atmosphere

Article Geoengineering: Impact of Marine Brightening Control on the Extreme Temperature Change over East Asia

Do-Hyun Kim 1,2 , Ho-Jeong Shin 3 and Il-Ung Chung 2,* 1 Innovative Meteorological Research Department, National Institute of Meteorological Sciences (NIMS), Seogwipo 63568, Korea; [email protected] 2 Department of Atmospheric and Environmental Sciences, Gangneung-Wonju National University, Gangneung 25457, Korea 3 Natural Science Research Institute, Gangneung-Wonju National University, Gangneung 25457, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-33-640-2325

 Received: 10 November 2020; Accepted: 8 December 2020; Published: 11 December 2020 

Abstract: We investigated the effect of artificial on extreme temperatures over East Asia. We used simulation data from five global models which have conducted the GeoMIP G4cdnc experiment. G4cdnc was designed to simulate an increase in the cloud droplet number concentration of the global marine lower by 50% under the forcing of the RCP4.5 scenario. G4cdnc decreased the net in the top of the atmosphere more over the ocean, alleviating the rise in mean temperature under RCP4.5 forcing. For extreme temperatures, G4cdnc reduced both the monthly minimum of daily minimum temperature (TNn) and monthly maximum of daily maximum temperature (TXx). The response of TNn was higher than that of TXx, especially in the winter, over the Sea of Okhotsk and the interior of the continent. This spatial heterogeneity and seasonality of the response were associated with sea ice– and snow–albedo feedbacks. We also calculated the efficacy of warming mitigation as a measure of the relative effect of geoengineering. The efficacy for TXx was higher than that for TNn, opposite to the absolute effect. After the termination of geoengineering, both TNn and TXx tended to rapidly revert to their trend under the RCP4.5 forcing.

Keywords: geoengineering; marine cloud brightening; climate model; extreme temperature; East Asia

1. Introduction Since the industrial revolution, atmospheric concentrations of greenhouse gases (GHGs) have risen owing to anthropogenic activities. In particular, the rise in concentration of has accelerated, recently exceeding 400 ppm. This has resulted in global warming, which is causing significant increases in the duration, frequency, and/or intensity of various types of extreme weather and climate events over East Asia and across the world. Many efforts have been devoted to solving the problems caused by global warming. For example, the Paris Agreement was signed by the United Nations Framework Convention on (UNFCCC) in 2015 and aimed at limiting the increase in the global mean temperature to well below 2 ◦C above pre-industrial levels, and pursuing efforts to limit the increase to 1.5 ◦C. Each nation participating in the Agreement thus set an individual goal for the reduction of greenhouse gases and consented to meet it. However, it has been suggested that the Agreement is not sufficient to alleviate the damage caused by climate change [1]. Therefore, the geoengineering field is emerging; it is aimed at mitigating the adverse effects of global warming and has received significant attention. Geoengineering includes a broad

Atmosphere 2020, 11, 1345; doi:10.3390/atmos11121345 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 1345 2 of 18 set of methods and technologies aimed at purposely altering the climate system to alleviate the impacts of climate change, although concerns have been expressed regarding its implementation [2–4]. Geoengineering methods can be divided into two categories: (CDR) and solar radiation management (SRM). The SRM method can be implemented in a variety of ways; the following has been suggested: using space mirrors in orbit [5], injecting aerosols into the stratosphere [6,7], and marine cloud brightening [8]. Marine cloud brightening is the focus of this study as it has received less attention. Since the introduction of SRM, many studies investigating the effect of SRM on temperature and precipitation through climate models have been conducted [9–11]. However, it is difficult to compare these studies owing to different experimental designs and the dependency of using specific climate models. To solve these problems, the Geoengineering Model Intercomparison Project (GeoMIP) was initiated, along with an experimental design of each SRM method [12]. GeoMIP has been progressed over Phase 1, Phase 2, and Phase 6. Among those phases, GeoMIP Phase 2 deals with marine cloud brightening with three experimental designs according to the degree of complexity: the G1ocean-albedo experiment simulates an increase in the global ocean albedo; the G4cdnc experiment simulates an increase in the cloud droplet number concentration (CDNC) in the global lower marine clouds; the G4sea- experiment injects sea salt particles into the tropical oceans [13]. This study focused on the G4cdnc experiment, which is based on the indirect effect of an increasing by adjusting the CDNC [14]. Various climate models were used in the simulation in this study owing to the simplistic design, which yields a more complete multi-model ensemble [15]. Global warming has increased the global mean temperature and frequency of extreme events [16]. Because increased extreme temperature events are more easily recognized by society and have more immediate detrimental consequences than changes in the mean temperature, studying the changes in temperature extremes is necessary. Changes in extreme temperatures are characterized by a significant amount of regional variations, and previous reports reveal that extreme temperatures have been increased more over mid to high-latitude regions, including East Asia [17–23]. However, a response of extreme temperature over East Asia under geoengineering has not been studied yet. Therefore, in this study, the effect of marine cloud brightening over East Asia is investigated using the output of the model which have conducted the GeoMIP Phase 2 G4cdnc experiment, focusing on the changes in extreme temperatures. In Section2, the data and methods used for the analysis are described. With a brief examination of changes in radiative flux and mean temperature, Section3 details the response of extreme temperature over East Asia via the G4cdnc experiment. Finally, Section4 summarizes the results and conclusions of this study.

2. Data and Methods

2.1. Data In this study, we used simulation data of the GeoMIP Phase 2 G4cdnc experiment from five global climate models (GCMs) out of nine models publicly available online (Table1). The G4cdnc experiment simulates an increase in the CDNC in marine low clouds for a 50-year period, from 2020 to 2069. After the year 2070, the increase in CDNC is terminated, and it is restored to the level before the implementation of G4cdnc. The experiment continued until 2090 to evaluate the effect of terminating the CDNC [13]. The G4cdnc experiment is simulated under the Coupled Model Intercomparison Project 5 (CMIP5) RCP4.5 scenario, a medium stabilization scenario among the four representative concentration pathways (RCPs), assuming emissions of GHGs resulting in an atmospheric CO2 concentration of 540 ppm by 2100 [24]. Atmosphere 2020, 11, 1345 3 of 18

Table 1. Description of earth system models of the GeoMIP G4cdnc experiment used in this study.

No. of Grid Cells Termination Effect Model Institution Reference (lon lat) Experiment × Beijing Normal BNU-ESM 128 64 Yes [25] University, China × Canadian Centre for CanESM2 Climate Modeling and 128 64 Yes [26] × Analysis, Canada University of New South CSIRO-Mk3L-1-2 64 56 Yes [27] Wales, Australia × Met Office Hadley HadGEM2-ES 192 145 Yes [28] Centre, UK × Japan Agency for MIROC-ESM Marine-Earth Science 128 64 No [29] × and Technology, Japan

To investigate the effects of the G4cdnc experiment for extreme climate events, we focused our analyses on monthly minimum and maximum extreme temperatures instead of annual values. Analysis using monthly extremes enables us to consider monthly variations (i.e., seasonality) and identify extremes more precisely. To this end, we calculated TNn, a monthly minimum of daily minimum temperatures, and TXx, a monthly maximum of daily maximum temperatures, as defined by the Expert Team of Climate Change Detection and Indices (ETCCDI) [30]. These extreme indices have been frequently used in previous research, both for observed weather [31] and model simulations (Orlowsky and Seneviratne with Curry et al. for GeoMIP Phase 1 G1 experiment, Aswathy et al. for G3 and Ji et al. for G1 and G4) [32–35]. In the GeoMIP G4cdnc experiment under the RCP4.5 scenario, four models simulated daily minimum (TASmin) and daily maximum (TASmax) temperatures with regard to extreme climatic events. The BNU-ESM model, however, did not simulate these variables. Moreover, the MIROC-ESM model simulated only the increase in CDNC, without the termination of the experiment. Therefore, we excluded the BNU-ESM model from the analysis of extreme temperatures and the MIROC-ESM model from the analysis after the termination of the experiment. As the spatial resolutions of simulation data differ between the models, as indicated in Table1, all outputs by G4cdnc and RCP4.5 models were re-gridded to 144 73 (2.5 longitude 2.5 latitude) × ◦ × ◦ for the convenience of comparison and calculation. The interpolation was carried out using the Climate Data Operators (CDO) package, in which we chose a first-order conservative remapping algorithm [36]. To analyze the changes over East Asia, we selected an analysis domain of 20–60◦ N, 90–160◦ E.

