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Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs Concentration Pathways

Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on... The National Institute of Meteorological Sciences-Korea Meteorological Administration (NIMS-KMA) has participated in the Coupled Model Inter-comparison Project (CMIP) and provided long-term simulations using the coupled climate model. The NIMS-KMA produces new future projections using the ensemble mean of KMA Advanced Community Earth system model (K- ACE) and UK Earth System Model version1 (UKESM1) simulations to provide scientific information of future climate changes. In this study, we analyze four experiments those conducted following the new shared socioeconomic pathway (SSP) based scenarios to examine projected climate change in the twenty-first century. Present day (PD) simulations show high performance skill in both climate mean and variability, which provide a reliability of the climate models and reduces the uncertainty in response to future forcing. In future projections, global temperature increases from 1.92 °C to 5.20 °C relative to the PD level (1995–2014). Global mean precipitation increases from 5.1% to 10.1% and sea ice extent decreases from 19% to 62% in the Arctic and from 18% to 54% in the Antarctic. In addition, climate changes are accelerating toward the late twenty-first century. Our CMIP6 simulations are released to the public through the Earth System Grid Federation (ESGF) international data sharing portal and are used to support the establishment of the national adaptation plan for climate change in South Korea. . . . . Keywords CMIP6 ScenarioMIP SSP-RCP Climate change Future projection 1 Introduction For the Intergovernmental Panel on Climate Change (IPCC) Responsible Editor: Eun-Soon Im. Assessment Report 6 (AR6), a new phase of model experimen- tation (CMIP6) is on progress. Projection of the future climate * Hyun Min Sung sunghm122@korea.kr change plays an important role in improving understanding of the climate system (O’Neilletal. 2016). The Scenario Model Inter-comparison Project (ScenarioMIP) is an essential protocol Innovative Meteorological Research Department, Climate Research Division, National Institute of Meteorological Sciences, Seogwipo, within CMIP6 that provide multi-model climate projections Jeju, South Korea based on new future simulations (Eyring et al. 2016). The highest Numerical Model Development Division, Numerical Modelling priority objective for the ScenarioMIP is to provide climate mod- Center, Seoul, South Korea el simulations that can facilitate a wide range of integrated studies Operational Systems Development Department, National Institute of for climate impact on societies, including the considerations of Meteorological Sciences, Seogwipo, Jeju, South Korea mitigation and adaptation. Planning and Finance Division, National Institute of Meteorological The NIMS-KMA has participated in the ScenarioMIP Sciences, Seogwipo, Jeju, South Korea since CMIP3. Min et al. (2006)produce theECHO-G model Program for Climate Model Diagnosis and Intercomparison, simulations under the Special Report on Emission Scenarios Lawrence Livermore National Laboratory, Livermore, CA, USA (SRES) of the CMIP3. Also, NIMS-KMA provides the Met Office Hadley Centre, Exeter, UK Hadley Global Environment Model (HadGEM2-AO; Collins Korean Meteorological Society 852 H. M. Sung et al. et al. 2011) simulations under Representative Concentration component of UKESM1 and K-ACE is the Nucleus for Pathway scenarios (RCPs; Moss et al. 2010). After CMIP5, a European Modelling of the Ocean dynamical model new concept for scenario has been developed by combination (NEMO) and MOM, respectively. This component is coupled of RCPs and SSPs. In this new concentration pathway, future through the OASIS3-MCT coupler. In addition, an aerosol climate response depends on the choices and implemen- component of K-ACE is the simple mode of the United tation of adaptation and mitigation options. The updated Kingdom Chemistry and Aerosol model (UKCA; Archibald projections of the climate state are required in CMIP6 et al. 2019, Mulcahy et al. 2018), while a fully coupled UKCA based on the new CO concentration pathways. Four is used in UKESM1. The detailed description of the model Tier 1 scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and components and the coupling process of two Earth system SSP5–8.5) are selected for our simulations. models are described in Lee et al. (2019a) and Sellar et al. For contributing to CMIP6, we use two Earth system (2019). These simulation results (both K-ACE and models (i.e., K-ACE and UKESM1) under the Met Office UKESM1) are released to the international data sharing portal collaboration agreement. Lee et al. (2019a) and Sellar et al. (ESGF) and are used to support the establishment of the na- (2019) report evaluation results for K-ACE and UKESM1, tional adaptation plan for climate change in South Korea. respectively, while there has been no study about the ensem- We simulate three ensemble members for each of the K- ble mean evaluation. In this study, we aim to show perfor- ACE (r1i1p1f1, r2i1p1f1, and r3i1p1f1) and the UKESM1 mance of the ensemble mean and their future climate changes (r13i1p1f2, r14i1p1f2, and r15i1p1f2). The ensemble mean in the twenty-first century. Climate change indicators, such as isused for analysis, which is described as NIMS-KMA- temperature, precipitation, sea-ice, and climate extreme CMIP6. Also, it is worth to document their comparison with indices are analyzed for future projections. We also fo- NIMS-KMA’s CMIP5 model (HadGEM2-AO; Baek cus on near-term (NT; 2021–2040), mid-term (MT; et al. 2013). The HadGEM2-AO comprises atmosphere 2041–2060), and long-term (LT; 2081–2100) period to component with N96 horizontal resolution and ocean provide information of future climate change. component with a 1 degree horizontal resolution (in- This study is organized as follows. The next section de- creasing to 1/3 degree at the equator). The atmospheric scribes the framework of CMIP6 experiments. The general component is the previous version of UM. performance of CMIP6 experiments is analyzed in section 3. Section 4 describes the climate change projections for the twenty-first century using the new SSP-RCP scenarios 2.2 Experimental Design and External Forcing focusing on temperature, precipitation, sea-ice, and cli- mate extreme indices. Finally, section 5 presents the Historical experiments (1850–2014) and future projection ex- discussion and conclusion. periments (2015 ~ 2100) have been performed with the K- ACE and the UKESM1. In CMIP6, similar to the earlier phases of CMIP, the pre-industrial control simulation is used 2 Model Description and Experiments to produce a stable quasi-equilibrium for initial state for the historical experiment (O’Neill et al. 2016). Following that, 2.1 Model Description historical experiments are performed using external condi- tions (e.g., GHGs, aerosols, land use changes, and natural The K-ACE has been developed by the NIMS-KMA under forcing). We select five gases (CO ,CH ,N O(WMO 2 4 2 the South Korea-United Kingdom (UK) Science and 2014), CFC-12-eq (Velders et al. 2009), HFC-134a-eq) to Technology Cooperation Program (Lee et al. 2019a). The represent anthropogenic radiative forcing (Meinshausen compositions of K-ACE are the Unified Model (UM) in the et al. 2017). Aerosol emission sources have been obtained Global Atmosphere 7.1 configuration (GA7.1; Walters et al. from Input4MIPs (Durack et al. 2018) and are associated with 2019), Modular Ocean Model of GFDL (MOM; Griffies et al. biofuel, fossil fuel, and biomass burning emissions (Bond 2007), sea ice model of Los Alamos (CICE; Hunke et al. et al., 2004). The ending states of the historical experiments 2015;Ridleyetal. 