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The integrated multi-satellite retrievals for the global precipitation measurement (IMERG) data, which is the latest generation of multi-satellite fusion inversion precipitation product provided by the Global Precipitation Measurement (GPM) mission, has been widely applied in hydrological research and applications. However, the quality of IMERG data needs to be validated, as this technology is essentially an indirect way to obtain precipitation information. This study evaluated the performance of IMERG final run (version 6.0) products from 2001 to 2020, using three sets of gauge-derived precipitation data obtained from the Integrated Surface Database, China Meteorological Administration, and U.S. Climate Reference Network. The results showed a basic consistency in the spatial pattern of annual precipitation total between IMERG data and gauge observations. The highest and lowest correlations between IMERG data and gauge observations were obtained in North Asia (0.373, p < 0.05) and Europe (0.308, p < 0.05), respectively. IMERG data could capture the bimodal structure of diurnal precipitation in South Asia but overestimates a small variation in North Asia. The disparity was attributed to the frequency overestimation but intensity underestimation in satellite inversion, since small raindrops may evaporate before arriving at the ground but can be identified by remote sensors. IMERG data also showed similar patterns of interannual precipitation variability to gauge observation, while overestimating the proportion of annual precipitation hours by 2.5% in North America, and 2.0% in North Asia. These findings deepen our understanding of the capabilities of the IMERG product to estimate precipitation at the hourly scale, and can be further applied to improve satellite precipitation retrieval. [ABSTRACT FROM AUTHOR]

Long-term PM 2.5 data are essential for the atmospheric environment, human health, and climate change. PM 2.5 measurements are sparsely distributed and of short duration. In this study, daily PM 2.5 concentrations are estimated using a machine learning method for the period from 1959 to 2022 in the Northern Hemisphere based on near-surface atmospheric visibility. They are extracted from the Integrated Surface Database (ISD). Daily continuous monitored PM 2.5 concentration is set as the target, and near-surface atmospheric visibility and other related variables are used as the inputs. A total of 80 % of the samples of each site are the training set, and 20 % are the testing set. The training result shows that the slope of linear regression with a 95 % confidence interval (CI) between the estimated PM 2.5 concentration and the monitored PM 2.5 concentration is 0.955 [0.955, 0.955], the coefficient of determination (R2) is 0.95, the root mean square error (RMSE) is 7.2 µ g m -3 , and the mean absolute error (MAE) is 3.2 µ g m -3. The test result shows that the slope within a 95 % CI between the predicted PM 2.5 concentration and the monitored PM 2.5 concentration is 0.864 [0.863, 0.865], the R2 is 0.79, the RMSE is 14.8 µ g m -3 , and the MAE is 7.6 µ g m -3. Compared with a global PM 2.5 concentration dataset derived from a satellite aerosol optical depth product with 1 km resolution, the slopes of linear regression on the daily (monthly) scale are 0.817 (0.854) from 2000 to 2021, 0.758 (0.821) from 2000 to 2010, and 0.867 (0.879) from 2011 to 2022, indicating the accuracy of the model and the consistency of the estimated PM 2.5 concentration on the temporal scale. The interannual trends and spatial patterns of PM 2.5 concentration on the regional scale from 1959 to 2022 are analyzed using a generalized additive mixed model (GAMM), suitable for situations with an uneven spatial distribution of monitoring sites. The trend is the slope of the Theil–Sen estimator. In Canada, the trend is -0.10 µ g m -3 per decade, and the PM 2.5 concentration exhibits an east–high to west–low pattern. In the United States, the trend is -0.40 µ g m -3 per decade, and PM 2.5 concentration decreases significantly after 1992, with a trend of -1.39 µ g m -3 per decade. The areas of high PM 2.5 concentration are in the east and west, and the areas of low PM 2.5 concentration are in the central and northern regions. In Europe, the trend is -1.55 µ g m -3 per decade. High-concentration areas are distributed in eastern Europe, and the low-concentration areas are in northern and western Europe. In China, the trend is 2.09 µ g m -3 per decade. High- concentration areas are distributed in northern China, and the low-concentration areas are distributed in southern China. The trend is 2.65 µ g m -3 per decade up to 2011 and -22.23 µ g m -3 per decade since 2012. In India, the trend is 0.92 µ g m -3 per decade. The concentration exhibits a north–high to south–low pattern, with high-concentration areas distributed in northern India, such as the Ganges Plain and Thar Desert, and the low-concentration area in the Deccan Plateau. The trend is 1.41 µ g m -3 per decade up to 2013 and -23.36 µ g m -3 per decade from 2014. The variation in regional PM 2.5 concentrations is closely related to the implementation of air quality laws and regulations. The daily site-scale PM 2.5 concentration dataset from 1959 to 2022 in the Northern Hemisphere is available at the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Atmos.tpdc.301127) (Hao et al., 2024). [ABSTRACT FROM AUTHOR]