2.2. Methods To investigate the effect of the GeoMIP G4cdnc geoengineering experiment over East Asia, we selected three periods for analysis: base period: 2006–2019, progress period: 2060–2069, and termination period: 2081–2090. For the base period, we used RCP4.5 data, and for the progress and termination periods, both G4cdnc and RCP4.5 data were used. Although many studies for geoengineering have adopted a 40-year period for analysis [33,35,37], we adopted only the last 10 years from the progress and termination periods to sufficiently consider the saturated conditions of forcing. To obtain better insights into the changes in extreme temperature, we checked the changes in mean temperature through the calculation of probability density functions (PDFs) of monthly temperatures. To create PDFs of monthly temperature, the method reported by Curry et al. and Ji et al. was employed [33,35]. At every grid point in each model, we first calculated the standardized monthly anomalies of monthly mean surface temperatures in the base period under RCP4.5;   Base Base Base Base i.e., τm = Tm Tm /σ , where the overline denotes the monthly mean during the last 10 years − Tm of the progress period and σBase the interannual monthly standard deviation for each month m. Tm Atmosphere 2020, 11, 1345 4 of 18

Next, we computed the monthly anomalies in perturbed experiments relative to the mean and   Exp Exp Base Base standard deviation under RCP4.5 in the base period, i.e., τm = Tm Tm /σ . For the progress − Tm period, PDFs were calculated for each individual model shown in Table1 and for the multi-model ensemble; equal weights were used for each model. Then, we conducted a two-sample non-parametric Kolmogorov–Smirnov test to determine if the distribution under G4cdnc differs significantly from that under the RCP4.5 scenario in the progress period [38]. For extreme temperatures, we took the results of the difference between G4cdnc and its base experiment of RCP4.5 to investigate the effect of marine cloud brightening over East Asia. The change in the extreme temperature was investigated in terms of both temporal and spatial aspects, which were displayed using a timeseries and a field of the difference, respectively. For temporal changes, the mean of the difference deduced from the multi-model ensemble over both the progress and termination periods are printed on the upper right of the time series. Extreme temperatures in summer (JJA) and winter (DJF) were calculated to examine the effects of seasonality. In the perspectives of establishing policies on local climate change, relative effects of geoengineering to warming magnitude under RCP4.5 may be more important than absolute effects. To consider this relative effect of G4cdnc, we adopted a simple quantitative metric, efficacy used in [33,39]. To do so, we first computed the root mean square (RMS) differences between experiments at each grid point as the following equation: sP  XExp XBase 2dA RMSExp Base(X) = P− (1) − dA where X is the extreme index of interest, Exp the perturbed experiment (G4cdnc or RCP4.5 in progress P period), Base the base experiment (RCP4.5 in base period), and a sum over the grid cells (dA) of a specified area A. Then, we calculated the efficacy e(X) of G4cdnc defined by the following equation:

RMSG4cdnc Base(X) e(X) 1 − (2) ≡ − RMSRCP4.5 Base(X) − In the case of e(X) 1, the temperature under G4cdnc approaches the temperature in the RCP4.5 → base period so that the G4cdnc experiment is the most effective in offsetting the warming under the RCP4.5 scenario. On the other hand, e(X) 0 indicates that the G4cdnd experiment has little effect in → reducing the warming. Values of e(X) can be negative if RMS differences for G4cdnc exceed those for RCP4.5. Efficacy was calculated for mean temperatures (TAS, TASmax, and TASmin) as well as for the two extreme temperature indices (TNn and TXx) to check if there are significant differences between relative effects on extreme temperatures and mean temperatures. We also analyzed the efficacy not only for the progress period but also for the period after the termination of geoengineering in order to investigate how long the relative effects can last.

3. Results

3.1. Radiation Budget at TOA As the G4cdnc experiment simulates the increase in the reflection of solar radiation by marine clouds, we first examined the changes in the radiation budget at the top of the atmosphere (TOA), expecting an increase in upward shortwave radiation. Figure1 shows the global distributions of the differences in radiative fluxes at the TOA between G4cdnc and the RCP4.5 experiments during the progress period. During this period, the upward shortwave radiation at the TOA increased by 2 2.95 Wm− in the global mean multi-model ensemble average. Although there was a decrease in certain areas, especially in the western equatorial Pacific, the reflection of solar radiation was increased overall around the globe with a large increase over the eastern parts of the Pacific and Atlantic Oceans (Figure1a). Atmosphere 2020, 11, x FOR PEER REVIEW 5 of 19

Except for some areas of the inter‐tropical convergence zone (ITCZ), the overall increase in reflective shortwave radiation at the TOA resulted in a decrease in outgoing longwave radiation (OLR) at the TOA, indicating a surface cooling not only over the global ocean but over the global land (Figure 1b). A significant decrease in OLR over tropical rainforest regions including the Amazon, equatorial Africa, and the Indo‐Pakistan region is noteworthy. In the global mean and multi‐model −2 Atmosphereensemble2020 average, 11, 1345, the reduction of OLR (−2.27 Wm ) largely compensates the energy loss by5 ofthe 18 shortwave reflection.

Figure 1. Geographical distribution of the difference of (a) top of the atmosphere (TOA) upward Figureshortwave 1. Geographical radiation, (b ) distribution TOA upward of longwavethe difference radiation, of (a) andtop ( ofc) TOA the atmosphere net radiative (TOA flux) betweenupward shortwaveG4cdnc and radiation, RCP4.5 during (b) TOA the upward progress longwave period. Results radiation are, shownand (c) for TOA multi-model net radiative ensemble flux between mean. G4cdnc and RCP4.5 during the progress period. Results are shown for multi‐model ensemble mean. Except for some areas of the inter-tropical convergence zone (ITCZ), the overall increase in reflectiveOverall, shortwave the changes radiation in the upward at the TOA shortwave resulted radiation in a decrease and OLR in tend outgoing to compensate longwave each radiation other. (OLR)Nevertheless, at the TOA,the net indicating radiation a at surface the TOA cooling (net shortwave not only over radiation the global minus ocean OLR) but decreased over the largely global overland (Figurethe oceans1b)., A except significant for the decrease ITCZ in area OLR and over an tropical increased rainforest OLR an regionsd Arctic including sea‐icethe region Amazon,, and increasedequatorial over Africa, most and of the the Indo-Pakistan land areas, except region for is the noteworthy. Tibetan Plateau In the globalwhere meanthe upward and multi-model shortwave ensemble average, the reduction of OLR ( 2.27 Wm 2) largely compensates the energy loss by the radiation significantly increased, unlike the− other land− areas (Figure 1c). The global mean multi‐ modelshortwave ensemble reflection. average of the net radiation at the TOA is −0.68 Wm−2 with a downward positive sign Overall,during the the progress changes inperiod the upward. This amount shortwave of change radiation in andthe net OLR radiation tend to compensatecorresponds each to a other. 15% reductionNevertheless, of the the radiative net radiation forcing at the (4.5 TOA Wm (net−2) induced shortwave by radiation anthropogenic minus OLR)activities decreased under largelythe RCP4.5 over scenariothe oceans,. except for the ITCZ area and an increased OLR and Arctic sea-ice region, and increased overOther most ofthan the the land sea areas,‐ice/snow except albedo for the feedback Tibetan effect, Plateau the where heterogeneity the upward in regional shortwave response radiations to G4cdncsignificantly can also increased, be explained unlike by the the other changes land areas in cloudiness, (Figure1c). soil The moisture global mean, and other multi-model climate ensemblevariables, average of the net radiation at the TOA is 0.68 Wm 2 with a downward positive sign during the but further investigation into the subject is beyond− the− scope of this paper. progressThe differences period. This in amountthe TOA of radiation changein fluxes the net are radiationreplicated corresponds in Figure 2 and to a focused 15% reduction on East ofAsia the, 2 theradiative analysis forcing domain (4.5 Wm for − this) induced study. by The anthropogenic marine cloud activities brightening under theincreased RCP4.5 the scenario. reflection of shortwaveOther radiation than the sea-iceat the /TOAsnow over albedo most feedback of the domain effect, thebut heterogeneitycaused a significant in regional decrease responses over the to GobiG4cdnc Desert can alsoarea be (Figure explained 2a). byThere the was changes a considerable in cloudiness, increase soil moisture, over the andSea otherof Okhotsk climate, which variables, can bebut related further to investigation a positive sea into ice the–albedo subject feedback. is beyond The the increase scope of over this paper.Southern China and the Tibetan PlateauThe can diff alsoerences be related in the TOAto a positive radiation snow fluxes–albedo are replicated feedback in(figures Figure are2 and not focused shown) on. Meanwhile, East Asia, OLRthe analysis decreased domain over formost this of study. the domain The marine area even cloud in brightening the area with increased a decrease the reflectiond shortwave of shortwave reflection radiation at the TOA over most of the domain but caused a significant decrease over the Gobi Desert area (Figure2a). There was a considerable increase over the Sea of Okhotsk, which can be related to a positive sea ice–albedo feedback. The increase over Southern China and the Tibetan Plateau can also be related to a positive snow–albedo feedback (figures are not shown). Meanwhile, OLR decreased over most of the domain area even in the area with a decreased shortwave reflection at the TOA (Figure2b). This reduction of OLR induced by the marine cloud brightening resulted in an increase in downward net radiation at the TOA except for the Sea of Okhotsk and Tibetan Plateau areas (Figure2c). Atmosphere 2020, 11, x FOR PEER REVIEW 6 of 19 at the TOA (Figure 2b). This reduction of OLR induced by the marine cloud brightening resulted in Atmospherean increase2020 in, 11 downward, 1345 net radiation at the TOA except for the Sea of Okhotsk and Tibetan Plateau6 of 18 areas (Figure 2c).

