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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 During the Joseon Dynasty, Korea established a network for measuring rainfall and recorded this information in historical documents known as Seungjeongwon Ilgi and Ilseongnok.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]

Cities represent a key space for our sustainable trajectory in a changing environment, and our society is steadily embracing urban green space for its role in mitigating heatwaves and anthropogenic CO2 emissions. This study reports two-year surface fluxes of energy and CO2 measured via the eddy covariance method in an artificially constructed urban forest to examine the impact of urban forests on air temperature and net CO2 exchange. The urban forest site shows typical seasonal patterns of forest canopies with the seasonal march of the East Asian summer monsoon. Our analysis indicates 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. We also stipulate that ecosystem respiration in urban forests is more pronounced than typical natural forests in a similar climate zone. This can be attributed to the substantial amount of soil organic carbon available due to intensive historical soil use and soil transplantation during forest construction, as well as relatively warmer temperatures in urban heat domes. Our observational study also indicates the need for caution in soil management for less CO2 emissions in urban areas. [ABSTRACT FROM AUTHOR]

Using the eddy covariance method, this study reports the one‐year turbulent fluxes of momentum, energy, and CO2, and their seasonal variations over a recently redeveloped high‐rise, high‐population, residential area in the metropolitan city of Seoul, Korea. The study area is affected by the Asian monsoon, which is accompanied by long rain spells and a related mid‐season depression of solar radiation in the summer. Our analysis shows that the urban surface energy balance and turbulence characteristics demonstrate typical urban properties. Unstable conditions dominate all day, and the storage heat flux (night‐time and morning) and sensible heat flux (afternoon) significantly affect the diurnal variations in the urban surface energy balance. Owing to the rough urban surface, the turbulence intensities are higher than those reported previously in other cities. The annual CO2 emission rate is approximately 13.1 kg CO2 m−2⋅year−1 with traffic, which is the major source of CO2 (+2.3 μmol⋅m−2⋅s−1 per 100 vehicles). Ecosystem respiration, including that by vegetation, soil, and humans, becomes dominant in the night‐time (00:00–05:00), thus contributing significantly to the annual CO2 budget. Further analysis indicates a unique coupling of urban surface energy partitioning and CO2 emission rates with the seasonal progression of the Asian monsoon: (a) surface albedo has annual minima in late summer when the sun elevation angle, is relatively higher and the urban surface condition is wetter than in other seasons; (b) the Bowen ratio ranges from 1.7 (summer) to 7.0 (winter); and (c) CO2 emission rates show seasonal variations with the progress of the summer monsoon. [ABSTRACT FROM AUTHOR]

The roughness sublayer (RSL) is one compartment of the surface layer (SL) where turbulence deviates from Monin–Obukhov similarity theory. As the computing power increases, model grid sizes approach the gray zone of turbulence in the energy-containing range and the lowest model layer is located within the RSL. From this perspective, the RSL has an important implication in atmospheric modeling research. However, it has not been explicitly simulated in atmospheric mesoscale models. This study incorporates the RSL model proposed by Harman and Finnigan (2007, 2008) into the Jiménez et al. (2012) SL scheme. A high-resolution simulation performed with the Weather Research and Forecasting model (WRF) illustrates the impacts of the RSL parameterization on the wind, air temperature, and rainfall simulation in the atmospheric boundary layer. As the roughness parameters vary with the atmospheric stability and vegetative phenology in the RSL model, our RSL implementation reproduces the observed surface wind, particularly over tall canopies in the winter season by reducing the root mean square error (RMSE) from 3.1 to 1.8 m s -1. Moreover, the improvement is relevant to air temperature (from 2.74 to 2.67 K of RMSE) and precipitation (from 140 to 135 mm per month of RMSE). Our findings suggest that the RSL must be properly considered both for better weather and climate simulations and for the application of wind energy and atmospheric dispersion. [ABSTRACT FROM AUTHOR]