2018), and the OASIS3-MCT coupler are applied as initial condition for future projection experi- (Craig et al. 2017; Valcke et al. 2015). The horizontal resolu- ments. Future projection experiments are performed using tion is a N96 regular latitude-longitude grid in atmosphere. Tier 1 scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and Land surface component is the joint UK land environment SSP5–8.5). According to Gidden et al. (2019), in SSP1, rapid simulator (JULES; Best et al. 2011). world energy transition from fossil-fuel related energy con- The UKESM1 has been developed by the UK Met Office sumption sharply decreases the emissions and SSP2 has a (UKMO) and the Natural Environment Research Council similar transition with delayed action. Only SSP3 shows sim- (NERC). The ocean and aerosol-chemistry processes are the ilar emissions to present-day levels due to the increasing de- key differences between K-ACE and UKESM1. The ocean mand of growing population. Korean Meteorological Society Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs... 853 3 Evaluation of Historical Experiments needed for area and mass average and σ is the interannual vn variance from the validation observations. This approach 3.1 General Performance of the Mean State helps to homogenize errors from different regions and vari- ables. Following that, the final model performance index is Evaluation of the historical simulation provides insights into calculated by averaging over all variables. The outcomes of the reliability of the climate model and reduces the uncertainty the comparison between K-ACE, UKESM1 and 22 CMIP5 in response to future forcing (Eyring et al. 2016). The purpose models are shown in Fig. 1. As the blue color becomes darker, of this section is to ensure that the two model ensembles with the error decreases. Note that the CMIP5 historical data are not similar structures use for supporting national policy of climate available after 2005 as the historical simulation ends in this change adaptation, but the calculated results also have com- year. Thus, the RCP 4.5 scenario is used for the period from parable from a scientific point of view. Reichler and Kim 2006 to 2014. Different scenarios show similar magnitude of (2008) suggest a performance index with aggregated errors climate change in near future (IPCC 2014) and most previous to simulate climatological mean states of multiple different studies use RCP 4.5 scenario for near future period. The climate variables. To apply this method, normalized errors NIMS-KMA-CMIP6 (Fig. 1) shows an improved perfor- of 20 key climate quantities (Table 1) are used. To determine mance in PD period (1995–2014) from historical simulation performance index, we first calculate normalized error vari- compared to CMIP5. In addition, K-ACE and UKESM1 show ance e for each model by normalizing the differences be- similar performance levels for present climate (Fig. 1b, 1c). tween simulated and observed climates based on grid point. This result indicates reduction of the uncertainty in response to The equation is given below: future forcing in CMIP6 simulations. S −O vmn vn B C e ¼ ∑ w @ A 3.2 Climate Variability vm vn It is important to examine the simulated performance of cli- Where, S indicates the simulated climatology for cli- mate variability for decadal time scale because the simulation vmn for climate projection is most likely to have hundreds time mate variable (v), model (m), and grid point (n). O is the vn scale integration. We investigate the performance on corresponding observed climatology. w is proper weights Table 1 Climate variables and Variable Domain Acronym Validation data corresponding validation data. Those listed as “land” or “ocean” 2 m air temperature global TAS CRU, ICOADS, NOAA, ERA-Interim are single-level fields over their respective regions Total cloudiness global CLT CERES Surface downwelling shortwave flux in air global RSDS CERES Surface upwelling shortwave flux in air global RSUS CERES Surface downwelling longwave flux in air global RLDS CERES Surface upwelling longwave flux in air global RLUS CERES TOA outgoing shortwave flux global RSUT CERES TOA outgoing longwave flux global RLUT CERES TOA cloud radiative forcing longwave flux global CRFLT CERES TOA cloud radiative forcing shortwave flux global CRFST CERES Precipitation global PR CMAP, GPCP Sea level pressure ocean PSL ICOADS Surface upward sensible heat flux ocean HFSS ICOADS, OAFLUX Surface upward latent heat flux ocean HFLS ICOADS, OAFLUX Surface skin temperature land TS ERA-Interim 200 hPa zonal wind global U200 ERA-Interim 200 hPa meridional wind global V200 ERA-Interim 200 hPa air temperature global T200 ERA-Interim 850 hPa zonal wind global U850 ERA-Interim 850 hPa meridional wind global V850 ERA-Interim Korean Meteorological Society 854 H. M. Sung et al. Korean Meteorological Society Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs... 855 Fig. 1 The calculated performance index over global for (a) climate change over the twenty-first century. In addition, this NIMS-KMA-CMIP6 results, (b)K-ACE, (c) UKESM1 and (d)- section provides information on how much change isprojected (y) 22 CMIP5 models. Low indices (blue color) denote better by the future projections for the new climate change scenario performance through comparing the NIMS-KMA’sCMIP5 results (Baek et al. 2013). This information supports the establishment of na- tional adaptation policies for climate change and can contribute reproducing various climate variability modes using the to the spread of the climate emergency. Program for Climate Model Diagnosis and Inter-comparison (PCMDI) Metrics Package (PMP; Gleckler et al. 2016,Lee 4.1 Surface Temperature et al. 2019b). The atmospheric modes (Northern Annular Mode (NAM), the North Atlantic Oscillation (NAO), the Figure 3 shows the time series of global mean surface tempera- Pacific North America pattern (PNA), the North Pacific ture changes for the twenty-first century relative to the PD period. Oscillation (NPO), and the Southern Annular Mode (SAM)) The simulated temperature projections have similar positive and SST based modes (Pacific Decadal Oscillation (PDO) and trends until 2030. As mentioned in AR5, near-term projections the North Pacific Gyre Oscillation (NPGO)) are examined in are dependent on internal variability rather than the emission this section. The winter season for the atmospheric modes are scenario. Impact of the emission scenario on future projections the primary focus (i.e., DJF, except for SAM where JJA used), becomes evident after the 2030s. In the late twenty-first century, because the variability signal is strongest in winter. The SST- rising temperature and ensemble spreads are proportional to the based modes are derived using monthly-mean time series. The concentration pathway of four scenarios. On average, the Common Basis Function (CBF) approach (Lee et al. 2019b)is projected range of temperature changes in the LT period relative used for evaluation of variability in this study. The twentieth to the PD period is expected to be 1.92 ± 0.22 °C, 3.02 ± 0.47 °C, Century Reanalysis (20CR; Compo et al. 2006, 2011) and Met 4.28 ± 0.62 °C, and 5.20 ± 0.71 °C for SSP1–2.6, SSP2–4.5, Office Hadley Centre Sea Ice and Sea Surface Temperature SSP3–7.0, and SSP5.8.5, respectively. In addition, the difference dataset (HadISST) version 1.1 (Rayner et al. 2003) are used in the temperature between SSP5–8.5 and SSP1–2.6 scenarios is for atmospheric and SST-based modes, respectively. The ob- about 3.3 °C by the end of the century, which is larger than the servations and models are both analyzed over the period temperature difference between RCP 2.6 and RCP 8.5 (3 °C) 1900–2005 (1956–2005 for observed SAM due to lower con- reported in Baek et al. (2013). According to O’Neill et al. fidence in observations over the Southern Hemisphere in the (2016), CO concentrations in SSP5–8.5 of CMIP6 are higher first half of the twentieth century). than RCP8.5 of CMIP5. This means that expected global The simulated patterns of each climate variability mode warming is higher in the new scenario of CMIP6. In addition, (Fig. 2) indicate that the models reasonably capture the CMIP6 models, such as K-ACE and UKESM1, have higher extra-tropical modes. For NAM, PNA, and NPO, K-ACE climate sensitivity (ECS; Equilibrium Climate Sensitivity) than and UKESM1 show larger variance over the Pacific Ocean, CMIP5 models (Zelinka et al. 2020, Sun et al. 2020). The but the locations of dipoles are comparable to the observation. projected future changes are described in Table 2.Consistent For NAO, models capture smaller variance over the Atlantic with the previous results (Baek et al. 2013), the temperature Ocean, but locations of dipoles are also comparable to the increase in East Asia is also larger for all scenarios. 