Long-term and high spatial resolution aerosol optical depth (AOD) data are essential for climate change detection and attribution. Global ground-based AOD observations are sparsely distributed, and satellite AOD retrievals have a low temporal frequency as well low accuracy before 2000 over land. In this study, AOD at 550 nm is derived from visibility observations collected at more than 5000 meteorological stations over global land regions from 1959 to 2021. The AOD retrievals (550 nm) of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua Earth observation satellite are used to train the machine learning model, and the ERA5 reanalysis boundary layer height is used to convert the surface visibility to AOD. Comparisons with an independent dataset (AERONET ground-based observations) show that the predicted AOD has a correlation coefficient of 0.55 at the daily scale. The correlation coefficients are higher at monthly and annual scales, which are 0.61 and 0.65, respectively. The evaluation shows consistent predictive ability prior to 2000, with correlation coefficients of 0.54, 0.66, and 0.66 at the daily, monthly, and annual scales, respectively. Due to the small number and sparse visibility stations prior to 1980, the global and regional analysis in this study is from 1980 to 2021. From 1980 to 2021, the mean visibility-derived AOD values over global land areas, the Northern Hemisphere, and the Southern Hemisphere are 0.177, 0.178, and 0.175, with a trend of - 0.0029 per 10 years, - 0.0030 per 10 years, and - 0.0021 per 10 years from 1980 to 2021. The regional means (trends) of AOD are 0.181 (- 0.0096 per 10 years), 0.163 (- 0.0026 per 10 years), 0.146 (- 0.0017 per 10 years), 0.165 (- 0.0027 per 10 years), 0.198 (- 0.0075 per 10 years), 0.281 (- 0.0062 per 10 years), 0.182 (- 0.0016 per 10 years), 0.133 (- 0.0028 per 10 years), 0.222 (0.0007 per 10 years), 0.244 (- 0.0009 per 10 years), 0.241 (0.0130 per 10 years), and 0.254 (0.0119 per 10 years) in Eastern Europe, Western Europe, Western North America, Eastern North America, Central South America, Western Africa, Southern Africa, Australia, Southeast Asia, Northeast Asia, Eastern China, and India, respectively. However, the trends decrease significantly in Eastern China (- 0.0572 per 10 years) and Northeast Asia (- 0.0213 per 10 years) after 2014, with the larger increasing trend found after 2005 in India (0.0446 per 10 years). The visibility-derived daily AOD dataset at 5032 stations over global land from 1959 to 2021 is available from the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Atmos.tpdc.300822) (Hao et al., 2023). [ABSTRACT FROM AUTHOR]

During the winter of 2017/18, Beijing endured the longest consecutive dry day spell (CDD) (145 days; 23 October 2017-16 March 2018) since recordings began in 1951 (see Fig. 1b). There [...]

