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Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area's precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen's Slope in the baseline and future periods with all RCPs. [ABSTRACT FROM AUTHOR]

An analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120‐year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Plain Language Summary: China is a vulnerable country to climate change due to its dense population, unbalanced social and economic development, shortage of water resources, and fragile ecosystems. How future precipitation will change over the region is of great concern for the general public and decision makers. This paper presents a first analysis of precipitation simulations from a set of five RCM (RegCM4) 21st century climate change projections, driven by coarse resolution general circulation models (GCMs) over China. We find that the spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by a precipitation underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in north China by both the GCMs and RegCM4 in all seasons except summer, when, weak correlations of the projected change patterns are found between the GCMs and nested RegCM4, indicating the greater role of the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Key Points: The spatial patterns of bias show common features between the GCMs and RegCM4RegCM4 provides greater spatial detail of present day precipitation simulation compared to the GCMs and finer structures of future changesThe change patterns across the RegCM4 projections show a high correlation, but not always between each pair of driving GCM and RegCM4 [ABSTRACT FROM AUTHOR]

To examine the characteristics of future precipitation under climate change is of great significance to urban water security. In this paper, multiple machine learning techniques, i.e., statistical downscaling model (SDSM), support vector machine (SVM), and multilayer perceptron (MLP), were used to downscale large-scale climatic variables simulated by the General Circulation Models (GCMs) to precipitation on a local scale. It was demonstrated in Shenzhen city, China, through multisite downscaling schemes based on projections from the Max Planck Institute Earth System Model (MPI-ESM1.2-HR), Meteorological Research Institute Earth System Model Version 2.0 (MRI-ESM2.0), and Beijing Climate Center Climate System Model (BCC-CSM2-MR). The obtained results showed that the downscaled precipitation would provide good monthly simulations against observations at 10 discrete stations. Regardless of superior performance of SVM and MLP over SDSM, the daily precipitation simulations should be further improved, and downscaling of heavy daily precipitations would be promoted by quantile mapping corrections. Due to the relatively poor simulation performance of BCC-CSM2-MR, the other two climate models were considered under the Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios) for ensemble precipitation projections for 2015–2100. Under the SSP1-2.6 scenario, the amounts of annual average precipitation for 10 stations were estimated to be higher relative to the historical period (2.7%–17%), and 9 out of 10 stations presented an increasing trend. However, downward trends also existed at three stations when it comes to scenarios SSP2-4.5 and SSP5-8.5. Moreover, a significantly positive trend was found to dominate the trend changes of annual extreme daily precipitation during 2015–2050, but the detected trends at stations were greatly dependent on the downscaling techniques and climate models. Besides, the increase in daily extreme precipitations for various return periods as well as statistically different precipitation characteristics for discrete stations would further shed light on urgent demands on urban resilient strategies for climate change adaptation. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

Extreme temperature events are becoming more frequent due to global warming, and have critical effects on natural ecosystems, social and economic spheres, human production and life. Predicting changes in temperature extremes and trends under future climate scenarios helps to assess the impact of climate change accurately. Based on climate observations from 54 meteorological stations in the North China Plain and the projection data from seven general circulation models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6), this paper researches nine representative extreme temperature indices under four typical climate scenarios. The aim is to reveal the temporal and spatial variations in extreme temperature indices in the North China Plain during the past (1971–2010) and the future (2061–2100). The results show that: using a support vector machine (SVM) to perform regression analysis on the multi-GCMs prediction results, the root mean square error (RMSE) and relative standard deviation (RSD) of the multi-model ensemble simulations obtained by the SVM method are lower than those of the arithmetic mean method and can better match the trend of the historical extreme temperature index; the extreme high temperature index is predicted to show a significant upward trend in the future, while the extreme low temperature index will decrease significantly; and there are significant spatial differences in the extreme temperature index in both historical and future periods, with the extreme temperature index under the high radiation forcing scenario (SSP585) showing the most considerable variation and the most significant spatial differences. [ABSTRACT FROM AUTHOR]

This paper looks at regional water security in eastern China in the context of global climate change. The response of runoff to climate change in the Qinhuai River Basin, a typical river in eastern China, was quantitatively investigated by using the Soil and Water Assessment Tool (SWAT) model and the ensemble projection of multiple general circulation models (GCMs) under three different shared socioeconomic pathways (SSPs) emission scenarios. The results show that the calibrated SWAT model is applicable to the Qinhuai River Basin and can accurately characterize the runoff process at daily and monthly scales with the Nash–Sutcliffe efficiency coefficients (NSE), correlation coefficients (R), and the Kling–Gupta efficiency (KGE) in calibration and validation periods being above 0.75 and relative errors (RE) are ±3.5%. In comparison to the baseline of 1980–2015, the mean annual precipitation in the future period (2025–2060) under the three emission scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 will probably increase by 5.64%, 2.60%, and 6.68% respectively. Correspondingly, the multiple-year average of daily maximum and minimum air temperatures are projected to rise by 1.6–2.1 °C and 1.4–2.0 °C, respectively, in 2025–2060. As a result of climate change, the average annual runoff will increase by 16.24%, 8.84%, and 17.96%, respectively, in the period of 2025–2060 under the three SSPs scenarios. The increase in runoff in the future will provide sufficient water supply to support socioeconomic development. However, increases in both rainfall and runoff also imply an increased risk of flooding due to climate change. Therefore, the impact of climate change on flooding in the Qinhuai River Basin should be fully considered in the planning of flood control and the basin's development. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are developed to simulate the past climate and generate future climate predictions. In the context of global warming, their important roles in identifying possible solutions to water resources planning/management are recognized by the world. However, in actual and regional implementation, due to many factors like initial and boundary conditions, parameters and model structures and so forth, there are great variabilities and uncertainties across the future climate projections of GCMs outputs, which has attracted criticism from water resources planners. Thus, the GCMs usually must be evaluated for assessing their performances in simulating the historical observations. Currently, there are many different conclusions and opinions as to whether the optimal individual model is more advantageous or whether the combined consideration of MME works better. The purpose of this paper is to compare the advantages and disadvantages between the selection of the optimal single GCM and multi‐model ensemble (MME) based on one of the more objective selection methods called TOPSIS. The results show that the performances of GCMs in simulating precipitation and temperature in different climate subregions over China are not identical. For CMIP6 GCMs simulations, the optimal precipitation GCM is CMCC‐CM2‐SR5 for EC, MIROC6 for SC, SWC and NWC, CESM2‐WACCM for NC and NEC, and FGOALS‐g3 for QTP. As for temperature, only NESM3 and BCC‐ESM1 are the optimal GCM for QTP and NWC, respectively; in other subregions, MME is better than single GCM. In general, a simple arithmetic averaging approach employed to generate the MME model is not superior to the optimal GCM, although the error metric with the observed data is reduced, at the cost of a severe compression of the interannual variability. [ABSTRACT FROM AUTHOR]

In aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information. [ABSTRACT FROM AUTHOR]

A large number of studies have shown that equatorial plasma bubbles (EPBs) occur mainly after sunset, and they usually drift eastward. However, in this paper, an unusual EPB event was simultaneously observed by an all-sky imager and the global navigation satellite system (GNSS) network in southern China, during the recovery phase of a geomagnetic storm that happened on 6–8 November 2015. Observations from both techniques show that the EPBs appeared near dawn. Interestingly, the observational results show that the EPBs continued to develop after sunrise, and they disappeared about 1 h after sunrise. The development stage of EPBs lasted for at least about 3 h. To our knowledge, this is the first time that the evolution of EPBs developing around sunrise was observed by an all-sky imager and the GNSS network. Our observation showed that the EPBs drifted westward, which was different from the usual eastward drifts of post-sunset EPBs. The simulation from the Thermosphere–Ionosphere–Electrodynamics General Circulation Model (TIE-GCM) suggest that the westward drift of EPBs should be related to the enhanced westward winds at storm time. Besides this, bifurcation and merging processes of EPBs were observed by the all-sky imager in the event. Associated with the development of EPBs, an increase in the peak height of the ionospheric F region was also observed near sunrise, and we suggest the enhanced upward vertical plasma drift during the geomagnetic storm plays a major role in triggering the EPBs near sunrise. [ABSTRACT FROM AUTHOR]

