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Estimating future sea level rise (SLR) projections is important for assessing coastal risks and planning of climate-resilient infrastructure. Therefore, in this study, we estimated the future projections of SLR from Coupled Model Intercomparison Project phase 6 (CMIP6) models for three climate targets (1.5 °C (T15), 2.0 °C (T20), and 3.0 °C (T30)) described by the Paris Agreement. The global SLR projections are 60, 140, and 320 mm for T15, T20, and T30, respectively, relative to the present-day levels. Similarly, around the Korean Peninsula, SLR projections become more intense with continuous global warming (20 mm (T15), 110 mm (T20), and 270 mm (T30)). Ocean variables show a slow response to climate change. Therefore, we developed the Emergence of Climate Change (EoC) index for determining the time when the variable is not following the present climate trend. The EoC of SLR appears after the EoC of sea-ice melting near the time of T15 warming. Moreover, the EoC of thermal expansion appears around the 2040s, which is similar to the time of the maximum of the T15 warming period and the median of the T20 warming period. Overall, our analysis suggests that the T15 warming may act as a trigger and SLR will accelerate after the T15 warming.

Land surface processes are rarely studied in Detection and Attribution Model Inter-comparison Project (DAMIP) experiments on climate change. We analyzed a CMIP6 DAMIP historical experiment by using multi-linear regression (MLRM) and analysis of variance methods. We focused on energy and water budgets, including gross primary productivity (GPP). In MLRM, we estimated each forcing’s contribution and identified the role of natural forcing, which is usually ignored. Contributions of the forcing factors varied by region, and high-ranked variables such as net radiation could receive multiple influences. Greenhouse gases (GHG) accelerated energy and water cycles over the global land surface, including evapotranspiration, runoff, GPP, and water-use efficiency. Aerosol (AER) forcing displayed the opposite characteristics, and natural forcing accounted for short-term changes. A long-term analysis of total soil moisture and water budget indicated that as the AER increases, the available water on the global land increases continuously. In the recent past, an increase in net radiation (i.e., a lowered AER) reduced surface moisture and hastened surface water cycle (GHG effect). The results imply that aerosol emission and its counterbalance to GHG are essential to most land surface processes. The exception to this is GPP, which was overdominated by GHG effects.

Potential intensity (PI) is a metric for climate model evaluation of TC-related thermodynamic conditions. However, PI is utilized usually for assessing basin-wide TC-related thermodynamic conditions, and not for evaluating TC passage to a certain region. Here we evaluate model-simulated PI over the passage of TCs affecting South Korea (KOR PI) as well as the PI over the entire western North Pacific basin (WNP PI) using 25 CMIP5 and 27 CMIP6 models. In terms of pattern correlations and bias-removed root mean square errors, CMIP6 model performances for KOR PI are found to be noticeably improved over CMIP5 models in contrast to negligible improvement for WNP PI, although it is not in terms of normalized standard deviations. This implies that thermodynamic condition on the route of TCs affecting South Korea is likely better captured by CMIP6 models than CMIP5 models.

The CO2 growth rate is one of the key geophysical quantities reflecting the dynamics of climate change as atmospheric CO2 growth is the primary driver of global warming. As recent studies have shown that TCCON (Total Carbon Column Observing Network) measurement footprints embrace quasi-global coverage, we examined the sensitivity of TCCON to the global CO2 growth. To this end, we used the aggregated TCCON observations (2006-2019) to retrieve Annual Growth Rate of CO2 (AGR) at global scales. The global AGR estimates from TCCON (AGRTCCON) are robust and independent, from (a) the station-wise seasonality, from (b) the differences in time series across the TCCON stations, and from (c) the type of TCCON stations used in the calculation (“background” or “contaminated” by neighboring CO2 sources). The AGRTCCON potential error, due to the irregular data sampling is relatively low (2.4–17.9%). In 2006–2019, global AGRTCCON ranged from the minimum of 1.59 ± 2.27 ppm (2009) to the maximum of 3.27 ± 0.82 ppm (2016), whereas the uncertainties express sub-annual variability and the data gap effects. The global AGRTCCON magnitude is similar to the reference AGR from satellite data (AGRSAT = 1.57–2.94 ppm) and the surface-based estimates of Global Carbon Budget (AGRGCB = 1.57–2.85). The highest global CO2 growth rate (2015/2016), caused by the record El Niño, was nearly perfectly reproduced by the TCCON (AGRTCCON = 3.27 ± 0.82 ppm vs. AGRSAT = 3.23 ± 0.50 ppm). The overall agreement between global AGRTCCON with the AGR references was yet weakened (r = 0.37 for TCCON vs. SAT; r = 0.50 for TCCON vs. GCB) due to two years (2008, 2015). We identified the drivers of this disagreement; in 2008, when only few stations were available worldwide, the AGRTCCON uncertainties were excessively high (AGRTCCON = 2.64 ppm with 3.92 ppm or 148% uncertainty). Moreover, in 2008 and 2015, the ENSO-driven bias between global AGRTCCON and the AGR references were detected. TCCON-to-reference agreement is dramatically increased if the years with ENSO-related biases (2008, 2015) are forfeited (r = 0.67 for TCCON vs. SAT, r = 0.82 for TCCON vs. GCB). To conclude, this is the first study that showed promising ability of aggregated TCCON signal to capture global CO2 growth. As the TCCON coverage is expanding, and new versions of TCCON data are being published, multiple data sampling strategies, dynamically changing TCCON global measurement footprint, and the irregular sensitivity of AGRTCCON to strong ENSO events; all should be analyzed to transform the current efforts into a first operational algorithm for retrieving global CO2 growth from TCCON data.

