Ocean alkalinity enhancement is considered as an effective atmospheric CO2 removal approach, but currently, little is known about the carbon sequestration potential of implementing olivine addition in offshore waters. We investigated the effect of olivine addition on the seawater carbonate system by carrying out a deck incubation experiment in the Northern Yellow Sea; the dissolution rate of olivine was calculated based on the increase in seawater alkalinity (TA), and the CO2 sequestration potential was evaluated. The results showed that the dissolution of olivine increased seawater TA and decreased partial pressure of CO2, resulting in oceanic CO2 uptake from the atmosphere through sea-air exchange; it also increased seawater pH and mitigated ocean acidification to a certain extent. The addition of 1 ‰ olivine had a more significant effect on the seawater carbonate system than 0.5 ‰ olivine addition. The average dissolution rate constant of olivine was 1.44 ± 0.15 μmol m−2 d−1. Assuming that olivine settles completely on the seabed due to gravity, the theoretically maximum amount of CO2 removed by applying 1 tonne of olivine per square meter area in the Northern Yellow Sea is only 2.0 × 10−4 t/m2. Therefore, when olivine addition is implemented in the offshore waters, it is necessary to consider reducing the olivine size, prolonging the settling time of olivine in the water column; and spreading olivine in well-mixed waters to prolong the residence time through repeated resuspension, thus increasing its potential in carbon sequestration. © 2024 Elsevier Ltd

Total alkalinity (TA) is the measure of water's ability to neutralize acids. Discrete TA samples are typically measured by acid titration, with one sample processed at a time, which is tedious and time-consuming. In this study, we assessed an automated titration system for TA batch measurements that included the washing procedure between each sample analysis; and evaluated the impact of various factors on the precision of TA measurement, including the sample mass, sample salinity, temperature variation, and the dilution effect from the residual washing water. The results showed that temperature variation within ±1 °C, salinity ranging from 0 to 40, sample mass from 20 to 30 g, and the residual washing water within 2 g did not affect the precision of TA measurement (±2 μmol/kg). In addition, the automated titration system encoded with Gran calculation managed to determine the negative TA as low as −100 μmol/kg. Our study suggests that the automated titration system with an auto-sampler is capable of high-precision TA batch measurements. © 2024 Elsevier B.V.

Gross primary production (GPP) is the largest flux and a crucial player in the terrestrial carbon cycle and has been studied extensively, yet large uncertainties remain in the spatiotemporal patterns of GPP in both observations and simulations. This study evaluates the performance of the second version of the Beijing Climate Center Atmosphere-Vegetation Interaction Model (BCC_AVIM2.0) in simulating GPP on multiple spatial and temporal scales in the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiments. Model simulations driven by two meteorological datasets were compared with two observation-based GPP products covering 1982-2008. Spatial patterns of annual GPP show a significant latitudinal gradient in each dataset, increasing from cold (tundra) and dry (desert) biomes to warm (temperate) and humid (tropical rainforest) biomes. BCC_AVIM2.0 overestimates GPP in most parts of the globe, especially in boreal forest regions and Southeast China, while underestimating GPP in subhumid regions in eastern South America and tropical Africa. The four datasets broadly agree on the GPP seasonal cycle, but BCC_AVIM2.0 predicts an earlier beginning of spring growth and a larger amplitude of seasonal variations than those in the ob-servations. The observation-based datasets exhibit slight interannual variability (IAV) and weak GPP linear trends, while the BCC_AVIM2.0 simulations demonstrate relatively large year-to-year variability and significant trends in the low-latitudes and temperate monsoon regions in North America and East Asia. Regarding the possible relationships between annual means of GPP and climate factors, BCC_AVIM2.0 predicts more extensive regions of the globe where the IAV of annual GPP is dominated by precipitation, especially in mid-to-high latitudes of the Northern Hemisphere and tropical Africa, while the observed GPP in the above regions is temperature-or radiation-dominant. The positive GPP biases due to earlier spring growth in boreal forest regions and negative GPP biases in off-equator tropical areas in the BCC_AVIM2.0 sim-ulations imply that cold stress on biomes in boreal mid-to-high latitudes should be strengthened to restrain plant growth, while drought stress in low-latitude regions might be eased to enhance plant production in the future version of BCC_AVIM.

This paper reports a comparative experiment using remote sensing underlying surface data (ESACCI) and Community Land Model underlying surface data (CLM_LS) to analyze the uncertainty of land surface types in land–atmosphere interaction. The results showed that the global distribution of ESACCI cropland is larger than that of CLM_LS, and there is a great degree of difference in some regions, which can reach more than 50% regionally. Furthermore, the changes of the underlying surface conditions can be transmitted to the model results through the data itself, resulting in the uncertainty of the surface energy balance, surface micro-meteorological elements, and surface water balance simulated by the model, which further affects the climate simulation effect. © 2023 by the authors.