The present paper examined the teleconnections between two huge Asian summer monsoon components (South and East Asia) during three time slices in future: near-(2010–2039), mid-(2040–2069) and far-(2070–2100) futures under the RCP4.5 and RCP8.5 scenarios. For this purpose, a high-resolution atmospheric general circulation model is used and integrated at 40 km horizontal resolution. To get more insight into the relationships between the two Asian monsoon components, we have studied the spatial displaying correlation coefficients (CCs) pattern of precipitation over the entire Asian monsoon region with that of South Asia and three regions of East Asia (North China, Korea–Japan and Southern China) separately during the same three time slices. The possible factors responsible for these teleconnections are explored by using mean sea level pressure (MSLP) and wind fields at 850 hPa. The CC pattern of precipitation over South Asia shows an in-phase relationship with North China and an out-of-phase relationship with Korea–Japan, while precipitation variations over Korea–Japan and Southern China exhibit an out-of-phase relationship with South Asia. The CCs analysis between the two Asian blocks during different time slices shows the strongest CCs during the near and far future with the RCP8.5 scenario. The CC pattern of precipitation over Korea–Japan and Southern China with the wind (at 850 hPa) and MSLP fields indicate that the major parts of the moisture over Korea–Japan gets transported from the west Pacific along the western limb of NPSH, while the moisture over Southern China comes from the Bay of Bengal and South China Seas for good monsoon activity. [ABSTRACT FROM AUTHOR]

ABSTRACT The biases in the Global Circulation Models ( GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed residual-based bagging tree ( RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios ( RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970-1999. [ABSTRACT FROM AUTHOR]

Climate change and land use/cover change (LUCC) are two major factors that alter hydrological processes. The upper reaches of the Tarim River, situated in the northwest region of China, experience a dry and less rainy climate and are significantly influenced by human activities. This study comprehensively assessed the impacts of individual and combined climate changes and LUCCs on streamflow. Three general circulation models (GCMs) were utilized to predict future climate changes under three shared socioeconomic pathways (SSP119, SSP245, and SSP585). Cellular Automata–Markov (CA–Markov) was employed to predict future LUCC under three scenarios (i.e., ecological protection, historical trend, and farmland development). Streamflow for the period 2021–2050 was simulated using the calibrated MIKE SHE model with multiple scenarios. The results showed that from 2021 to 2050, increments in both average annual precipitation and average annual temperature under the three SSPs were predicted to lead to an increased streamflow. In comparison to the conditions observed in 2000, under three LUCC scenarios for 2030, the grassland area decreased by 1.04% to 1.21%, while the farmland area increased by 1.97% to 2.26%, resulting in reduced streamflow. The related changes analysis indicated that the variation in streamflow during winter is most significant, followed by spring. The study predicted that climate change would increase streamflow, while LUCC would decrease it. Due to the greater impact of LUCC, considering the combined effect of both factors, runoff would decrease. The contribution analysis indicated that climate change contributed between −7.16% and −18.66%, while LUCC contributed between 107.16% and 118.66%. [ABSTRACT FROM AUTHOR]

The first rainy season (FRS) in South China from late-spring to early-summer represents the beginning of the summer monsoon rainy season over eastern China. This study investigates the interannual relationship between the South Pacific meridional sea surface temperature (SST) dipole (SPMD) and the concurrent FRS precipitation anomalies over South China during April–June (AMJ) without El Niño/Southern Oscillation (ENSO) impact. During AMJ, after removing the concurrent ENSO signal through the linear regression, the first empirical orthogonal function (EOF) mode of SST anomalies in the South Pacific exhibits a meridional dipole with warming in the tropical southeastern Pacific and cooling in mid-latitudes of the South Pacific. The first EOF mode is referred to as the SPMD pattern. The off-equatorial enhanced diabatic heating associated with the northeastern pole of the SPMD triggers low-level cross-equatorial northerlies in the tropical north central Pacific (TNCP) through the Matsuno–Gill type response. The anomalous northerly and its associated TNCP SST cooling lead to a Northwest Pacific anomalous anticyclone (NWP-AAC) via the wind-evaporation-SST feedback. The atmospheric general circulation model sensitivity experiments further suggest that the SPMD can induce the NWP-AAC directly or collaboratively with the TNCP SST cooling. Additionally, anomalous moisture can be transported from the tropical western Pacific into South China, giving rise to enhancement of the FRS precipitation. The Liang–Kleeman information flow for causality analysis and ensemble mean of 30-member atmospheric model simulation results further suggest a robust physical pathway that the SPMD is related to the FRS precipitation over South China when ENSO absents. Our results may deepen the understanding of interannual rainfall variability in South China in non-ENSO years. [ABSTRACT FROM AUTHOR]

This study develops a four-module-based multimodel framework, coupled with the projections of general circulation models (GCMs), a hydrological model (SWAT), and two sets of evaluation indicators [Indicators of Hydrologic Alteration (IHA) and River Regime Index (RRI)], to systematically quantify the response of ecohydrological regimes to climatic change. The Jinsha River Basin (JRB) was selected as a target area for both framework verification and application analysis because of its precious natural conditions, privileged abundant hydropower, and extremely rich fish resources. Results revealed that: (1) temperature and precipitation in the JRB would show increasing trends to varying degrees in the future. The increase in temperature would be much higher than that in precipitation, and the increment would be more significant during the end of the century (2068–2097). (2) The future runoff in the JRB may face an overall decreasing trend, leading to more and more frequent drought disasters. (3) The disturbances in the ecohydrological regime would result in more concentrated runoff and smoother hydrological pulse fluctuations in the JRB, meaning human-assisted participation would be required for the conservation of reproduction and development of ecological resources like Coreius guichenoti. Our findings suggest that effective management of future watershed resources can only be accomplished if an in-depth and comprehensive hydrological evaluation of changing climate is made, and the protection and sustainable development of ecological resources of the JRB in the future will require the participation of stakeholders. Figuring out the alteration in ecohydrological regimes under natural variation and its potential hazards may guide the degree of human participation in futural river protection. [ABSTRACT FROM AUTHOR]

Glyptostrobus pensilis (Cupressaceae) is the only surviving species of the genus Glyptostrobus. Although the species is widely cultivated throughout China, in the wild it only occurs as small stands in southeastern China, central Laos, and southern Vietnam. However, its low genetic variability, lack of recruitment, and the progressive destruction of its habitat caused by humans, have meant that the populations are showing a clear declining trend and the species as a whole is threatened. Ecological niche modeling is used here to study the present potential distribution, as well as in the future (2061–2080) using several global circulation models under two of the shared socioeconomic pathways (SSP 126 and SSP 585) that are being used to produce the IPCC Sixth Assessment Report. The current potential area of G. pensilis is of 1,452,481 km2 with the areas of greatest probability of presence located in southeastern China. This potential area is reduced for the future according to most models, with greater losses for the SSP 585 scenario. Between 6.9 and 31.3% of all wild populations of G. pensilis would be outside potential areas (including the Lao populations, which harbor the highest levels of genetic variability). Conservation measures include the expansion of the current network of protected areas (since over 90% of wild populations do not occur within them), the development of propagation techniques, and the carrying out of translocation activities that should require international collaboration among the countries in which the species is found. With the current knowledge, we have reassessed the threat status of the species under the IUCN criteria, downgrading it from CR to EN both for China and at global level. [ABSTRACT FROM AUTHOR]

In most cases, climate change projections from General Circulation Models (GCM) and Regional Climate Models cannot be directly applied to climate change impact studies, and downscaling is, therefore, needed. A large number of statistical downscaling methods exist, but no clear recommendations exist of which methods are more appropriate, depending on the application. This paper compares two different statistical downscaling methods, Pre and Pre, using the Coupled Model Intercomparison Project Phase 5 (CMIP5) datasets and station observations. Both methods include two steps, but the major difference between them is how the CMIP5 dataset and the station data used. The downscaled precipitation data are validated with observations through China and Jiangxi province from 1976 to 2005. Results show that GCMs cannot be used directly in climate change impact studies. In China, the second method Pre, which establishes regression model based on the station data, has a tendency to overestimate or underestimate the real values. The accuracy of Pre is much better than Pre based on mean absolute error, mean relative error and root mean square error. Pre fuses the mode data and station data effectively. Results also show the importance of the meteorological station data in the process of residual modification. [ABSTRACT FROM AUTHOR]

