Mechanism of the quasi-biweekly precipitation variability over the Mongolian Plateau and its simulations in CMIP6 models

ContextMongolian Plateau is one of the largest contingent arid and semi-arid regions of the world. Rivers on the plateau provide vital water for millions of indigenous Mongolian people and numerous endangered wildlife, but are increasingly disturbed by climate change and human activities. Yet, long-term changes in river runoff across the plateau remain poorly studied due to data unavailability.ObjectivesThis study aimed to analyze the temporal trends in gauged river runoff on the Mongolian Plateau, identify drivers of the observed changes, and evaluate CMIP6 models' performance in simulating historical runoff changes across the plateau.MethodsWe compiled possibly the largest database of long-term (20 - 71 years) river runoff for the plateau comprising measurements over 30 major rivers. Statistical analyses were conducted to assess trends in river runoff and correlations between runoff and climatic variables. Additionally, we applied the Budyko curve framework to identify the influence of human activities on river runoff in specific basins. Furthermore, we compared ground-measured runoff data with simulations from CMIP6 models to evaluate the ability of CMIP6 models to replicate runoff dynamics in typical arid and semi-arid regions.ResultsWe observed pervasive and abrupt reduction in runoff in 21 out of the 30 rivers within 5 years before or after the year of 2000. Variations in river runoff were most significantly caused by changes in total precipitation (TP). In particular, 27 rivers experienced abrupt TP changes around 2000, and there was a significant positive correlation between annual fluctuations in TP and river runoff for 18 rivers. In addition to climate factors, the influence of human activities was identified in certain basins. The CMIP6 simulations failed to capture the abrupt changes in runoff occurred pervasively across the plateau around 2000.ConclusionsAround 2000, major rivers on the Mongolian Plateau, especially in Inner Mongolia, experienced runoff declines, primarily due to TP changes. Human activities like dam construction and water diversion further influenced local runoff. CMIP6 historical runoff simulations were inaccurate across the plateau, highlighting the difficulty of simulating river runoff in this critical region. Our study could contribute to a more comprehensive understanding of the water resource changes on the Mongolian Plateau, with direct implications for enhancing ecological conservation and management practice.

The Mongolian Plateau (MP) is located in the eastern part of arid Central Asia (ACA). Climatically, much of the MP is dominated by the westerly circulation and has an arid and semi-arid climate; however, the eastern part of the MP is also influenced by the East Asian summer monsoon (EASM) and has a humid and semi-humid climate. Several studies have shown that precipitation variability in the MP differs from that in western ACA but is consistent with that in the EASM region. Here we use monthly precipitation data for 1979-2016 to characterize and determine the origin of the summer precipitation variability of the MP and the EASM region. The results show that the MP and the mid-latitude EASM region exhibit a consistent pattern of precipitation variability on interannual and decadal timescales; specifically, the consistent regions are the MP and North and Northeast China. We further investigated the physical mechanisms responsible for the consistent interdecadal precipitation variability between the MP and the mid-latitude EASM region, and found that the mid-latitude wave train over Eurasia, with positive (negative) geopotential height anomalies over the North Atlantic and ACA and negative (positive) geopotential height anomalies over Europe and the MP, is the key factor responsible for the consistency of precipitation variability in the MP and the mid-latitude EASM region. The positive anomalies over the North Atlantic and ACA and negative anomalies over Europe and the MP would enhance the transport of westerly and monsoon moisture to the MP and North and Northeast China. They could also strengthen the Northeast Asian low, enhance the EASM, and trigger the anomalous ascending motion over the MP which promotes precipitation in the MP and in the mid-latitude EASM region. Overall, our results help explain the spatial variations of paleo-precipitation/humidity reconstructions in East Asia and clarify the reasons for the consistency of the regional climate.