In recent decades, extremely cold winters have occurred repeatedly throughout the Northern Hemisphere, including the Korean Peninsula (hereafter, Korea). Typically, cold winter temperatures in Korea can be linked to the strengthening of the Siberian High (SH). Although previous studies have investigated the typical relationship between the SH and winter temperatures in Korea, this study uniquely focused on a change in the relationship, which reflects the influence of the Arctic Oscillation (AO) and El Niño–Southern Oscillation (ENSO). A significant change in the 15-year moving correlation between the SH and the surface air temperature average in Korea (K-tas) was observed in January. The correlation changed from −0.80 during 1971–1990 to −0.16 during 1991–2010. The mean sea-level pressure pattern regressed with the temperature, and a singular value decomposition analysis that incorporated the temperature and pressure supports that the negative high correlation during 1971–1990 was largely affected by AO. This connection with AO is substantiated by empirical orthogonal function (EOF) analysis with an upper-level geopotential height at 300 hPa. In the second mode of the EOF, the temperature and pressure patterns were primarily affected by ENSO during 1991–2010. Consequently, the interdecadal change in correlation between K-tas and the SH in January can be attributed to the dominant effect of AO from 1971–1990 and of ENSO from 1991–2010. Our results suggest that the relative importance of these factors in terms of the January climate in Korea has changed on a multidecadal scale.

Understanding the response of the Earth system to CO2 removal (CDR) is crucial because the possibility of irreversibility exists. Therefore, the Carbon Dioxide Removal Model Inter-comparison Project (CDRMIP) for the protocol experiment in the Coupled Model Inter-comparison Project Phase 6 (CMIP6) has been developed. Our analysis focuses on the regional response in the hydrological cycle, especially in East Asia (EA). The peak temperature changes in EA (5.9 K) and the Korean peninsula (KO) (6.1 K) are larger than the global mean surface air temperature (GSAT) response. The precipitation changes are approximately 9.4% (EA) and 23.2% (KO) at the phase change time (130–150 years); however, the largest increase is approximately 16.6% (EA) and 36.5% (KO) in the ramp-down period (150–160 years). In addition, the differences are below 5 mm/day and 1 day for the precipitation intensity indices (Rx1day and Rx5day) and frequency indices (R95 and R99), respectively. Furthermore, the monsoon rainband of the ramp-down period moves northward as the earlier onset with high confidence compared to the ramp-up period; however, it does not move north to the KO region. The results suggest that reducing CO2 moves the rainband southward. However, a detailed interpretation in terms of the mechanism needs to be carried out in further research.

Scientists occasionally predict projected changes in extreme climate using multi-model ensemble methods that combine predictions from individual simulation models. To predict future changes in precipitation extremes in the Korean peninsula, we examined the observed data and 21 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over East Asia. We applied generalized extreme value distribution (GEVD) to a series of annual maximum daily precipitation (AMP1) data. Multivariate bias-corrected simulation data under three shared socioeconomic pathway (SSP) scenarios—namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5—were used. We employed a model weighting method that accounts for both performance and independence (PI-weighting). In calculating the PI-weights, two shape parameters should be determined, but usually, a perfect model test method requires a considerable amount of computing time. To address this problem, we suggest simple ways for selecting two shape parameters based on the chi-square statistic and entropy. Variance decomposition was applied to quantify the uncertainty of projecting the future AMP1. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1973–2010), were estimated for three overlapping periods in the future, namely, period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From these analyses, we estimated that the relative increases in the observations for the spatial median 20-year return level will be approximately 18.4% in the SSP2-4.5, 25.9% in the SSP3-7.0, and 41.7% in the SSP5-8.5 scenarios, respectively, by the end of the 21st century. We predict that severe rainfall will be more prominent in the southern and central parts of the Korean peninsula.

