Substantial Climate Response outside the Target Area in an Idealized Experiment of Regional Radiation Management

Dipu, Sudhakar et al. Article — Published Version Substantial Climate Response outside the Target Area in an Idealized Experiment of Regional Radiation Management Climate Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Dipu, Sudhakar et al. (2021) : Substantial Climate Response outside the Target Area in an Idealized Experiment of Regional Radiation Management, Climate, ISSN 2225-1154, MDPI, Basel, Vol. 9, Iss. 4, https://doi.org/10.3390/cli9040066 This Version is available at: https://hdl.handle.net/10419/240193 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ climate Article Substantial Climate Response outside the Target Area in an Idealized Experiment of Regional Radiation Management Sudhakar Dipu 1,* , Johannes Quaas 1, Martin Quaas 1,2, Wilfried Rickels 3and Johannes Mülmenstädt 1,4 and Olivier Boucher 5,6   Citation: Dipu, S.; Quaas, J.; Quaas, M.; Rickels, W.; Mülmenstädt, J.; Boucher, O. Substantial Climate Response outside the Target Area in an Idealized Experiment of Regional Radiation Management. Climate 2021, 9, 66. https://doi.org/10.3390/ cli9040066 Academic Editor: Rajib Shaw Received: 15 March 2021 Accepted: 13 April 2021 Published: 16 April 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Institute for Meteorology, Universität Leipzig, 04109 Leipzig, Germany; [email protected] (J.Q.); [email protected] (M.Q.); [email protected] (J.M.) 2German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, 04103 Leipzig, Germany 3Kiel Institute for the World Economy Kiel, 24105 Kiel, Germany; [email protected] 4Presently at Pacific Northwest National Laboratory, Richland, WA 99354, USA 5Institut Pierre-Simon Laplace, Sorbonne Université, 75005 Paris, France; olivier[email protected] 6CNRS, 75016 Paris, France *Correspondence: [email protected] Abstract: Radiation management (RM) has been proposed as a conceivable climate engineering (CE) intervention to mitigate global warming. In this study, we used a coupled climate model (MPI-ESM) with a very idealized setup to investigate the efficacy and risks of CE at a local scale in space and time (regional radiation management, RRM) assuming that cloud modification is technically possible. RM is implemented in the climate model by the brightening of low-level clouds (solar radiation management, SRM) and thinning of cirrus (terrestrial radiation management, TRM). The region chosen is North America, and we simulated a period of 30 years. The implemented sustained RM resulted in a net local radiative forcing of − 9.8Wm −2 and a local cooling of − 0.8K. Surface temperature (SAT) extremes (90th and 10th percentiles) show negative anomalies in the target region. However, substantial climate impacts were also simulated outside the target area, with warming in the Arctic and pronounced precipitation change in the eastern Pacific. As a variant of RRM, a targeted intervention to suppress heat waves (HW) was investigated in further simulations by implementing intermittent cloud modification locally, prior to the simulated HW situations. In most cases, the intermittent RRM results in a successful reduction of temperatures locally, with substantially smaller impacts outside the target area compared to the sustained RRM. Keywords: regional radiation management; climate engineering; radiative forcing 1. Introduction Climate engineering (CE), also referred to as geoengineering, encompasses a set of technologies and methods to deliberately intervene in the climate system to counteract global warming [ 1 ]. The approach consists of either reducing the amount of solar radiation absorbed by the Earth, facilitating outgoing long-wave radiation (radiation management, RM) or enhancing the net carbon sink from the atmosphere (carbon dioxide removal, CDR) in order to mitigate global warming [ 2 ]. In the past few years, CE has garnered significant attention because, if adequate measures to curb greenhouse gases in the atmosphere are not implemented rapidly, substantial warming over pre-industrial times can be expected [3–6]. To tackle global warming, the Paris agreement (2015) aims to limit the increase in globalmean near-surface temperature to below 2 ◦ C in comparison to pre-industrial times and to pursue efforts to limit the increase to below 1.5 ◦ C [ 7 , 8 ]. Substantial reductions in greenhouse gas emissions as well as some amount of CDR are required to do so, but, if such measures are insufficient or come too late, achieving these goals would imply some sort of RM. However, RM is expected to be imperfect (e.g., it may lead to overcooling of the tropics and undercooling of the poles) with potentially severe side effects (e.g., it modifies some precipitation patterns). Climate 2021,9, 66. https://doi.org/10.3390/cli9040066 https://www.mdpi.com/journal/climate Climate 2021,9, 66 2 of 22 Furthermore, it would not solve the issues of ocean acidification and ocean deoxygenation, and a putative early termination would cause rapid climate change [ 9 , 10 ]. Thus, RM entails many social and ethical issues [ 11 ] which to some extent also apply to research on RM [ 12 ]. However, without a strong reduction in greenhouse