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Precipitation plays a crucial role in the global energy and water cycle and has important implications for food, water, and energy security. To enhance our understanding of the water cycle, it is invaluable to have a comprehensive historical record of precipitation. However, obtaining such records, especially for the period before the Industrial Revolution, can be challenging. During the Joseon Dynasty, Korea established a network for measuring rainfall and recorded this information in historical documents known as Seungjeongwon Ilgi and Ilseongnok. Recently, these documents have been digitized, providing us with daily precipitation data for Seoul spanning 130 years, from 1778 to 1907. By combining and analyzing these two documents, we were able to address inconsistencies found in previous studies and improve the quality of the data. Notably, this dataset is free of any missing values, making it the longest daily precipitation record in the world before the Industrial Revolution. Its availability to the public holds great potential for climate research in East Asia during the late Little Ice Age. [ABSTRACT FROM AUTHOR]

In this study, the WRF-Chem v4.4 model was utilized to evaluate the sensitivity of O 3 simulations with three bottom-up emission inventories (EDGAR-HTAP v2 and v3 and KORUS v5) using surface and aircraft data in East Asia during the Korea-United States Air Quality (KORUS-AQ) campaign period in 2016. All emission inventories were found to reproduce the diurnal variations of O 3 and its main precursor NO 2 as compared to the surface monitor data. However, the spatial distributions of the daily maximum 8 h average (MDA8) O 3 in the model do not completely align with the observations. The model MDA8 O 3 had a negative (positive) bias north (south) of 30° N over China. All simulations underestimated the observed CO by 50 %–60 % over China and South Korea. In the Seoul Metropolitan Area (SMA), EDGAR-HTAP v2 and v3 and KORUS v5 simulated the vertical shapes and diurnal patterns of O 3 and other precursors effectively, but the model underestimated the observed O 3 , CO, and HCHO concentrations. Notably, the model aromatic volatile organic compounds (VOCs) were significantly underestimated with the three bottom-up emission inventories, although the KORUS v5 shows improvements. The model isoprene estimations had a positive bias relative to the observations, suggesting that the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.04 overestimated isoprene emissions. Additional model simulations were conducted by doubling CO and VOC emissions over China and South Korea to investigate the causes of the model O 3 biases and the effects of the long-range transport on the O 3 over South Korea. The doubled CO and VOC emission simulations improved the model O 3 simulations for the local-emission-dominant case but led to the model O 3 overestimations for the transport-dominant case, which emphasizes the need for accurate representations of the local VOC emissions over South Korea. [ABSTRACT FROM AUTHOR]

Severe spatiotemporal heterogeneity of emissions sources and limited measurement networks have been hampering the monitoring and understanding of CO2 fluxes in large cities, a great concern in climate research as big cities are among the major sources of anthropogenic CO2 in the climate system. To understand the CO2 fluxes in Seoul, Korea, CO2 fluxes at eight surface energy balance sites, six urban (vegetation-area fraction < 15%) and two suburban (vegetation-area fraction > 60%), for 2017–2018 are analyzed and attributed to the local land-use and business types. The analyses show that the CO2 flux variations at the suburban sites are mainly driven by vegetation and that the CO2 flux differences between the urban and suburban sites originate from the differences in the vegetation-area fraction and anthropogenic CO2 emissions. For the CO2 fluxes at the urban sites; (1) vehicle traffic (traffic) and heating-fuel consumption (heating) contribute > 80% to the total, (2) vegetation effects are minimal, (3) the seasonal cycle is driven mainly by heating, (4) the contribution of heating is positively related to the building-area fraction, (5) the annual total is positively (negatively) correlated with the commercial-area (residential-area) fraction, and (6) the traffic at the commercial sites depend further on the main business types to induce distinct CO2 flux weekly cycles. This study shows that understanding and estimation of CO2 fluxes in large urban areas require careful site selections and analyses based on detailed consideration of the land-use and business types refined beyond the single representative land-use type widely-used in contemporary studies. [ABSTRACT FROM AUTHOR]

A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling. Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022). [ABSTRACT FROM AUTHOR]

To validate the Geostationary Environment Monitoring Spectrometer (GEMS), the GEMS Map of Air Pollution (GMAP) campaign was conducted during 2020–2021 by integrating Pandora Asia Network, aircraft, and in situ measurements. In the present study, GMAP-2020 measurements were applied to evaluate urban air quality and explore the synergy of Pandora column (PC) NO2 measurements and surface in situ (SI) NO2 measurements for Seosan, South Korea, where large point source (LPS) emissions are densely clustered. Due to the difficulty of interpreting the effects of LPS emissions on air quality downwind of Seosan using SI monitoring networks alone, we explored the combined analysis of both PC- NO2 and SI- NO2 measurements. Agglomerative hierarchical clustering using vertical meteorological variables combined with PC- NO2 and SI- NO2 yielded three distinct conditions: synoptic wind-dominant (SD), mixed (MD), and local wind-dominant (LD). These results suggest meteorology-dependent correlations between PC- NO2 and SI- NO2. Overall, yearly daytime mean (11:00–17:00 KST) PC- NO2 and SI- NO2 statistical data showed good linear correlations (R=∼0.73); however, the differences in correlations were largely attributed to meteorological conditions. SD conditions characterized by higher wind speeds and advected marine boundary layer heights suppressed fluctuations in both PC- NO2 and SI- NO2 , driving a uniform vertical NO2 structure with higher correlations, whereas under LD conditions, LPS plumes were decoupled from the surface or were transported from nearby cities, weakening correlations through anomalous vertical NO2 gradients. The discrepancies suggest that using either PC- NO2 or SI- NO2 observations alone involves a higher possibility of uncertainty under LD conditions or prevailing transport processes. However, under MD conditions, both pollution ventilation due to high surface wind speeds and daytime photochemical NO2 loss contributed to stronger correlations through a decline in both PC- NO2 and SI- NO2 towards noon. Thus, Pandora Asia Network observations collected over 13 Asian countries since 2021 can be utilized for detailed investigation of the vertical complexity of air quality, and the conclusions can be also applied when performing GEMS observation interpretation in combination with SI measurements. [ABSTRACT FROM AUTHOR]

