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Stomatal closure is a major physiological response to the increasing atmospheric carbon dioxide (CO2), which can lead to surface warming by regulating surface energy fluxes—a phenomenon known as CO2 physiological forcing. The magnitude of land surface warming caused by physiological forcing is substantial and varies across models. Here we assess the continental warming response to CO2 physiological forcing and quantify the resultant climate feedback using carbon–climate simulations from phases 5 and 6 of the Coupled Model Intercomparison Project, with a focus on identifying the cause of inter-model spread. It is demonstrated that the continental (40°–70°N) warming response to the physiological forcing in summer (~0.55 K) is amplified primarily due to cloud feedback (~1.05 K), whereas the other climate feedbacks, ranged from –0.57 K to 0.20 K, show relatively minor contributions. In addition, the strength of cloud feedback varies considerably across models, which plays a primary role in leading large diversity of the continental warming response to the physiological forcing.

South Korea’s heat wave events over 39 years (1980–2018) were defined by spatiotemporal criteria, and their quantitative characteristics were analyzed. The duration and intensity of these events ranked highest in 2016 and 2018. An examination of synoptic conditions of heat wave events in 2016 and 2018 based on a reanalysis dataset revealed a positive anomaly of 500-hPa geopotential height, which could have induced warm conditions over the Korean Peninsula in both years. However, a difference prevailed in that there was a blocking high over the Kamchatka Peninsula and a continental thermal high over northern China in 2016, while the expansion of the western North Pacific subtropical high was mainly associated with 2018 heat wave events. Numerical experiments using the Weather Research and Forecasting (WRF) Model were conducted to 1) evaluate how distinct meteorological characteristics of heat wave events in 2016 and 2018 were reproduced by the model, and 2) investigate how they affect extreme temperature events. Typical synoptic features of the 2016 heat wave events (i.e., Kamchatka blocking and continental thermal high) were not captured well by the WRF Model, while those of 2018 were reasonably reproduced. On the contrary, the heat wave event during late August 2016 related to the Kamchatka blocking high was realistically simulated when the blocking was artificially sustained by applying spectral nudging. In conclusion, the existence of a blocking high over the Kamchatka region (i.e., northern Pacific region) is an important feature to accurately predict long-lasting heat waves in East Asia.

Global warming and abnormal climate change have resulted in an increase in the frequency of severe heatwave events. Recently, a series of extreme heatwave events have occurred in South Korea, and the damage from these events has also been increasing. Thus, it is necessary to analyze the mechanisms for generating and developing heatwaves. In this study, the long-term trend for heatwave events in South Korea was investigated using cluster analysis. Heatwave events in a 38-year period in South Korea were defined, and their synoptic patterns were categorized into three clusters. The number of heatwave days of cluster 2, which is related to the anomalous positive geopotential height (GPH) over the Kamchatka Peninsula, was found to significantly increase in recent years (2000–2018) compared with the past (1981–1999). In contrast, the frequency of cluster 3 associated with a negative GPH anomaly over the Kamchatka Peninsula decreased in the same period. There were five regions, including northern China and the Kamchatka Peninsula, where the mid-level GPH significantly increased between 2000 and 2018. This change in GPH was positively (negatively) correlated with the patterns associated to long-term variability of heatwave days of cluster 2 (cluster 3). The long-term trends of the GPH anomalies over five regions showed a significant correlation with the North Atlantic Oscillation (NAO) index during midsummer. As a result, it is likely that the heatwave events related to cluster 2 (cluster 3) have increased (decreased) in South Korea because the long-term variability of the summer NAO has recently induced a favorable (unfavorable) atmospheric condition for cluster 2 (cluster 3).

The harmony of the Southern Ocean and Antarctic terrain is the pacemaker of Antarctic climate change under global warming., Recent Antarctic surface climate change has been characterized by greater warming trends in West Antarctica than in East Antarctica. Although this asymmetric feature is well recognized, its origin remains poorly understood. Here, by analyzing observation data and multimodel results, we show that a west-east asymmetric internal mode amplified in austral winter originates from the harmony of the atmosphere-ocean coupled feedback off West Antarctica and the Antarctic terrain. The warmer ocean temperature over the West Antarctic sector has positive feedback, with an anomalous upper-tropospheric anticyclonic circulation response centered over West Antarctica, in which the strength of the feedback is controlled by the Antarctic topographic layout and the annual cycle. The current west-east asymmetry of Antarctic surface climate change is undoubtedly of natural origin because no external factors (e.g., orbital or anthropogenic factors) contribute to the asymmetric mode.