gases and in the absence of CE methods (CDR and RM), anthropogenic climate change could result devastating consequences with 3–4 ◦ C or more temperature rise by the end of the 21st century [ 13 – 16 ], which would also generate significant social and ethical concerns [17]. In this context, RM might be proposed to “shave off” the peak of climate warming due to anthropogenic greenhouse gases, before the CO 2 removal and greenhouse gases mitigations become sufficient [ 3 , 10 , 18 – 20 ]. Quaas et al. (2016) [ 21 ] argued that local implementation of RM seems more likely than a global implementation. One key reason for this is that different countries or different regions of the world have different preferences with regard to climate change. A regional implementation might also occur as an interim step before global action is taken [ 22 ]. Various climate projections with RM techniques propose that the radiative forcing (RF), a measure of energy budget perturbation, is substantially localized to the region of implementation [23–26]. Local mitigation seems a necessary but not a sufficient condition for regional RM (RRM) to be of interest, because the climate effects may extend outside the region. The extended climate effect may be beneficial or detrimental, while the pattern of influence strongly depends on the region of RRM implementation [ 10 , 25 ]. By using an example, here we demonstrate that RRM may lead to non-local responses which are modulated by the atmospheric circulation, and subsequently we demonstrate that limiting RRM also in time substantially reduces these side-effects. Proposed RM management schemes involve reflecting solar radiation away from the Earth’s atmosphere (solar radiation management, SRM [ 27 ]) and increasing the outgoing long-wave radiation at the top of the atmosphere (terrestrial radiation management, TRM [26,28]) . SRM techniques aim to manipulate the global temperature by increasing the albedo of the atmosphere. Among CE options, some SRM techniques are potentially comparatively inexpensive, technologically feasible and would lead to a rapid response of the climate system [ 29 , 30 ]. SRM includes methods such as the sulfate aerosol injection into the stratosphere or increasing the reflectivity of low-level clouds, and possibly also their lifetime, by adding aerosols to the troposphere [13,18,29,31–34]. The response of climate to stratospheric aerosol injection (SAI) has been investigated in many modeling studies (e.g., [ 10 , 18 , 19 , 24 , 25 , 34 – 37 ]). These suggest that SAI could possibly stabilize the global mean surface temperature. The problem that equatorial injection of stratospheric aerosols leads to an overcooling of the tropics relative to the higher latitudes can possibly be overcome by optimized injection at multiple locations [ 35 ]. SAI focusing on the polar regions can even have a larger influence in high compared to low latitudes [ 38 , 39 ]. Besides cooling, large-scale SAI could lead to consequences such as a shift in precipitation patterns [ 20 , 40 ], reduction in monsoon precipitation [ 41 ] and unmitigated characteristics of temperature and precipitation extremes [ 24 ]. Further, SAI would also delay the recovery of the ozone layer and enhance environmental risks [ 18 , 33 , 34 , 42 ]. However, MacMartin et al. (2019) [ 3 ] suggested that, for a limited deployment of SAI, the projected changes in surface temperature, precipitation and precipitation minus evaporation are typically smaller than natural variability. In addition to SAI, marine cloud brightening (MCB) has been proposed as a possible SRM approach [ 43 – 45 ]. The suggestion is to modify low-level marine clouds by injecting aerosols into the marine boundary layer and so increase cloud albedo. Such modification would produce a negative RF, which implies a cooling of surface temperature [ 46 ]. This approach is most effective in relatively clean areas [ 43 ]. Ship tracks and the impact of volcanic eruptions on marine clouds provide observational evidence of the cloud albedo effect [ 47 ]. Several modeling studies reported that, in principle, MCB has the potential to cool the Earth substantially [25,29,48]. Climate 2021,9, 66 3 of 22 Aswathy et al. (2015) [ 24 ] examined multi-model simulations of SAI and MCB. They demonstrated that both methods offset the effect of global warming, with more cooling in lower latitudes and residual warming in the Arctic. Aswathy et al. (2015) [ 24 ] further discussed the discrepancy in extreme temperature and precipitation for the two different CE schemes (SAI and MCB). Finally, some studies suggest that sudden termination of SRM may cause an acceleration of global warming, which is another important risk of SRM [ 49 , 50 ]. However, the sudden termination of strong SRM implementation might not be the most realistic scenario [51]. Another way of manipulating the net radiative flux of the planet could be through the thinning of high-level cirrus clouds (deliberate reduction of the cloud cover and optical thickness) [52,53]. Cirrus thinning reduces the absorption of long-wave radiation emitted from the Earth’s surface and the atmosphere beneath it, which results in a cooling [ 26 , 54 , 55 ]. However, altitude, optical