Figure 2. (a) top of the atmosphere (TOA) upward shortwave radiation, (b) TOA upward longwave Figure 2. Same as Figure 1, except for over East Asia. radiation, and (c) TOA net radiative flux between G4cdnc and RCP4.5 during the progress period. Results are shown for multi-model ensemble mean, over East Asia. 3.2. Mean Temperature 3.2. Mean Temperature To check whether the global warming effect under the RCP4.5 scenario has been alleviated over East AsiaTo check through whether the radiative the global forcing warming changes eff inducedect under by thethe RCP4.5marine cloud scenario brightening, has been probability alleviated overdistribution East Asia function throughs (PDF thes) radiativefor surface forcing air mean changes temperature induced are by shown the marine in Figure cloud 3 for brightening, each of the probabilityfive models distributionas well as their functions ensemble (PDFs) mean, for using surface sta airndarized meantemperature monthly mean are anomalie shown ins Figurethroughout3 for eachthe given of the period five modelss. In the as figure, wellas the their PDFs ensemble from the mean, progress using period standarized of the G4cdnc monthly and mean RCP4.5 anomalies results throughoutare compared the with given the periods. baseline In(the the base figure, period the RCP4.5) PDFs from results the. progress period of the G4cdnc and RCP4.5 results are compared with the baseline (the base period RCP4.5) results. In Figure3, the PDFs of the multi-model ensemble mean exhibit a modest increase in mean temperature between G4cdnc and the baseline, indicating warming despite the implementation of the G4cdnc experiment. The PDFs in the progress period, however, show that the distribution mean under G4cdnc is lower than that under RCP4.5 (G4cdnc: 1.206, RCP4.5: 3.469). The difference in PDFs between the G4cdnc and RCP4.5 experiments is statistically significant at the 95% level by the Kolmogorov–Smirnov test. Hence, this feature implies that the G4cdnc experiment significantly alleviates the additional rise in mean temperature caused by the RCP4.5 scenario over East Asia. The mean of the PDFs from all the models under the G4cdnc experiment is lower than of RCP4.5; the CSIRO-Mk3L-1-2 model resulted in the lowest mean of 0.326, and the MIRCO-ESM model resulted − in the greatest mean of 1.198. This cooling effect of mean temperature is consistent with the results of previous studies investigating the effects of other geoengineering experiments. For example, a study conducted by Curry et al. calculated the PDFs, the same as our method but for the globe, and focused on the difference in mean temperature between the GeoMIP G1 and CMIP5 abrupt4xCO2 experiments [33]. In that study, PDFs obtained from the G1 experiment are located further left compared to the PDFs obtained from the abrupt4xCO2 experiment, indicating a very high cooling effect caused by the G1 experiment. Ji et al. also calculated PDFs using the same algorithm for the globe and reported the Atmosphere 2020, 11, 1345 7 of 18 difference between GeoMIP G4 and CMIP5 RCP4.5 [35]. Similarly, in that study, there was a discernable difference between the PDFs from the G4 experiment and those from RCP4.5, representing an alleviation ofAtmosphere the increase 2020, 11 in, x mean FOR PEER temperature REVIEW caused by RCP4.5. 7 of 19

Figure 3. Probability distributions of standardized monthly mean anomalies for three experiments: Figure 3. Probability distributions of standardized monthly mean anomalies for three experiments: G4cdnc (blue), RCP4.5 (red), and base period RCP4.5 (black). Probability density functions (PDFs) are G4cdnc (blue), RCP4.5 (red), and base period RCP4.5 (black). Probability density functions (PDFs) are over East Asia during the progress period (year 2060 to 2069). Anomalies are calculated with respect over East Asia during the progress period (year 2060 to 2069). Anomalies are calculated with respect to the mean and standard deviation of the base period for the RCP4.5 scenario for each experiment. to the mean and standard deviation of the base period for the RCP4.5 scenario for each experiment. “False” shown in the upper right corner indicates that the difference in the distribution between G4cdnc “False” shown in the upper right corner indicates that the difference in the distribution between and RCP4.5 is significant at the 95% level by the Kolmogorov–Smirnov test and “True” indicates G4cdnc and RCP4.5 is significant at the 95% level by the Kolmogorov–Smirnov test and “True” vice versa. indicates vice versa. Figure3 also shows that the variance of the multi-model ensemble mean PDF obtained from the In Figure 3, the PDFs of the multi‐model ensemble mean exhibit a modest increase in mean G4cdnc experiment is lower than that from the RCP4.5 experiment (G4cdnc: 1.655, RCP4.5: 2.748), temperature between G4cdnc and the baseline, indicating warming despite the implementation of which is consistent with a previous study result for the G1 experiment compared with the CMIP5 the G4cdnc experiment. The PDFs in the progress period, however, show that the distribution mean abrupt4xCO2 experiment [33]. A greater variance in PDFs equates to a larger inter-annual variability in under G4cdnc is lower than that under RCP4.5 (G4cdnc: 1.206, RCP4.5: 3.469). The difference in PDFs the temperature and probably to the more frequent occurrence of extreme temperatures. Assuming the between the G4cdnc and RCP4.5 experiments is statistically significant at the 95% level by the latter case, extreme temperatures under the G4cdnc experiment may occur less frequently than under Kolmogorov–Smirnov test. Hence, this feature implies that the G4cdnc experiment significantly the RCP4.5 scenario. Most of the models resulted in a lower variance in PDFs under the G4cdnc alleviates the additional rise in mean temperature caused by the RCP4.5 scenario over East Asia. The experiment than under RCP4.5, with the exception of the CSIRO-Mk3L-1-2 model, presenting the mean of the PDFs from all the models under the G4cdnc experiment is lower than of RCP4.5; the opposite. Meanwhile, the skewness of the multi-model ensemble’s PDFs under G4cdnc also decreases CSIRO‐Mk3L‐1‐2 model resulted in the lowest mean of −0.326, and the MIRCO‐ESM model resulted slightly compared with that under RCP4.5 (G4cdnc: 0.380, RCP4.5: 0.457). in the greatest mean of 1.198. 3.3. TemporalThis cooling and Spatial effect Response of mean in temperatureExtreme Temperatures is consistent with the results of previous studies investigating the effects of other geoengineering experiments. For example, a study conducted by CurryTo etinvestigate al. calculated the temporal the PDFs changes, the same in extreme as our method temperatures but for over the Eastglob Asiae, and under focuse thed G4cdncon the experiment,difference in the mean diff temperatureerences between between G4cdnc the GeoMIP and RCP4.5 G1 and for CMIP5 the TNn abrupt4xCO and TXx2 indicesexperiment haves [33] been. In that study, PDFs obtained from the G1 experiment are located further left compared to the PDFs obtained from the abrupt4xCO2 experiment, indicating a very high cooling effect caused by the G1 experiment. Ji et al. also calculated PDFs using the same algorithm for the globe and reported the difference between GeoMIP G4 and CMIP5 RCP4.5 [35]. Similarly, in that study, there was a

Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 19 discernable difference between the PDFs from the G4 experiment and those from RCP4.5, representing an alleviation of the increase in mean temperature caused by RCP4.5. Figure 3 also shows that the variance of the multi‐model ensemble mean PDF obtained from the G4cdnc experiment is lower than that from the RCP4.5 experiment (G4cdnc: 1.655, RCP4.5: 2.748), which is consistent with a previous study result for the G1 experiment compared with the CMIP5 abrupt4xCO2 experiment [33]. A greater variance in PDFs equates to a larger inter‐annual variability in the temperature and probably to the more frequent occurrence of extreme temperatures. Assuming the latter case, extreme temperatures under the G4cdnc experiment may occur less frequently than under the RCP4.5 scenario. Most of the models resulted in a lower variance in PDFs under the G4cdnc experiment than under RCP4.5, with the exception of the CSIRO‐Mk3L‐1‐2 model, presenting the opposite. Meanwhile, the skewness of the multi‐model ensemble’s PDFs under G4cdnc also decreases slightly compared with that under RCP4.5 (G4cdnc: 0.380, RCP4.5: 0.457).

3.3. Temporal and Spatial Response in Extreme Temperatures AtmosphereTo investigate2020, 11, 1345 the temporal changes in extreme temperatures over East Asia under the8 G4cdnc of 18 experiment, the differences between G4cdnc and RCP4.5 for the TNn and TXx indices have been analyzedanalyzed and and their their time time series series are are shown shown in in Figure 4 4.. Hereafter, Hereafter,∆ ∆denotes denotes the the multi-model multi‐model ensemble ensemble meanmean of a of difference a difference between between G4cdnc G4cdnc and and RCP4.5 averagedaveraged over over the the progress progress period period (2060–2069). (2060–2069).

Figure 4. Timeseries of the difference in extreme temperature indices between G4cdnc and RCP4.5 Figureover 4. East Timeseries Asia. The of timeseriesthe difference shows in the extreme differences temperature in (a) monthly indices minimum between of G4cdnc daily minimum and RCP4.5 overtemperature East Asia. (TNn)The timeseries and (b) monthly shows maximumthe difference of dailys in maximum (a) monthly temperature minimum (TXx). of daily The minimum heavy temperaturedark curve (TNn represents) and ( theb) monthly multi-model maximum ensemble of meandaily ofmaximum all models temperature used in thisstudy, (TXx) excluding. The heavy the dark MIROC-ESM model. The multi-model ensemble means over the last 10 years for the progress and curve represents the multi‐model ensemble mean of all models used in this study, excluding the termination periods are located in upper right corner of each plot. MIROC‐ESM model. The multi‐model ensemble means over the last 10 years for the progress and terminationThe results period of thes are G4cdnc located experiment in upper right are ascorner follows: of each∆TNn plot=. 1.128 C (minimum of 1.695 C − ◦ − ◦ by HadGEM2-ES and maximum of 0.505 C by CanESM2) and ∆TXx = 0.998 C (minimum of − ◦ − ◦ T1.368he resultsC by MIROC-ESMof the G4cdnc and experiment maximum ofare0.730 as follows:C by CanESM2). ∆TNn = −1.128 As aresult, °C (minimum G4cdnc lowers of −1.695 not °C − ◦ − ◦ by HadGEM2only the mean‐ES temperatureand maximum butalso of − extreme0.505 °C temperatures by CanESM2 over) and East ∆TXx Asia by = more−0.998 or °C less (minimum than one of −1.368degree. °C by Among MIROC the‐ESM models, and CanESM2 maximum produced of −0.730 the °C least by change CanESM2 in the). As extreme a result, temperature G4cdnc indices,lowers not onlywhich the mean is consistent temperature with the but result also for extreme mean temperature temperature presenteds over East in Section Asia by3.2 .more or less than one degree. ItAmong is notable the thatmodels, the ∆ CanESM2TNn is larger produced than the the∆TXx, least suggesting change in an the implication extreme temperature for extreme cold.indices, whichAmong is consistent the extreme with temperature the result studies, for mean many temperature SRM geoengineering presented studies in Section and studies 3.2. of other forcing scenariosIt is notable reported that similar the ∆TNn results: is larger a larger than change the in ∆ TNnTXx, thansuggesting in TXx (GeoMIP an implication G1 and G4for simulations extreme cold. Amonganalyzed the byextreme Ji et al., temperature G3 simulation studies analyzed, many by Aswathy SRM geoengineering et al., and studies studies on simulations and studies under GHGof other forcing analyzed by Tebaldi et al., Orlowsky and Seneviratne, and Sillmann et al.) [32,34,35,40,41]. forcing scenarios reported similar results: a larger change in TNn than in TXx (GeoMIP G1 and G4 Figure5 shows the geographical distribution of the di fference in extreme temperatures between the simulations analyzed by Ji et al., G3 simulation analyzed by Aswathy et al., and studies on G4cdnc and RCP4.5 experiments over East Asia during the progress period. The figures demonstrate negative values of ∆TNn and ∆TXx over the entire area of East Asia. However, there appears to be evident regional heterogeneity in the response of extreme temperatures to the G4cdnc simulation. Notably, the ∆TNn in Figure5a has a considerably large negative value over the Sea of Okhotsk, lower than 4 C, whereas ∆TXx has a minimum value of 2 C over the same region. − ◦ − ◦ Several studies noted the larger temperature cooling effect over high-latitude oceanic regions including the Sea of Okhotsk due to SRM geoengineering [35,42,43]. This large temperature response over high-latitude oceanic regions appears to be in line with the well-known global warming effect, polar amplification [44–46]. Ji et al. showed that both solar dimming (G1) and stratospheric aerosol (G4) geoengineering resulted in a large ∆TNn over high-latitude oceanic regions in both hemispheres, which was not seen in TXx [35]. It has been found that this high-latitude cooling by geoengineering Atmosphere 2020, 11, x FOR PEER REVIEW 9 of 19 simulations under GHG forcing analyzed by Tebaldi et al., Orlowsky and Seneviratne, and Sillmann et al.) [32,34,35,40,41].

AtmosphereFigure 20205 show, 11, 1345s the geographical distribution of the difference in extreme temperatures betwee9 of 18 n the G4cdnc and RCP4.5 experiments over East Asia during the progress period. The figures demonstrate negative values of ∆TNn and ∆TXx over the entire area of East Asia. However, there appearsexperiments, to be evident such as regional cloud brightening heterogeneity [42] and in the solar response dimming of [43 extreme], could temperature restore the seas iceto the extent G4cdnc to the state of their control experiments. simulation.