Because of the limitations of coarse-resolution general circulation models (GCMs), delta change (DC) methods are generally used to derive scenarios of future climate as inputs into impact models. In this paper, the impact of future climate change on irrigation was investigated over China using the Community Land Model, version 4 (CLM4), which was calibrated against observed irrigation water demand (IWD) at the provincial level. The results show large differences in projected changes of IWD variability, extremes, timing, and regional responses between the DC and bias-corrected (BC) methods. For example, 95th-percentile IWD increased by 62% in the BC method compared to only a 28% increase in the DC method. In addition, a shift of seasonal IWD peaks (averaged over the country) to one month later in the year was projected when using the BC method, whereas no evident changes were predicted when using the DC method. Furthermore, low-percentile runoff has larger impacts in the BC method compared with proportional changes in the DC method, indicating that hydrological droughts seem to be exacerbated by increased climate variability. The discrepancies between the two methods were potentially due to the inability of the DC method to capture the changes in precipitation variability. Therefore, the authors highlight the potential effects of climate variability and the sensitivity to the choice of particular strategy-adjusting climate projection in assessing climate change impacts on irrigation. Some caveats, however, should be placed around interpretation of simulated percentage changes for all of China since a large model bias was found in southern China. [ABSTRACT FROM AUTHOR]

General circulation model (GCM) outputs archived by the Coupled Model Intercomparison Project (CMIP) are widely used for climate projection. A comprehensive evaluation of the applicability of GCMs is crucial for generating reliable projections. However, previous GCM evaluation studies mainly focused on the reproducibility of the historical simulation to the observation data, ignoring the uncertainty involved in the future climate projection. In this study, an innovative and comprehensive index, named the history–future applicability score (HFAS), is proposed for evaluating the applicability of GCMs. Through incorporating the Taylor skill score (S) and the analysis of variance (ANOVA) within a general framework, the HFAS can reflect both the historical reproducibility and future uncertainty of GCMs. The HFAS framework is applied to quantitatively evaluating the skills of 28 GCMs (14 from CMIP5 and 14 from CMIP6) in modeling temperature and precipitation at 631 reference sites across China. Results indicate that (i) both CMIP5 and CMIP6 models underestimate the historical temperature of China and overestimate the historical precipitation; (ii) the total uncertainties in future temperature and precipitation projections increase along with time; compared to CMIP5, temperature uncertainty of CMIP6 is narrowed, while the precipitation uncertainty is increased; (iii) taking into account historical reproducibility and future uncertainty, CMIP6 models generally have increased applicability compared to CMIP5; and (iv) the best-performing GCMs in modeling temperature and precipitation of China are ACCESS-CM2 and MPI-ESM-LR, respectively, with the HFAS being 68.4 and 63.5. The findings are helpful for researchers to select locally appropriate GCMs and generate reliable regional climate projection. [ABSTRACT FROM AUTHOR]

Highlights • First attempt to apply concept of corporate memory to study strategic planning. • Original empirical study on reclamation for a small open economy. • Original neo-institutional economic inquiry of reclamation. Abstract Informed by property rights economics, this paper explores some unique advantages of reclamation, as a form of expansionist dynamic zoning, compared to densification of existing built up urban land and conversion of rural and peri-urban land. One major argument is that modern reclamation package, in light of global circulation of capital, can be one comprehensive project that avoids old mistakes and tries out new ideas without upsetting pre-existing development or interests. It then explains, with the help of statistics collected from a historical study of land reclamation of Hong Kong, that the future urban development in this open economy will be arrested if she gives up reclamation. The loss of corporate memory, using civil service staff movements based on the Staff List, seldom used in Hong Kong studies on town planning, as a proxy, in strategic planning is advanced as a factor behind the current situation. [ABSTRACT FROM AUTHOR]

The frequency of large-scale anomalous precipitation events over China has increased during 1961–2018. However, it remains challenging to understand the mechanisms associated with these anomalous events showing different spatial patterns. Here, we applied an autoencoder technique to identify large-scale anomalous precipitation events for both observations and model simulations, which were then classified into several patterns through a self-organizing map method. The synoptic behavior and atmospheric circulation background of different anomalous patterns were also analyzed using simultaneous composite analyses. Results show that occurrences of different anomalous precipitation patterns have increased significantly, except those centered in North China, Northeast China, and the Yangtze River basin. The anomalous precipitation patterns manifest various intraseasonal distributions, which are linked to zonal oscillations of the western North Pacific subtropical high (WNPSH) and meridional displacements of East Asia westerly jet (EAJ). Accompanied by the westward movement of WNPSH, anticyclonic systems transport warm moist air from the Indian Ocean and the South China Sea to converge with the cold air caused by anomalous cyclones over the northwest flank of the WNPSH, leading to large-scale anomalous precipitation in these regions. Besides WNPSH, the northward and southward displacements of EAJ also favor the occurrence of anomalous precipitation events in northern and southern China, respectively. Our study also illustrates that the occurrence frequency of anomalous precipitation events is projected to increase remarkably under the Shared Socioeconomic Pathway 5–8.5 (SSP585) scenario by rates of twofold to fourfold. [ABSTRACT FROM AUTHOR]

Given the interdependence of climate change (CLC) and urban expansion (URE) on ecosystem productivity in China and India, hybrid physics-based models were fitted in this study to evaluate the effects of these variables on photosynthetically active radiation (PAR). This was accomplished by interpolating the most recent five general circulation models (GCMs) from coupled model intercomparison project phase 6 (CMIP6) into the CMIP6 multi-ensemble model. The potential of PAR is projected to increase by 0.001 to 2.077% in China and by 0.002 to 6.737% in India, on a seasonal and annual basis, if the warming is kept at 1.5 °C from now until the end of this century. The effects of CLC and URE on the changes in PAR in China and India were investigated, and URE had a greater impact than CLC when compared to effective contributions, with 49.47% for China and 28.41% for India in the entire case scenario. In contrast, CLC and PAR residual factor (PRF) have a greater impact in India than in China, with effects of 13.79% and 57.79% compared to 0.89% and 49.64%, respectively. Preferences for exotic, high-productivity plant species, irrigation, CO2 fertilization, and nitrogen deposition are suggested as measures for replenishing PAR in both countries. [ABSTRACT FROM AUTHOR]

Climate data with high spatial and temporal resolution were of great significance for regional environmental management, such as for early response to possible predicted local climate changes and extreme weather. However, the current downscaling targets for CMIP6 climate simulations were mostly medium-resolution (MR) reanalysis data, which were still coarse for local analysis. A two-step downscaling method was proposed for 100 × resolution enhancements of general circulation model (GCM) daily temperature data in this study. First, the historical GCM outputs were 10 × downscaled to a set of dynamically predictable MR data using a deep convolutional neural network (CNN), which included both encode-decode structure and long-short skip connections. Then, using high-resolution (HR) topographic data and MR climate data as auxiliary data, the GCM data were super-resolved to a series of images with spatial resolution of 1 km. A one-step downscaling analysis combined only with HR topographic data was performed as comparison. Seven evaluation metrics were selected to evaluate the prediction accuracy, and the results showed that the overall performance of two-step downscaling method was better than one-step downscaling method. Higher Nash–Sutcliffe efficiency (NSE) and lower mean absolute relative error (MARE) indicated that the two-step method performed better prediction of peak and low values. It was further confirmed by accuracy evaluation on the 10% max and 10% min values of the testing dataset. The introduction of dynamically predictable MR data could provide effective detailed information during the downscaling process and improve the prediction accuracies. Finally, the projected data of four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) during 2015–2050 were downscaled to the study area. The complex temporal and spatial variations indicated that there were great differences in temperature changes in a basin, and differentiated management measures should be proposed in advance. [ABSTRACT FROM AUTHOR]