This study investigates the prediction skill of sub-seasonal prediction models that vary based on the choice of two dynamical cores: the finite volume (FV) dynamical core on a latitude-longitude grid system and the spectral element (SE) dynamical core on a cubed-sphere grid system. Recent research showed that the SE dynamical core on a uniform grid system increases parallel scalability and removes the need for polar filters for mitigating uncertainty in climate prediction, particularly for the Arctic region. However, it still remains questionable whether the choice of dynamical cores can actually yield significant changes in prediction skill. To tackle this issue, we implemented a sub-seasonal prediction model based on the Community Atmospheric Model version 5 by incorporating the above two dynamical cores with virtually the same physics schemes. Sub-seasonal prediction skills of the SE dynamical core and FV dynamical core are verified with ERA-Interim reanalysis during the early winter (November–December) and the late winter (January–February) from 2001/2002 to 2017/2018. The prediction skills of two different dynamical cores were significantly different regardless of the similar physics scheme. In the ocean, the predictability of the SE dynamical core is similar to that of the FV dynamical core, mostly because our simulation configuration imposes the same boundary and initial conditions at the surface. Notable differences in the one-month predictability between the two cores are observed for the wintertime Arctic and mid-latitudes, particularly over North America and Eurasia continents. With a one-month lead, the SE dynamical core exhibited higher predictability over North America in late winter (r ≈ 0.45 in SE, r ≈ 0.10 in FV) whereas the FV dynamical core showed relatively higher predictability in East Asia and Eurasia in early winter (r ≈ 0.15 in SE, r ≈ 0.43 in FV). Therefore, we conclude that caution is needed when selecting the dynamical cores of sub-seasonal prediction models. Partially, these differences can be ascribed to the different manifestations of Arctic-mid-latitude linkage in the two dynamical cores; the SE dynamical core captures warmer Arctic and colder mid-latitudes relatively better than the FV dynamical core.

Paleoclimate data shows a good correlation between the concentration of CO2 and atmospheric temperature in the geological timescale. Many studies compare the Last Glacial Maximum (LGM) and the Pre-Industrial era (PI), to understand the coupling processes. A popular mechanism explaining this coupling process is a modulation of the ocean circulation and related CO2 emission over the Southern Ocean due to atmospheric westerly. The atmospheric westerly plays an important role in driving ocean circulation; however, the related processes are not fully understood for the LGM period.In this study, we examine physical processes determining the characteristics of the atmospheric westerly focusing on the Southern Ocean. Atmospheric states for LGM and PI are reproduced using a coupled earth system model with different sea ice conditions. A poleward intensification of the Southern Hemispheric Westerlies is observed for the LGM experiment. A comparison to PI shows that the meridional temperature gradient largely determines this intensification, and the enhanced meridional gradient is observed due to decreased heat flux from the subantarctic ocean in the LGM experiment. This result suggests that the Antarctic sea ice is a crucial component for understanding the Southern Hemispheric Westerly.

The southern hemisphere (SH) westerly wind change in the LGM is critical in understanding the glacial-interglacial carbon cycle since its strength and position influence the upwelling of the carbon rich deep water to the surface. To examine the change in SH westerly wind in the LGM, we adopted CAM5 atmosphere general circulation model (GCM) and performed LGM simulation with sensitivity experiments by specifying the LGM sea ice in the Southern Ocean (SO), ice sheet over Antarctica, and tropical pacific sea surface temperature. The SH westerly response to LGM boundary conditions in the CAM5 was compared with those from CMIP5 LGM simulations. In the CAM5 LGM simulation, the SH westerly wind substantially increases between 40°S and 65°S, while the zonal-mean zonal wind decreases at latitudes higher than 65°S. The position of the SH maximum westerly wind moves poleward by about 8° in the LGM simulation. Sensitivity experiments suggest that the increase in SH westerly winds is mainly due to the increase in sea ice in the SO that accounts for 60% of total wind change. In the CMIP5-PMIP3 LGM experiments, most of the models show the slight increase and poleward shift of the SH westerly wind as in the CAM5 experiment. The increased and poleward shifted westerly wind in the LGM obtained in the current model result is consistent with previous model results and some lines of proxy evidence, though opposite model responses and proxy evidence exist for the SH westerly wind change.