Figure 5. Geographical distribution of the difference in the extreme temperature indices between the FigureG4cdnc 5. Geographical and RCP4.5 simulations distribution during of the the difference progress period in the over extreme East Asia.temperature The distributions indices between show the the G4cdncdifferences and RCP4.5 in (a) TNn simulations and (b) TXx. during The resultsthe progress are shown period for theover multi-model East Asia. ensembleThe distributions mean. show the differences in (a) TNn and (b) TXx. The results are shown for the multi‐model ensemble mean. Although ∆TNn and ∆TXx resulted in cooling all over the area, a land–sea difference of the resultsNotabl isy, distinct the ∆TNn in Figure in Fig5.ure The 5 G4cdnca has a considerably experiment has large a larger negative cooling value e ff ectover of the∆TNn Sea and of Okhotsk∆TXx , lowerover than the − interior4 °C, whereas of the continent ∆TXx has compared a minimum to the value ocean, of − with2 °C theover exception the same of region. the Sea Several of Okhotsk. studies notedThis the land–sea larger ditemperaturefference in response cooling can effect also over be seen high in‐latitude previous ocean studiesic thatregions have including investigated the other Sea of Okhotskgeoengineering due to SRM experiments geoengineering such as the [35,42,43] injection. ofThis stratospheric large temperature aerosols orresponse marine seaover salt high [35,‐47latitude,48]. oceanicAlterskjær regions et al.appears conducted to be ain model line with experiment the well with‐known sea saltglobal injection warming and showedeffect, polar that theamplification cooling [44e–ff46]ect. Ji in et mean al. showed temperature that both is larger solar over dimming the interior (G1) ofand the stratospheric continent than aerosol in the (G4) oceans, geoengineering except for high-latitude northern hemisphere regions [47]. Volodin et al. and Ji et al. showed a larger cooling effect resulted in a large ∆TNn over high‐latitude oceanic regions in both hemispheres, which was not seen of stratospheric aerosol injection over tropical and mid-latitude land areas than in the corresponding in TXx [35]. It has been found that this high‐latitude cooling by geoengineering experiments, such as oceanic areas [35,48]. cloud brightening [42] and solar dimming [43], could restore the sea ice extent to the state of their control3.4. Seasonality experiments of Extreme. Temperature Response Although ∆TNn and ∆TXx resulted in cooling all over the area, a land–sea difference of the This section examines the effect of seasonality on the changes in the extreme temperature indices, results is distinct in Figure 5. The G4cdnc experiment has a larger cooling effect of ∆TNn and ∆TXx TNn and TXx. To investigate the seasonality, temporal and spatial changes were analyzed and overare the shown interior in Figures of the 6continent and7, respectively, compared for to Eastthe ocean Asia in, with boreal the summer exception and of winter the Sea during of Okhotsk. the Thisprogress land–sea period. difference in response can also be seen in previous studies that have investigated other geoengineeringFigure6 presents experiments the time such series as for the the injectio entire simulationn of stratospheric period, but aerosol now onlys or the marine last 10-year sea salt [35,47,48]averages. Alterskjær of the progress et al. period conducted from 2060a model to 2069 experiment are used. Inwith both sea seasons, salt injection G4cdnc and resulted showed in cooling that the coolingof the effect extreme in temperatures.mean temperature In winter, is larger the multi-model over the interior ensemble of meansthe continent of ∆TNn than= 1.425in theC ocean and s, − ◦ except∆TXx for= high1.072‐latitudeC are largernorthern than hemisphere the annual response regions shown[47]. Volodin in Figure et4 .al. The and inter-model Ji et al. showed di fference a larger is − ◦ cooling1.771 effectC for of TNn, stratospheric ranging from aerosol2.222 injectionC to 0.451 over C,tropical and 0.990 and Cmid for‐latitude TXx, ranging land fromareas than1.555 inC the ◦ − ◦ − ◦ ◦ − ◦ correspondingto 0.565 C. ocean Theseic inter-model areas [35,48] diff. erences are larger than the annual range. Unlike the winter case, − ◦ G4cdnc has a larger cooling effect on TXx than on TNn in summer, having multi-model means of 3.4.∆ SeasoTNn nality= 0.994 of ExtremeC and ∆TemperatureTXx = 1.119 ResponseC. The inter-model difference is lower than the annual range: − ◦ − ◦ 0.362 ◦C for TNn, ranging from 1.356 ◦C to 0.994 ◦C, and 0.444 for TXx, ranging from 1.458 ◦C This section examines the effect− of seasonality− on the changes in the extreme temperature− indices, to 1.014 C. In short, G4cdnc has a greater cooling effect on TNn in winter and on TXx in summer. − ◦ TNnThe and inter-model TXx. To investigate difference for the TNn seasonality, is larger thantemporal the ensemble and spatial mean changes annual were range analyzed during winter, and are shownindicating in Figure a larges 6 and uncertainty 7, respectively, in simulating for East the Asia extreme in boreal cold. summer and winter during the progress period.

Atmosphere 2020, 11, 1345 10 of 18 Atmosphere 2020, 11, x FOR PEER REVIEW 10 of 19

Figure 6. Time series of the difference in the extreme temperature indices (a,b) TNn and (c,d) TXx Figure 6. Time series of the difference in the extreme temperature indices (a,b) TNn and (c,d) TXx between the G4cdnc and RCP4.5 simulations over East Asia for (a,c) December-January-February (DJF) between the G4cdnc and RCP4.5 simulations over East Asia for (a,c) December‐January‐February and (b,d) June-July-August (JJA). The heavy dark curve is the multi-model ensemble mean used in this (DJF) and (b,d) June‐July‐August (JJA). The heavy dark curve is the multi‐model ensemble mean used study, excluding the MIROC-ESM model. The multi-model ensemble means for the last 10 years of the in this study, excluding the MIROC‐ESM model. The multi‐model ensemble means for the last 10 progress and termination periods are located in right upper corner of each plot. years of the progress and termination periods are located in right upper corner of each plot. Figure7 presents the di fference in TNn and TXx between the G4cdnc and RCP4.5 experiments over East Asia in boreal winter and summer during the progress period (2060–2069). The same as the annual case shown in Figure5, the cooling e ffect caused by G4cdnc is dominant over the whole of East Asia in both winter and summer. The particularly large cooling effect over the Sea of Okhotsk shown in the annual case for TNn (Figure5a) does not appear in summer but in winter (Figure7a,b). An increase in sea ice lowers the air temperature, particularly the minimum temperature [35] and, according to this effect, the G4cdnc experiment demonstrated the largest cooling over the Sea of Okhotsk in winter (Figure7a). Sea ice in the Sea of Okhotsk varies with season, reaching its maximum in March and minimum in September [49]. Many CMIP5 models projected that the extent of Arctic sea ice will decline under a GHG forcing scenario of RCP4.5 in both summer and winter seasons in the future [50]. In the meantime, a study by Moore et al. demonstrated that the G1 scenario restores the extent of Arctic sea ice from conditions under the high GHG scenario to a preindustrial state [43]. Because of the removal of snow and sea ice in summer, the TNn response to the G4cdnc experiment is weaker in summer over the East Asian continent and the Sea of Okhotsk compared to winter (Figure7b). The sea

Atmosphere 2020, 11, 1345 11 of 18 ice over the Sea of Okhotsk also causes a larger TXx cooling compared to the rest of the ocean, but a Atmospheremoderate 2020 response, 11, x FOR compared PEER REVIEW to the TNn cooling in winter (Figure7c,d). 11 of 19