A global eddy-resolving forecast system LFS is developed based on a primitive ocean general circulation model. The system's configuration, forecast experiments, and a preliminary evaluation of the preoperational system are shown. In the preoperational stage, since the full data assimilation has not yet been set up for LFS, the initial state is obtained by nudging the ocean temperature and salinity from an ocean analysis dataset. Despite this, the LFS demonstrates a generally good performance in short-term oceanography forecasting, except for sea level anomaly (SLA). The median values of the 1-day forecast leading root mean square error (RMSE) for the sea surface temperature (SST), SLA, upper 2000 m temperature, and salinity are approximately 0.52°C, 0.10 m, 0.57°C and 0.13 psu, respectively. Although there are slight warm biases in the forecasted SST, the forecasts of temperature and salinity in the thermocline by the LFS are comparable with the results of operational oceanography systems under the framework of the Intercomparison and Validation Task Team. However, the forecast SLA has a relatively large RMSE related to the absence of direct observational constraints in the initial state. Further investigations are needed to improve the performance of LFS. [ABSTRACT FROM AUTHOR]

Robustness and cost effectiveness are major concerns for sustainable stormwater management under deep uncertainty of climate change. Given that many traditional static planning strategies are not working with unpredictable future conditions, the possibility of system failure, and the lock-in effects, the Adaptation Pathway (AP) approach was adopted for dynamically robust and cost-effective planning in this paper. In order to increase optimization accuracy of multi-staged planning, a continuous definition of the AP optimization problem was raised by improving the simplified versions in existing studies. A case study in Suzhou, a provincial pilot Sponge City in China undergoing increasing annual rainfall and severe water environment deterioration, was included by integrating Long-Term Hydrologic Impact Assessment-Low Impact Development model with optimization methods, aiming to persistently control the non-point source total phosphorus loading below an acceptable amount in the following unforeseen 20 years via multi-staged low-impact development (LID) construction. A novel optimization method developed by the authors in a companion paper, namely marginal-cost-based greedy strategy (MCGS), was successfully applied to efficiently solve the continuous version of the AP optimization problem. The popular genetic algorithm (GA) was used as a contrast. A weather generator was elaborated based on four Representative Concentration Pathway scenarios and 17 spatial downscaled general circulation models to simulate the unforeseen future annual rainfalls that helped with evaluating cost effectiveness of each prospective LID plan. Results showed that the adaptation pathways optimized by MCGS could save the whole life net present cost of an LID plan by 1%–60% compared with those optimized by GA, and the computational efficiency of MCGS was over 13 times faster than GA. • Deep uncertainty of climate change requires dynamically robust LID planning. • The Adaptation Pathway (AP) approach guarantees robustness and cost effectiveness. • The AP optimization problem was redefined in a continuous manner. • A greedy strategy was designed to optimize the adaptation pathway of LID placement. • The greedy strategy prevailed over the popular genetic algorithm significantly. [ABSTRACT FROM AUTHOR]

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Under global warming, according to results obtained from offline drought indices driven by projections of general circulation models (GCMs), future droughts in China will worsen but the results are not consistent. We analyzed changes in droughts covering the entire hydrologic cycle using outputs of GCMs of the 6th Coupled Model Intercomparison Project (CMIP6) for SSP2-4.5 and SSP5-8.5 climate scenarios, and compared the results with that of popular, offline drought indices [the self-calibrating Palmer Drought Severity Index (scPDSI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Actual Evapotranspiration Index (SPAEI)]. Among meteorological, agricultural, and hydrological drought indices tested under both SSP scenarios, the results obtained from SPAEI and scPDSI agree better with univariate drought indices than SPEI. scPDSI generally agrees well with agricultural droughts (Standardized Soil Moisture Index with the surface soil moisture content; SSIS). Future droughts estimated using soil moisture analysis are more widespread than that from precipitation and runoff analysis in humid regions of South China by the end of the twenty-first century. In arid northwestern China and Inner Mongolia, drought areas and severity based on scPDSI and SSIS forced with the SSP scenarios show obvious decreasing trends, in contrast to increasing trends projected in South China. Trends projected using SPEI contradict those projected by other drought indices in non-humid regions. Therefore, selecting appropriate drought indices is crucial in project representative future droughts and provides meaningful information needed to achieve effective regional drought mitigation strategies under climate warming impact. [ABSTRACT FROM AUTHOR]

Reanalysis datasets have been widely used in meteorological research, including studies of Northeast China cold vortices (NCCVs), where these datasets act as effective substitutes for observations. However, to date, no studies have focused on their performance in reproducing NCCVs. To address this knowledge gap, we adopted an automatic three-step identification algorithm (TIA) and used it to detect NCCVs from ERA5 and MERRA-2 reanalysis datasets spanning 39 warm seasons (May–September) during the period from 1980 to 2018. A comparative method was employed for a rough verification of the characteristics of the reproduced NCCVs. Moreover, a dataset derived from 1370 Chinese ground-based observational stations was used to verify the performance of the reanalysis models in reproducing the precipitation and air temperature associated with NCCVs. The results show that the TIA identified the majority of NCCVs, with an accuracy of approximately 90% from ERA5 or MERRA-2. Both reanalysis models can reproduce the characteristics of NCCVs (including location, strength, and duration), and both replicate air temperature better than precipitation. ERA5 and MERRA-2 showed strong consistency in reproducing the central longitude, central latitude, central height, and range of NCCVs, with correlation coefficients of 0.974, 0.972, 0.996, and 0.919, respectively, at the 99.9% significance level. The daily average 2-m temperatures in both reanalysis datasets were in good agreement with observations; however, overestimations of approximately 7°–8°C arose in steep high-altitude regions. In addition, both models tended to overestimate light rain (≤5 mm day−1) by approximately 1.2 mm and underestimate heavy rain (≥20 mm day−1) by over 6.7 mm. [ABSTRACT FROM AUTHOR]

Simple Summary: Adopting deficit irrigation (DI) to improve crop production and safeguard groundwater resources is of great importance in water scarce regions, e.g., the North China Plain (NCP). Under the background of global warming, it is worth investigating whether DI continues to play such a key role under future climate change scenarios. Thus, we studied the effect of DI on wheat yield and water use efficiency under future climate change scenarios. We found that moderate deficit irrigation (DI3, ≤0.4 PAWC at sowing to flowering stage) under the N3 (150 kg N ha−1) condition was identified as the optimum irrigation schedule for the study site under future climate change scenarios. However, the compensation effect of DI3 on yield and water use efficiency (WUE) became weak in the future. To conclude, in water scarce regions of NCP, DI remains an effective strategy to maintain higher yield and enhance water use under future climate scenarios. Background. Deficit irrigation (DI) is a feasible strategy to enhance crop WUE and also has significant compensation effects on yield. Previous studies have found that DI has great potential to maintain crop production as full irrigation (FI) does. Therefore, adopting DI to improve crop production and safeguard groundwater resources is of great importance in water scarce regions, e.g., the North China Plain (NCP). Under the background of global warming, it is worth investigating whether DI continues to play such a key role under future climate scenarios. Methods. We studied the response of winter wheat yield and WUE to different DI levels at pre-anthesis under two Shared Socioeconomic Pathways (SSPs) scenarios (SSP245 and SSP585) using the Agricultural Production Systems Simulator (APSIM) model driven by 21 general circulation models (GCMs) from the Coupled Model Inter-Comparison Project phase 6 (CMIP6). Additionally, we explored the effects of different nitrogen (N) fertilizer application rates on DI. Results. We found that simulated wheat yield would increase by 3.5–45.0%, with WUE increasing by 8.8–46.4% across all treatments under future climate change. Moderate deficit irrigation (DI3, ≤0.4 PAWC at the sowing to flowering stage) under the N3 (150 kg N ha−1) condition was identified as the optimum irrigation schedule for the study site under future climate change. However, compensation effects of DI3 on yield and WUE became weak in the future, which was mainly due to increased growing season rainfall projected by GCMs. In addition, we found that N fertilizer application could mitigate the effect of DI3. Conclusions. We highlight that in water scarce regions of NCP, DI remains an effective strategy to maintain higher yield and enhance water use under future climate scenarios. Results strongly suggest that moderate deficit irrigation under a 150 kg N ha−1 condition could mitigate the contradiction between production and water consumption and ensure food safety in the NCP. [ABSTRACT FROM AUTHOR]