Long-term PM2.5 data are needed to study the atmospheric environment, human health, and climate change. PM2.5 measurements are sparsely distributed and of short duration. In this study, daily PM2.5 concentrations are estimated from 1959 to 2022 using a machine learning method at 4011 terrestrial sites in the Northern Hemisphere based on hourly atmospheric visibility data, which are extracted from the Meteorological Terminal Aviation Routine Weather Report (METAR). PM2.5 monitoring is the target of machine learning, and atmospheric visibility and other related variables are the inputs. The training results show that the slope between the estimated PM2.5 concentration and the monitored PM2.5 concentration is 0.946± 0.0002 within the 95 % confidence interval (CI), the coefficient of determination (R2) is 0.95, the root mean square error (RMSE) is 7.0 μg/m3, and the mean absolute error (MAE) is 3.1 μg/m3. The test results show that the slope between the predicted PM2.5 concentration and the monitored PM2.5 concentration is 0.862 ± 0.0010 within a 95 % CI, the R2 is 0.80, the RMSE is 13.5 μg/m3, and the MAE is 6.9 μg/m3. The multiyear mean PM2.5 concentrations from 1959 to 2022 in the United States, Canada, Europe, China, and India are 11.2 μg/m3, 8.2 μg/m3, 20.1 μg/m3, 51.3 μg/m3 and 88.6 μg/m3, respectively. PM2.5 is low and continues to decrease from 1959 to 2022. PM2.5 in the United States increases slightly at a rate of 0.38 μg/m3/decade from 1959 to 1990 and decreases at a rate of -1.32 μg/m3/decade from 1991 to 2022. Trends in Europe are positive (5.69 μg/m3/decade) from 1959 to 1972 and negative (-1.91 μg/m3/decade) from 1973 to 2022. Trends in China and India are increasing (3.04 and 3.35 μg/m3/decade, respectively) from 1959 to 2012 and decreasing (-38.82 and -42.84 μg/m3/decade, respectively) from 2013 to 2022. The dataset is available at National Tibetan Plateau / Third Pole Environment Data Center (https://doi.org/10.11888/Atmos.tpdc.301127) (Hao et al., 2024). [ABSTRACT FROM AUTHOR]

The temporal distribution of precipitation is of great importance in water availability and the hydrological cycle. Existing studies, directly comparing observed and simulated precipitation amount and intensity over different temporal intervals, show that reanalyses overestimate precipitation frequency and underestimate precipitation intensity, and therefore cannot characterize extreme precipitations. In this study, relative values of precipitation (i.e., the cumulative fractions of precipitation on different percentiles of wettest hours or days to the annual total) were used to evaluate ERA5 over China during the warm seasons of from 1979 to 2015. We found that ERA5 well reproduced the relative values of daily precipitation to the annual total, although hourly results were less satisfied. The cumulative fractions at the hourly time scale at the 99th, 95th, and 90th percentiles were 15.2% ± 3.4%, 38.7% ± 6.6%, and 54.5% ± 7.4% for the gauge observations, and 9.0% ± 1.7%, 27.2% ± 4.0%, and 41.5% ± 5.0% for ERA5, respectively. ERA5 had excellent agreement with gauge observations at daily time scales: gauge observations were 13.0% ± 3.4%, 32.1% ± 5.1%, and 47.6% ± 5.7%, compared to 11.5% ± 3.7%, 30.7% ± 7.0%, and 46.0% ± 8.8% for ERA5, respectively. This difference was mainly due to the high frequency of hourly precipitation in the reanalyses, which were cancelled out at the daily scale. Gauge observations at the hourly scale showed an increase cumulative fraction at the 99th, 95th, and 90th percentiles (0.07% ± 0.03% decade−1, 0.15% ± 0.03% decade−1, and 0.17% ± 0.03% decade−1, respectively) from 1979 to 2015, but did not show an obvious trend at the daily scale. These tendencies were underestimated at the hourly scale but overestimated at the daily scale in ERA5. The results helped us to characterize the temporal distributions and variations of precipitation, and to deepen our understanding of hydrological processes and reanalyses performance. [ABSTRACT FROM AUTHOR]

