CMIP5 Models Research Articles

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.

Process-based evaluation of intraseasonal oceanic Kelvin waves in the Pacific Ocean in CMIP6 models

Abstract Oceanic intraseasonal Kelvin waves (KWs) help modulate upper-ocean thermal characteristics, providing feedbacks to important coupled air-sea phenomena in the tropics. The recent availability of daily thermocline depth fields from several CMIP6 models makes it possible to evaluate the performance of KWs and identify potential sources of bias. Most models fail to simulate a realistic spatial distribution of KW variability. Models simulate large variability of KWs in the western or eastern Pacific rather than in the central Pacific as observed. The modeled KWs propagate slowly (about 1.5 m/s) compared to observations (about 2.5 m/s). This slow propagation is also identified in wavenumber-frequency spectra for KWs and meridional KW structures, which is more consistent with a second baroclinic mode structure in models compared to the first baroclinic mode structure in observations. An analysis of the relative contributions of vertical wavenumber and background ocean stability to KW phase speeds indicates that the high vertical wavenumber bias in models contributes most to the slow propagation, in which the higher-than-observed vertical wavenumbers implying the biased incorporation of higher baroclinic modes in model KW structure. This finding is further supported by the results of vertical mode decomposition that incorporates background density profiles. These results indicate that a realistic representation of the KW vertical structure is essential to produce realistic KW propagations in models.

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.

CMIP6 models underestimate Arctic sea ice loss during the Early Twentieth-Century Warming, despite simulating large low-frequency sea ice variability

Abstract The variability of Arctic sea ice extent (SIE) on interannual and multi-decadal timescales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in 20th-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850-1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE), but exhibit pronounced inter-model spread in multi-decadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the sub-polar North Atlantic. We find that this is associated with differences in models’ sensitivity to northern hemispheric sea surface temperatures (SST). Additionally, we show that while CMIP6 models are generally capable of simulating multi-decadal changes in Arctic sea ice from the mid-20th century to present day, they tend to underestimate the observed sea ice decline during the Early Twentieth-Century Warming (ETCW; 1915-1945). These results suggest the need for an improved characterization of the sea ice response to multi-decadal climate variability, in order to address the sources of model bias and reduce the uncertainty in future projections arising from inter-model spread.

Two Perspectives on Amplified Warming over Tropical Land Examined in CMIP6 Models

Abstract Climate change projections show amplified warming associated with dry conditions over tropical land. We compare two perspectives explaining this amplified warming: one based on tropical atmospheric dynamics and the other focusing on soil moisture and surface fluxes. We first compare the full spatiotemporal distribution of changes in key variables in the two perspectives under a quadrupling of CO2 using daily output from the CMIP6 simulations. Both perspectives center around the partitioning of the total energy/energy flux into the temperature and humidity components. We examine the contribution of this temperature/humidity partitioning in the base climate and its change under warming to rising temperatures by deriving a diagnostic linearized perturbation model that relates the magnitude of warming to 1) changes in the total energy/energy flux, 2) the base-climate temperature/humidity partitioning, and 3) changes in the partitioning under warming. We show that the spatiotemporal structure of warming in CMIP6 models is well predicted by the inverse of the base-climate partition factor, which we term the base-climate sensitivity: conditions that are drier in the base climate have a higher base-climate sensitivity and experience more warming. On top of this relationship, changes in the partition factor under intermediate (between wet and dry) surface conditions further enhance or dampen the warming. We discuss the mechanistic link between the two perspectives by illustrating the strong relationships between lower-tropospheric temperature lapse rates, a key variable for the atmospheric perspective, and surface fluxes, a key component of the land surface perspective. Significance Statement Understanding what conditions give rise to the largest magnitude of warming in response to rising CO2 concentrations is not only scientifically important but also critical from a climate impact standpoint. Two main perspectives, one focusing on atmospheric dynamics and the other focusing on land surface processes, have been proposed to explain the stronger warming associated with drier conditions in the tropics. Here, we compare and contrast these two perspectives. We demonstrate that amplified warming in CMIP6 models can largely be predicted from base-climate dryness alone in both perspectives but is further modified based on changes in the partitioning of energy between temperature and moisture. We highlight the spatiotemporal conditions where assumptions in the two perspectives hold and where deviations occur within CMIP6 climate models.