Climate sensitivity and feedback processes are important for understanding Earth’s system response to increased CO2 concentration in the atmosphere. Many modelling groups that contribute to Coupled Model Intercomparison Project phase 6 (CMIP6) have reported a larger equilibrium climate sensitivity (ECS) with their models compared to CMIP5 models. This consistent result is also found in the Korea Meteorological Administration Advanced Community Earth System model (K-ACE). Idealized climate simulation is conducted as an entry card for CMIP6 to understand Earth’s system response in new coupled models and compared to CMIP5 models. The ECS in the K-ACE is 4.83 K, which is higher than the range (2.1–4.7 K) of CMIP5 models in sensitivity to CO2 change and higher bound (1.8–5.6 K) of CMIP6 models. The radiative feedback consists of clear-sky and cloud radiative feedback. Clear-sky feedback of K-ACE is similar to CMIP5 models whereas cloud feedback of K-ACE is more positive. The result is attributable for strong positive shortwave cloud radiative effect (CRE) feedback associated with reduced low-level cloud cover at mid latitude in both hemispheres. Despite the cancellations in strong negative long wave CRE feedback with the changes in high-level clouds in the tropics, shortwave CRE has a dominant effect in net CRE. Detailed understanding of cloud feedback and cloud properties needs further study.

This study analyzes the relationship between various atmospheric fields and the observed PM10 concentrations in the Seoul metropolitan area, South Korea, during the winters of the 2001–2014 period to find suitable atmospheric indices for predicting high PM10 episodes in the region. The analysis shows that PM10 concentration in the metropolitan area is mainly affected by the intensity of horizontal ventilation and the 500 hPa high-pressure system over the Korean peninsula. The modified Korea particulate matter index (MKPI) is proposed based on a 10 m wind speed for surface ventilation and 500 hPa zonal wind for the intensity of a 500 hPa high-pressure system over the Korean peninsula. It is found that a positive MKPI value is closely correlated with the occurrence of high PM10 concentration episodes, and hence, can be used as a predictor for high PM10 episodes in the area. A future projection of the MKPI using two three-member general circulation model (GCM) ensembles with four shared socioeconomic pathway (SSP) scenarios in Coupled Model Intercomparison Project Phase 6 (CMIP6) shows that positive MKPI events and high PM10 episodes are expected to increase by 5.4−16.4% depending on the SSP scenarios in the 2081−2100 period from the present-day period of 1995−2014.

A cavity ring-down spectroscopy (CRDS) G-2401m analyzer onboard a Beechcraft King Air 350, a new Korean Meteorological Administration (KMA) research aircraft measurement platform since 2018, has been used to measure in situ CO2, CH4, and CO. We analyzed the aircraft measurements obtained in two campaigns: a within-boundary layer survey over the western Republic of Korea (hereafter Korea) for analyzing the CO2 and CH4 emission characteristics for each season (the climate change monitoring (CM) CM mission), and a low altitude survey over the Yellow Sea for monitoring the pollutant plumes transported into Korea from China (the environment monitoring (EM) mission). This study analyzed CO2, CH4, and CO data from a total of 14 flights during 2019 season. To characterize the regional combustion sources signatures of CO2 and CH4, we calculated the short-term (1-min slope based on one second data) regression slope of CO to CO2 and CH4 to CO enhancements (subtracted with background level, present as ∆CO, ∆CO2, and ∆CH4); slope filtered with correlation coefficients (R2) (2 with of 1.8–6% and 0.3–0.7 between ∆CH4 and ∆CO. In particular, the regression slope between ∆CO and ∆CO2 increased to >4% when air flows from east-central China such as Hebei, Shandong, and Jiangsu provinces, etc., sustained for 1–3 days, suggesting pollutants from these regions were most likely characterized by incomplete fossil fuel combustions at the industries. Over 80% of the observations in the Western Korea missions were attributed to Korean emission sources with regression slope between ∆CO and ∆CO2 of 0.5–1.9%. The CO2 emissions hotspots were mainly located in the north-Western Korea of high population density and industrial activities. The higher CH4 were observed during summer season with the increasing concentration of approximately 6% over the background level, it seems to be attributed to biogenic sources such as rice paddies, landfill, livestock, and so on. It is also noted that occurrences of high pollution episodes in North-Western Korea are more closely related to the emissions in China than in Korea.