20CR as well. It is similar for SAM and PDO where models Figure 4 illustrates the spatial pattern of global surface tem- are performing well at capturing the pattern. For NPGO, how- perature in the twenty-first century under four future scenari- ever, there are discrepancies between HadISSTv1.1 (Fig. 2y) os. The temperature projections are estimated for three pe- and models (Fig. 2z-ab), which are more noticeable than other riods, NT (2021–2040), MT (2041–2060), and LT (2081– modes. Overall, models are promising to capture patterns and 2100). Continuous increases in temperature are expected in amplitudes of modes in general except for the NPGO (ampli- NT, MT, and LT under all scenarios. NT projections are sim- tude not shown). With the analysis from previous session, ilar in all scenarios. However, LT projections show significant these results demonstrate the high reliability of future projec- discrepancies in different scenarios compared to NT and MT tions for the twenty-first century. periods. A larger scale warming in the late twenty-first century over land rather than ocean (right column in Fig. 4)is projected under SSP3–7.0 and SSP5–8.5 (high concentration 4 Future Climate Projections scenarios). The highest temperature increase is expected to occur in the Arctic regions, with less warming over In section 3, we investigated the performance of K-ACE and the North Atlantic and the Southern Ocean. These spa- UKESM1 for the PD period compared to observations. Two tial changes are accelerated due to sea ice melting climate models show similar performances for climate mean state which is based on increasing temperature (positive and variability. Therefore, we analyzed ensemble mean for future surface albedo feedback; IPCC, 2014). Korean Meteorological Society 856 H. M. Sung et al. Korean Meteorological Society Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs... 857 Table 2 Projected global mean surface temperature change (tas) and Fig. 2 The observed (EOF-1 or 2; first column) and simulated (CBF) precipitation change in each future period, NT (2021–2040), MT (2041– modes of variability from HadGEM2-AO (second column), K-ACE 2060), and LT (2081–2100) relative to PD period (third column), and UKESM1 (fourth column), respectively. The percent of variance [%] explained by each EOF or CBF is noted at the upper-right Global Near-Term Mid-Term Long-Term corner of each plot. Units for atmospheric and SST-based modes are hPa and °C, respectively Tas (°C) SSP1–2.6 1.19 1.67 1.92 SSP2–4.5 1.25 1.99 3.02 SSP3–7.0 1.22 2.18 4.28 4.2 Precipitation SSP5–8.5 1.33 2.42 5.20 precip. (%) SSP1–2.6 2.82 4.11 5.10 The twenty-first century projection of precipitation also shows SSP2–4.5 2.52 4.23 6.85 larger response in this study than the result reported by Baek et al. SSP3–7.0 2.09 3.84 7.88 (2013). Projected global mean precipitation changes (Fig. 5) SSP5–8.5 2.58 4.66 10.08 demonstrate that all scenarios show significant increases in pre- East-Asia Near-Term Mid-Term Long-Term cipitation with similar trends projected until 2050. Precipitation Tas (°C) SSP1–2.6 1.19 1.69 1.97 changes in the LT period relative to the PD period are expected to SSP2–4.5 1.27 2.03 2.99 increase from 5.1% to 10.1%. Further, SSP1–2.6 and SSP5–8.5 SSP3–7.0 1.24 2.18 4.33 show 5% precipitation difference in the LT period, and this pre- SSP5–8.5 1.39 2.45 5.29 cipitation differenc’e in 2100 is twice that in CMIP5 (2.5% in precip. (%) SSP1–2.6 3.39 5.30 6.31 Baek et al. 2013). Additionally, changes in global precipitation −1 with temperature are within the range of 1–3%°C in most SSP2–4.5 3.49 4.63 6.67 climate models (IPCC 2014), and NIMS-KMA-CMIP6 shows SSP3–7.0 3.03 5.08 7.97 −1 −1 comparable precipitation sensitivity (2.7%°C ,1.9%°C for SSP5–8.5 3.68 5.16 10.21 SSP1–2.6 and SSP5–8.5, respectively). Spatial patterns of future precipitation changes (%) in NT projections are similar in all scenarios and the impact of dif- 4.3 Sea Ice ferent emission scenarios begins to appear in LT period (Fig. 6). All scenarios reveal the same spatial pattern (increase Figure 7 shows the time series of the sea ice changes. Similar in tropics and decrease in subtropics) of precipitation changes to temperature changes, the reduction of the sea ice extent until with different magnitudes. Moreover, the East Asia monsoon around 2030 is consistent in all four scenarios and the melting and Indian monsoon regions tend to be wetter while the South trend differs significantly among the scenarios after 2030. The Asia monsoon and South America monsoon regions become projected sea ice extent in SSP1–2.6 stabilizes after the MT pe- drier. The magnitudes of these patterns are significant in riod and the reduction continues in SSP2–4.5. The acceleration higher concentration scenarios (SSP3–7.0, SSP5–8.5). of melting occurs after the MT period in SSP3–7.0 and SSP5– Overall, East Asian changes are larger than the global climate 8.5. This is especially evident in the Arctic region, where accel- changes for all scenarios (Table 2). eration is significant and is influenced by the positive ice albedo feedback (increased air temperature reduces sea ice cover, allowing more energy to be absorbed on the sea surface, accelerating the melting; Gregory et al. 2002; Cvijanovic and Ken 2015). In LT period, the sea ice extent decreases from 19% (SSP1–2.6) to 62% (SSP5–8.5) in the Arctic and from 18% (SSP1–2.6) to 54% (SSP5–8.5) in the Antarctic. The rapid sea ice melting rates in SSP3–7.0 and SSP5–8.5 are associated with high CO concentrations (> 600 ppm) and temperatures (above 4 °C) relative to the pre-industrial levels. Additionally, according to the IPCC AR5, sea ice reduction of the Arctic has been most rapid in summer. A nearly ice-free Arctic (sea ice 6 2 extent less than 10 km for at least five consecutive years) in September is likely after the NT period in all scenarios (not shown). The rate of decline in the Arctic is faster in this study Fig. 3 Time series of global mean surface temperature changes for the historical simulation (black) from 1995 to 2014, and future simulations than reportedbyBaeketal. (2013). However, the Antarctic sea for four SSP-RCPs (SSP5–8.5 (red), SSP3–7.0 (yellow), SSP2–4.5 ice loss is projected to continue through the NT period depending (blue), and SSP1–2.6 (green)) from 2015 to 2100, respectively. The shad- on the magnitude of global warming (from 35% for SSP1–2.6 to ed area indicates the ensemble spread of six members (both K-ACE and 93% for SSP5–8.5). UKESM1) Korean Meteorological Society 858 H. M. Sung et al. Fig. 4 Global distribution of the 20 year averaged temperature change in (fourth row)) with three future periods (Near-Term (NT; left column), twenty-first century of four SSP-RCP future scenarios (SSP1–2.6 (first Mid-Term (MT; mid column), and Long-Term (LT; right column)) com- row), SSP2–4.5 (second row), SSP3–7.0 (third row), and SSP5–8.5 pared to PD period (1995–2014) 4.4 Climate Extremes Index We use the Expert Team on Climate Change Detection and Indices (ETCCDI) to define a set of climate indices (Klein Tank et al. 2009, Sillmann et al. 2013). To investigate the future projections of climate extremes, warm days (above the 90th percentile of daily maximum temperature; TX90p) and cold nights (below the 10th percentile of daily minimum tempera- ture; TN10p), and very wet days (above the 95th percentile of daily precipitation amount; R95p) of ETCCDI are used. Figure 8 shows the spatial distributions of extreme indices (TX90p, TN10p, and R95p) for the LT period. Relative to PD level, TX90p increases three times especially in Central Africa, western