Vegetation of the Northern Hemisphere plays a vital role in global ecosystems and the carbon cycle. Variations in precipitation profoundly affect vegetation productivity, plant growth, and species communities. Precipitation frequency directly controls soil moisture availability, which has an impact on the vegetation carbon sink. However, it is unclear how precipitation frequency affects the vegetation productivity of different land cover types in different seasons. In this study, the sensitivities of the gross primary production (GPP) of six vegetation types (forest, cropland, grassland, shrubland, tundra and barren land) in response to the frequency of five categories of precipitation (trace: 0.1–5 mm/day, small: 5–10 mm/day, moderate: 10–15 mm/day, heavy: 15–20 mm/day, and very heavy: >20 mm/day) were analyzed based on the XGBoost model. The results showed that, between 1982 and 2015, precipitation frequency declined in most land cover types but increased significantly in the pan-Arctic. Differences in the sensitivity to precipitation frequency were observed between seasons and precipitation categories in northern latitudes. The GPP values of forest and barren land vegetation were less sensitive to precipitation frequency than grassland, shrubland and tundra. This may be related to different vegetation community structures and underlying surfaces and gradually increasing drought resistance capability. The sensitivity to precipitation frequency declined for moderate and heavy precipitation in cropland, but it increased in winter. As the frequency of trace precipitation diminishes in winter, the sensitivity of each vegetation type reduces by an average of 0.03%/decade. Conversely, the sensitivities to small and moderate rain increase by 0.01%/decade and 0.02%/decade, respectively, for ecosystems such as cultivated land, forests, and shrubs. However, shrubs and tundra exhibit distinct behaviors, where shifts in precipitation frequency align directly with trends in sensitivity. These results show that the frequency of precipitation significantly affects vegetation productivity and has different sensitivities, and vegetation shows different feedback mechanisms in the face of environmental changes. [ABSTRACT FROM AUTHOR]

Climate change research has received increasing attention from both researchers and the public, and the analysis of precipitation is one of the most important topics in this field. As a supplement to gauge observations, satellite-derived precipitation data present advantages, as they have high spatiotemporal resolution and good continuity. The Global Precipitation Measurement (GPM) mission is the newest generation of precipitation measurement products designed to conduct quasi-global satellite observations. This study used the latest Integrated Multi-satellitE Retrievals for GPM data collected between 2001 and 2020 to analyze changes in precipitation amount, frequency, and intensity on global land and ocean surfaces. The results showed that precipitation intensity over the ocean was generally higher than on land, and the two were more similar at the hourly scale than at the daily scale, as shown by the JS divergence statistics: 0.0323 and 0.0461, respectively. This may be due to the thermodynamic differences between land and the ocean, which can accumulate over a longer time scale. The average number of annual precipitation hours and days increased by 50 h and 5 days during 2011–2020 compared with the previous decade. The absence of obvious changes in annual precipitation amounts led to a decrease in annual precipitation intensity and the weakening of extreme precipitation on land. The analysis of precipitation regimes in nine mainland regions of the globe showed a significant increasing trend for both hourly and daily precipitation in North Asia, while insignificant changes or even decreasing trends were detected in the other regions. The results of this study elucidated the variations in precipitation between land and ocean areas and can contribute to the understanding of global precipitation. [ABSTRACT FROM AUTHOR]