Understanding the variations of future drought under climate warming can provide the basis for mitigation efforts. This study utilized the standardized precipitation evapotranspiration index (SPEI), empirical mode decomposition (EMD) and empirical orthogonal function analysis (EOF) to predict the spatiotemporal variation of future drought under the representative concentration pathway RCP4.5 and RCP8.5 scenarios within the Mongolian Plateau over the period 2020–2100. The SPEI was computed using temperature and precipitation data generated by the fifth stage of the Coupled Model Intercomparison Project (CMIP5). The results under both the RCP4.5 and RCP8.5 scenarios showed increasing changes in temperature and precipitation. Both scenarios indicated increases in drought, with those under RCP8.5 much more extreme than that under RCP4.5. Under both scenarios, the climate showed an abrupt change to become drier, with the change occurring in 2041 and 2054 for the RCP4.5 and RCP8.5 scenarios, respectively. The results also indicated future drought to be more extreme in Mongolia than in Inner Mongolia. The simulated drought pattern showed an east–west antiphase and a north–south antiphase distribution based on EOF. The frequency of drought was higher under RCP8.5 compared to that under RCP4.5, with the highest frequencies under both scenarios occurring by the end of the 21st century, followed by the mid-21st century and early 21st century. The findings of this research can provide a solid foundation for the prevention, early warning and mitigation of drought disasters within the context of climate change in the Mongolian Plateau.

Summer precipitation on the Mongolian Plateau (MP) is a crucial climate indicator in arid and semiarid regions, significantly influencing water resources and climate variability. This study evaluates the capability of the Coupled Model Intercomparison Project phase 6 (CMIP6) models to reproduce the observed spatial pattern of precipitation climatology and interannual variability over the MP. Although the multimodel ensemble mean (MME) of the CMIP6 models can roughly capture the overall spatial distribution of MP precipitation, there are significant differences among the models, particularly in the simulation of precipitation variability on the MP. Here, we extract the leading intermodel mode of the MP precipitation variability, accounting for over 50% of the total intermodel uncertainty, and investigate the sources of these uncertainties. The results show that the dominant intermodel precipitation uncertainty mode reveals a meridional dipole pattern across the 30-model ensemble members, with flooding in northern MP but drought in southern MP. This structure is attributed to El Niño–Southern Oscillation (ENSO)-interference circulation anomalies in MP, driven by an extratropical teleconnection wave train triggered by the Indian summer monsoon rainfall. Further analysis investigates the role of ENSO in affecting MP precipitation patterns, revealing that models with greater ENSO intensity tend to have amplified interannual variability and lower similarity to observed precipitation distribution. Enhancing the reproduction of ENSO variability in models could mitigate biases in the simulated relationship between MP precipitation and ENSO, thereby improving the accuracy of MP precipitation pattern simulations.

<h3>Knowledge of aerosol concentration, type, and physical and chemical properties is necessary to understand their role in Earth’s climate system. However, CMIP6 models’ performance of AOD simulation in China lacks a comprehensive evaluation and the potential improvement for CMIP6 models is still unclear. Here, we assess the performance of CMIP6 models in simulating annual mean AOD climatology and its seasonality over China from 2000 to 2014 and explore the underlying reasons for its performance. Compared with CMIP5, CMIP6 models can better capture the annual mean AOD climatology magnitude over Eastern Central China (ECC) with a notable enhancement of 52.98% due to a significant increase in the dominate sulfate aerosol. However, the majority of CMIP6 models fail to capture the observed inverted “V-like” pattern that depicts two centers of maximum AOD in spring over northeast China (NEC) and in summer over southeast China (SEC), respectively. The deficiency of two maximums by CMIP6 models is separately due to the negative bias in the simulation of organic aerosol (OA) AOD and sulfate AOD. Our analysis suggests that the deviation of simulated precipitation, relative humidity (RH), and liquid water path (LWP) in CMIP6 models contributes to the deviation of simulated sulfate AOD through affecting sulfate aerosol concentration by wet deposition and aqueous-phase production. Therefore, this study illustrates the urgent need to improve AOD simulation in global climate models.</h3>

ABSTRACTTwenty‐one climate models from Coupled Model Intercomparison Project Phase 3 (CMIP3) and thirty‐one models from the project's Phase 5 (CMIP5) were used to evaluate model reproducibility in assessing interannual variability of summer precipitation in Pan‐Asian monsoon region. The results show that both the multi‐model ensemble means of the best eight models and of the thirty‐one CMIP5 models are more skilful than those of the CMIP3 models in simulating the climatological pattern and the dominant mode of summer precipitation in Pan‐Asian monsoon region. CMIP5 models show improved skill in representing the main characteristic of the first mode of summer precipitation in Pan‐Asian monsoon region, which is a meridional tripole pattern from north to south occurring east of the 80°E region. That is, owing to the improved El Niño–Southern Oscillation (ENSO) pattern and the relationship between Antarctic oscillation in the southern Pacific Ocean (AAOSP) and ENSO, the first dominant mode of summer precipitation in Pan‐Asian monsoon region are captured by CMIP5 models, which indicates that these models are more skilful in simulating the air–sea interaction of the Southern Hemisphere.