depth, cloud microphysics and reflectivity of sunlight play a pivotal role in cirrus radiative effects [ 56 , 57 ]. Storelvmo et al. (2013) [ 58 ] tested the cirrus thinning hypothesis in a global climate model and found that it has the potential to counteract anthropogenic global warming. For cirrus cloud modification, a preliminary estimate of the potential global change in cloud radiative effect of up to − 2.8Wm −2 has been reported, which could almost offset the RF due to CO 2 doubling [ 26 ]. However, the effect of this magnitude is quite theoretical. It could be a complementary measure to SAI if implemented regionally over polar regions in the winter season. However, cirrus thinning in the polar regions would modify the equator to the pole temperature gradient [ 22 , 38 , 59 ]. Previous studies (some of which are discussed above) have provided insight into various RM methods, their efficacy and risks. Studies that have examined the possibility of RRM through the dimming of solar radiation were limited to the Arctic [ 39 , 59 ]. Outside the Arctic, RRM raises critical questions if different countries and regions of the world have a different perspective on climate change and/or CE. Nevertheless, it is essential to identify the regional response to climate change [60]. In this context, Quaas et al. (2016) [ 21 ] pointed out that RRM could further be limited by implementing them only “on demand” to target certain climate extreme events, in particular, heat waves (HW). It is much more homogenous than other climate extremes, for example, thunderstorms and extreme precipitations. From observations and model projections, it is evident that, with climate change, the present-day HWs are likely to become more frequent, intense and longer with a substantial impact on human health [ 61 – 65 ]. In recent decades, climate models are increasingly able to reproduce climate extremes as well as their response to forcings [ 66 ]. Wang et al. (2013) [ 67 ] simulated the effect of RRM by increasing the surface albedo of urban roofs, which allows for some HW suppression. This implies that mitigation measures such as RRM could potentially reduce the impact of the HW and its consequences. In RM research, deployment scenarios play a pivotal role in assessing efficacy and risks. Most of the RM scenarios are aiming to offset the global mean temperature rise. However, the recent emphasis is on moderate and restrained deployment [ 68 – 70 ]. In this study, we considered the scenario of a regional intervention (local mitigation or RRM) in a mid-latitude region, using a state-of-the-art climate model under the assumption that cloud modification is technically possible. This study investigated the efficacy and impacts of regional mitigation by sustained RM, the HW suppression by intermittent RM and its impacts outside the target region. 2. Data and Methodology 2.1. Model Description The simulations on which this study relies was performed with a coupled atmosphere– ocean–land surface model, the Max Planck Institute Earth system model (MPI-ESM) [ 71 ]. It consists of the atmospheric component ECHAM6 [ 72 ] with T63L47 spectral resolution (about 1.8 ◦ in the horizontal, uppermost of the 47 levels at 0.01hPa) and the ocean component Max Planck Institute Ocean Model (MPIOM) [ 73 ], which applies an idealized control mapping grid of about 1.5 ◦ with 40 levels. The atmospheric composition, as well as other Climate 2021,9, 66 4 of 22 boundary conditions, are prescribed at pre-industrial conditions. The simulations were initialized with existing pre-industrial equilibrium simulation and were run for 30years. The two reasons to choose the pre-industrial climate are: (i) the practical one that a balanced equilibrium atmosphere–ocean state is available; and (ii) that the analysis is facilitated since the only transient perturbation is the imposed one. Although we agree that RRM would be more realistically tested in a future scenario, it is very unlikely to change the results. The key mechanisms documented in our study would be equally present no matter what the baseline climate is. Furthermore, the choice of the scenario would be arbitrary. 2.2. Experimental Design The aim of these experiments was an analysis of RRM, targeting a continental area encompassing 32.5 ◦ N to 47.5 ◦ N and 112.0 ◦ W to 92.0 ◦ W (FigureA1). North America was chosen somewhat arbitrarily, but there is one key argument: it is a mid-latitude region where no directly neighboring countries are located in the zonal direction, so that comparatively little effects of RRM in other countries may be expected. The exact location of the box within North America is arbitrary and again idealized. Three types of model experiments were performed. First, a control simulation was performed without any cloud modification. A second type of simulations was performed where an idealized regional cloud modification (see Section 2.2.1) was sustained throughout the simulation over the targeted region. This second type of simulations is referred to as the “sustained mitigation” experiment and is evaluated against the control simulation. A third type of simulations was also performed where cloud modification was implemented only for the periods when in the control