India, southern China, Southeast Asia, Fig. 5 Time series of global mean precipitation changes (%) for the historical simulation (black) from 1995 to 2014, and future simulations Central America, and northern South America regions. Also, for four SSP-RCPs (SSP5–8.5 (red), SSP3–7.0 (yellow), SSP2–4.5 TN10p is decreased about 93% compared with the PD level (blue), and SSP1–2.6 (green)) from 2015 to 2100, respectively. The shad- especially in northern and southern Africa, Europe, Russia, ed area indicates the ensemble spread of six members (both K-ACE and Australia, and high elevation regions including major UKESM1) Korean Meteorological Society Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs... 859 Fig. 6 Global distribution of the 20 year precipitation change (%) in (fourth row)) with three future periods (Near-Term (NT; left column), twenty-first century of four SSP-RCP future scenarios (SSP1–2.6 (first Mid-Term (MT; mid column), and Long-Term (LT; right column)) com- row), SSP2–4.5 (second row), SSP3–7.0 (third row), and SSP5–8.5 pared to PD period (1995–2014) 6 2 Fig. 7 Time series of sea ice extent change (10 km )in(a) the Arctic and (blue), and SSP1–2.6 (green)) relative to PD level (1995–2014), respec- (b) the Antarctic for historical simulation (black) and future simulations tively. Shaded area indicates ensemble spread of six members (both K- for four SSP-RCPs (SSP5–8.5 (red), SSP3–7.0 (yellow), SSP2–4.5 ACE and UKESM1) Korean Meteorological Society 860 H. M. Sung et al. Fig. 8 Spatial distributions of warm days (TX90p; top), cold nights (TN10p; middle), and very wet days (R95p; bottom) in LT (2081 ~ 2100) relative to the PD (1995 ~ 2014). Unit is % mountain ranges (e.g., the Rockies, the Andes, and the Alps) The results of performance index for 20 climate var- and the Tibetan plateau. Overall, change in TX90p and iables reveal that the NIMS-KMA-CMIP6 simulation TN10p mainly occurs in low to mid latitude regions with high shows better performance in PD period compared to elevation, respectively. These results are consistent with the CMIP5 simulations. In addition to the mean state, model CMIP5 projections (IPCC 2014, 2019). Furthermore, the simulations capture the observed characteristics of R95p projections in LT period from 15% in SSP1–2.6 to extratropical modes of variability (i.e., NAM, NAO, 54% in SSP5–8.5, and the spatial pattern of R95p changes PNA, NPO, SAM, PDO, and NPGO). Overall, these mainly occur in low-latitude and high-latitude regions (e.g., results demonstrate the high reliability of future projec- Central Africa, Southeast Asia, northern South America, tions for the twenty-first century. Alaska, and Scandinavia). This increasing trend in cli- (2) Projected global warming and increasing precipitation mate extreme indices is similar with Baek et al. are proportional to scenarios. Future changes in the LT (2013), however the rate of change in the LT period period are larger than in the NT period. These results are is higher. This demonstrates that the response to future consistent with CMIP5, but the increments are larger in extremes due to SSP-RCP is stronger than in RCP this study based on SSP-RCPs. scenarios. In the LT period, projected temperature and precipi- tation depend on the scenarios associated with GHG forcing. On average, the projected range of temperature changes in the LT period relative to the PD period is 5 Summary and Discussion expected to be 1.92 ± 0.22 °C, 3.02 ± 0.47 °C, 4.28 ± 0.62 °C, and 5.20 ± 0.71 °C for SSP1–2.6, SSP2–4.5, Understanding climate change induced from anthropogenic SSP3–7.0, and SSP5.8.5, respectively. Precipitation in- forcing is important to determine the future directions of so- creases from 5.1% to 10.1% and sea ice extent reduces cioeconomic development. The SSP-RCP scenarios have from 19% to 62% in the Arctic and from 18% to 54% in been developed for the new phase of CMIP that is currently the Antarctic. Future changes in temperature and precip- underway (O’Neill et al., 2016). The NIMS-KMA produces itation increments are larger than results reported by climate projections with new scenarios and this study summa- Baek et al. (2013), owing to the large CO concentrations rizes the main findings of that effort. 2 in the SSP-RCP scenario and higher climate sensitivity of CMIP6 models. (1) NIMS-KMA produces new CMIP6 scenario using the (3) To investigate the future projection of climate extremes, ensemble mean of two models (K-ACE and UKESM1) TX90p, TN10p, and R95p indices are used in this study. and an evaluation of CMIP6 historical simulation is per- Spatial patterns of these indices are similar to CMIP5, formed in this study compared to CMIP5 simulations. Korean Meteorological Society Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs... 861 Development and evaluation of an earth system model-HadGEM2. but the magnitudes are significantly larger than CMIP5. Geosci. Model Dev. 4,1051–1075 (2011) The spatial pattern of the indices (Fig. 8)isingood Compo, G.P., Whitaker, J.S., Sardeshmukh, P.D.: Feasibility of a 100- agreement with the CMIP5 models. In LT period, TX90p year reanalysis using only surface pressure data. Bull. Am. increases three times and TN10p decreases about 93% Meteorol. Soc. 87,175–190 (2006) Compo, G.P., Whitaker, J.S., Sardeshmukh, P.D., Matsui, N., Allan, R.J., relative to PD period. The R95p increases from 15% in Yin, X., Gleason, B.E., Vose, R.S., Rutledge, G., Bessemoulin, P., SSP1–2.6 to 54% in SSP5–8.5. This increasing trend of BroNnimann, S., Brunet, M., Crouthamel, R.I., Grant, A.N., climate extreme indices is similar to CMIP5, but the Groisman, P.Y., Jones, P.D., Kruk, M.C., Kruger, A.C., Marshall, magnitudes are larger than CMIP5. G.J., Maugeri, M., Mok, H.Y., Nordli, O., Ross, T.F., Trigo, R.M., Wang, X.L., Woodruff, S.D., Worley, S.J.: The twentieth century reanalysis project. Q. J. R. Meteorol. Soc. 137,1–28 (2011) Acknowledgments This work was funded by the Korea Meteorological Cvijanovic, I., Ken, C.: Atmospheric impacts of sea ice decline in CO Administration Research and Development Program “Development and induced global warming. Clim. Dyn. 44,1173–1186 (2015) Assessment of IPCC AR6 Climate Change Scenarios” under Grant Craig, A., Valcke, S., Coquart, L.: Development and performance of a (KMA2018-00321). UKESM1 is in collaboration with U.K. Met new version of the OASIS coupler, OASIS3-MCT_3.0. Geosci Office. Work of Jiwoo Lee was performed under the auspices of the Model Dev. 10,3297–3308 (2017) U.S. Department of Energy (BER, RGMA Program) by Lawrence Durack, P.J., Taylor, K.E., Eyring, V., Ames, S.K., Hoang, T., Nadeau, Livermore National Laboratory under Contract DE-AC52-07NA27344. D., Doutriaux, C., Stockhause, M., Gleckeler, P.J.: Toward stan- dardized data sets for climate model experimentation, Eos, 99,doi: https://doi.org/10.1029/2018EO101751, (2018) Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adap- Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, tation, distribution and reproduction in any medium or format, as long as R.J., Taylor, K.E.: Overview of the coupled model Intercomparison you give appropriate credit to the original author(s) and the source, pro- project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9,1937–1958 (2016) vide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included Gidden, M.J., Riahi, K., Smith, S.J., Fujimori, S., Luderer, G., Kriegler, in the article's Creative Commons licence, unless indicated otherwise in a E., van Vuuren, D.P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J.C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., Takahashi, K.: Global emissions pathways under different socio- statutory regulation or exceeds the permitted use, you will need to obtain economic scenarios for use in CMIP6; a dataset of harmonized permission directly from the copyright holder. 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Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs Concentration Pathways

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Springer Journals
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1976-7633