The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. Multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skillful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, CMIP6 MME showed a significant improvement in simulating rainfall and temperature over Bangladesh compared to CMIP5 MME. The highest improvements were found in simulating cold season (winter and post-monsoon) rainfall and temperature in higher elevated areas. The improvement was relatively more for rainfall than for temperature. The models could capture the interannual variability of annual and seasonal rainfall and temperature reliably, except for the winter rainfall. However, systematic wet and cold/warm biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlations with observed data, but the higher difference in standard deviations and centered root mean square errors compared to CMIP5 GCMs indicates better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables except for minimum temperature at different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature than CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is not consistent among the climate models used in this research. However, it sets a precedent for future research on climate change risk assessment for the scientific community.

The lake area in the Inner Mongolian Plateau (IMP) has experienced a rapid reduction in recent decades. Previous studies have highlighted the important role of intensive human activities in IMP lake shrinkage. However, this study found that climate change–induced summer precipitation variations can exert great influences on the IMP lake area variations. The results suggest that the decadal shift in the IMP summer precipitation may be the predominant contributor to lake shrinkage. Further analysis reveals that the Atlantic multidecadal oscillation (AMO) and Arctic sea ice concentration (SIC) play important roles in the IMP summer precipitation variations. The AMO seems to provide beneficial large-scale circulation fields for the decadal variations in the IMP summer precipitation, and the Arctic SIC decline is favorable for weakening the IMP summer precipitation intensity after the late 1990s. Evidence indicates that the vorticity advection related to the Arctic SIC decline can result in the generation of Rossby wave resources in the midlatitudes. Then, the strengthened wave resources become favorable for enhancing the stationary wave propagation across Eurasia and inducing cyclonic circulation over the Mongolia–Baikal regions, which might bring more rainfall northward and weaken the IMP summer precipitation intensity. Consequently, due to the decreased rainfall and gradual warming after the late 1990s, the lake area in the IMP has experienced a downward trend in recent years.

Bias evaluation in rainfall over Southeast Asia in CMIP6 models

       Fire is the primary form of terrestrial ecosystem disturbance globally and a critical Earth system process. So far, most Earth system models (ESMs) have incorporated fire modeling, with 19 out of them submitted fire simulations to the CMIP6. Transitioning from CMIP5 to CMIP6, much more models submitted fire simulations and the dominant fire scheme has evolved from GlobFIRM to the Li scheme. However, it remains unknown how well CMIP6 ESMs perform in fire simulations. This study provides the first comprehensive evaluation of CMIP6 fire simulations, through comparisons with multiple satellite-based datasets and the Reading Paleofire Database of global charcoal records (RPD).        Our results show that most CMIP6 models simulate the global amounts of present-day burned area and fire carbon emissions within the range of satellite-based products, and reproduce observed major features of spatial pattern and seasonal cycle as well as the relationships of fires with precipitation and population density, except for models employing the GlobFIRM fire scheme. Additionally, most CMIP6 models can reproduce the response of interannual variability of tropical fires to ENSO, except for some models incorporating the SPITFIRE fire scheme. From 1850 to 2015, CMIP6 models generally agree with RPD, with some discrepancies in southern South America before 1920 and in temperate and eastern boreal North America, Europe, and boreal Asia after 1990. Compared with CMIP5, CMIP6 has solved the serious issues of CMIP5 which simulates the global burned area less than half of observations, fails to capture the high burned area fraction in Africa, and underestimates seasonal variability. CMIP6 fire carbon emissions simulations are also closer to RPD. However, CMIP6 models still fail to capture the present-day significant decline in observed global burned area and fire carbon emissions partly due to underestimation in anthropogenic fire suppression, and fail to reproduce the spring peak in NH mid-latitudes mainly due to an underestimation of crop fires. Based on our findings, we identify potential biases in fire and carbon projection based on CMIP6 models. We also provide suggestions for the fire scheme development, and bias correction methods when generating multi-source merged fire products.