Soil moisture is a key variable of the climate system. Microwave remote sensing has become an essential means of obtaining soil moisture because of the unique advantages of its all-day and all-weather observation capability. Theoretically, low-frequency C-band observations are highly suitable for soil moisture retrieval because of their high sensitivity to soil moisture at vegetation roots. However, the quality of C-band observations suffers from radio frequency interference (RFI) over the United States. This paper used the iterative principal component analysis (PCA) method to repair RFI-contaminated second-generation Advanced Microwave Scanning Radiometer (AMSR-2) C-band observations, and the results of soil moisture retrieval based on restored data were evaluated. It was found that RFI could lead to nonconvergence in the retrieval of a large amount of data, and the application of repaired data in retrieval could result in the recovery of more than 80% of nonconvergent data, especially in spring and autumn. The retrieval results based on restored data attained a satisfactory correlation with ERA5 reanalysis data and European Space Agency Climate Change Initiative (ESA-CCI) soil moisture data and suitably agreed with precipitation observation data. Soil moisture generally exhibited a gradual increase from west to north and east. This feature was weakened due to the influence of monsoons in the east in summer. The western side of the Cascade Mountains is the wettest area of the United States, with soil moisture exceeding 0.4 m(3) m(-3). The driest region of the United States is located between the Rockies and Cordillera Mountains, and the soil moisture value is lower than 0.1 m(3) m(-3).

Thailand is located in the Southeast Asian region, where the summer rainfall exhibits strong interannual variability, and the successful simulation of rainfall variation in Thailand by current climate models remains a challenge. Therefore, this paper evaluates the capability of the state-of-the-art Atmospheric GCM of the Institute of Atmospheric Physics (IAP-AGCM) in simulating summer rainfall over Thailand by comparing the model's results with ground-truth observation during 1981-2012. Generally, the model shows a certain skill in reproducing the observed spatial distribution of the summer rainfall climatology and its interannual variability over Thailand, although the model underestimated both rainfall amount and its variability. Using Empirical Orthogonal Function (EOF) analysis, it is found that the IAP climate model reproduced creditably the spatial patterns of the first three dominant modes of summer rainfall in Thailand, whereas it underestimated the explained variance of the observed EOF-1 and overestimated the explained variance of the observed EOF-2 significantly. It was further found that the correlation between the observed rainfall anomalies in Thailand and the Nino3.4 index can be reproduced by the IAP model. However, the observed negative correlation is largely underestimated by the IAP climate model, and this could be the reason for the underestimation of explained variance of the EOF-1 by the IAP model. The evaluation results would be of great importance for further model improvement and thus potential application in seasonal prediction in the region.

利用GLEAM V3.3a实际蒸散发资料,评估了中国科学院地球系统模式（CAS-ESM2）对青藏高原蒸散发的模拟性能,并给出了模式对未来气候变化情景下高原蒸散发变化的预估。结果表明:CAS-ESM2可以较好地模拟出青藏高原蒸散发的空间分布与季节循环特征,以及1981—2014年蒸散发的增加趋势,但趋势的幅值相对观测偏弱。未来预估试验结果显示,4种不同未来共享社会经济路径（SSPs）情景下青藏高原蒸散发均普遍增加,其中SSP585情景下的增加最为显著,且喜马拉雅山脉地区蒸散发的增加量值最大。相较于1995—2014年历史时期,年均蒸散发在2041—2060年增加46.3～65.8 mm,增幅为13.4%～19.0%;2081—2100年,年均蒸散发增加75.7～151.1 mm,增幅为21.7%～43.6%。影响蒸散发未来变化的因素具有区域性差异,高原中部和南部受气温变化影响更大,而柴达木盆地、羌塘高原中部受降水变化影响更大。

地表覆盖是陆面和气候模式中的一个重要基础数据。以陆面过程模式BCCAVIM为例,介绍模式中的地表覆盖数据变量、数据分辨率、不同类型数据的来源,重点比较分类方法差异巨大且类型众多的植被覆盖。综述比较了国际和国内常用的几套全球地表覆盖数据的来源、分类系统和分类方法以及空间分辨率,根据陆面过程模式的地表覆盖数据需求,确定不同全球土地覆盖数据在模式中的应用方法,讨论分析了全球地表覆盖产品在模式应用中存在的差距,提出不同遥感数据产品之间一致性较差的可能解决方案,探讨遥感数据产品在模式中应用的可能方式,以期更好地发挥全球地表覆盖数据产品的作用。