Oak pollen is a major respiratory allergen in Korea, and the distribution of oak trees is expected to increase by ecological succession and climate change. One of the drivers of climate change is increasing CO, which is also known to amplify the allergy risk of weed pollen by inducing elevated allergenic protein content. However, the impact of CO concentration on tree pollen is not clearly understood due to the experimental difficulties in carrying out extended CO treatment. To study the response of pollen production of sawtooth oak trees (Quercus acutissima) to elevated levels of ambient CO, three open-top chambers at the National Institute of Forest Science in Suwon, Korea were utilized with daytime (8 am-6 pm) CO concentrations of ambient (× 1.0, ~ 400 ppm), × 1.4 (~ 560 ppm), and × 1.8 (~ 720 ppm) treatments. Each chamber had three sawtooth oak trees planted in September 2009. One or two trees per chamber matured to bloom in 2016. Five to six catkins were selected per tree and polyethylene bags were attached to collect pollen grains. The total number of catkins per tree was counted and the number and weight of pollen grains per catkin were measured. Oak allergen-Que a 1 (Allergon Co., Uppsala, Sweden)-was extracted and purified to make an ELISA kit by which the antigen levels in the pollen samples were quantified. Total pollen counts per tree of the × 1.4 and × 1.8 treatments showed significant increase of 353 and 1299%, respectively, from the × 1.0 treatment (p < 0.001). Allergenic protein contents at the × 1.4 and × 1.8 treatments also showed significant increase of 12 and 11%, respectively (p = 0.011). The × 1.8 treatment induced significant difference from the × 1.0 treatment in terms of pollen production and allergenic protein content, whereas the × 1.4 treatment showed mixed significance. In summary, the oak trees under the elevated CO levels, which are expected in the changing climate, produced significantly higher amount of pollen and allergenic protein than under the present air conditions.

Attempts to assess the changes between the observed (or historical) and future projected daily rainfall extremes for 59 stations throughout Korea have been made with descriptive statistics and extreme value analysis. For the comparison, three different periods and four different data sets are considered: observation and historical data from 1976 to 2005 (period 0), simulation from 2021 to 2050 (period 1) and from 2066 to 2095 (period 2). The historical and projected rainfalls are obtained from RCP 4.5 and RCP 8.5 scenarios, which are based on a regional climate model HadGEM3‐RA. For the comparison of extreme values, the 20‐ and 50‐year return levels and the return period estimates are obtained by using the best one between two extreme value distributions, the method of L‐moments and the regional frequency analysis. From the descriptive statistics, we find that the numbers of heavy rainfall events will increase in the future. The total precipitation is projected to remain unchanged or slightly increased, compared to the observation. From the extreme value analysis, we realize that a 1‐in‐20 year and a 1‐in‐50 year annual maximum daily precipitation will likely become a 1‐in‐10 year and a 1‐in‐16 year event, respectively, when compared to the observation (a 1‐in‐5 year and a 1‐in‐7 year event, compared to the historical data), by the end of the 21st century. But this finding is based on only one simulation model, which confines the confidence of the result and suggests an ensemble approach based on multiple models to get more reliable result.

Tuning a complex simulation code refers to the process of improving the agreement of a code calculation with respect to a set of experimental data by adjusting parameters implemented in the code. This process belongs to the class of inverse problems or model calibration. For this problem, the approximated nonlinear least squares (ANLS) method based on a Gaussian process (GP) metamodel has been employed by some researchers. A potential drawback of the ANLS method is that the metamodel is built only once and not updated thereafter. To address this difficulty, we propose an iterative algorithm in this study. In the proposed algorithm, the parameters of the simulation code and GP metamodel are alternatively re-estimated and updated by maximum likelihood estimation and the ANLS method. This algorithm uses both computer and experimental data repeatedly until convergence. A study using toy-models including inexact computer code with bias terms reveals that the proposed algorithm performs better than the ANLS method and the conditionallikelihood-based approach. Finally, an application to a nuclear fusion simulation code is illustrated. 1

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