Combined effects of ocean-land processes on spring precipitation variability in Mongolian Plateau

Contribution of internal variability to the Mongolian Plateau summer precipitation trends in MPI-ESM large-ensemble model

Cloud and its radiative effects are major sources of uncertainty that lead to simulation discrepancies in climate models. In this study, shortwave cloud radiative forcing (SWCF) over major stratus regions is evaluated for Atmospheric Models Intercomparison Project (AMIP)-type simulations of models involved in the third and fifth phases of the Coupled Models Intercomparison Project (CMIP3 and CMIP5). Over stratus regions, large deviations in both climatological mean and seasonal cycle of SWCF are found among the models. An ambient field sorted by dynamic (vertical motion) and thermodynamic (inversion strength or stability) regimes is constructed and used to measure the response of SWCF to large-scale controls. In marine boundary layer regions, despite both CMIP3 and CMIP5 models being able to capture well the center and range of occurrence frequency for the ambient field, most of the models fail to simulate the dependence of SWCF on boundary layer inversion and the insensitivity of SWCF to vertical motion. For eastern China, there are large differences even in the simulated ambient fields. Moreover, almost no model can reproduce intense SWCF in rising motion and high stability regimes. It is also found that models with a finer grid resolution have no evident superiority than their lower resolution versions. The uncertainties relating to SWCF in state-of-the-art models may limit their performance in IPCC experiments.

Given the climatic importance of the Madden–Julian oscillation (MJO), this study evaluates the capability of CMIP6 models in simulating MJO eastward propagation in comparison with their CMIP5 counterparts. To understand the representation of MJO simulation in models, a set of diagnostics with respect to MJO-associated dynamic and thermodynamic structures is applied, including large-scale zonal circulation, vertical structures of diabatic heating and equivalent potential temperature, moisture convergence at the planetary boundary layer (PBL), and the east–west asymmetry of moisture tendency relative to the MJO convection. The simulated propagation of the MJO in CMIP6 models shows an overall improvement in realistic speed and longer distance, which displays a robust linear regression relationship against the above-mentioned dynamic and thermodynamic structures. The improved MJO propagation in CMIP6 largely benefits from better representation of premoistening processes that is primarily contributed by improved PBL moisture convergence. In addition, the convergence of moisture and meridional advection of moisture prior to the MJO convection are enhanced in CMIP6, while the zonal advection of moisture is as weak as that in CMIP5. The increased convergence of moisture is a result of enhanced lower-tropospheric moisture and divergence, and the enhanced meridional advection of moisture can be caused by sharpened meridional gradient of mean lower-tropospheric moisture in the western Pacific. Further examination of the lower-tropospheric moisture budget reveals that the enhanced zonal asymmetry of the moisture tendency in CMIP6 is driven by the drying process to the west of the MJO convection, which is attributed to the negative vertical and zonal advections of moisture.

Historical simulations of models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are evaluated over 10 Australian regions for their performance in simulating extreme temperatures, among which three models with initial‐condition large ensembles (LEs) are used to estimate the effects of internal variability. Based on two observational data sets, the Australian Water Availability Project (AWAP) and the Berkeley Earth Surface Temperatures (BEST), we first analyze the models' abilities in simulating the probability distributions of daily maximum and minimum temperature (TX and TN), followed by the spatial patterns and temporal variations of the extreme indices, as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). Overall, the CMIP6 models are comparable to CMIP5, with modest improvements shown in CMIP6. Compared to CMIP5, the CMIP6 ensemble tends to have narrower interquartile model ranges for some cold extremes, as well as narrower ensemble ranges in temporal trends for most indices. Over southeast, tropical, and southern regions, both CMIP ensembles generally exhibit relatively large deficiencies in simulating temperature extremes. We also confirm that internal variability can affect the trends of the extremes and there is uncertainty in representing the irreducible variability among different LEs in CMIP6. Furthermore, the evaluation based on Perkins' skill score (PSS) and root‐mean‐square error (RMSE) in the three LEs does not directly correlate with the ranges of the trends for extreme temperatures. The findings of this study are useful in informing and interpreting future projections of temperature‐related extremes over Australia.