Core IdeasIn the proposed model, the wilting point and porosity are a function of organic matter.In organic‐rich soil, the model improves accuracy of the microwave radiative transfer model.The model is applicable for both portable and satellite soil moisture sensors. Most dielectric mixing models have been developed for mineral soils without extensive consideration of organic matter (OM). In addition, when used for in situ measurement, most of these models focus only on the real part of the effective dielectric constant without the corresponding imaginary part. Organic matter fractions in soils are found globally (57%), with an especially significant amount in the boreal region (17%). Without proper consideration of OM in dielectric mixing models and subsequent microwave radiative transfer modeling, brightness temperature (TB) calculations may be erroneous. This would lead to uncertainties in the estimation of higher level products, such as soil moisture retrievals from portable soil moisture sensors (e.g., time‐domain reflectometers) or passive microwave sensors onboard the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Microwave Scanning Radiometer (AMSR2) satellites. We incorporated OM into a dielectric mixing model by adjusting the wilting point and porosity according to the OM content, i.e., the effective soil dielectric constant decreases with higher OM due to a decrease in the fraction of free water and an increase in bound water. With the proposed soil parameters in the dielectric mixing model, high levels of OM increase the TB for a specific soil moisture by decreasing the microwave effective dielectric constant. The simulated TB better reproduced SMAP‐observed TB (11% in RMSE) through the improvement of the effective dielectric constant (40% reduction in RMSE). We anticipate that the application of our approach can improve microwave‐based surface soil moisture retrievals in areas with high OM. [ABSTRACT FROM AUTHOR]

In this study, the performance of wind forecasts over Svalbard, located between the Arctic Ocean and the Norwegian Sea, was evaluated using the Polar Weather Research and Forecasting (PWRF) model and three-dimensional variational data assimilation (DA) system. The forecasts of the analysis–forecast cycling experiment using the PWRF 3DVAR were compared with those of the cold start experiment using reanalysis as the initial condition. Three strong wind cases that occurred during January and February 2011–2012 were selected, where polar lows were generated on the east coast of Greenland and generated a wind speed above 20 m s−1 in Svalbard. The wind speed forecasts for both cycling and cold start experiments were similar to the highest 10-minute average wind speed for the last 1 hour (HAW). The average root mean square error (RMSE) of the forecasts in the cold start experiment from HAW was 3.78 m s−1 for three cases and was greater than that in the cycling experiment. The forecast performance in the cycling experiment was comparable to, or even better than, that in the cold start experiment, which implies that the cycling system with DA is more useful than the cold start system in forecasting polar weather to support research activities. [ABSTRACT FROM AUTHOR]

Accurate and consistent monitoring of anthropogenic combustion is imperative because of its significant health and environmental impacts, especially at city-to-regional scale. Here, we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) global prediction system using measurements from aircraft, ground sites, and ships during the Korea-United States Air Quality (KORUS-AQ) field study in May to June 2016. Our evaluation focuses on CAMS CO and CO2 analyses as well as two higher-resolution forecasts (16 and 9 km horizontal resolution) to assess their capability in predicting combustion signatures over east Asia. Our results show a slight overestimation of CAMS CO2 with a mean bias against airborne CO2 measurements of 2.2, 0.7, and 0.3 ppmv for 16 and 9 km CO2 forecasts, and analyses, respectively. The positive CO2 mean bias in the 16 km forecast appears to be consistent across the vertical profile of the measurements. In contrast, we find a moderate underestimation of CAMS CO with an overall bias against airborne CO measurements of -19.2 (16 km), -16.7 (9 km), and -20.7 ppbv (analysis). This negative CO mean bias is mostly seen below 750 hPa for all three forecast/analysis configurations. Despite these biases, CAMS shows a remarkable agreement with observed enhancement ratios of CO with CO2 over the Seoul metropolitan area and over the West (Yellow) Sea, where east Asian outflows were sampled during the study period. More efficient combustion is observed over Seoul (dCO = dCO2 = 9 ppbv ppmv-1) compared to theWest Sea (dCO=dCO2 = 28 ppbv ppmv-1). This "combustion signature contrast" is consistent with previous studies in these two regions. CAMS captured this difference in enhancement ratios (Seoul: 8-12 ppbv ppmv-1, the West Sea: ~30 ppbv ppmv-1) regardless of forecast/analysis configurations. The correlation of CAMS CO bias with CO2 bias is relatively high over these two regions (Seoul: 0.64-0.90, the West Sea: ~0:80) suggesting that the contrast captured by CAMS may be dominated by anthropogenic emission ratios used in CAMS. However, CAMS shows poorer performance in terms of capturing local-to-urban CO and CO2 variability. Along with measurements at ground sites over the Korean Peninsula, CAMS produces too high CO and CO2 concentrations at the surface with steeper vertical gradients (~0:4 ppmv hPa-1 for CO2 and 3.5 ppbv hPa-1 for CO) in the morning samples than observed (0:25 ppmv hPa-1 for CO2 and 1.7 ppbv hPa-1 for CO), suggesting weaker boundary layer mixing in the model. Lastly, we find that the combination of CO analyses (i.e., improved initial condition) and use of finer resolution (9 km vs. 16 km) generally produces better forecasts. [ABSTRACT FROM AUTHOR]

