomes of this study will play a role in estimating future GHG emissions and understanding river metabolism, as well as providing a valuable reference for tackling emerging challenges in resource acquisition in high-latitude and high-altitude regions.

Local Analysis of Drought and Climate Change Projection in Future Periods under the CMIP6 Model (Case Study: Mazandaran Province)

Local Analysis of Drought and Climate Change Projection in Future Periods under the CMIP6 Model (Case Study: Mazandaran Province)

Historical Trends in Ocean Heat, Carbon, Salinity, and Oxygen Simulations: Impact of a Changing Ocean Transport

AbstractExamination of historical simulations from CMIP6 models shows substantial pre‐industrial to present‐day changes in ocean heat (ΔH), salinity (ΔS), oxygen (ΔO2), dissolved inorganic carbon (ΔDIC), chlorofluorocarbon‐12 (ΔCFC12), and sulfur hexafluoride (ΔSF6). The spatial structure of the changes and the consistency among models differ among tracers: ΔDIC, ΔCFC12, and ΔSF6 all are largest near the surface, are positive throughout the thermocline with weak changes below, and there is good agreement among the models. In contrast, the largest ΔH, ΔS, and ΔO2 are not necessarily at the surface, their sign varies within the thermocline, and there are large differences among models. These differences between the two groups of tracers are linked to climate‐driven changes in the ocean transport, with this tracer “redistribution” playing a significant role in changes in ΔH, ΔS, and ΔO2 but not the other tracers. The spatial structure, and differences between models, of changes in age tracers are consistent with ΔH, ΔS, and ΔO2, supporting the hypothesis that redistribution plays a major role for these tracers. Further, the impact of the vertical displacement of isopycnals (heave) plays a major role in the differing impact of redistribution between the two groups, with this process causing insignificant changes to ΔDIC, ΔCFC12, and ΔSF6 due to their weak spatial gradients. A similar multi‐tracer analysis of observations could provide insights into the relative role of the addition and redistribution of tracers in the ocean.

Northern Hemisphere Atmospheric Blocking in CMIP6 Climate Projections using a Hybrid Index

Abstract Atmospheric blocking is quantified by a variety of different blocking indices. Here, the index by Davini et al. (DAV12) is modified to a hybrid index by additionally considering the extent of the blocking anticyclone. Applying both indices, the DAV12 and the hybrid index, to the 20th century reanalyses ERA-20C and the 20CRv3 (hereafter 20CR) ensemble reveals large differences between the reanalyses. The annual blocking frequency increases widespread and significantly in ERA-20C, but only regionally and non-significantly in the 20CR ensemble-mean. Trend analysis reveals a higher reliability of the 20CR ensemble-mean than ERA-20C blocking results. Seasonally, the changes in summer blocking are most pronounced and significantly positive around Greenland. The CMIP6-mean historical climate simulation agrees well in magnitude with 20CR during the period 1900–2014, with some underestimation of blocking frequency in the last decades related to inconsistent trends. For a future climate, the average of the CMIP6 models projects a decrease in blocking frequency in major parts of the Northern Hemisphere in summer and in winter. A CMIP6 sub-set of models which agree best to reanalyses shows a less negative future trend. Because of the mismatch of historical trends, especially in summer, the future projections are uncertain.

A multi-scenario ensemble approach incorporating stepwise cluster analysis to reduce uncertainty in large-scale watershed precipitation projections: a case study of Pearl River Basin, South China.

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds.

Substantial Cold Bias During Wintertime Cold Extremes in the Southern Cascadia Region in Historical CMIP6 Simulations

AbstractGlobal climate models often simulate atmospheric conditions incorrectly due to their coarse grid resolution, flaws in their dynamics, and biases resulting from parameterization schemes. Here we document a bias in the magnitude and extent of minimum temperature extremes in the CMIP6 model ensemble, relative to ERA5. The bias is present in the southern Cascadia region (i.e., Pacific Northwestern United States and southwestern British Columbia, Canada, spanning from the coast to the Rocky Mountains), with some models showing a bias magnitude in excess of −10°C in the first percentile of daily winter minimum temperature. The sea level pressure pattern for these events is similar in CMIP6 models and ERA5, showing high anomalies in the Northeast Pacific that are indicative of an atmospheric blocking pattern and consequently more northerly flow. Though this atmospheric blocking pattern is typically concurrent with cold winter temperatures across much of North America, Rocky and Cascade mountain ranges prevent the cold air from reaching the southern Cascadia region as confirmed by the observations and reanalysis. Our results suggest that the bias in CMIP6 minimum temperatures is a result of unresolved topography in the Rockies and Cascade mountain ranges, such that the terrain does not adequately block cold air advection from reaching the southern Cascadia region.

Decadal Relationship Between Arctic SAT and AMOC Changes Modulated by the North Pacific Oscillation

AbstractThe faster warming for Arctic Ocean surface air temperature (SAT) relative to that at lower latitude is connected with various processes, including local radiation feedback, poleward oceanic and atmospheric heat transport. It is unclear how combinations of different low‐frequency internal climate modes influence Arctic amplification on the decadal timescale. Here, the decadal Arctic SAT variation, its connection with the Atlantic meridional overturning circulation (AMOC) and possible underlying mechanisms, are investigated based on several independent observational proxies, pre‐industrial experiments, and historical large ensembles of two CMIP6 models. Our study suggests that AMOC and Arctic SAT vary in phase on the decadal timescale, whereas this relationship is insignificant at the interannual timescale. Further analysis shows that the AMOC accompanied with cross‐basin oceanic water/heat transport between Atlantic and Arctic would alter air–sea interface exchange over the melting ice regions, and then amplified poleward atmospheric heat and moisture transports. The resulting enhanced downward longwave radiation ultimately warms the Arctic SAT. Additionally, the decadal‐scale North Pacific Oscillation (NPO) can modulate the relationship between AMOC and Arctic SAT by influencing poleward moisture transport and cross‐basin circulation. Specifically, the phase shift of combined NPO and AMOC can contribute 14%–41% covariance relationship between AMOC and Arctic SAT. Our study provides potential sources for predicting the Arctic climate and constraining its uncertainty in future projections.