The so-called Green Sahara (GS), which was a wet and vegetative Sahara region in the early to mid-Holocene, provides useful information on our climate simulation because it is a consequence of complex interaction between biophysical and climatic processes. It is still a challenge to simulate the GS in terms of vegetative extent and precipitation using current climate models. This study attempts to simulate the Green Sahara 8000 years ago by using the state-of-the-art Earth system model CESM that incorporates the nitrogen cycle and the soil–precipitation feedbacks. Our study puts more emphasis on the impact of soil biophysical properties (e.g., bare-soil albedo, porosity, heat capacity, and hydraulic conductivity) and soil nitrogen influenced by soil organic matter on the simulation of the GS. In this coupled simulation, vegetation interacts with changes in soil properties and soil organic matter by phenology, decomposition, and allocation of carbon and nitrogen. With changes in the Earth's orbit and dust in the early to mid-Holocene, the model simulates increased precipitation in North Africa but does not capture the extent of the GS. Our analysis shows that the Holocene greening is simulated better if the amount of soil nitrogen and soil texture is properly modified for the humid and vegetative GS period. Soil biochemical and physical properties increase precipitation and vegetation cover in North Africa through their influence on photosynthesis and surface albedo as well as their consequent enhanced albedo–precipitation and evapotranspiration–precipitation feedbacks. Our findings suggest that future climate simulation needs to consider consequent changes in soil nitrogen and texture with changes in vegetation cover and density for proper climate simulations. [ABSTRACT FROM AUTHOR]

The prevalent soil moisture probe algorithms are based on a polynomial function that does not account for the variability in soil organic matter. Users are expected to choose a model before application: either a model for mineral soil or a model for organic soil. Both approaches inevitably suffer from limitations with respect to estimating the volumetric soil water content in soils with a wide range of organic matter content. In this study, we propose a new algorithm based on the idea that the amount of soil organic matter (SOM) is related to major uncertainties in the in situ soil moisture data obtained using soil probe instruments. To test this theory, we derived a multiphase inversion algorithm from a physically based dielectric mixing model capable of using the SOM amount, performed a selection process from the multiphase model outcomes, and tested whether this new approach improves the accuracy of soil moisture (SM) data probes. The validation of the proposed new soil probe algorithm was performed using both gravimetric and dielectric data from the Soil Moisture Active Passive Validation Experiment in 2012 (SMAPVEX12). The new algorithm is more accurate than the previous soil-probe algorithm, resulting in a slightly improved correlation (0.824 to 0.848), 12 % lower root mean square error (RMSE; 0.0824 to 0.0727 cm 3 cm -3), and 95 % less bias (-0.0042 to 0.0001 cm 3 cm -3). These results suggest that applying the new dielectric mixing model together with global SOM estimates will result in more reliable soil moisture reference data for weather and climate models and satellite validation. [ABSTRACT FROM AUTHOR]

Cities represent a key space for a sustainable society in a changing environment, and our society is steadily embracing urban green space for its role in mitigating heat waves and anthropogenic CO 2 emissions. This study reports 2 years of surface fluxes of energy and CO 2 in an artificially constructed urban forest measured by the eddy covariance method to examine the impact of urban forests on air temperature and net CO 2 exchange. The urban forest site shows typical seasonal patterns of forest canopies with the seasonal march of the East Asian summer monsoon. This study shows that the urban forest reduces both the warming trend and urban heat island intensity compared to the adjacent high-rise urban areas and that photosynthetic carbon uptake is large despite relatively small tree density and leaf area index. During the significant drought period in the second year, gross primary production and evapotranspiration decreased, but their reduction was not as significant as those in natural forest canopies. We speculate that forest management practices, such as artificial irrigation and fertilization, enhance vegetation activity. Further analysis reveals that ecosystem respiration in urban forests is more pronounced than for typical natural forests in a similar climate zone. This can be attributed to the substantial amount of soil organic carbon due to intensive historical soil use and soil transplantation during forest construction, as well as relatively warmer temperatures in urban heat domes. Our findings suggest the need for caution in soil management when aiming to reduce CO 2 emissions in urban areas. [ABSTRACT FROM AUTHOR]