For regional weather forecasts and climate predictions, it is important to determine the optimal domain size, location, and top height. A wide model domain can be chosen to avoid noises from lateral boundaries but this can include the Tibetan Plateau and areas of northern Manchuria to the Kamchatka Peninsula in Northeast Asia. This study shows that topographic regions around the Tibetan Plateau and warm pool areas over the Manchuria in an extended model domain may have harmful effects on the accuracy of short‐ to medium‐range regional predictions on the downwind side in spring. The inaccuracy is related to model errors due to steep terrain regions in the Tibetan Plateau and cold bias in the lower stratosphere north of Manchuria. Well‐designed spectral nudging over the eastern flank of the Tibetan Plateau and the use of a higher model top are found to improve regional predictions for Northeast Asia in spring by effectively eliminating errors associated with steep topography and temperature biases in the upper troposphere and lower stratosphere, respectively. Our findings suggest possible ways to mitigate biases due to steep mountains and upper‐level processes in regional modeling. We discuss the role of our method in terms of uncertainties in regional weather forecasts and climate predictions. [ABSTRACT FROM AUTHOR]

Net ecosystem carbon dioxide (CO2) exchange (NEE) is a key parameter for understanding the terrestrial plant ecosystems, but it is difficult to monitor or predict over large areas at fine temporal resolutions. In this research, we estimated the hourly NEE using a combination of the integrated neural network (NN) model with geostationary satellite imagery to overcome the limitations of existing daily polar orbiting satellite-derived carbon flux products. Two sets of satellite imageries (i.e. the meteorological imager (MI) and geostationary ocean colour imager (GOCI) aboard communication, ocean, and meteorological satellite (COMS)) and CO2 flux data derived from eddy covariance measurements were used to verify the feasibility of applying hourly geostationary satellite imagery with an NN-based approach for estimating NEE at high temporal resolutions. For the NN model, the optimum neuronal architecture was established using an NN with one hidden layer that was trained using the Levenberg-Marquardt back propagation algorithm. The hourly NEE values estimated in test period from the NN model using the combined COMS MI and GOCI imagery and ground measurements as model inputs were compared with the eddy covariance NEE values from the measurement tower, which yielded reliable statistical agreement. The hourly NEE results from the NNmodel based on COMS MI and GOCI imagery and ground measurement data had the highest accuracy (RMSE = 2.026 ìmol m-2 s-2, R = 0.975), while the root mean square error (RMSE) and the regression coefficient (R) generated by the NN model based on satellite imagery as the sole input variable were relatively lower (RMSE = 3-230 ìmol m-2 s-2, R = 0.952). Although the simulations for the satellite-only NEE were showed as lower accuracy than the NN model that included all input variables, the hourly variations in NEE also appeared to describe its daily growth and development pattern well, indicating the possibility of deriving hourly-based products from the proposed NN model using geostationary satellite data as inputs. [ABSTRACT FROM AUTHOR]