The present work investigates leading modes of winter fog days over eastern China and possible association with sea surface temperature (SST) variability. Based on the Empirical Orthogonal Function (EOF) analysis, two dominant interannual modes of winter fog days are identified: a monopole mode and a meridional dipole mode. The monopole mode displays a significantly positive relationship with El Niño-Southern Oscillation (ENSO). The meridional dipole mode is closely linked to North Atlantic tripole (NAT) SST anomalies. A quasi-barotropic Rossby wave train can be excited by the NAT SST anomalies and propagates eastwards to East Asia. During winters with the positive phase of the NAT SST anomalies, there appear cyclonic and anticyclonic circulation anomalies over northern and southern China, favoring less and more winter fog frequency, respectively. These observed impacts from the ENSO and NAT SST anomalies can be further reproduced by atmospheric general circulation model (AGCM) experiments. Consideration of the NAT SST anomalies can apparently improve the explained variance of winter fog days over eastern China on the basis of the common-used ENSO signal. When ENSO and NAT occur as in-phase combinations and out-of-phase combinations, winter fog days anomalies appear over southern and northern China, respectively. These results have important implications on the seasonal-to-interannual predictability of the winter fog frequency over eastern China. [ABSTRACT FROM AUTHOR]

A global model formulation for unified weather‐climate forecast is evaluated, with emphasis on the climate simulations at typical hydrostatic resolutions. The internal sensitivity is explored by considering different dynamical configurations (resolution, solver type, transport scheme). After a basic assessment of the global mean climate, a detailed analysis of precipitation characteristics is extended to East Asia. The model shows a reasonable mean state, seasonal variation, frequency–intensity structure, and diurnal phase time. The artificial rainfall around the steep slopes of the Tibetan Plateau can be improved through choices in the dynamical configuration. The regional features characterized by "afternoon versus nocturnal‐to‐early‐morning peaks" are properly distinguished. The hourly climatic features are comparable to super‐parameterized CAM5. Different dynamical configurations demonstrate unique sensitivities related to underling physical mechanisms, which are studied from the perspective of the diurnal cycle for three representative regions. Over South China, the higher‐resolution models decrease the weak‐precipitation while increase intense rainfall, thus reducing the dry biases. This is contributed by enhanced grid and sub‐grid scale motions associated with daytime convection progression. Over central western China, the variable‐resolution model better simulates the eastward propagating episodes characterized by a transition from convective to stratiform rainfall along the eastern slope of the Plateau. This reduces the positive biases at the high topography of the Plateau and alleviates the negative biases at the lower foot. Over central eastern China, the model replicates the dominant role of large‐scale governing factors in regulating the early morning rainfall peaks, and produces stratiform heating patterns. Plain Language Summary: We examine the statistical simulation of a model developed within the Global‐to‐Regional Integrated forecast SysTem, using Atmospheric Model Intercomparison Project style integration. This is among an effort in exploring a unified system for weather and climate modeling. Because of the evolutionary nature of model development, the absolute model performance can change. Thus, special attention is paid to the model sensitivity related to dynamical configurations, which helps to gain understanding that can support continuous research and development. Precipitation is one of the most important variables in a general circulation model as it encapsulates the synergistic effects of resolvable and non‐resolvable processes, the hydrological cycle, and radiative balance. Simulating precipitation faces the challenges in representing the moist physics and its interaction with dynamics. We use precipitation as performance metrics for a better understanding of the model behavior, focusing on a unique region–East Asia. The diurnal cycle is given particular emphasis because it reveals the direct physical mechanism of precipitation, and can help to disentangle the interactions between model dynamics and physics. We show that the models produce hourly rainfall features that are comparable to a global multiscale modeling framework that has two interactive dynamical solvers, and support some beneficial sensitivity. Key Points: To evaluate a model formulation for unified weather‐climate forecast using the Atmospheric Model Intercomparison Project‐style experimentTo understand model precipitation characteristics over East Asia by analyzing hourly scale apparent sources/sinks and grid‐scale tendenciesTo understand model sensitivity related to dynamical configurations, including grid resolution, moisture transport scheme and solver type [ABSTRACT FROM AUTHOR]

Two different bias correction methods, the quantile mapping (QM) and quantile delta mapping (QDM), are applied to simulated daily temperature and precipitation over China from a set of 21st century regional climate model (the ICTP RegCM4) projections. The RegCM4 is driven by five different general circulation models (GCMs) under the representative concentration pathway RCP4.5 at a grid spacing of 25 km using the CORDEX East Asia domain. The focus is on mean temperature and precipitation in December–January–February (DJF) and June–July–August (JJA). The impacts of the two methods on the present day biases and future change signals are investigated. Results show that both the QM and QDM methods are effective in removing the systematic model biases during the validation period. For the future changes, the QDM preserves the temperature change signals well, in both magnitude and spatial distribution, while the QM artificially modifies the change signal by decreasing the warming and modifying the patterns of change. For precipitation, both methods preserve the change signals well but they produce greater magnitude of the projected increase, especially the QDM. We also show that the effects of bias correction are variable- and season-dependent. Our results show that different bias correction methods can affect in different way the simulated change signals, and therefore care has to be taken in carrying out the bias correction process. [ABSTRACT FROM AUTHOR]

Projections of future sea level changes are usually based on global climate models (GCMs). However, the changes in shallow coastal regions, like the marginal seas near China, cannot be fully resolved in GCMs. To improve regional sea level simulations, a high-resolution (~8 km) regional ocean model is set up for the marginal seas near China for both the historical (1994–2015) and future (2079–2100) periods under representative concentration pathways (RCPs) 4.5 and 8.5. The historical ocean simulations are evaluated at different spatiotemporal scales, and the model is then integrated for the future period, driven by projected monthly climatological climate change signals from eight GCMs individually via both surface and open boundary conditions. The downscaled ocean changes derived by comparing historical and future experiments reveal greater spatial details than those from GCMs, such as a low dynamic sea level (DSL) center of -0.15min the middle of the South China Sea (SCS).As a novel test, the downscaled results driven by the ensemble mean forcings are almost identical with the ensemble average results from individually downscaled cases. Forcing of the DSL change and increased cyclonic circulation in the SCS are dominated by the climate change signals from the Pacific, while the DSL change in the East China marginal seas is caused by both local atmosphere forcing and signals from the Pacific. The method of downscaling developed in this study is a useful modeling protocol for adaptation and mitigation planning for future oceanic climate changes. [ABSTRACT FROM AUTHOR]

The arid and semi‐arid regions are highly vulnerable to climate change and variability. Agricultural production in these regions is particularly vulnerable because of its heavy dependence on climate conditions. Therefore, it is important to improve the projections of future agro‐climatic conditions. This study investigates the changes in agroclimatic conditions for growing Chinese Medicinal Yam (CMY) in the semi‐arid North China during 2031–2050 under the Representative Concentration Pathway (RCP) 4.5 and 8.5 emission scenarios. It employs high‐resolution climate data provided by the regional climate model (RCM) of Providing Regional Climates for Impacts Studies (PRECIS‐2.0). The changes in CMY yield and producing area under future climate change are simulated by the Agro‐Ecological Zone model. The simulations are based on newly calibrated cultivar parameters. The results show that the high‐resolution RCM simulation corresponds better with the observations of precipitation and temperature than the general circulation model (GCM). Because of the increased temperature and precipitation, the agroclimatic conditions for CMY are going to improve. While the traditional yam production area will maintain suitable for CMY production, the CMY production areas will further expand northward in the future. The average yield will increase by 1,196 and 1,336 kg DM/ha under RCP 4.5 and 8.5 emission scenarios, respectively. Key Points: Improving the projections of future agro‐climatic conditions in the semi‐arid North ChinaThe high‐resolution Providing Regional Climates for Impacts Studies projections correspond better with the observationsThe Chinese Medicinal Yam production areas will expand northward in the future, the average yield will increase by 1196–1336 kg DM/ha in the future [ABSTRACT FROM AUTHOR]