Figure 7. Geographical distribution of the difference in extreme temperature indices (a,b) TNn and Figure 7. Geographical distribution of the difference in extreme temperature indices (a,b) TNn and (c,d) TXx between the G4cdnc and RCP4.5 simulations during the (a,c) DJF and (b,d) JJA seasons in the (c,d) TXx between the G4cdnc and RCP4.5 simulations during the (a,c) DJF and (b,d) JJA seasons in progress period over East Asia. The results show the multi-model ensemble mean. the progress period over East Asia. The results show the multi‐model ensemble mean. The land–sea difference revealed in the annual response of the extreme temperature indices in Figure 6 presents the time series for the entire simulation period, but now only the last 10‐year Figure5 can also be found in the seasonal responses of TNn, especially in winter. Compared to averages of the progress period from 2060 to 2069 are used. In both seasons, G4cdnc resulted in the oceanic response, the G4cdnc experiment results in a larger cooling effect over the East Asian cooling of the extreme temperatures. In winter, the multi‐model ensemble means of ∆TNn = −1.425 °C continent, except for Southern China in winter and summer (Figure7a,c). This continental region with and ∆TXx = −1.072 °C are larger than the annual response shown in Figure 4. The inter‐model a larger cooling effect is frequently covered by snow during winter, suggesting snow–albedo feedback. difference is 1.771 °C for TNn, ranging from −2.222 °C to −0.451 °C, and 0.990 °C for TXx, ranging A previous study showed that G3 and G4 geoengineering would restore the snow cover that is reduced from −1.555 °C to −0.565 °C. These inter‐model differences are larger than the annual range. Unlike by GHG forcing, which decreases the surface-sensible heat flux, raises heat capacity, and thus reduced the winter case, G4cdnc has a larger cooling effect on TXx than on TNn in summer, having multi‐ the sensitivity of temperature to radiative forcing changes [35,51]. model means of ∆TNn = −0.994 °C and ∆TXx = −1.119 °C. The inter‐model difference is lower than the Figure8 shows the di fference in snow cover and cloudiness between the G4cdnc and RCP4.5 annual range: 0.362 °C for TNn, ranging from −1.356 °C to −0.994 °C, and 0.444 for TXx, ranging from experiments over East Asia in winter during the progress period, but it has been drawn using only two −1.458 °C to −1.014 °C. In short, G4cdnc has a greater cooling effect on TNn in winter and on TXx in model outputs (CanESM2 and MIROC-ESM) currently available from the data archive. The pattern summer. The inter‐model difference for TNn is larger than the ensemble mean annual range during of snow cover change with a decrease approximately north of 40◦ N and an increase in the south winter, indicating a large uncertainty in simulating the extreme cold. is consistent with that of the upward shortwave radiation change at the TOA shown in Figure2a. Figure 7 presents the difference in TNn and TXx between the G4cdnc and RCP4.5 experiments This result illustrates positive feedback between snow and albedo. The net incoming solar radiation over East Asia in boreal winter and summer during the progress period (2060‐2069). The same as the increases over the northern area and it would warm the atmosphere. Then, the resultant decrease in annual case shown in Figure 5, the cooling effect caused by G4cdnc is dominant over the whole of vertical temperature gradient near the surface can cause a decrease in latent and sensible heat fluxes East Asia in both winter and summer. The particularly large cooling effect over the Sea of Okhotsk from the surface, followed by a decrease in cloudiness (Figure8b) and snowfall, daytime warming, shown in the annual case for TNn (Figure 5a) does not appear in summer but in winter (Figure 7a,b). and nighttime cooling of the surface after all. This feedback process can possibly be related to the An increase in sea ice lowers the air temperature, particularly the minimum temperature [35] and, greater cooling of TNn (Figure7a) over the northern area with the decreased shortwave flux and the according to this effect, the G4cdnc experiment demonstrated the largest cooling over the Sea of smaller cooling of TXx over the area (Figure7c). Such positive snow–albedo feedback can be applied Okhotsk in winter (Figure 7a). Sea ice in the Sea of Okhotsk varies with season, reaching its maximum to the southern area with the increased upward shortwave radiation with the opposite signs as well. in March and minimum in September [49]. Many CMIP5 models projected that the extent of Arctic sea ice will decline under a GHG forcing scenario of RCP4.5 in both summer and winter seasons in the future [50]. In the meantime, a study by Moore et al. demonstrated that the G1 scenario restores the extent of Arctic sea ice from conditions under the high GHG scenario to a preindustrial state [43]. Because of the removal of snow and sea ice in summer, the TNn response to the G4cdnc experiment is weaker in summer over the East Asian continent and the Sea of Okhotsk compared to winter

Atmosphere 2020, 11, x FOR PEER REVIEW 12 of 19

(Figure 7b). The sea ice over the Sea of Okhotsk also causes a larger TXx cooling compared to the rest of the ocean, but a moderate response compared to the TNn cooling in winter (Figure 7c,d). The land–sea difference revealed in the annual response of the extreme temperature indices in Figure 5 can also be found in the seasonal responses of TNn, especially in winter. Compared to the oceanic response, the G4cdnc experiment results in a larger cooling effect over the East Asian continent, except for Southern China in winter and summer (Figure 7a,c). This continental region with a larger cooling effect is frequently covered by snow during winter, suggesting snow–albedo feedback. A previous study showed that G3 and G4 geoengineering would restore the snow cover that is reduced by GHG forcing, which decreases the surface‐sensible heat flux, raises heat capacity, and thus reduced the sensitivity of temperature to radiative forcing changes [35,51]. Figure 8 shows the difference in snow cover and cloudiness between the G4cdnc and RCP4.5 experiments over East Asia in winter during the progress period, but it has been drawn using only two model outputs (CanESM2 and MIROC‐ESM) currently available from the data archive. The pattern of snow cover change with a decrease approximately north of 40°N and an increase in the south is consistent with that of the upward shortwave radiation change at the TOA shown in Figure 2a. This result illustrates positive feedback between snow and albedo. The net incoming solar radiation increases over the northern area and it would warm the atmosphere. Then, the resultant decrease in vertical temperature gradient near the surface can cause a decrease in latent and sensible heat fluxes from the surface, followed by a decrease in cloudiness (Figure 8b) and snowfall, daytime warming, and nighttime cooling of the surface after all. This feedback process can possibly be related to the greater cooling of TNn (Figure 7a) over the northern area with the decreased shortwave flux and the smaller cooling of TXx over the area (Figure 7c). Such positive snow–albedo feedback can be Atmosphereapplied to2020 the, 11 southern, 1345 area with the increased upward shortwave radiation with the opposite12 signs of 18 as well.

Figure 8. Geographical distributions of the differences in (a) snow cover and (b) cloud cover between Figurethe G4cdnc 8. Geographical and RCP4.5 simulationsdistributions during of the DJF differences season in in the (a) progress snow cover period and over (b) Eastcloud Asia. cover The between results theare derivedG4cdnc fromand RCP4.5 two models simulations of CanESM2 during and DJF MIROC-ESM. season in the progress period over East Asia. The results are derived from two models of CanESM2 and MIROC‐ESM. Such a large response over the continent has been shown not only in GHG forcing experiments but also inSuch other a large geoengineering response over experiments. the continent Orlowsky has been and shown Seneviratne not only and in Sillmann GHG forcing et al. experiments showed that butthe responsealso in other of extreme geoengineering temperatures experiments. is projected Orlowsky to be larger and Seneviratne over mid- and and high-latitude Sillmann et continentalal. showed thatbands the during response winter of underextreme CMIP3 temperatures and CMIP5 is projected GHG forcing to be scenarios, larger over respectively mid‐ and [ 32 high,41].‐latitude Ji et al. continentalshowed that bands G1 and during G4 geoengineering winter under experiments CMIP3 and have CMIP5 more GHG significant forcing e scenarios,ffects over respectively continental [32,41]land masses. Ji et al. of showed the same that latitudinal G1 and G4 band geoengineering compared to experiments those of the oceanhave more [35]. significant effects over continental land masses of the same latitudinal band compared to those of the ocean [35]. 3.5. Relative Effect of Extreme Temperature 3.5. Relative Effect of Extreme Temperature We calculated annual and monthly efficacies based on root mean square (RMS) differences to Atmosphereinvestigate 2020W, 11e calculated, x FOR the relativePEER annual REVIEW eff ectand on monthly extreme efficac temperaturesies based ofon theroot G4cdnc mean square experiment (RMS over) difference East Asia.s to 13 of 19 Theinvestigate annual the effi caciesrelative of effect extreme on extreme temperatures temperature (TNn ands of TXx) the G4cdnc and mean experiment temperatures over (TAS,East Asia. TASmin, The and TASmax)aandnnual TASmax) areefficac shownies are of shown extremein Fig inure Figure temperatures 9, 9a,nd and seasonal seasonal (TNn and variationsvariations TXx) and in ein meanffi efficacycacy temperatures are displayedare displayed (TAS, using TASmin monthly using , monthly values invalues Figure in Figure10. In 10 this. In s thisect section,ion, we we refer refer to to the the eefficacyfficacy in in terms terms of percentages. of percentages.

Figure 9. Annual efficacy of the G4cdnc experiment for neutralizing extreme (TNn and TXx) and daily Figure 9.mean Annual (TAS), efficacy daily minimum of the G4cdnc (TASmin), experiment and daily maximum for neutralizing (TASmax) extreme temperatures (TNn in and RCP4.5 TXx) over and daily mean (TASEast), Asia. daily Results minimum are shown (TASmin for multi-model), and daily ensemble maximum mean. (TASmax) temperatures in RCP4.5 over East Asia. Results are shown for multi‐model ensemble mean.

Figure 10. Timeseries of monthly efficacy of the G4cdnc experiment for neutralizing extreme (TNn and TXx) temperatures during the progress period (2060–2069) in RCP4.5 over East Asia. Results are shown for multi‐model ensemble mean.