simulation there is a HW in the region of interest. This third type of simulations is used to evaluate the impact of “intermittent mitigation”. In the above case, the simulation is stopped a little after a HW is detected over the target area; it is then rewound and restarted with the cloud modification applied for a short period before, during and after the HW is simulated in the initial simulation. The scenario is meant to represent the fact that RRM is triggered and then a HW is forecast by numerical weather prediction. We define HW conditions as periods when the area mean of daily maximum temperature within the target region exceeds a threshold value, selected here as 32 ◦ C, for at least three consecutive days. In such an event, RRM is implemented starting 10 days preceding the HW (FigureA2). This 10-day period is a lead time at which numerical weather prediction is reliable, and long enough to allow the surface temperature to respond to the cloud modification. RRM is then sustained until one week after the end of the HW in the original simulation. The simulation with HW suppression then becomes the main simulation and is continued (consistent with the scenario, FigureA2). If multiple HW episode occurs within a period (less than 10 days between the HWs), then such events are combined and treated as a long single HW condition. In this third type of simulations, the periods with HW suppressions are evaluated against the corresponding periods without HW suppression that were simulated before the simulation is rewound to apply the cloud modification. To reduce the uncertainty associated with the simulated interannual variability, a six-member ensemble was performed and analyzed. A small ensemble size would be sufficient for global mean temperature response, while, for deep ocean processes or some atmospheric extremes, a larger ensemble is required [ 74 ]. The ensemble members were performed only for sustained and intermittent experiments. For the sustained experiment, each ensemble member used the same external forcing besides a small perturbation in the atmospheric initial conditions. Thus, the statistics were performed on a period of 6 × 30years=180years. For the intermittent experiment, the perturbation was applied only during the HW suppression period (FigureA2). The cloud modification influences the Earth’s climate by perturbing the Earth’s energy budget at the top of the atmosphere, which is referred to as the effective radiative forcing (ERF). It is defined as the difference between the net radiative flux at the top of the atmosphere for the experiment (with RRM) and the control simulation (without RRM). Climate 2021,9, 66 5 of 22 However, since the integration time is short enough, there is still the bulk of the top-of- atmosphere radiation imbalance that makes up the ERF. To compute statistical significance levels, a Welch’s unpaired t -test was used [ 75 , 76 ]. In both experiments, a set of climate extremes was identified with the upper and lower end of the distribution of meteorological variables, for instance, top and bottom deciles (90th and 10th percentiles, respectively) of surface temperature [ 24 ]. In the following text, the changes in temperature, precipitation, wind, etc., are the mean changes over 30 years (experiment − control) and local/locally denote the experiment region. 2.2.1. Cloud Modification Cloud optical properties have a profound impact on the global radiative effect [ 46 ]. Optically thick boundary layer clouds exert a negative radiative effect, by reflecting solar radiation and little greenhouse effect [ 46 , 77 ], whereas optically thin high-level clouds have a positive radiative effect by blocking the terrestrial radiation [ 26 ]. Here, the cloud modification is implemented as an alteration to both types of clouds by multiplying the liquid cloud water content ql by a factor of 10 and multiplying the cloud ice content qi by a factor of 0.1 in the model, specifically over the target region ( ql and qi are local variables in the radiation module). This modification is made at every time step because the change does not affect processes other than the radiation. The change intentionally is large to obtain a climate signal. The MPI-ESM uses a single moment cloud microphysics scheme. A change in the cloud microphysics would modify the particle size, which feedbacks in the next time step to the changed cloud water content. This effectively assumes that, technologically, such a cloud modification is feasible and neglects possible implications of the specific technology. Since the above modification will work only if ql> 0 and/or qi> 0, the magnitude of the cloud modification strongly depends on the presence and thermodynamic phase of cloud layers in the atmospheric column. The cloud modification effects scale about linearly with the forcing to a first approximation. Here, the climate model scales the liquid/ice to reduce the vapor, which conserves the cloud water. 