Aerosol effects on clouds and precipitation account for a large portion of uncertainties in the prediction of the future course of global hydrologic circulations and climate. As a process of a better understanding of interactions between aerosol, clouds and precipitation, simulations are performed for a mixed-phase convective multiple-cloud system over the tropics. Studies on single-cloud systems have shown that aerosol-induced increases in freezing, associated increases in parcel buoyancy and thus the intensity of clouds (or updrafts) are a main mechanism which controls aerosol-cloud-precipitation interactions in convective clouds. However, in the multiple-cloud system that plays much more important roles in global hydrologic circulations and thus climate than single-cloud systems, aerosol effects on condensation play the most important role in aerosol-induced changes in the intensity of clouds and the effects on freezing play a negligible role in those changes. Aerosol-induced enhancement in evaporation intensifies gust fronts and increases the number of subsequently developing clouds, which leads to the substantial increases in condensation and associated intensity of convection. Although aerosol-induced enhancement in freezing takes part in the increases in condensation by inducing stronger convergence around cloud bottom, the increases in condensation are ~one order of magnitude larger than those in freezing. It is found that while aerosol-induced increases in freezing create intermittent extremely heavy precipitation, aerosol-induced increases in evaporation enhance light and medium precipitation in the multiple-cloud system here. This increase in light and medium precipitation makes it possible that cumulative precipitation increases with increasing aerosol concentration, although the increase is small. It is interesting that the altitude of the maximum of the time- and domain-averaged hydrometeor mass densities is quite robust to increases in aerosol concentration. This is because locations of gust fronts and homogeneous freezing do not vary significantly with changing aerosol concentration and this outweighs aerosol effects on hydrometeor size. [ABSTRACT FROM AUTHOR]

ABSTRACT Electricity demand is influenced by atmospheric conditions, and, therefore it is important to quantify their relationships suitably for accurate electricity demand forecasting and the implementation of power-saving policies. However, interdependencies and characteristics of covariance among meteorological variables within the same periodicities hinder the quantification of their direct and indirect impacts on electric power load. To investigate the strength of the direct correlation between atmospheric conditions and electric power load, this study harnessed a new partialization analysis method based on a partial phase synchronization index combined with wavelet transformation. The advantage of the proposed method is that it can be used to evaluate the degree of independent contribution of the variables over different spatiotemporal scales. Compared with traditional statistical analyses, this new partialization analysis shows that air temperature is the principal variable associated directly with electricity demand, but that the strength of the relationship varies with season and time scale. Relative humidity and wind speed have strong direct correlations with electricity in summer and winter, respectively. Insolation is directly coupled to the electric power load only on sub-diurnal time scales. This investigation indicates that for accurate forecasting of electricity demand, changes in the coupling strengths of different atmospheric variables should be incorporated into the electric power load forecasting process. The study shows that a partial phase synchronization index, combined with wavelet transformation, is a useful tool that could be used in other studies to assess complex interacting atmospheric oscillations that cannot be assessed properly by traditional approaches. [ABSTRACT FROM AUTHOR]

The monsoon system is an important natural driver of ecosystem carbon and water exchanges in Asia and is being altered by anthropogenic forcings. This system is accompanied by heavy rainfall and typhoons in the main growing season, thus causing alterations of environmental conditions such as rainfall, wind, and temperature; therefore, it acts as a natural disturbance to forests in Asia. Therefore, degradation of ecosystem service by monsoon activity reinforced by anthropogenic factors in a changing climate is of great concern. In this study, we presented observational evidences for the interplay of terrestrial carbon and water dynamics with the Asian monsoon and their implication in ecosystem modeling. We analyzed 3-year eddy-covariance data at a temperate deciduous forest in Korea. We used wavelet power and coherence spectra to investigate the Asian monsoon system and to determine its impact on the ecosystem. During the study period, our analysis showed strong coupling between ecosystem functioning and temporal variations of monsoon climate. Further scrutiny on the model outputs showed that the model did not accurately reproduce the observed plant phenology and thus ecosystem carbon and water exchanges disturbed by monsoon activities. Our findings suggest that under projected climate scenarios, terrestrial carbon sinks in monsoon Asia will decline if the monsoon disturbance will exceed its natural range of variation and if there is no enhancement in the robustness of the ecosystem in this region. [ABSTRACT FROM AUTHOR]