MJO Diversity in CMIP6 Models

Abstract Recent studies have shown that individual Madden–Julian oscillation (MJO) events can be categorized into four types based on their propagation characteristics: standing, jumping, slow-propagating, and fast-propagating MJOs. While their structures and impacts are well documented in observations, their representation in state-of-the-art climate models has not been investigated. This study evaluates the four types of MJOs in phase 6 of Coupled Model Intercomparison Project (CMIP6) models. While most models show reasonable MJO propagation characteristics, their performance varies among MJO types, with better performance for the propagating MJO types. To identify the factors that drive each MJO type, background conditions at the surface, troposphere, and stratosphere are examined. We found that the standing and fast MJOs are closely associated with sea surface temperature (SST) and precipitable water (PW) distributions. Specifically, negative and positive SST and PW anomalies are observed over the equatorial Pacific during the standing and fast-propagating MJOs, respectively. In contrast, the jumping and slow-propagating MJOs do not show noticeable SST and PW distributions, suggesting that they may be more strongly influenced by the internal dynamics than by the background conditions. It is further found that the models with more frequent standing MJO than fast MJO generally have stronger negative biases in both SST and PW over the equatorial western Pacific. Although the standing MJO is also preferred when the stratospheric wind at 50 hPa is westerly, such a relationship is absent in the models. These results suggest that MJO diversity in climate models could be improved by better simulation of the mean state, especially for the standing and fast-propagating MJOs.

Understanding the Biases in Global Monsoon Simulations from the Perspective of Atmospheric Energy Transport

Abstract Understanding global monsoon (GM) variability and projecting its future changes rely heavily on climate models. However, climate models generally show pronounced biases in GM simulations, and the reasons for this remain unclear. Here, we evaluate the performance of 20 pairs of climate models that participated in both phase 5 of the Coupled Model Intercomparison Project (CMIP5) and phase 6 of CMIP (CMIP6) and identify the sources of their GM simulation biases from an energy transport perspective. The multimodel mean improvement in CMIP6 compared to CMIP5 is demonstrated by the increasing skill scores for various GM metrics from 0.20–0.79 to 0.48–0.83. More specifically, the dry biases in the Northern Hemisphere Summer Monsoon (NHSM) precipitation in CMIP5 [root-mean-square error (RMSE): 1.85 mm day−1] are reduced in CMIP6 (RMSE: 1.66 mm day−1). This higher simulation skill is associated with higher skill in simulating the precipitation-solstitial mode, monsoon intensity, and monsoon domains. The improvement in the NHSM precipitation simulation results from that in the meridional transport of atmospheric energy. Atmospheric energy budget analysis shows that the negative biases in downward surface longwave radiation and northward energy transport are smaller in CMIP6 than in CMIP5 in the boreal summer, resulting in a more realistic interhemispheric thermal contrast and meridional gradient of moist static energy. However, a major weakness of the CMIP6 models is found in the Southern Hemisphere Summer Monsoon precipitation simulation due to the positive bias in the top-of-the-atmosphere downward longwave radiation. This study shows that reasonably reproducing the meridional global atmospheric energy transportation is necessary for skillful GM simulation.

Improved simulation of the influence of the North Pacific Oscillation on El Niño-Southern Oscillation in CMIP6 than in CMIP5 models

Improved simulation of the influence of the North Pacific Oscillation on El Niño-Southern Oscillation in CMIP6 than in CMIP5 models

Teleconnection and the Antarctic response to the Indian Ocean Dipole in CMIP5 and CMIP6 models

Teleconnection and the Antarctic response to the Indian Ocean Dipole in CMIP5 and CMIP6 models

Assessment of the Southern Ocean Sea Surface Temperature Biases in CMIP5 and CMIP6 Models

Assessment of the Southern Ocean Sea Surface Temperature Biases in CMIP5 and CMIP6 Models

Robust future intensification of winter precipitation over the United States

We investigate 21st-century hydroclimate changes over the United States (US) during winter and the sources of projection uncertainty under three emission scenarios (SSP2–4.5, SSP3–7.0, and SSP5–8.5) using CMIP6 models. Our study reveals a robust intensification of winter precipitation across the US, except in the Southern Great Plains, where changes are very small. By the end of the 21st century, winter precipitation is projected to increase by about 2–5% K−1 over most of the US. The frequency of very wet winters is also expected to increase, with 6–7 out of 30 winters exceeding the very wet threshold under the different scenarios. Our results suggest that the enhancement of future winter precipitation is modulated largely by coupled dynamic and thermodynamic responses, though partly offset by thermodynamic responses. Overall, our results highlight a high likelihood of increasing impacts from winter precipitation due to climate change.