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10.1007/s13143-021-00225-6
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Abstract

The National Institute of Meteorological Sciences-Korea Meteorological Administration (NIMS-KMA) has participated in the Coupled Model Inter-comparison Project (CMIP) and provided long-term simulations using the coupled climate model. The NIMS-KMA produces new future projections using the ensemble mean of KMA Advanced Community Earth system model (K- ACE) and UK Earth System Model version1 (UKESM1) simulations to provide scientific information of future climate changes. In this study, we analyze four experiments those conducted following the new shared socioeconomic pathway (SSP) based scenarios to examine projected climate change in the twenty-first century. Present day (PD) simulations show high performance skill in both climate mean and variability, which provide a reliability of the climate models and reduces the uncertainty in response to future forcing. In future projections, global temperature increases from 1.92 °C to 5.20 °C relative to the PD level (1995–2014). Global mean precipitation increases from 5.1% to 10.1% and sea ice extent decreases from 19% to 62% in the Arctic and from 18% to 54% in the Antarctic. In addition, climate changes are accelerating toward the late twenty-first century. Our CMIP6 simulations are released to the public through the Earth System Grid Federation (ESGF) international data sharing portal and are used to support the establishment of the national adaptation plan for climate change in South Korea. . . . . Keywords CMIP6 ScenarioMIP SSP-RCP Climate change Future projection 1 Introduction For the Intergovernmental Panel on Climate Change (IPCC) Responsible Editor: Eun-Soon Im. Assessment Report 6 (AR6), a new phase of model experimen- tation (CMIP6) is on progress. Projection of the future climate * Hyun Min Sung sunghm122@korea.kr change plays an important role in improving understanding of the climate system (O’Neilletal. 2016). The Scenario Model Inter-comparison Project (ScenarioMIP) is an essential protocol Innovative Meteorological Research Department, Climate Research Division, National Institute of Meteorological Sciences, Seogwipo, within CMIP6 that provide multi-model climate projections Jeju, South Korea based on new future simulations (Eyring et al. 2016). The highest Numerical Model Development Division, Numerical Modelling priority objective for the ScenarioMIP is to provide climate mod- Center, Seoul, South Korea el simulations that can facilitate a wide range of integrated studies Operational Systems Development Department, National Institute of for climate impact on societies, including the considerations of Meteorological Sciences, Seogwipo, Jeju, South Korea mitigation and adaptation. Planning and Finance Division, National Institute of Meteorological The NIMS-KMA has participated in the ScenarioMIP Sciences, Seogwipo, Jeju, South Korea since CMIP3. Min et al. (2006)produce theECHO-G model Program for Climate Model Diagnosis and Intercomparison, simulations under the Special Report on Emission Scenarios Lawrence Livermore National Laboratory, Livermore, CA, USA (SRES) of the CMIP3. Also, NIMS-KMA provides the Met Office Hadley Centre, Exeter, UK Hadley Global Environment Model (HadGEM2-AO; Collins Korean Meteorological Society 852 H. M. Sung et al. et al. 2011) simulations under Representative Concentration component of UKESM1 and K-ACE is the Nucleus for Pathway scenarios (RCPs; Moss et al. 2010). After CMIP5, a European Modelling of the Ocean dynamical model new concept for scenario has been developed by combination (NEMO) and MOM, respectively. This component is coupled of RCPs and SSPs. In this new concentration pathway, future through the OASIS3-MCT coupler. In addition, an aerosol climate response depends on the choices and implemen- component of K-ACE is the simple mode of the United tation of adaptation and mitigation options. The updated Kingdom Chemistry and Aerosol model (UKCA; Archibald projections of the climate state are required in CMIP6 et al. 2019, Mulcahy et al. 2018), while a fully coupled UKCA based on the new CO concentration pathways. Four is used in UKESM1. The detailed description of the model Tier 1 scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and components and the coupling process of two Earth system SSP5–8.5) are selected for our simulations. models are described in Lee et al. (2019a) and Sellar et al. For contributing to CMIP6, we use two Earth system (2019). These simulation results (both K-ACE and models (i.e., K-ACE and UKESM1) under the Met Office UKESM1) are released to the international data sharing portal collaboration agreement. Lee et al. (2019a) and Sellar et al. (ESGF) and are used to support the establishment of the na- (2019) report evaluation results for K-ACE and UKESM1, tional adaptation plan for climate change in South Korea. respectively, while there has been no study about the ensem- We simulate three ensemble members for each of the K- ble mean evaluation. In this study, we aim to show perfor- ACE (r1i1p1f1, r2i1p1f1, and r3i1p1f1) and the UKESM1 mance of the ensemble mean and their future climate changes (r13i1p1f2, r14i1p1f2, and r15i1p1f2). The ensemble mean in the twenty-first century. Climate change indicators, such as isused for analysis, which is described as NIMS-KMA- temperature, precipitation, sea-ice, and climate extreme CMIP6. Also, it is worth to document their comparison with indices are analyzed for future projections. We also fo- NIMS-KMA’s CMIP5 model (HadGEM2-AO; Baek cus on near-term (NT; 2021–2040), mid-term (MT; et al. 2013). The HadGEM2-AO comprises atmosphere 2041–2060), and long-term (LT; 2081–2100) period to component with N96 horizontal resolution and ocean provide information of future climate change. component with a 1 degree horizontal resolution (in- This study is organized as follows. The next section de- creasing to 1/3 degree at the equator). The atmospheric scribes the framework of CMIP6 experiments. The general component is the previous version of UM. performance of CMIP6 experiments is analyzed in section 3. Section 4 describes the climate change projections for the twenty-first century using the new SSP-RCP scenarios 2.2 Experimental Design and External Forcing focusing on temperature, precipitation, sea-ice, and cli- mate extreme indices. Finally, section 5 presents the Historical experiments (1850–2014) and future projection ex- discussion and conclusion. periments (2015 ~ 2100) have been performed with the K- ACE and the UKESM1. In CMIP6, similar to the earlier phases of CMIP, the pre-industrial control simulation is used 2 Model Description and Experiments to produce a stable quasi-equilibrium for initial state for the historical experiment (O’Neill et al. 2016). Following that, 2.1 Model Description historical experiments are performed using external condi- tions (e.g., GHGs, aerosols, land use changes, and natural The K-ACE has been developed by the NIMS-KMA under forcing). We select five gases (CO ,CH ,N O(WMO 2 4 2 the South Korea-United Kingdom (UK) Science and 2014), CFC-12-eq (Velders et al. 2009), HFC-134a-eq) to Technology Cooperation Program (Lee et al. 2019a). The represent anthropogenic radiative forcing (Meinshausen compositions of K-ACE are the Unified Model (UM) in the et al. 2017). Aerosol emission sources have been obtained Global Atmosphere 7.1 configuration (GA7.1; Walters et al. from Input4MIPs (Durack et al. 2018) and are associated with 2019), Modular Ocean Model of GFDL (MOM; Griffies et al. biofuel, fossil fuel, and biomass burning emissions (Bond 2007), sea ice model of Los Alamos (CICE; Hunke et al. et al., 2004). The ending states of the historical experiments 2015;Ridleyetal. 