Using observational datasets and numerical model experiments, the mechanism on the slowly varying change in the relationship between the El Nino-Southern Oscillation (ENSO) and the East Asian winter monsoon (EAWM) is investigated. The decadal-window (11-, 15-, and 21-year) moving correlations show a significant change in the boreal wintertime ENSO–EAWM relationship between two sub-periods of 1976‒1992 and 1997‒2013. Such recent change in ENSO–EAWM relationship is mainly attributed to the changes in the intensity and zonal location of the anomalous lower-tropospheric northwest Pacific anticyclone (NWP-AC). NWP-AC commonly develops near the region of the Philippine Sea during the ENSO’s peak phase and plays an important role of bridging the tropical convection and mid-latitude teleconnection. On one hand, the intensity of the NWP-AC is influenced by the interdecadal variation in a linkage between ENSO and the Indian Ocean sea surface temperature (SST) variability, referring that a strong connection between the Pacific and Indian Oceans results in the strengthening of NWP-AC response to ENSO. On the other hand, the zonal displacement of the NWP-AC is associated with the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). That is, the tropical Pacific mean state (i.e., zonal SST gradient between climatologically warm western Pacific and cold eastern Pacific)—strengthened by either the negative PDO phase or the positive AMO phase—drives the anomalous ENSO-induced convection to be shifted to the west. With this westward shift, the zonal center of the NWP-AC also migrates westward over the Philippine Islands and exerts stronger connection between ENSO and EAWM. In contrast, the relaxed zonal SST contrast associated with either the positive PDO phase or the negative AMO phase tends to exhibit weaker ENSO–EAWM relationship via both of eastward shifted zonal centers of the anomalous ENSO-induced convection and the NWP-AC. Finally, a series of the numerical experiments conducted by an atmospheric general circulation model supports the observational findings.

Substantial reduction in Arctic sea ice in recent decades has intensified air-sea interaction over the Arctic Ocean and has altered atmospheric states in the Arctic and surrounding high-latitude regions. This study has found that the atmospheric responses related to Arctic sea-ice melt in the cold season (October–March) depend on sea-ice fraction and are very sensitive to in situ sea surface temperature (SST) from a series of atmospheric general circulation model (AGCM) simulations in which multiple combinations of SSTs and sea-ice concentrations are prescribed in the Arctic Ocean. It has been found that the amplitude of surface warming over the melted sea-ice region is controlled by concurrent in situ SST even if these simulations are forced by the same sea-ice concentration. Much of the sensitivity of surface warming to in situ SST are related with large changes in surface heat fluxes such as the outgoing long-wave flux in early winter (October–December) and the sensible and latent heat fluxes for the entire cold season. Vertical extension of surface warming and moistening is sensitive to these changes as well; the associated condensational heating modulates a static stability in the lower troposphere. This study also indicates that changes in SST fields in AGCM simulations must be implemented with extra care, especially in the melted sea-ice region in the Arctic. The statistical method introduced in this study for adjusting SSTs in conjunction with a given sea-ice change can help to model the atmospheric response to sea-ice loss more accurately.

In January 2016, the Arctic experienced an extremely anomalous warming event after an extraordinary increase in air temperature at the end of 2015. During this event, a strong intrusion of warm and moist air and an increase in downward longwave radiation, as well as a loss of sea ice in the Barents and Kara seas, were observed. Observational analyses revealed that the abrupt warming was triggered by the entry of a strong Atlantic windstorm into the Arctic in late December 2015, which brought enormous moist and warm air masses to the Arctic. Although the storm terminated at the eastern coast of Greenland in late December, it was followed by a prolonged blocking period in early 2016 that sustained the extreme Arctic warming. Numerical experiments indicate that the warming effect of sea ice loss and associated upward turbulent heat fluxes are relatively minor in this event. This result suggests the importance of the synoptically driven warm and moist air intrusion into the Arctic as a primary contributing factor of this extreme Arctic warming event.