In this study, the interdecadal variability of the relationship between ENSO and winter synoptic temperature variability (STV) over the Asian–Pacific–American region is investigated based on observational data from 1951 to 2018. An interdecadal shift in the ENSO–STV relationship occurred in the 1980s over Eastern China, changing from significant in period 1 (P1; 1951–87) to insignificant in period 2 (P2; 1988–2018). But the ENSO–STV relationship is significantly stable over North America for the whole period. In addition, a possible reason for this interdecadal shift in the ENSO–STV relationship over Eastern China is also investigated. During P1, the ENSO pattern is significantly correlated to the temperature gradient over Northeast Asia, which is the key region influencing the intensification of extratropical eddies. The intensification of extratropical eddies over Northeast Asia is directly associated with the magnitude of STV over Eastern China. But in P2, the ENSO pattern is not related to the temperature over Northeast Asia. Therefore, the change in the ENSO pattern from P1 to P2 contributes to the interdecadal shift in the ENSO–STV relationship in the 1980s over Eastern China by influencing the temperature gradient over Northeast Asia, while ENSO can influence the temperature gradient over North America for the whole period. Furthermore, the possible role of the ENSO patterns in P1 and P2 is also examined by using an atmospheric general circulation model, highlighting that the pattern of SST variation is a determining factor in regulating STV in different regions. [ABSTRACT FROM AUTHOR]

The present study examines subseasonal prediction skills and biases of the summer rainfall over eastern China in the Beijing Climate Center (BCC) Atmospheric General Circulation Model (BCC_AGCM2.2) and assesses the predictability of eastern China summer rainfall based on the multi-member forecasts. The BCC_AGCM2.2 model shows some skill in predicting summer rainfall over eastern China within the lead-times of 0–9 days. However, the subseasonal prediction skill is low on average, which is linked to the ability of the model in predicting the western Pacific subtropical high (WPSH). The low prediction skills may partially be attributed to biases in the model, including a wider meridional span and a weaker intensity of rainbelt in Yangtze River valley, earlier meridional movement, and a further northward shift of WPSH compared to that in the observations. These biases result in an obvious dry bias along the monsoon rainbelt, and a wet bias to both the north and south sides. Moreover, as a major contributor to the predictability, the Pacific-Japan pattern is not well predicted for both its spatial pattern and subseasonal evolution. The low forecast skill of summer rainfall in eastern China seems due to a dominant role of the atmospheric internal variability and a minor influence of the sea surface temperature in extratropical climate variability. Nevertheless, enhanced prediction skills under the assumption of a perfect model imply the potential to improve the prediction skill of the summer rainfall over eastern China through reducing model biases. [ABSTRACT FROM AUTHOR]

The international community is paying more attention to climate change because a consensus has been reached that climate change has an adverse effect not only on the environment but also on agriculture. Therefore, in this study, present and future climate datasets (obtained from general circulation models) including atmospheric carbon concentration were used to assess the impact of climate change on grain production for an important base of China (Northeast). An empirical model has been developed using climate and other additional variables (effective irrigation area, fertilizer, and labor force) to assess the effect of climate change on grain production. The results revealed that maximum temperature is a key climate determinant in grain production of the study area. Atmospheric carbon concentration showed a significant impact on grain outputs in most of the cases. During the analysis, it was observed that precipitation displayed a declining trend while an effective irrigation area showed positive non-significant contribution to grain production. Analysis based on different representative concentration pathways exhibited that maximum temperature may contribute negatively to grain production in the future. Overall, the analysis showed that climate change has a significant contribution to grain production. In conclusion, the implications for future research and policymakers have been addressed. Particularly, the importance of considering regional differences in adaptation planning in agricultural regions was also considered. [ABSTRACT FROM AUTHOR]

In this study, we investigated the effects of temperature frequency trends on the projected yield and dry matter distribution of japonica rice (Oryza sativa L.) with elevated carbon dioxide (CO2) under future climate change scenarios in northwestern China. The Crop Environment Resource Synthesis (CERES)-Rice model was forced with the outputs from three general circulation models (GCMs) to project the rice growth and yield. Future temperature trends had the most significant impact on rice growth, and the frequency of higher than optimal temperatures (~24-28 oC) for rice growth showed a marked increase in the future, which greatly restricted photosynthesis. The frequency of extreme temperatures (>35 oC) also increased, exerting a strong impact on rice fertilization and producing a significantly reduced yield. Although the increased temperature suppressed photosynthetic production, the elevated CO2 stimulated this production; therefore, the net result was determined by the dominant process. The aboveground biomass at harvest trended downward when temperature became the major factor in photosynthetic production and trended upward when CO2-fertilization dominated the process. The trends for the leaf and stem dry matter at harvest were affected not only by changes in photosynthesis but also by the dry matter distribution to the panicles. The trends for the rice panicle dry matter at harvest were closely related to the effects of temperature andCO2 on photosynthetic production, and extreme temperatures also remarkably affected these trends by reducing the number of fertilized spikelets. The trends of rice yield were very similar to those of panicle dry matter because the panicle dry matter is mostly composed of grain weight (yield). This study provides a better understanding of the japonica rice processes, particularly under extreme climate scenarios, which will likely become more frequent in the future. [ABSTRACT FROM AUTHOR]

Reference crop evapotranspiration (ET0) is of great importance in assessing the potential impacts of climate changes on hydrological cycles and the global energy balance. The spatiotemporal change of ET0 and the drought response over Poyang Lake watershed of China from 2011 to 2100 are the main concern in this work. Based on the meteorological data and the output of the general circulation model (GCM) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we used the Penman–Monteith formula and downscaling model to calculate the history and future ET0 in Poyang Lake watershed, respectively. Major results are drawn as follows. First, the annual average ET0 decreased during 1961–2014 and the average ET0 of the basin is high in the north and south, but low in the middle. The ET0 was most dominated by sunshine duration in the spring, summer, and fall and by relative humidity in the winter. Second, the downscaling model has a good simulation effect, and the GCM data-downscaling simulation results are significantly improved after the deviation correction. Third, under the representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios, ET0 in the Poyang Lake watershed will increase over the next three periods, with the middle future (2041–2070) as the largest increase period. The spatial distribution of ET0 is generally high in the east and low in the west. Fourth, under the RCP 8.5 scenario, the drought index (DI) of the watershed showed an increasing trend, the seasonal distribution of DI is fall>summer>spring>winter , and the Ganjiang River subbasin will be the key prevention area for future drought risks. The results can provide basic data support for the optimal management of water resources and scientific response to the impact of climate change on agricultural production in the watershed for associated policymakers and stakeholders. [ABSTRACT FROM AUTHOR]

Maize production on the North China Plain (NCP) is critical to food security in China; however, currently, it is affected by climate change. Understanding the spatiotemporal distribution of the climate suitability for maize on the NCP in the present and future may help sustainably use climate‐related resources to ensure food security in China. In this study, 30 general circulation models from the Coupled Model Intercomparison Project Phase 5 and a statistical downscaling model (NWAI‐WG) were used to project meteorological data in 2021–2100 under representative concentration pathway 4.5 (RCP4.5) and RCP8.5 for 23 national climatic stations on the NCP. Based on agricultural climate suitability theory and the fuzzy mathematics method, the suitability of temperature, precipitation, and solar radiation on summer maize were analysed. The results showed that temperature suitability is decreasing for 2021–2100, especially during the stages from jointing to maturity; temperature suitability is the lowest in the southwest and increases to the northeast under both scenarios. Compared to 1996–2015, the precipitation suitability in 2021–2100 increases greatly under both scenarios, especially in the central part under RCP4.5 and in the north part under RCP8.5. Solar radiation suitability shows a decreasing trend for 1996–2015, however, an increasing trend for 2021–2100 under both scenarios. At spatial scale, the solar suitability increases from southwest to northeast. The integrated climate suitability under RCP4.5 in 2021–2100 is averaged approximately 0.8 and varies slightly, indicating climate change may do small effect on maize growth, though the high values shifting from the central part in 2021–2040 to the northern part in 2081–2100; however, under RCP8.5, the integrated climate suitability shows a downward trend, indicating that climate change will make many regions less suitable for maize growth. These results could provide basic information for agriculture to adapt to climate change and ensure food security for China. [ABSTRACT FROM AUTHOR]