2018), and the OASIS3-MCT coupler are applied as initial condition for future projection experi- (Craig et al. 2017; Valcke et al. 2015). The horizontal resolu- ments. Future projection experiments are performed using tion is a N96 regular latitude-longitude grid in atmosphere. Tier 1 scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and Land surface component is the joint UK land environment SSP5–8.5). According to Gidden et al. (2019), in SSP1, rapid simulator (JULES; Best et al. 2011). world energy transition from fossil-fuel related energy con- The UKESM1 has been developed by the UK Met Office sumption sharply decreases the emissions and SSP2 has a (UKMO) and the Natural Environment Research Council similar transition with delayed action. Only SSP3 shows sim- (NERC). The ocean and aerosol-chemistry processes are the ilar emissions to present-day levels due to the increasing de- key differences between K-ACE and UKESM1. The ocean mand of growing population. Korean Meteorological Society Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs... 853 3 Evaluation of Historical Experiments needed for area and mass average and σ is the interannual vn variance from the validation observations. This approach 3.1 General Performance of the Mean State helps to homogenize errors from different regions and vari- ables. Following that, the final model performance index is Evaluation of the historical simulation provides insights into calculated by averaging over all variables. The outcomes of the reliability of the climate model and reduces the uncertainty the comparison between K-ACE, UKESM1 and 22 CMIP5 in response to future forcing (Eyring et al. 2016). The purpose models are shown in Fig. 1. As the blue color becomes darker, of this section is to ensure that the two model ensembles with the error decreases. Note that the CMIP5 historical data are not similar structures use for supporting national policy of climate available after 2005 as the historical simulation ends in this change adaptation, but the calculated results also have com- year. Thus, the RCP 4.5 scenario is used for the period from parable from a scientific point of view. Reichler and Kim 2006 to 2014. Different scenarios show similar magnitude of (2008) suggest a performance index with aggregated errors climate change in near future (IPCC 2014) and most previous to simulate climatological mean states of multiple different studies use RCP 4.5 scenario for near future period. The climate variables. To apply this method, normalized errors NIMS-KMA-CMIP6 (Fig. 1) shows an improved perfor- of 20 key climate quantities (Table 1) are used. To determine mance in PD period (1995–2014) from historical simulation performance index, we first calculate normalized error vari- compared to CMIP5. In addition, K-ACE and UKESM1 show ance e for each model by normalizing the differences be- similar performance levels for present climate (Fig. 1b, 1c). tween simulated and observed climates based on grid point. This result indicates reduction of the uncertainty in response to The equation is given below: future forcing in CMIP6 simulations. S −O vmn vn B C e ¼ ∑ w @ A 3.2 Climate Variability vm vn It is important to examine the simulated performance of cli- Where, S indicates the simulated climatology for cli- mate variability for decadal time scale because the simulation vmn for climate projection is most likely to have hundreds time mate variable (v), model (m), and grid point (n). O is the vn scale integration. We investigate the performance on corresponding observed climatology. w is proper weights Table 1 Climate variables and Variable Domain Acronym Validation data corresponding validation data. Those listed as “land” or “ocean” 2 m air temperature global TAS CRU, ICOADS, NOAA, ERA-Interim are single-level fields over their respective regions Total cloudiness global CLT CERES Surface downwelling shortwave flux in air global RSDS CERES Surface upwelling shortwave flux in air global RSUS CERES Surface downwelling longwave flux in air global RLDS CERES Surface upwelling longwave flux in air global RLUS CERES TOA outgoing shortwave flux global RSUT CERES TOA outgoing longwave flux global RLUT CERES TOA cloud radiative forcing longwave flux global CRFLT CERES TOA cloud radiative forcing shortwave flux global CRFST CERES Precipitation global PR CMAP, GPCP Sea level pressure ocean PSL ICOADS Surface upward sensible heat flux ocean HFSS ICOADS, OAFLUX Surface upward latent heat flux ocean HFLS ICOADS, OAFLUX Surface skin temperature land TS ERA-Interim 200 hPa zonal wind global U200 ERA-Interim 200 hPa meridional wind global V200 ERA-Interim 200 hPa air temperature global T200 ERA-Interim 850 hPa zonal wind global U850 ERA-Interim 850 hPa meridional wind global V850 ERA-Interim Korean Meteorological Society 854 H. M. Sung et al. Korean Meteorological Society Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs... 855 Fig. 1 The calculated performance index over global for (a) climate change over the twenty-first century. In addition, this NIMS-KMA-CMIP6 results, (b)K-ACE, (c) UKESM1 and (d)- section provides information on how much change isprojected (y) 22 CMIP5 models. Low indices (blue color) denote better by the future projections for the new climate change scenario performance through comparing the NIMS-KMA’sCMIP5 results (Baek et al. 2013). This information supports the establishment of na- tional adaptation policies for climate change and can contribute reproducing various climate variability modes using the to the spread of the climate emergency. Program for Climate Model Diagnosis and Inter-comparison (PCMDI) Metrics Package (PMP; Gleckler et al. 2016,Lee 4.1 Surface Temperature et al. 2019b). The atmospheric modes (Northern Annular Mode (NAM), the North Atlantic Oscillation (NAO), the Figure 3 shows the time series of global mean surface tempera- Pacific North America pattern (PNA), the North Pacific ture changes for the twenty-first century relative to the PD period. Oscillation (NPO), and the Southern Annular Mode (SAM)) The simulated temperature projections have similar positive and SST based modes (Pacific Decadal Oscillation (PDO) and trends until 2030. As mentioned in AR5, near-term projections the North Pacific Gyre Oscillation (NPGO)) are examined in are dependent on internal variability rather than the emission this section. The winter season for the atmospheric modes are scenario. Impact of the emission scenario on future projections the primary focus (i.e., DJF, except for SAM where JJA used), becomes evident after the 2030s. In the late twenty-first century, because the variability signal is strongest in winter. The SST- rising temperature and ensemble spreads are proportional to the based modes are derived using monthly-mean time series. The concentration pathway of four scenarios. On average, the Common Basis Function (CBF) approach (Lee et al. 2019b)is projected range of temperature changes in the LT period relative used for evaluation of variability in this study. The twentieth to the PD period is expected to be 1.92 ± 0.22 °C, 3.02 ± 0.47 °C, Century Reanalysis (20CR; Compo et al. 2006, 2011) and Met 4.28 ± 0.62 °C, and 5.20 ± 0.71 °C for SSP1–2.6, SSP2–4.5, Office Hadley Centre Sea Ice and Sea Surface Temperature SSP3–7.0, and SSP5.8.5, respectively. In addition, the difference dataset (HadISST) version 1.1 (Rayner et al. 2003) are used in the temperature between SSP5–8.5 and SSP1–2.6 scenarios is for atmospheric and SST-based modes, respectively. The ob- about 3.3 °C by the end of the century, which is larger than the servations and models are both analyzed over the period temperature difference between RCP 2.6 and RCP 8.5 (3 °C) 1900–2005 (1956–2005 for observed SAM due to lower con- reported in Baek et al. (2013). According to O’Neill et al. fidence in observations over the Southern Hemisphere in the (2016), CO concentrations in SSP5–8.5 of CMIP6 are higher first half of the twentieth century). than RCP8.5 of CMIP5. This means that expected global The simulated patterns of each climate variability mode warming is higher in the new scenario of CMIP6. In addition, (Fig. 2) indicate that the models reasonably capture the CMIP6 models, such as K-ACE and UKESM1, have higher extra-tropical modes. For NAM, PNA, and NPO, K-ACE climate sensitivity (ECS; Equilibrium Climate Sensitivity) than and UKESM1 show larger variance over the Pacific Ocean, CMIP5 models (Zelinka et al. 2020, Sun et al. 2020). The but the locations of dipoles are comparable to the observation. projected future changes are described in Table 2.Consistent For NAO, models capture smaller variance over the Atlantic with the previous results (Baek et al. 2013), the temperature Ocean, but locations of dipoles are also comparable to the increase in East Asia is also larger for all scenarios. 