There are still large uncertainties in the estimates of net ecosystem exchange of CO2 (NEE) with atmosphere in Asia, particularly in the boreal and eastern part of temperate Asia. To understand these uncertainties, we assessed the CarbonTracker Asia (CTA2017) estimates of the spatial and temporal distributions of NEE through a comparison with FLUXCOM and the global inversion models from the Copernicus Atmospheric Monitoring Service (CAMS), Monitoring Atmospheric Composition and Climate (MACC), and Jena CarboScope in Asia, as well as examining the impact of the nesting approach on the optimized NEE flux during the 2001−2013 period. The long-term mean carbon uptake is reduced in Asia, which is −0.32 ± 0.22 PgC yr−1, whereas −0.58 ± 0.26 PgC yr−1 is shown from CT2017 (CarbonTracker global). The domain aggregated mean carbon uptake from CTA2017 is found to be lower by 23.8%, 44.8%, and 60.5% than CAMS, MACC, and Jena CarboScope, respectively. For example, both CTA2017 and CT2017 models captured the interannual variability (IAV) of the NEE flux with a different magnitude and this leads to divergent annual aggregated results. Differences in the estimated interannual variability of NEE in response to El Niño−Southern Oscillation (ENSO) may result from differences in the transport model resolutions. These inverse models’ results have a substantial difference compared to FLUXCOM, which was found to be −5.54 PgC yr−1. On the one hand, we showed that the large NEE discrepancies between both inversion models and FLUXCOM stem mostly from the tropical forests. On the other hand, CTA2017 exhibits a slightly better correlation with FLUXCOM over grass/shrub, fields/woods/savanna, and mixed forest than CT2017. The land cover inconsistency between CTA2017 and FLUXCOM is therefore one driver of the discrepancy in the NEE estimates. The diurnal averaged NEE flux between CTA2017 and FLUXCOM exhibits better agreement during the carbon uptake period than the carbon release period. Both CTA2017 and CT2017 revealed that the overall spatial patterns of the carbon sink and source are similar, but the magnitude varied with seasons and ecosystem types, which is mainly attributed to differences in the transport model resolutions. Our findings indicate that substantial inconsistencies in the inversions and FLUXCOM mainly emerge during the carbon uptake period and over tropical forests. The main problems are underrepresentation of FLUXCOM NEE estimates by limited eddy covariance flux measurements, the role of CO2 emissions from land use change not accounted for by FLUXCOM, sparseness of surface observations of CO2 concentrations used by the assimilation systems, and land cover inconsistency. This suggested that further scrutiny on the FLUXCOM and inverse estimates is most likely required. Such efforts will reduce inconsistencies across various NEE estimates over Asia, thus mitigating ecosystem-driven errors that propagate the global carbon budget. Moreover, this work also recommends further investigation on how the changes/updates made in CarbonTracker affect the interannual variability of the aggregate and spatial pattern of NEE flux in response to the ENSO effect over the region of interest.

The effects of the emissions of anthropogenic greenhouse gases (GHGs), aerosols, and natural forcing on the summer-mean surface air temperature (TAS) in the East Asia (EA) land surface in the 20th century are analyzed using six-member coupled model inter-comparison project 5 (CMIP5) general circulation model (GCM) ensembles from five single-forcing simulations. The simulation with the observed GHG concentrations and aerosol emissions reproduces well the land-mean EA TAS trend characterized by warming periods in the early (1911−1940; P1) and late (1971−2000; P3) 20th century separated by a cooling period (1941−1970; P2). The warming in P1 is mainly due to the natural variability related to GHG increases and the long-term recovery from volcanic activities in late-19th/early-20th century. The cooling in P2 occurs as the combined cooling by anthropogenic aerosols and increased volcanic eruptions in the 1960s exceeds the warming by the GHG increases and the nonlinear interaction term. In P3, the combined warming by GHGs and the interaction term exceeds the cooling by anthropogenic aerosols to result in the warming. The SW forcing is not driving the TAS increase in P1/P3 as the shortwave (SW) forcing is heavily affected by the increased cloudiness and the longwave (LW) forcing dominates the SW forcing. The LW forcing to TAS cannot be separated from the LW response to TAS, preventing further analyses. The interaction among these forcing affects TAS via largely modifying the atmospheric water cycle, especially in P2 and P3. Key forcing terms on TAS such as the temperature advection related to large-scale circulation changes cannot be analyzed due to the lack of model data.