Socioeconomic development and natural ecosystems of China are strongly affected by droughts. The socioeconomic risk of droughts in China should be quantified under climate change. This study assessed drought impacts on population and gross domestic product (GDP) under the 2.0°C‐warmer climate scenario, by implementing multiple general circulation model (GCM) simulations under the representative concentration pathway (RCP). Using the standardized precipitation evapotranspiration index (SPEI), we calculated drought frequencies in the base period (1986–2005) and 2.0°C‐warmer climate (2040–2059 in RCP4.5). Then, population and GDP exposures were evaluated by combining the drought frequency with population and GDP simulations under a shared socioeconomic pathway (SSP). Droughts were likely to occur more frequently under the 2°C‐warmer climate, especially for inland areas of northern China (i.e., Xinjiang, Xizang, Qinghai, Ningxia, Shaanxi, Gansu provinces), consisting mainly of mild and moderate droughts. Population exposure to drought was projected at 4.94 ± 0.36 × 109 person‐months, around 2% increase over the base period value. GDP exposure would increase significantly to 2.08 ± 0.15 × 1014 PPP $‐months, approximately 14‐fold over the base period. Probability analysis showed nearly 50% of the grid cells of China's entire territory undergoing more than an 11‐fold increase in GDP exposure. Our findings indicated the climate and GDP effects were primary contributors, respectively, to the changed population and GDP exposures to drought. Population and GDP exposures were distributed unequally across the country, having a large impact in the east where both population and economic activity are highly agglomerated. Our results reveal an urgent need to design and implement effective adaptation measures to overcome droughts, especially in eastern China. [ABSTRACT FROM AUTHOR]

The future vegetation–climate system over East Asia, as well as its dependence on Representative Concentration Pathways (RCPs), is investigated using a regional climate–vegetation model driven with boundary conditions from Flexible Global Ocean–Atmosphere–Land System Model: Grid-point Version 2. Over most of the region, due to the rising CO2 concentration and climate changes, the model projects greater vegetation density (leaf area index) and gradual shifts of vegetation type from bare ground to grass or from grass to trees; the projected spatial extent of the vegetation shift increases from RCP2.6 to RCP8.5. Abrupt shifts are projected under RCP8.5 over northeast China (with grass replacing boreal needleleaf evergreen trees due to heat stress) and India (with tropical deciduous trees replacing grass due to increased water availability). The impact of vegetation feedback on future precipitation is relatively weak, while its impact on temperature is more evident, especially during DJF over northeast China and India with differing mechanisms. In northeast China, the projected forest loss induces a cooling through increased albedo, and daytime high temperature (Tmax) is influenced more than nighttime low temperature (Tmin); in India, increased vegetation cover induces an evaporative cooling that outweighs the warming effect of an albedo decrease in DJF, leading to a weaker impact on Tmax than on Tmin. Based on a single model, the qualitative aspects of these results may hold while quantitative assessment will benefit from a follow-up regional model ensemble study driven by multiple general circulation models. [ABSTRACT FROM AUTHOR]

Climate change is one of the most significant causes of species range shift and extinction. Based on a citizen science dataset of birds in China, the Bird Report, we developed a high-resolution map of bird species richness in China, and simulated the range shifts and area changes of the 1,042 birds through the year 2070 using three different General Circulation Models and two different Representative Concentration Pathways (RCPs, including RCP 2.6 and RCP 8.5). It was found that 241–244 (under different scenarios) bird species would lose a portion of their distribution ranges; and that most species in China would move to either higher elevations or northward. The other 798–801 species would experience range expansion. Compared to resident species (n = 516), migratory birds (n = 526) may undergo more limited range expansion but a longer range shift distance on average. The species diversity of birds will considerably increase in areas higher than 1,500 m in elevation under both RCPs. Conservation priorities with higher species richness were also identified using the Zonation model. The existing national nature reserves are not sufficient for protecting important bird habitats, especially after range shifts. Significant gaps in protected areas were observed in the northern Xinjiang, southern Tibet, Greater Khingan, Sanjiang Plain, Songnen Plain, northern Bohai Rim, and southeastern coastline areas. Many of these areas are characterized by high human populations and intensive development, and establishing sizable protected areas has become difficult. Inclusive conservation mechanisms that include restoring habitats in urban parks and sharing habitats in farmland areas, may be a feasible solution. [ABSTRACT FROM AUTHOR]

Landslides result in a significant number of casualties every year in China. The frequency and intensity of extreme precipitation are expected to increase due to climate change, leading to a change in landslide occurrence. This study focuses on climate change impacts on event rainfall characteristics that are commonly linked to landslide occurrence in China. A modelling framework was proposed to quantitatively assess the spatiotemporal change in event rainfall characteristics influencing landslide occurrences in China under future scenarios. First, an algorithm was used to extract the rainfall events from observed precipitation data and the 21 Global Circulation Models dataset. Then, the cumulative event rainfall-rainfall duration (E-D) threshold was identified and used as a proxy of landslide occurrence. Finally, the historical (1971–2000) and future (2031–2060 and 2066–2095) data of 21 GCMs were then applied to determine the E-D threshold in areas highly susceptible to landslides in China to assess the impact of climate change. Landslide occurrence is projected to increase potentially under all GCMs, by amounts ranging from 19.9% to 33.2% in the late 21st century compared to the historical period under the RCP4.5 and RCP85 scenarios, respectively. There are regional differences in the impact of climate change. Future landslide increases in the Northwest region and the Qinghai-Tibet region are the most significant, with consistency among multiple GCMs. However, there is only a slight increase in the South China region with high uncertainty. The monthly variations in landslides are bimodal, with the largest increases in spring and autumn. The results indicate that using a single GCM to assess climate change impacts may have biases, and consideration of median trends and variations among multiple GCMs is suggested. However, the study is a first hint on how climate change may affect landslide occurrence in the future, as the assessment of the effect of climate change on landslides is not straightforward based on only the precipitation-related proxy. The influence on air temperature and soil moisture and the selection of projection datasets and proxies should be carefully considered when applying the presented methods for climate change impacts on landslide studies. [ABSTRACT FROM AUTHOR]

The East Asian summer monsoon is unique among summer monsoon systems in its complex seasonality, exhibiting distinct intraseasonal stages. Previous studies have alluded to the downstream influence of the westerlies flowing around the Tibetan Plateau as key to its existence. We explore this hypothesis using an atmospheric general circulation model that simulates the intraseasonal stages with fidelity. Without a Tibetan Plateau, East Asia exhibits only one primary convective stage typical of other monsoons. As the plateau is introduced, the distinct rainfall stages—spring, pre-mei-yu, mei-yu, and midsummer—emerge, and rainfall becomes more intense overall. This emergence coincides with a pronounced modulation of the westerlies around the plateau and extratropical northerlies penetrating northeastern China. The northerlies meridionally constrain the moist southerly flow originating from the tropics, leading to a band of lower-tropospheric convergence and humidity front that produces the rainband. The northward migration of the westerlies away from the northern edge of the plateau leads to a weakening of the extratropical northerlies, which, coupled with stronger monsoonal southerlies, leads to the northward migration of the rainband. When the peak westerlies migrate north of the plateau during the midsummer stage, the extratropical northerlies disappear, leaving only the monsoon low-level circulation that penetrates northeastern China; the rainband disappears, leaving isolated convective rainfall over northeastern China. In short, East Asian rainfall seasonality results from the interaction of two seasonally evolving circulations—the monsoonal southerlies that strengthen and extend northward, and the midlatitude northerlies that weaken and eventually disappear—as summer progresses. [ABSTRACT FROM AUTHOR]

We simulate the response of Asian summer climate to Atlantic multidecadal oscillation (AMO)-like sea surface temperature (SST) anomalies using an intermediate-complexity general circulation model (IGCM4). Experiments are performed with seven individual AMO SST anomalies obtained from CMIP5/PMIP3 global climate models as well as their multimodel mean, globally and over the North Atlantic Ocean only, for both the positive and negative phases of the AMO. During the positive (warm) AMO phase, a Rossby wave train propagates eastward, causing a high pressure and warm and dry surface anomalies over eastern China and Japan. During the negative (cool) phase of the AMO, the midlatitude Rossby wave train is less robust, but the model does simulate a warm and dry South Asian monsoon, associated with the movement of the intertropical convergence zone in the tropical Atlantic. The circulation response and associated temperature and precipitation anomalies are sensitive to the choice of AMO SST anomaly pattern. A comparison between global SST and North Atlantic SST perturbation experiments indicates that East Asian climate anomalies are forced from the North Atlantic region, whereas South Asian climate anomalies are more strongly affected by the AMO-related SST anomalies outside the North Atlantic region. Experiments conducted with different amplitudes of negative and positive AMO anomalies show that the temperature response is linear with respect to SST anomaly but the precipitation response is nonlinear. [ABSTRACT FROM AUTHOR]