A series of coupled atmosphere-ocean-land global climate model (GCM) simulations using the National Center for Atmospheric Research (NCAR) Community Climate System Model 3 (CCSM3) has been performed for the period 1870–2099 at a T85 horizontal resolution following the GCM experimental design suggested in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). First, a hindcast was performed using the atmospheric concentrations of three greenhouse gases (CO2, CH4, N2O) specified annually and globally on the basis of observations for the period 1870–1999. The hindcast results were compared with observations to evaluate the GCM’s reliability in future climate simulations. Second, climate projections for a 100-year period (2000–2099) were made using six scenarios of the atmospheric concentrations of the three greenhouse gases according to the A1FI, A1T, A1B, A2, B1, and B2 emission profiles of the Special Report on Emissions Scenarios. The present CCSM simulations are found to be consistent with IPCC’s AR4 results in the temporal and spatial distributions for both the present-day and future periods. The GCM results were used to examine the changes in extreme temperatures and precipitation in East Asia and Korea. The extreme temperatures were categorized into warm and cold events: the former includes tropical nights, warm days, and heat waves during summer (June–July–August) and the latter includes frost days, cold days, and cold surges during winter (December–January–February). Focusing on Korea, the results predict more frequent heat waves in response to future emissions: the projected percentage changes between the present day and the late 2090s range from 294% to 583% depending on the emission scenario. The projected global warming is predicted to decrease the frequency of cold extreme events; however, the projected changes in cold surge frequency are not statistically significant. Whereas the number of cold surges in the A1FI emission profile decreases from the present-day value by up to 24%, the decrease in the B1 scenario is less than 1%. The frequency and intensity of extreme precipitation events year-round were examined. Both the frequency and the intensity of these events are predicted to increase in the region around Korea. The present results will be helpful for establishing an adaptation strategy for possible climate change nationwide, especially extreme climate events, associated with global warming.

We present a study that suggests greening in the circumpolar high-latitude regions amplifies surface warming in the growing season (May–September) under enhanced greenhouse conditions. The investigation used a series of climate simulations with the Community Atmospheric Model version 3—which incorporates a coupled, dynamic global vegetation model—with and without vegetation feedback, under both present and doubled CO2 concentrations. Results indicate that climate warming and associated changes promote circumpolar greening with northward expansion and enhanced greenness of both the Arctic tundra and boreal forest regions. This leads to additional surface warming in the high-latitudes in the growing season, primarily through more absorption of incoming solar radiation. The resulting surface and tropospheric warming in the high-latitude and Arctic regions weakens prevailing tropospheric westerlies over 45–70N, leading to the formation of anticyclonic pressure anomalies in the Arctic regions. These pressure anomalies resemble the anomalous circulation pattern during the negative phase of winter Arctic Oscillation. It is suggested that these circulation anomalies reinforce the high-latitude and Arctic warming in the growing season.

Observations and modeling studies indicate that enhanced vegetation activities over high latitudes under an elevated CO2 concentration accelerate surface warming by reducing the surface albedo. In this study, we suggest that vegetation-atmosphere-sea ice interactions over high latitudes can induce an additional amplification of Arctic warming. Our hypothesis is tested by a series of coupled vegetation-climate model simulations under 2xCO2 environments. The increased vegetation activities over high latitudes under a 2xCO2 condition induce additional surface warming and turbulent heat fluxes to the atmosphere, which are transported to the Arctic through the atmosphere. This causes additional sea-ice melting and upper-ocean warming during the warm season. As a consequence, the Arctic and high-latitude warming is greatly amplified in the following winter and spring, which further promotes vegetation activities the following year. We conclude that the vegetation-atmosphere-sea ice interaction gives rise to additional positive feedback of the Arctic amplification.