The annual efficacies of G4cdnc for decreasing the warming of TNn and TXx under the RCP4.5 conditions in the multi‐model ensemble mean over East Asia are 56.6% and 63.4%, respectively. In other words, the efficacy of TXx is larger than that of TNn by 7%, which implies that marine cloud brightening is a relatively effective method of alleviating the warming of TXx compared to TNn under GHG forcing. This asymmetric relative effect on extreme temperatures is the opposite to that represented by the absolute effect on extreme temperatures (∆TNn ≥ ∆TXx in Section 3.3). However, Curry et al., for G1 over the globe, showed that the difference between the efficacies of TNn and TXx is not significant (TNn: 87% and TXx: 89%) [33]. Therefore, the relative effect of marine cloud brightening over East Asia has a different asymmetry from that reported by the G1 experiment. Meanwhile, the annual efficacy for mean temperature over the progress period by the G4cdnc simulation is 60.9% for TAS, 61% for TASmin, and 60.4% for TASmax, which implies that G4cndc is similarly effective in alleviating the warming of the mean temperature. In addition, the efficacies for all mean temperatures are larger than that for TNn but lower than that for TXx. The monthly efficacy was investigated for extreme temperatures during the progress period (Figure 10). The efficacy of TXx was larger than that of TNn during all the months, except from

Atmosphere 2020, 11, x FOR PEER REVIEW 13 of 19 and TASmax) are shown in Figure 9, and seasonal variations in efficacy are displayed using monthly values in Figure 10. In this section, we refer to the efficacy in terms of percentages.

Figure 9. Annual efficacy of the G4cdnc experiment for neutralizing extreme (TNn and TXx) and daily mean (TAS), daily minimum (TASmin), and daily maximum (TASmax) temperatures in RCP4.5 over East Asia. Results are shown for multi‐model ensemble mean. Atmosphere 2020, 11, 1345 13 of 18

Figure 10. Timeseries of monthly efficacy of the G4cdnc experiment for neutralizing extreme (TNn and TXx) temperatures during the progress period (2060–2069) in RCP4.5 over East Asia. Results are shown Figure 10. Timeseries of monthly efficacy of the G4cdnc experiment for neutralizing extreme (TNn for multi-model ensemble mean. and TXx) temperatures during the progress period (2060–2069) in RCP4.5 over East Asia. Results are shown forThe multi annual‐model efficacies ensemble of G4cdnc mean. for decreasing the warming of TNn and TXx under the RCP4.5 conditions in the multi-model ensemble mean over East Asia are 56.6% and 63.4%, respectively. In other Thewords, annual the efficac efficacyies of TXx of G4cdnc is larger than for thatdecreasing of TNn by the 7%, whichwarming implies of that TNn marine and cloudTXx brighteningunder the RCP4.5 is a relatively effective method of alleviating the warming of TXx compared to TNn under GHG forcing. conditionsThis in asymmetric the multi relative‐model e ffensembleect on extreme mean temperatures over East is Asia the opposite are 56.6% to that and represented 63.4%, respectively. by the In other words,absolute the eff efficacyect on extreme of TXx temperatures is larger (than∆TNn that∆TXx of TNn in Section by 7% 3.3)., which However, implies Curry etthat al., formarine G1 cloud ≥ brighteningover theis a globe, relatively showed ef thatfective the di methodfference between of alleviat the eingfficacies the ofwarming TNn and TXx of TXx is not compared significant to TNn under GHG(TNn: forcing. 87% and This TXx: asymmetr 89%) [33]. Therefore,ic relative the effect relative on e ffextremeect of marine temperature cloud brightenings is the overopposite East to that representedAsia hasby the a di ffabsoluteerent asymmetry effect on from extreme that reported temperatures by the G1 (∆ experiment.TNn ≥ ∆TXx Meanwhile, in Section the 3.3). annual However, efficacy for mean temperature over the progress period by the G4cdnc simulation is 60.9% for TAS, Curry et61% al., forfor TASmin, G1 over and the 60.4% globe for, TASmax,showed which that impliesthe difference that G4cndc between is similarly the e efficacffectiveies in alleviating of TNn and TXx is not significantthe warming (TNn: of the mean 87% temperature. and TXx: In 89%) addition, [33] the. T ehereforefficacies for, the all meanrelative temperatures effect of are marine larger cloud brighteningthan thatover for East TNn Asiabut lower has than a different that for TXx. asymmetry from that reported by the G1 experiment. Meanwhile, Thethe monthlyannual effi efficacycacy was for investigated mean temperature for extreme temperatures over the progress during the period progress by period the G4cdnc simulation(Figure is 60.9% 10). The for eTAS,fficacy 61% of TXx for was TASmin larger, than and that60.4% of TNn for TASmax, during all thewhich months, implies except that from G4cndc is August to October. The relative effect of TXx reached its maxima of 75% in December to March and similarly effective in alleviating the warming of the mean temperature. In addition, the efficacies for its minima of 55% in April and September to November. The efficacy, however, rebounded to 65% all meanfrom temperatures May to July, despiteare larger this beingthan during that for the TNn minima but period. lower The than relative that e ffforect TXx. of TXx shown by the TheG4cdnc monthly experiment efficacy demonstrated was investigated a seasonal for response; extreme it reached temperature maxima durings during winter the and progress minima period (Figure 10during). The spring efficacy and fall. of OnTXx the was other larger hand, the than relative that e offfect TNn of TNn during reached all its the maxima months of 65%, except in from February, March, August, and December and its minima of 50% in the other months. Hence, the relative effect of TNn revealed its double maximum peaks in February and August and its minimum in May, showing relatively low efficacies in spring and fall. In short, the relative effect of extreme temperatures produced by the G4cdnc experiment demonstrates seasonal dependency over East Asia, and the range between the maximum and minimum efficacies reaches up to 30%.

3.6. Durability of Effects after the Termination of G4cdnc The last 20-year simulation period (2071 to 2090) highlights the consequences of terminating marine cloud brightening after the restoration of CDNC, and the termination period has been specified as the latter 10 years (2081–2090) in this study. Here, we analyze the changes in TNn and TXx indices over East Asia during the termination period, whereas previous studies have researched changes in mean temperatures [4,47,52] after impeding other geoengineering experiments over the globe. Atmosphere 2020, 11, 1345 14 of 18

The multi-model ensemble mean of differences in extreme temperatures between the G4cdnc and RCP4.5 experiments over East Asia during the termination period shown in Figure4 are ∆TNn = 0.321 C for TNn with an inter-model range of 0.366 C to 0.255 C and ∆TXx = 0.278 C − ◦ − ◦ − ◦ − ◦ for TXx with a range of 0.417 C to 0.013 C. Although the 10-year averaged values of TNn and TXx − ◦ − ◦ are still lower than the values under the RCP4.5 experiment, the year-to-year values asymptote to the RCP4.5 level till 2090, the last simulation year of the experiment. This termination effect on extreme temperatures was examined in a previous study that showed a 10th percentile global temperature increase of 0.61 ◦C and 90th percentile increase of 0.59 ◦C after shutting down injections of sea salt into the tropical marine atmospheric boundary layer [34]. In the MIROC-ESM model, which did not simulate the termination effect, the values of ∆TNn and ∆TXx remained at around 1 C during the − ◦ termination period. Figure 11 shows the difference pattern of extreme temperatures between the G4cdnc and RCP4.5 experiments over East Asia averaged during the termination period. Both ∆TNn and ∆TXx increased and approached the RCP4.5 results compared to the differences obtained from the progress period. Other studies also presented an increase in extreme temperature over the globe after terminating the forcing of their geoengineering methods [34,47]. It is noteworthy that while the cloud brightening control was implemented in the G4cdnc experiment, the extreme temperatures remained cooler than the RCP4.5 experiment results over the whole East Asian region, but after the termination of the control showing restoration of the experimental forcing CDNC, they increased and, in some regions, showed higher temperature values than the RCP4.5 results. These regionally different responses can Atmospherecause a 2020 political, 11, x FOR problem PEER betweenREVIEW the countries taking control and other countries not participating15 inof 19 the control experiment.