The GEOS-Chem simulation of atmospheric CH4 was evaluated against observations from the Thermal and Near Infrared Sensor for Carbon Observations Fourier Transform Spectrometer (TANSO-FTS) on the Greenhouse Gases Observing Satellite (GOSAT), the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and the Total Carbon Column Observing Network (TCCON). We focused on the model simulations at the 4∘×5∘ and 2∘×2.5∘ horizontal resolutions for the period of February–May 2010. Compared to the GOSAT, TCCON, and ACE-FTS data, we found that the 2∘×2.5∘ model produced a better simulation of CH4 , with smaller biases and a higher correlation to the independent data. We found large resolution-dependent differences such as a latitude-dependent XCH4 bias, with higher column abundances of CH4 at high latitudes and lower abundances at low latitudes at the 4∘×5∘ resolution than at 2∘×2.5∘. We also found large differences in CH4 column abundances between the two resolutions over major source regions such as China. These differences resulted in up to 30 % differences in inferred regional CH4 emission estimates from the two model resolutions. We performed several experiments using 222Rn , 7Be , and CH4 to determine the origins of the resolution-dependent errors. The results suggested that the major source of the latitude-dependent errors is excessive mixing in the upper troposphere and lower stratosphere, including mixing at the edge of the polar vortex, which is pronounced at the 4∘×5∘ resolution. At the coarser resolution, there is weakened vertical transport in the troposphere at midlatitudes to high latitudes due to the loss of sub-grid tracer eddy mass flux in the storm track regions. The vertical air mass fluxes are calculated in the model from the degraded coarse-resolution wind fields and the model does not conserve the air mass flux between model resolutions; as a result, the low resolution does not fully capture the vertical transport. This produces significant localized discrepancies, such as much greater CH4 abundances in the lower troposphere over China at 4∘×5∘ than at 2∘×2.5∘. Although we found that the CH4 simulation is significantly better at 2∘×2.5∘ than at 4∘×5∘ , biases may still be present at 2∘×2.5∘ resolution. Their importance, particularly in regards to inverse modeling of CH4 emissions, should be evaluated in future studies using online transport in the native general circulation model as a benchmark simulation. [ABSTRACT FROM AUTHOR]

Chemistry plays an indispensable role in investigations of the atmosphere; however, many climate models either ignore or greatly simplify atmospheric chemistry, limiting both their accuracy and their scope. We present the development and evaluation of the online global atmospheric chemical model BCC-GEOS-Chem v1.0, coupling the GEOS-Chem chemical transport model (CTM) as an atmospheric chemistry component in the Beijing Climate Center atmospheric general circulation model (BCC-AGCM). The GEOS-Chem atmospheric chemistry component includes detailed tropospheric HOx – NOx –volatile organic compounds–ozone–bromine–aerosol chemistry and online dry and wet deposition schemes. We then demonstrate the new capabilities of BCC-GEOS-Chem v1.0 relative to the base BCC-AGCM model through a 3-year (2012–2014) simulation with anthropogenic emissions from the Community Emissions Data System (CEDS) used in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The model captures well the spatial distributions and seasonal variations in tropospheric ozone, with seasonal mean biases of 0.4–2.2 ppbv at 700–400 hPa compared to satellite observations and within 10 ppbv at the surface to 500 hPa compared to global ozonesonde observations. The model has larger high-ozone biases over the tropics which we attribute to an overestimate of ozone chemical production. It underestimates ozone in the upper troposphere which is likely due either to the use of a simplified stratospheric ozone scheme or to biases in estimated stratosphere–troposphere exchange dynamics. The model diagnoses the global tropospheric ozone burden, OH concentration, and methane chemical lifetime to be 336 Tg, 1.16×106 molecule cm -3 , and 8.3 years, respectively, which is consistent with recent multimodel assessments. The spatiotemporal distributions of NO2 , CO, SO2 , CH2O , and aerosol optical depth are generally in agreement with satellite observations. The development of BCC-GEOS-Chem v1.0 represents an important step for the development of fully coupled earth system models (ESMs) in China. [ABSTRACT FROM AUTHOR]

In this study, three representative concentration pathways (RCPs) and 15 general circulation models of the Coupled Model Intercomparison Project Phase 5 were used to assess the behaviour of precipitation (P) and surface air temperature (SAT) over part of the Songhua River Basin. The Water Evaluation and Planning (WEAP) model linked with SAT and P was used for monthly simulation of streamflow to assess the influence of land use/land cover and climate change on the streamflow. The results suggest that, under RCP2.6, RCP4.5 and RCP8.5, the SAT over the study area may increase in the 21st century by 1.12, 2.44 and 5.82°C, respectively. Moreover, by the middle of the 21st century, streamflow in the basin may have decreased by 19%. The decrease in streamflow may be due to changed land use conditions and water withdrawal, having critical implications for management and future planning of water resources in the basin. [ABSTRACT FROM AUTHOR]

Near-surface temperature records show that China warmed by about 0.8 °C from 1950 to 2010. However, there exists an ongoing debate about whether this warming might have been partially due to urbanization bias. In fact, homogenization approaches may be inefficient in densely populated provinces that have experienced a significant urban development since the 1940s. This paper aims to complement previous research on the topic by showing that an alternative approach based on the analysis of the divergence between the minimum (Tmin) and maximum (Tmax) near-surface temperature records since the 1940s could be useful to clarify the issue because urban heat island (UHI) effects stress the warming of nocturnal temperatures more than the diurnal ones. Then, the significance of the divergence observed in the data could be evaluated against the expectations produced by the CMIP5 general circulation model simulations. From 1945–1954 to 2005–2014, on average and over China, these models predict that Tmin had to warm 0.19 ± 0.06 °C more than Tmax. However, during the same period, the climatic records show that Tmin warmed 0.83 ± 0.15 °C more than Tmax. A similar analysis demonstrates that the effect is more pronounced during the colder months from November–April than during the warmer ones from May to October. A comparison versus China urbanization records demonstrates that the regions characterized by a large Tmin-Tmax divergence are also the most densely populated ones, such as north-east China, that have experienced a diffused and fast urbanization since the 1940s. The results are significant and may indicate the presence of a substantial uncorrected urbanization bias in the Chinese climate records. Under the hypothesis that Tmax is a better metric for studying climatic changes than Tmean or Tmin, we conclude that about 50% of the recorded warming of China since the 1940s could be due to uncorrected urbanization bias. In addition, we also find that the Tmax record from May to October over China shows the 1940s and the 2000s equally warm, in contrast to the 1 °C warming predicted by the CMIP5 models. • Mean surface temperature in China has warmed by 0.8 °C since 1950. • Atmospheric boundary layer physics predicts higher UHI warming during night. • The divergence between Tmin and Tmax is used to detect urbanization biases. • The China regions with the strongest Tmin-Tmax divergence are also the most populated. • Thus, the temperature warming in China is partially due to UHI biases. [ABSTRACT FROM AUTHOR]

Drought has become one of the most serious meteorological disasters for agricultural production in many areas around the world, and the situation could be worse under the impact of climate change. To facilitate better adaptation planning, this study proposed a drought assessment framework integrating downscaling method, drought index, copula technique, and bivariate frequency analysis, and applied it to investigate the change of the drought characteristics and drought risks from the past to the future in Huang-Huai-Hai River basin (HRB), North China. Drought was firstly defined by standardized precipitation evapotranspiration index (SPEI) based on 1497 observed grid data from 1979 to 2004. Then, we constructed the joint distribution of drought duration and severity based on copulas to detect and quantify the drought risks. To address the effect of climate change, similar calculation process was applied to the future climate data, which was downscaled using delta change method from representative concentration pathway (RCP 8.5) of 12 general circulation models (GCMs). The study results suggested that, under climate change condition, most irrigation districts over HRB would generally experience lower frequency of drought events but with extended duration; some districts would have more serious drought, but majority would experience similar or even lower level of severity. In light of the mean joint occurrence probability, the irrigation district at the south part of Huai River basin would likely experience the highest increase of drought risks in near future (by 0.86%) and distant future (by 0.76%), while most of other districts over HRB would face low risk of serious drought risks. The obtained results offer useful information to agricultural managers or water resources authorities who are interested in the development of effective long-term adaptation strategies for drought management. [ABSTRACT FROM AUTHOR]