20CR as well. It is similar for SAM and PDO where models Figure 4 illustrates the spatial pattern of global surface tem- are performing well at capturing the pattern. For NPGO, how- perature in the twenty-first century under four future scenari- ever, there are discrepancies between HadISSTv1.1 (Fig. 2y) os. The temperature projections are estimated for three pe- and models (Fig. 2z-ab), which are more noticeable than other riods, NT (2021–2040), MT (2041–2060), and LT (2081– modes. Overall, models are promising to capture patterns and 2100). Continuous increases in temperature are expected in amplitudes of modes in general except for the NPGO (ampli- NT, MT, and LT under all scenarios. NT projections are sim- tude not shown). With the analysis from previous session, ilar in all scenarios. However, LT projections show significant these results demonstrate the high reliability of future projec- discrepancies in different scenarios compared to NT and MT tions for the twenty-first century. periods. A larger scale warming in the late twenty-first century over land rather than ocean (right column in Fig. 4)is projected under SSP3–7.0 and SSP5–8.5 (high concentration 4 Future Climate Projections scenarios). The highest temperature increase is expected to occur in the Arctic regions, with less warming over In section 3, we investigated the performance of K-ACE and the North Atlantic and the Southern Ocean. These spa- UKESM1 for the PD period compared to observations. Two tial changes are accelerated due to sea ice melting climate models show similar performances for climate mean state which is based on increasing temperature (positive and variability. Therefore, we analyzed ensemble mean for future surface albedo feedback; IPCC, 2014). Korean Meteorological Society 856 H. M. Sung et al. Korean Meteorological Society Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs... 857 Table 2 Projected global mean surface temperature change (tas) and Fig. 2 The observed (EOF-1 or 2; first column) and simulated (CBF) precipitation change in each future period, NT (2021–2040), MT (2041– modes of variability from HadGEM2-AO (second column), K-ACE 2060), and LT (2081–2100) relative to PD period (third column), and UKESM1 (fourth column), respectively. The percent of variance [%] explained by each EOF or CBF is noted at the upper-right Global Near-Term Mid-Term Long-Term corner of each plot. Units for atmospheric and SST-based modes are hPa and °C, respectively Tas (°C) SSP1–2.6 1.19 1.67 1.92 SSP2–4.5 1.25 1.99 3.02 SSP3–7.0 1.22 2.18 4.28 4.2 Precipitation SSP5–8.5 1.33 2.42 5.20 precip. (%) SSP1–2.6 2.82 4.11 5.10 The twenty-first century projection of precipitation also shows SSP2–4.5 2.52 4.23 6.85 larger response in this study than the result reported by Baek et al. SSP3–7.0 2.09 3.84 7.88 (2013). Projected global mean precipitation changes (Fig. 5) SSP5–8.5 2.58 4.66 10.08 demonstrate that all scenarios show significant increases in pre- East-Asia Near-Term Mid-Term Long-Term cipitation with similar trends projected until 2050. Precipitation Tas (°C) SSP1–2.6 1.19 1.69 1.97 changes in the LT period relative to the PD period are expected to SSP2–4.5 1.27 2.03 2.99 increase from 5.1% to 10.1%. Further, SSP1–2.6 and SSP5–8.5 SSP3–7.0 1.24 2.18 4.33 show 5% precipitation difference in the LT period, and this pre- SSP5–8.5 1.39 2.45 5.29 cipitation differenc’e in 2100 is twice that in CMIP5 (2.5% in precip. (%) SSP1–2.6 3.39 5.30 6.31 Baek et al. 2013). Additionally, changes in global precipitation −1 with temperature are within the range of 1–3%°C in most SSP2–4.5 3.49 4.63 6.67 climate models (IPCC 2014), and NIMS-KMA-CMIP6 shows SSP3–7.0 3.03 5.08 7.97 −1 −1 comparable precipitation sensitivity (2.7%°C ,1.9%°C for SSP5–8.5 3.68 5.16 10.21 SSP1–2.6 and SSP5–8.5, respectively). Spatial patterns of future precipitation changes (%) in NT projections are similar in all scenarios and the impact of dif- 4.3 Sea Ice ferent emission scenarios begins to appear in LT period (Fig. 6). All scenarios reveal the same spatial pattern (increase Figure 7 shows the time series of the sea ice changes. Similar in tropics and decrease in subtropics) of precipitation changes to temperature changes, the reduction of the sea ice extent until with different magnitudes. Moreover, the East Asia monsoon around 2030 is consistent in all four scenarios and the melting and Indian monsoon regions tend to be wetter while the South trend differs significantly among the scenarios after 2030. The Asia monsoon and South America monsoon regions become projected sea ice extent in SSP1–2.6 stabilizes after the MT pe- drier. The magnitudes of these patterns are significant in riod and the reduction continues in SSP2–4.5. The acceleration higher concentration scenarios (SSP3–7.0, SSP5–8.5). of melting occurs after the MT period in SSP3–7.0 and SSP5– Overall, East Asian changes are larger than the global climate 8.5. This is especially evident in the Arctic region, where accel- changes for all scenarios (Table 2). eration is significant and is influenced by the positive ice albedo feedback (increased air temperature reduces sea ice cover, allowing more energy to be absorbed on the sea surface, accelerating the melting; Gregory et al. 2002; Cvijanovic and Ken 2015). In LT period, the sea ice extent decreases from 19% (SSP1–2.6) to 62% (SSP5–8.5) in the Arctic and from 18% (SSP1–2.6) to 54% (SSP5–8.5) in the Antarctic. The rapid sea ice melting rates in SSP3–7.0 and SSP5–8.5 are associated with high CO concentrations (> 600 ppm) and temperatures (above 4 °C) relative to the pre-industrial levels. Additionally, according to the IPCC AR5, sea ice reduction of the Arctic has been most rapid in summer. A nearly ice-free Arctic (sea ice 6 2 extent less than 10 km for at least five consecutive years) in September is likely after the NT period in all scenarios (not shown). The rate of decline in the Arctic is faster in this study Fig. 3 Time series of global mean surface temperature changes for the historical simulation (black) from 1995 to 2014, and future simulations than reportedbyBaeketal. (2013). However, the Antarctic sea for four SSP-RCPs (SSP5–8.5 (red), SSP3–7.0 (yellow), SSP2–4.5 ice loss is projected to continue through the NT period depending (blue), and SSP1–2.6 (green)) from 2015 to 2100, respectively. The shad- on the magnitude of global warming (from 35% for SSP1–2.6 to ed area indicates the ensemble spread of six members (both K-ACE and 93% for SSP5–8.5). UKESM1) Korean Meteorological Society 858 H. M. Sung et al. Fig. 4 Global distribution of the 20 year averaged temperature change in (fourth row)) with three future periods (Near-Term (NT; left column), twenty-first century of four SSP-RCP future scenarios (SSP1–2.6 (first Mid-Term (MT; mid column), and Long-Term (LT; right column)) com- row), SSP2–4.5 (second row), SSP3–7.0 (third row), and SSP5–8.5 pared to PD period (1995–2014) 4.4 Climate Extremes Index We use the Expert Team on Climate Change Detection and Indices (ETCCDI) to define a set of climate indices (Klein Tank et al. 2009, Sillmann et al. 2013). To investigate the future projections of climate extremes, warm days (above the 90th percentile of daily maximum temperature; TX90p) and cold nights (below the 10th percentile of daily minimum tempera- ture; TN10p), and very wet days (above the 95th percentile of daily precipitation amount; R95p) of ETCCDI are used. Figure 8 shows the spatial distributions of extreme indices (TX90p, TN10p, and R95p) for the LT period. Relative to PD level, TX90p increases three times especially in Central Africa, western India, southern China, Southeast