Figure 11. Geographical distribution of the differences in extreme temperature indices between G4cdnc Figureand RCP4.5 11. Geographical for termination distribution period over of East the Asia. differences The distributions in extreme show tempe therature difference indices in (a ) between TNn G4cdncand ( band) TXx. RCP4.5 The multi-model for termination mean period results over are shown, East Asia. excluding The distributions the MIROC-ESM show model. the difference in (a) TNn and (b) TXx. The multi‐model mean results are shown, excluding the MIROC‐ESM model. Finally, Figure 12 shows the monthly efficacy of the G4cdnc experiment for extreme temperatures over East Asia during the termination period. As the G4cndc is terminated, the relative effects on both Finally, Figure 12 shows the monthly efficacy of the G4cdnc experiment for extreme TNn and TXx also decreased significantly. Moreover, the relative effect on TNn during the termination temperatures over East Asia during the termination period. As the G4cndc is terminated, the relative period is larger than TXx in all months except for February, March, and November. For TNn, the efficacy effectreacheds on both its maxima TNn and of 15%TXx onalso average decreased in March significantly. and November Moreover, and its the minima relative of 5%effect in theon restTNn of during the themonths. termination TXx reached period itsis maximalarger than of 20% TXx on in average all months in May except to August for February, and December March and, and minima November. of 5% Forin TNn, January the toefficacy April. reached In short, its the maxim relativea eofffect 15% on on extreme average temperature in March duringand November the termination and its period minima of 5%also in shows the rest a seasonal of thevariation months. but TX withx reached much weakerits maxim seasonalitya of 20% than on thataverage during inthe Ma progressy to August period. and December and minima of 5% in January to April. In short, the relative effect on extreme temperature during the termination period also shows a seasonal variation but with much weaker seasonality than that during the progress period.

Figure 12. Timeseries of monthly efficacy of the G4cdnc experiment for neutralizing extreme (TNn and TXx) temperatures during the termination period for the RCP4.5 scenario over East Asia. Results are shown for multi‐model ensemble mean, excluding MIROC‐ESM model.

4. Summary and Conclusions We have investigated the marine cloud brightening effects on extreme temperature indices, TNn and TXx, over East Asia by the GeoMIP Phase 2 G4cdnc experiment under the RCP4.5 warming scenario. For this study, we used five climate model datasets that participated in the G4cdnc experiment and analyzed their multi‐model ensemble mean and the individual model results. Through a brief analysis of the radiation fluxes at the TOA, our results confirmed that the marine cloud brightening could increase the reflection of shortwave radiation at the TOA and decrease the outgoing longwave radiation, resulting in a net radiation loss over ocean and a gain over land, largely

Atmosphere 2020, 11, x FOR PEER REVIEW 15 of 19

Figure 11. Geographical distribution of the differences in extreme temperature indices between G4cdnc and RCP4.5 for termination period over East Asia. The distributions show the difference in (a) TNn and (b) TXx. The multi‐model mean results are shown, excluding the MIROC‐ESM model.

Finally, Figure 12 shows the monthly efficacy of the G4cdnc experiment for extreme temperatures over East Asia during the termination period. As the G4cndc is terminated, the relative effects on both TNn and TXx also decreased significantly. Moreover, the relative effect on TNn during the termination period is larger than TXx in all months except for February, March, and November. For TNn, the efficacy reached its maxima of 15% on average in March and November and its minima of 5% in the rest of the months. TXx reached its maxima of 20% on average in May to August and December and minima of 5% in January to April. In short, the relative effect on extreme temperature Atmosphereduring the2020 termination, 11, 1345 period also shows a seasonal variation but with much weaker seasonality15 of 18 than that during the progress period.

Figure 12. Timeseries of monthly efficacy of the G4cdnc experiment for neutralizing extreme (TNn and Figure 12. Timeseries of monthly efficacy of the G4cdnc experiment for neutralizing extreme (TNn TXx) temperatures during the termination period for the RCP4.5 scenario over East Asia. Results are and TXx) temperatures during the termination period for the RCP4.5 scenario over East Asia. Results shown for multi-model ensemble mean, excluding MIROC-ESM model. are shown for multi‐model ensemble mean, excluding MIROC‐ESM model. 4. Summary and Conclusions 4. Summary and Conclusions We have investigated the marine cloud brightening effects on extreme temperature indices, TNnWe and have TXx, investigated over East Asia the by marine the GeoMIP cloud brightening Phase 2 G4cdnc effects experiment on extreme under temperature the RCP4.5 indice warmings, TNn scenario.and TXx, Forover this East study, Asia we by used the five GeoMIP climate Phase model 2 datasets G4cdnc that experiment participated under in the the G4cdnc RCP4.5 experiment warming andscenario analyzed. For theirthis multi-modelstudy, we used ensemble five climate mean and model the individual datasets that model participated results. in the G4cdnc experimentThrough and a briefanalyzed analysis their of multi the radiation‐model ensemble fluxes at themean TOA, and our the results individual confirmed model that results. the marine cloudThrough brightening a brief could analysis increase of the theradiation reflection fluxes of at shortwave the TOA, our radiation results atconfirmed the TOA tha andt the decrease marine thecloud outgoing brightening longwave could radiation,increase the resulting reflection in aof net shortwave radiation radiation loss over at ocean the TOA and aand gain decrease over land, the largelyoutgoing compensating longwave radiation, the loss resulting in East Asia. in a net Our radiation results alsoloss over confirmed ocean theand sea–icea gain over albedo land feedback, largely over ocean, especially over the Sea of Okhotsk and the snow albedo feedback could be a crucial factor determining the radiation response at the TOA in East Asia. The G4cdnc experiment shows the capability of counteracting the warming of extreme temperatures as well as the mean temperature under the RCP4.5 scenario, though the amount was insufficient to remove the warming to the level of the base period (2006–2019). The efficacy of the cloud control for mean temperature reaches approximately 60%, which is a middle of the efficacies for extreme temperatures: 56% for TNn and 63% for TXx annually. The efficacies for the extreme temperatures revealed a significant seasonality, showing the highest efficacy in winter and relatively low efficacies in spring and fall during the progress period (2060–2069). Although the cloud control was implemented over ocean only, the changes in the extreme temperatures were larger over land than over ocean in East Asia. The considerable cooling of TNn over the Sea of Okhotsk in winter implies the restoration of the extent of sea ice by the G4cdnc experiment. These findings need to be highlighted since the extent of sea ice has a great influence on the wintertime temperature in mid-latitude land areas of the Northern Hemisphere. It is also probable that the significant cooling of TNn in winter can be related to the increase in wintertime cold events. In the context that extreme cold events as well as hot spells have occurred more frequently under global warming, how these findings can be used to act on severe weather events is an important issue to be answered prior to any implementation of the cloud control experiment in reality. Thus, more in-depth analysis on the extreme values not only for temperature but also other variables is required on a regional scale. The authors expect this study can be a trigger for these further studies.

Author Contributions: Conceptualization, D.-H.K., I.-U.C., and H.-J.S.; Methodology, D.-H.K., I.-U.C., and H.-J.S.; Validation, D.-H.K., H.-J.S., and I.-U.C.; Visualization, D.-H.K.; Writing—original draft, D.-H.K.; Writing—review and editing, D.-H.K., H.-J.S., and I.-U.C. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by the Korea Meteorological Administration Research and Development Program “Development and Assessment of IPCC AR6 Climate Change Scenario” under Grant (KMA2018-00321). I.-U. Chung was also supported by the Korean Meteorological Administration Research and Development Program Atmosphere 2020, 11, 1345 16 of 18 under Grant (KMI2018-03412). H.-J. Shin was also supported by the National Research Foundation of Korea (NRF) and the Center for Women in Science, Engineering and Technology (WISET) Grant (2018-072) funded by the Ministry of Science and ICT (MSIT) under the Program for Returners into R&D. Acknowledgments: The authors thank the anonymous reviewers for their helpful comments. Conflicts of Interest: The authors declare no conflict of interest. Data Availability: All simulation data in CMIP5 and GeoMIP are publicly available through the Earth System Grid Federation. The BNU-ESM data are archived at http://climatemodeling.bnu.edu.cn (last access: 16 September 2020).

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