Unraveling Projected Changes in Spatiotemporal Patterns and Drought Events across Mainland China Using CMIP6 Models and an Intensity–Area–Duration Algorithm

Unraveling Projected Changes in Spatiotemporal Patterns and Drought Events across Mainland China Using CMIP6 Models and an Intensity–Area–Duration Algorithm

Development of DRIP - drought representation index for CMIP climate model performance, application to Southeast Brazil

With the escalating impacts of drought events driven by climate change, reducing the uncertainty of drought projections becomes critical for enhancing risk management and adaptation strategies. This study aimed to develop an index for assessing the performance of CMIP6 Global Climate Models in simulating meteorological drought scenarios across regional hydrological systems, intended to provide more reliable information for management purposes. Named the ‘Drought Representation Index for CMIP Climate Model Performance’ (DRIP), this index evaluates CMIP models' performance to represent drought severity, duration, and return period. DRIP was used to select CMIP models and create an ensemble of the best-performing models (E-DRIP) to improve the reliability of drought projections. E-DRIP was then compared with a general ensemble of available CMIP6 models (E-CMIP). We applied this method in Southeast Brazil, a region known for its climate uncertainties and low predictability; specifically, it was implemented within the Paraíba do Sul River Basin, a nationally strategic watershed in a highly populated and industrialized area, which has recently faced unprecedented drought-related water crises. Results showed that DRIP effectively assessed the individual performance of CMIP models, which exhibited considerable variability, and identified the top-performing models for a multi-model ensemble. Additionally, the E-DRIP ensemble significantly reduced uncertainties in drought projections, achieving an average reduction of 63 % in the study area compared to E-CMIP. Furthermore, the proposed method enables evaluations across any standardized drought index scale, reference period, or threshold, and can be readily adapted to other hydrological systems.

A dataset of high-resolution climate change projections over South America with bias correction

Accurate and detailed datasets are crucial for assessing climate change impacts. Regional climate models provide high-resolution simulations and are key tools but often exhibit systematic biases. Therefore, this paper presents a dataset derived from bias-corrected Eta regional model simulations and projections driven by four global CMIP5 models. The correction applied to daily precipitation, potential evapotranspiration, actual evapotranspiration, and 2-m air temperature, was conducted on a 0.2° x 0.2° grid over South America. The dataset covers two periods: 1976-2005 (baseline) and 2006-2099 (future) under RCP4.5 and RCP8.5 scenarios. Empirical quantile mapping was used to adjust the Eta model outputs to better match observational data. This method modified the accumulated probability curves of the Eta model outputs to align with observational curves for both baseline and future climates. Two observational datasets were used for correction and evaluation. The new dataset shows that bias correction significantly reduced the errors in the Eta simulations, especially for the frequent values of precipitation, potential evapotranspiration, and 2-m temperature, and also corrected the annual cycle and frequency distribution of these variables, approaching the observations. The pattern of extreme precipitation indices from the bias-corrected Eta dataset also reduced error. Bias correction was applied to future projections. The comparison against the raw Eta dataset showed that the trends of changes were preserved, but in general, the peaks of the changes were smoothed. As in the raw Eta dataset, the RCP8.5 scenario showed a higher change rate than RCP4.5. This work also revealed the large uncertainty of the observational dataset; some of the remaining errors after the bias correction were mostly due to differences between the two correction and evaluation observational datasets. The described dataset is freely available from the CNPq LattesData repository at the following link: https://doi.org/10.57810/lattesdata/WAVGSL.

Increasing exposure of global croplands productivity to growing season heatwaves under climate warming

Abstract Growing season heatwaves that occur simultaneously over global croplands can negatively impact global food baskets. The long-term changes of growing season heatwaves, as well as their impacts on croplands productivity, are crucial to food security, but remain unclear. Here, we investigated changes in the frequency, intensity and magnitude of growing season heatwaves from the past to the future over the global croplands, based on observations and Coupled Model Intercomparison Project Phase 6 models. We introduced an index, gross primary productivity (GPP) exposure, as a proxy of the overall impact of heatwaves on cropland productivity. The results show that the frequency and intensity of growing season heatwaves have increased since 1950 and will continue throughout the 21st century. The increase of the annual accumulated magnitude of growing season heatwaves in the future is mainly contributed by the increase of heatwave frequency. This leads to a global-scale increase in the GPP exposure to growing season heatwaves, with Asia, North America, and Europe being the most affected. The continued increase in GPP exposure is dominated by increases in heatwaves rather than GPP itself. Under the lower emission scenario SSP1-2.6, the global cropland GPP exposure will reduce by 86.11% and 330.47% relative to that under SSP2-4.5 and SSP5-8.5 scenarios, respectively, by the end of 21st century. Our results provide crucial insights into potential impacts of heatwaves on cropland productivity and hence food security.