Asia, Fig. 5 Time series of global mean precipitation changes (%) for the historical simulation (black) from 1995 to 2014, and future simulations Central America, and northern South America regions. Also, for four SSP-RCPs (SSP5–8.5 (red), SSP3–7.0 (yellow), SSP2–4.5 TN10p is decreased about 93% compared with the PD level (blue), and SSP1–2.6 (green)) from 2015 to 2100, respectively. The shad- especially in northern and southern Africa, Europe, Russia, ed area indicates the ensemble spread of six members (both K-ACE and Australia, and high elevation regions including major UKESM1) Korean Meteorological Society Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs... 859 Fig. 6 Global distribution of the 20 year precipitation change (%) in (fourth row)) with three future periods (Near-Term (NT; left column), twenty-first century of four SSP-RCP future scenarios (SSP1–2.6 (first Mid-Term (MT; mid column), and Long-Term (LT; right column)) com- row), SSP2–4.5 (second row), SSP3–7.0 (third row), and SSP5–8.5 pared to PD period (1995–2014) 6 2 Fig. 7 Time series of sea ice extent change (10 km )in(a) the Arctic and (blue), and SSP1–2.6 (green)) relative to PD level (1995–2014), respec- (b) the Antarctic for historical simulation (black) and future simulations tively. Shaded area indicates ensemble spread of six members (both K- for four SSP-RCPs (SSP5–8.5 (red), SSP3–7.0 (yellow), SSP2–4.5 ACE and UKESM1) Korean Meteorological Society 860 H. M. Sung et al. Fig. 8 Spatial distributions of warm days (TX90p; top), cold nights (TN10p; middle), and very wet days (R95p; bottom) in LT (2081 ~ 2100) relative to the PD (1995 ~ 2014). Unit is % mountain ranges (e.g., the Rockies, the Andes, and the Alps) The results of performance index for 20 climate var- and the Tibetan plateau. Overall, change in TX90p and iables reveal that the NIMS-KMA-CMIP6 simulation TN10p mainly occurs in low to mid latitude regions with high shows better performance in PD period compared to elevation, respectively. These results are consistent with the CMIP5 simulations. In addition to the mean state, model CMIP5 projections (IPCC 2014, 2019). Furthermore, the simulations capture the observed characteristics of R95p projections in LT period from 15% in SSP1–2.6 to extratropical modes of variability (i.e., NAM, NAO, 54% in SSP5–8.5, and the spatial pattern of R95p changes PNA, NPO, SAM, PDO, and NPGO). Overall, these mainly occur in low-latitude and high-latitude regions (e.g., results demonstrate the high reliability of future projec- Central Africa, Southeast Asia, northern South America, tions for the twenty-first century. Alaska, and Scandinavia). This increasing trend in cli- (2) Projected global warming and increasing precipitation mate extreme indices is similar with Baek et al. are proportional to scenarios. Future changes in the LT (2013), however the rate of change in the LT period period are larger than in the NT period. These results are is higher. This demonstrates that the response to future consistent with CMIP5, but the increments are larger in extremes due to SSP-RCP is stronger than in RCP this study based on SSP-RCPs. scenarios. In the LT period, projected temperature and precipi- tation depend on the scenarios associated with GHG forcing. On average, the projected range of temperature changes in the LT period relative to the PD period is 5 Summary and Discussion expected to be 1.92 ± 0.22 °C, 3.02 ± 0.47 °C, 4.28 ± 0.62 °C, and 5.20 ± 0.71 °C for SSP1–2.6, SSP2–4.5, Understanding climate change induced from anthropogenic SSP3–7.0, and SSP5.8.5, respectively. Precipitation in- forcing is important to determine the future directions of so- creases from 5.1% to 10.1% and sea ice extent reduces cioeconomic development. The SSP-RCP scenarios have from 19% to 62% in the Arctic and from 18% to 54% in been developed for the new phase of CMIP that is currently the Antarctic. Future changes in temperature and precip- underway (O’Neill et al., 2016). The NIMS-KMA produces itation increments are larger than results reported by climate projections with new scenarios and this study summa- Baek et al. (2013), owing to the large CO concentrations rizes the main findings of that effort. 2 in the SSP-RCP scenario and higher climate sensitivity of CMIP6 models. (1) NIMS-KMA produces new CMIP6 scenario using the (3) To investigate the future projection of climate extremes, ensemble mean of two models (K-ACE and UKESM1) TX90p, TN10p, and R95p indices are used in this study. and an evaluation of CMIP6 historical simulation is per- Spatial patterns of these indices are similar to CMIP5, formed in this study compared to CMIP5 simulations. Korean Meteorological Society Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs... 861 Development and evaluation of an earth system model-HadGEM2. but the magnitudes are significantly larger than CMIP5. Geosci. Model Dev. 4,1051–1075 (2011) The spatial pattern of the indices (Fig. 8)isingood Compo, G.P., Whitaker, J.S., Sardeshmukh, P.D.: Feasibility of a 100- agreement with the CMIP5 models. In LT period, TX90p year reanalysis using only surface pressure data. Bull. Am. increases three times and TN10p decreases about 93% Meteorol. Soc. 87,175–190 (2006) Compo, G.P., Whitaker, J.S., Sardeshmukh, P.D., Matsui, N., Allan, R.J., relative to PD period. The R95p increases from 15% in Yin, X., Gleason, B.E., Vose, R.S., Rutledge, G., Bessemoulin, P., SSP1–2.6 to 54% in SSP5–8.5. This increasing trend of BroNnimann, S., Brunet, M., Crouthamel, R.I., Grant, A.N., climate extreme indices is similar to CMIP5, but the Groisman, P.Y., Jones, P.D., Kruk, M.C., Kruger, A.C., Marshall, magnitudes are larger than CMIP5. G.J., Maugeri, M., Mok, H.Y., Nordli, O., Ross, T.F., Trigo, R.M., Wang, X.L., Woodruff, S.D., Worley, S.J.: The twentieth century reanalysis project. Q. J. R. Meteorol. Soc. 137,1–28 (2011) Acknowledgments This work was funded by the Korea Meteorological Cvijanovic, I., Ken, C.: Atmospheric impacts of sea ice decline in CO Administration Research and Development Program “Development and induced global warming. Clim. Dyn. 44,1173–1186 (2015) Assessment of IPCC AR6 Climate Change Scenarios” under Grant Craig, A., Valcke, S., Coquart, L.: Development and performance of a (KMA2018-00321). UKESM1 is in collaboration with U.K. Met new version of the OASIS coupler, OASIS3-MCT_3.0. Geosci Office. Work of Jiwoo Lee was performed under the auspices of the Model Dev. 10,3297–3308 (2017) U.S. Department of Energy (BER, RGMA Program) by Lawrence Durack, P.J., Taylor, K.E., Eyring, V., Ames, S.K., Hoang, T., Nadeau, Livermore National Laboratory under Contract DE-AC52-07NA27344. D., Doutriaux, C., Stockhause, M., Gleckeler, P.J.: Toward stan- dardized data sets for climate model experimentation, Eos, 99,doi: https://doi.org/10.1029/2018EO101751, (2018) Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adap- Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, tation, distribution and reproduction in any medium or format, as long as R.J., Taylor, K.E.: Overview of the coupled model Intercomparison you give appropriate credit to the original author(s) and the source, pro- project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9,1937–1958 (2016) vide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included Gidden, M.J., Riahi, K., Smith, S.J., Fujimori, S., Luderer, G., Kriegler, in the article's Creative Commons licence, unless indicated otherwise in a E., van Vuuren, D.P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J.C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., Takahashi, K.: Global emissions pathways under different socio- statutory regulation or exceeds the permitted use, you will need to obtain economic scenarios for use in CMIP6; a dataset of harmonized permission directly from the copyright holder. 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Journal

"Asia-Pacific Journal of Atmospheric Sciences"Springer Journals

Published: Nov 1, 2021

Keywords: CMIP6; ScenarioMIP; SSP-RCP; Climate change; Future projection

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