Soil moisture plays a crucial role in surface hydrological processes and land–atmosphere interactions. It can influence vegetation growth directly, serving as a significant indicator for monitoring agricultural drought. However, spatially continuous datasets of root zone soil moisture rely on model simulations, introducing numerous uncertainties associated with model parameters and input data. Currently, multiple soil moisture products derived from model simulations exist, but their representation at spatial scales remains unclear. Moreover, their abilities to express soil–atmosphere and soil–vegetation interactions within land–atmosphere coupling are not understood, leading to divergent inclinations toward drought. This study investigates the performance of five soil moisture products, European Centre for Medium-Range Weather Forecasts Reanalysis v5-Land (ERA5-Land), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and SoMo.ml, under drought conditions. The bias, correlation, and difference of standard deviation (STDD) were calculated between these products and the observations from International Soil Moisture Network stations. The causal probability of soil, meteorological, and agricultural drought was calculated using the causal-effect Peter and Clark (PC) Momentary Conditional Independence (MCI) method to evaluate the data propensity of these products. ERA5-Land and SoMo.ml gave a similar performance with the highest accuracy, which was attributed to the use of the same meteorological forcing data. The biases of soil moisture from these two products at surface, middle and deep depths against station observations are below 0.1 m3/m3, and the STDD is within 0.05 m3/m3. The accuracy of GLDAS is comparatively lower, characterized by lower correlations (below 0.2 for deeper layers) and high bias (above 0.15 and 0.2 for middle and deep layers, respectively). This discrepancy could be attributed to substantial biases in the precipitation forcing data. ERA5-Land shows higher spatial resolution and greater spatial heterogeneity, whereas MERRA-2 underperformed in this area. MERRA-2 had the strongest connection to agricultural drought, with a propensity probability of 0.477. Conversely, SoMo.ml demonstrates the strongest connection to meteorological drought, with a propensity probability of 0.234. Due to the errors in simulated and observational data during the MERRA data assimilation, substantial biases in the soil moisture data, and low accuracy in meteorological forcing of GLDAS, there was no clear causal relationship between soil moisture drought and meteorological drought between these two products. These findings provide recommendations for the use of soil moisture products in drought research. • ERA5-Land and SoMo.ml soil moisture demonstrate superior performance in drought. • GLDAS showed less spatial heterogeneity and almost no long-term trend. • MERRA2 and GLDAS do not show a causal relationship to atmospheric drought. • The discrepancy could be attributed to substantial biases in the forcing data. [ABSTRACT FROM AUTHOR]

The rising of near‐surface air temperature is the most obvious manifestation of global warming. The analysis of the probability distribution function (PDF) of temperature can deep our understanding of the current characteristics of climate and extreme temperature changes. In this study, the daily maximum, minimum and mean temperature observations from meteorological stations are analysed to construct the temperature anomaly PDF during 1961–2016 over China. The skewness and kurtosis of the PDF are compared between the 1961–1990 and 1991–2016 periods. The non‐Gaussian tails of the PDF are also identified to qualify the deviation of the temperature probability density curve. The results show that near‐surface air temperature in China increased during the recent decades. The daily minimum temperature increased more than the daily maximum temperature did, while the temperature increase in winter was larger than that in summer. The largest increase detected was 1.29°C for the daily minimum temperature in winter, contrasting with the smallest increase of 0.51°C recorded for the daily minimum temperature in summer between the two study periods. The temperature anomaly PDF generally presented a positive skewness in winter and a slightly negative skewness in summer. Obvious non‐Gaussian tails in the temperature anomaly PDF were present in the majority of the study sites. Most areas showed a short cold tail in winter (except for southwestern China), and long cold tails were mainly observed in summer. These results can improve our understanding of the variation of extreme temperatures, and of the responses to global warming. [ABSTRACT FROM AUTHOR]

As a direct manifestation of global warming, the air temperature has been reported to rise significantly during the last several decades. Meanwhile, precipitation around the world has also changed profoundly. Therefore, it is important to unravel the long‐term changes in temperature and precipitation as well as their relationship. This study investigates the characteristics of temperature and precipitation in China from 1961 to 2018. Based on theoretical simulations, the effect of cloud droplet growth and precipitation formation on cloud temperature change has been determined. It has been found that for a specific cloud, the change in surface tension with temperature can increase the supersaturation rate in the cloud, which promotes precipitation by about 10%·K−1. A real dataset was then used to analyse the state change in temperature and precipitation under climate change. Precipitation showed an increasing trend by 13.9%·K−1 throughout China with increasing temperature, which reflects the combined effect of the decrease in rainfall frequency and the increase in rainfall intensity. This indicates that the theoretical simulations could provide a reference model to analyse the actual precipitation–temperature relationship (PTR). In addition, the PTR showed large regional variations: 5.1%·K−1 in North China, 5.6%·K−1 in the Northeast region, 19.6%·K−1 in the Yangtze River valley, 20.4%·K−1 in the Southeast, 14.1%·K−1 in the Southwest, and 9.2%·K−1 in the Northwest, which may be limited by the relative humidity. [ABSTRACT FROM AUTHOR]