Stochastic Approach to the Evolution of the Global Water Cycle: Results of Historical Experiments on the CMIP-6 Models

Stochastic Approach to the Evolution of the Global Water Cycle: Results of Historical Experiments on the CMIP-6 Models

Future changes in extremes across China based on NEX-GDDP-CMIP6 models

This paper evaluates the NASA Earth Exchange Global Daily Downscaled Projections’ (NEX-GDDP) CMIP6 models’ performance in simulating extreme climate indices across China and its eight subregions for the period 2081–2100 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios. The models effectively reproduce the spatial patterns of extreme high temperatures, especially in northern China. They show enhanced capabilities in accurately simulating the maximum daily maximum temperature (TXx) and the number of high temperature days (T35). They improve the cold bias of the TXx index in Northwest China and warm bias in South China. In terms of precipitation, the models demonstrate strong performance, evidenced by significant spatial correlations in total wet day precipitation (PTOT) simulations. They reduce the biases of PTOT and simple daily intensity (SDII) compared to CMIP6 models. Regionally, they enhance PTOT accuracy along southern coasts and in Yunnan, better captures very heavy precipitation days (R20) in the Southwest region, max 5-day precipitation (RX5D) in North China and Southwest region, and SDII in the Northeast region and Yunnan. Under SSP5-8.5 scenario, significant impacts include increased TXx in Northwest China, more heatwave days in Southwest China, and more T35 in South China. Extreme precipitation will become more frequent in South and East China, with the greatest intensity increases in Southwest China (SWC1). North China will see fewest consecutive dry days (CDD) indices, while consecutive wet days (CWD) will prominently rise in SWC1.

Климатические изменения температуры воздуха западной части российской Арк­тики в 1940—2099 гг. по данным ERA5 и моделям CMIP6

The study deals with the climatic changes in surface air temperature (SAT) in the western part of the Russian Arctic (60—80° N, 30—90° E). To analyze the changes in SAT that occurred over the period 1940—2023, we use data from the ERA5 reanalysis and the results of the Historical model experiment CMIP6. Future changes in SAT until the end of the 21st century we consider based on the results of SSP experiments of CMIP6 models. An increase in the average SAT of the studied region by 2—4°C is shown from approximately the mid-1970s to 2023. Moreover, this increase in SAT is most noticeable in the White and Kara Seas, as well as in the north and east of the Barents Sea. CMIP6 models based on different greenhouse gas emission scenarios produce markedly different forecasts for the increase in SAT for the region under study until the end of the 21st century. Thus, depending on the scenario, the average increase in SAT in the region under study by the end of the 21st century can range from 2—4°C to 6—10°, with stronger growth of SAT in the north of the region under study. At the same time, with little dependence on the future scenario, CMIP6 models predict that in the next 30 years the average SAT of the western part of the Russian Arctic will increase by approximately 2—3°C, and in the north of the region under study its increase may be more than 3°C, and in south — about 2°C. Thus, the difference in average SAT between the north and south of the region under study will decrease throughout the 21st century under any of the SSP scenarios considered.

Quantifying the Impact of Future Climate Change on Flood Susceptibility: An Integration of CMIP6 Models, Machine Learning, and Remote Sensing

Quantifying the Impact of Future Climate Change on Flood Susceptibility: An Integration of CMIP6 Models, Machine Learning, and Remote Sensing

Assessing Past and Future Heat and Cold Waves over Iraq Using CMIP6 Model

The United Nations classified Iraq as one of the most vulnerable countries to climate change. Temperatures continue to rise leading to more heat waves, droughts, sandstorms, and desertification. This study aims to investigate the characteristics of heat waves over Iraq from 1982 to 2050 using CMIP6 model at RCP4.5 scenarios. The results clearly showed that the effects of climatic changes were evident in the maximum and minimum temperatures inside Iraq. Average temperature increased of 1.9°C between 1982 and 2020. During 1999, there was a sharp increase in summer days for all stations. The annual number of summer days for the stations (Basra, Nasiriyah, Baghdad, Rutba, Sulaymaniyah, and Mosul) was (195, 193, 177, 108, 106, and 142), while in 2039 the number of summer days was found to be (212, 206, 194, 143, 144, and 149), respectively. The increase in the number of discrete heat wave events began in 2020 and will continue until 2050 for almost the entire territory of Iraq. The warmest day in Basra was in July 2016 with 53.2°C, while the projection of the warmest day was found in July 2046 with 55.7°C. The coldest daily temperature reached -5.03 °C for Baghdad in 1997 and -10.1°C for Sulaymaniyah in 1985, while the projection of the coldest day was -16.2°C over Sulaymaniyah in 2039. The trend of the number of days contributing to cold wave events was decreasing for all stations whereas the coldest year for all stations was 1992 for the entire period (1982 to 2020). Heat waves accelerated from the beginning of the study period in 1982 and continued to increase until the end of the study in 2050 in Iraq.

Changes in the Boreal Summer Intraseasonal Oscillation under Global Warming in CMIP6 Models

Changes in the Boreal Summer Intraseasonal Oscillation under Global Warming in CMIP6 Models

Evaluation of ocean wave power utilizing COWCLIP 2.0 datasets: a CMIP5 model assessment

Evaluation of ocean wave power utilizing COWCLIP 2.0 datasets: a CMIP5 model assessment