The pollen calendar is the simplest forecasting method for pollen concentrations. As pollen concentrations are liable to seasonal variations due to alterations in climate and land-use, it is necessary to update the pollen calendar using recent data. To attenuate the impact of considerable temporal and spatial variability in pollen concentrations on the pollen calendar, it is essential to employ a new methodology for its creation. A pollen calendar was produced in Korea using data from recent observations, and a new method for creating the calendar was proposed, considering both risk levels and temporal resolution of pollen concentrations. A probability distribution was used for smoothing concentrations and determining risk levels. Airborne pollen grains were collected between 2007 and 2017 at 8 stations; 13 allergenic pollens, including those of alder, Japanese cedar, birch, hazelnut, oak, elm, pine, ginkgo, chestnut, grasses, ragweed, mugwort and Japanese hop, were identified from the collected grains. The concentrations of each pollen depend on locations and seasons due to large variability in species distribution and their environmental condition. In the descending order of concentration, pine, oak and Japanese hop pollens were found to be the most common in Korea. The pollen concentrations were high in spring and autumn, and those of oak and Japanese hop were probably the most common cause of allergy symptoms in spring and autumn, respectively. High Japanese cedar pollen counts were observed in Jeju, while moderate concentrations were in Jeonju, Gwangju and Busan. A new methodology for the creation of a pollen calendar was developed to attenuate the impact of large temporal and spatial variability in pollen concentrations. This revised calendar should be available to the public and allergic patients to prevent aggravation of pollen allergy.

Estimating future sea level rise (SLR) and sea surface temperature (SST) is essential to implement mitigation and adaptation options within a sustainable development framework. This study estimates regional SLR and SST changes around the Korean peninsula. Two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) scenarios and nine Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations are used to estimate the changes in SLR and SST. At the end of the 21st century, global SLR is expected to be 0.28 m (0.17–0.38 m) and 0.65 m (0.52–0.78 m) for SSP 1–2.6 and SSP5-8.5, respectively. Regional change around the Korean peninsula (0.25 m (0.15–0.35 m; SSP1-2.6) and 0.63 m (0.50–0.76 m; SSP5-8.5)) is similar with global SLR. The discrepancy between global and regional changes is distinct in SST warming rather than SLR. For SSP5-8.5, SST around the Korean peninsula projects is to rise from 0.49 °C to 0.59 °C per decade, which is larger than the global SST trend (0.39 °C per decade). Considering this, the difference of regional SST change is related to the local ocean current change, such as the Kuroshio Current. Additionally, ocean thermal expansion and glacier melting are major contributors to SLR, and the contribution rates of glacier melting increase in higher emission scenarios.

Our study analyzed the occurrence frequency and distribution of seedable clouds around the Korean Peninsula in order to better secure water resources. Cloud products from the Communication, Ocean, and Meteorological Satellite (COMS), including cloud fraction, cloud top height, cloud top temperature, cloud phase, cloud top pressure, cloud optical thickness, and rainfall intensity, were used. Daytime hourly data between 0900 and 1800 local standard time (LST) observed from December 2016 to November 2019 was used. Seedable clouds occurring within this period were evaluated based on seasonal cloud phases, occurrence frequency, and cloud characteristics according to land, sea, and cloud type. These clouds exhibited varying average occurrence frequencies in different seasons. Sc (stratocumulus) clouds exhibited the highest occurrence frequency for all seasons, with an average of 63%, followed by Cu (cumulus) at 15%, As (altostratus) at 13%, and Ac (altocumulus) at 6%. We determined that low-level clouds primarily occurred around the Korean Peninsula, and the occurrence frequency of stratiform clouds was highest for water phase seedable clouds, while the occurrence frequency of cumuliform clouds was highest for ice phase seedable clouds. We believe that precipitation enhancement experiments could be suitable for western and eastern seas around the Korean Peninsula as well as for mountainous regions on land.

This study investigates changes in fine particulate matter (PM_2.5) concentration and air-quality index (AQI) in Asia using nine different Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from historical and future scenarios under shared socioeconomic pathways (SSPs). The results indicated that the estimated present-day PM_2.5 concentrations were comparable to satellite-derived data. Overall, the PM_2.5 concentrations of the analyzed regions exceeded the WHO air-quality guidelines, particularly in East Asia and South Asia. In future SSP scenarios that consider the implementation of significant air-quality controls (SSP1-2.6, SSP5-8.5) and medium air-quality controls (SSP2-4.5), the annual PM_2.5 levels were predicted to substantially reduce (by 46% to around 66% of the present-day levels) in East Asia, resulting in a significant improvement in the AQI values in the mid-future. Conversely, weak air pollution controls considered in the SSP3-7.0 scenario resulted in poor AQI values in China and India. Moreover, a predicted increase in the percentage of aged populations (>65 years) in these regions, coupled with high AQI values, may increase the risk of premature deaths in the future. This study also examined the regional impact of PM_2.5 mitigations on downward shortwave energy and surface air temperature. Our results revealed that, although significant air pollution controls can reduce long-term exposure to PM_2.5, it may also contribute to the warming of near- and mid-future climates.

Leaf wetness duration (LWD) models have been proposed as an alternative to in situ LWD measurement, as they can predict leaf wetness using physical mechanism and empirical relationship with meteorological conditions. Applications of advanced machine learning (ML) algorithms in the development of empirical LWD model can lead to improvements in the LWD prediction. The current study developed LWD model using extreme learning machine, random forest method, and a deep neural network. Additionally, performances of these ML-based LWD models are evaluated and compared with existing models. Observed LWD and meteorological variable data are obtained from nine farms in South Korea. Temporal and geographical information were also used. Additionally, the priorities of the employed variables in the development of the ML-based LWD models were analyzed. As a result, the ML-based LWD models outperformed the existing models; the random forest led to the best performance for LWD prediction among the tested LWD models. Strengths of associations between input variables and leaf wetness were relative humidity, short wave radiation, air temperature, hour, latitude, longitude, and wind speed in descending order. Uses of the geographical and time information in development of LWD model can improve the performance of LWD model.

The large population growth has significantly altered the thermal characteristics of the atmosphere, including decreased albedo and increased heat capacity; thus, urban areas experience unique climatic phenomena. We conducted sensitivity experiments using Unified Model Local Data Assimilation and Prediction-Met-Office-Reading Urban Surface Exchange Scheme (LDAPS-MORUSES) to investigate the response of surface energy budget to albedo changes in the Seoul Metropolitan Area. We compared 1.5-m temperature at 56 automatic weather station (AWS) sites and showed underestimations of approximately 0.5–2 K, but the diurnal cycle was well simulated. We changed the wall and road albedo parameters by ±50% from the default values for sensitivity experiments. With increasing albedo, 1.5-m temperature decreased by approximately 0.06 °C and 0.01 °C in urban and suburban areas, respectively. These changes are responses to decreased net radiation and sensible heat during daytime, whereas sensible heat mainly contributes to the surface cooling during nighttime. Furthermore, the decrease in albedo leads to altered vertical structure of potential temperature and atmospheric circulations at altitudes of 300–1000 m. Results show that albedo modification can affect not only surface temperature but also the entire urban boundary layer.