3. Results 3.1. Sustained Mitigation The implemented RM in the climate model (see Section 2.2.1) increases the reflection of solar radiation by liquid-water clouds (negative radiative effect) and reduces the cirrus greenhouse effect by allowing more terrestrial radiation to escape to space (i.e., to reduce the absorption of long-wave radiation emitted from the Earth’s surface and the atmosphere beneath; negative radiative effect). Both lead to a negative local RF. Figure 1a shows the diagnosed effective RF (ERF) at the top of the atmosphere, which yields a magnitude of − 9.8 ± 5Wm −2 over the target/experiment region. The negative forcing leads to a cooling of the near surface air temperature (SAT) with a mean of − 0.8 ± 0.7K in the target region (Figure 1b). Further, the mean temperature profile also illustrates a reduction compared to the control simulation (figure not shown). Climate 2021,9, 66 6 of 22 Figure 1. ( a ) Effective radiative forcing (ERF, Wm −2 ) at the top of the atmosphere; and ( b ) nearsurface air temperature (SAT, K) change as 30-year average (experiment − control), ensemble average differences between the sustained RRM and control simulations. Hatched areas are grid cells where the changes are statistically significant at the 90% level according to a t-test. The radiative effect of RRM was further untangled by a separate assessment of the two different cloud modifications (thickening of liquid clouds, mainly in the solar spectrum, and thinning of ice clouds, mainly in the terrestrial spectrum) to find that the thickening of the liquid cloud contributes 54% to the total regional forcing, with the remainder from the thinning of ice clouds (figure not shown). An important result of the simulation is that, besides this intended effect, also a high latitude warming is evident over the Alaskan region, which is statistically significant at 90% confidence level. From the geographical distribution, the main contributor to the high latitude warming is the anomalous warming simulated to the northwest of the experimental region (Alaskan region). As a consequence of the local cooling, there is a weakening of surface westerly wind flow resulting in an anomalous north to northwesterly flow in the western Pacific Climate 2021,9, 66 7 of 22 between 30 ◦ N and 60 ◦ N and between 120 ◦ W and 180 ◦ W (Figure 2a). This anomalous flow favors incursions of warm air masses from mid-latitude to high latitudes. Associated with RRM and high latitude warming, significant changes in circulation and geopotential height are also noted at higher altitudes. The positive anomalies of geopotential and temperature at 500hPa result in an anomalous anticyclonic circulation over the warm region and a cyclonic circulation over the target region (Figure 2b). Figure 2. The same as Figure 1but for: ( a ) 1000hPa air temperature (K) and wind vector (ms −1 ); and ( b ) 500hPa air temperature (color shades), wind vector (ms −1 ), geopotential height (m, contours from −4 to 4 by 2) and wind vector anomaly. The above circulations result in the convergence of warm air (anticyclonic) and divergence of cold air (cyclonic) above the respective regions. This teleconnection is analogous to the finding of Kug et al. (2015) [ 78 ], although it suggests an influence of Arctic warming on North American cold winters, which is the opposite interpretation of causation. Note that, in our simulations, the causation is imposed by construction. There is some seasonality to the results. The colder winters in North America in response to RRM are the major contributor to anomalous Arctic warming. The anomalous cold winter due to RRM cooling provides a favorable condition for the Arctic warming through the poleward intrusion of warm air from mid-latitudes (Figure 2). Furthermore, the sea ice area fraction shows a decrease over the Alaskan region, which is associated with sustained warming of the Alaskan region due to the North American Climate 2021,9, 66 8 of 22 RRM. In turn, in the polar region, the sea ice fraction shows an increase (Figure A3a). The change in sea ice fraction could be related to the seasonality in ERF. It has both contributions from ice and liquid cloud modifications (figure not shown), with a relatively significant negative ERF in the winter season, which leads to seasonality in SAT as well. The seasonality in the RRM induced SAT anomaly (Figure A4) leads to an imbalance between summer ice melt and winter ice growth (Figure A3b,c), which accelerates sea ice loss around the Alaskan region, especially in the Bering Sea and the Sea of Okhotsk. An even more pronounced effect outside the targeted area is found when considering SAT extremes as defined by the top and bottom deciles of the temporal distribution at each grid point (Figure 3). The geographical distribution of change in the top decile of the SAT shows a cooling of the temperatures over the experiment region and exhibits a spatial pattern that is similar to the mean SAT change pattern, with local cooling. However, in the bottom decile of the SAT distribution, along with the expected reduction over the target area, significant warming is simulated over much of the high latitudes (between 60 ◦ N and 90 ◦ N) of the Northern Hemisphere, with a statistical significance at a confidence level of 90%. Indeed, in the bottom decile of the SAT, the warmings are statistically significant, especially over the Arctic, emphasizing the non-local influence of RRM, attributable to the teleconnection mechanism discussed above. The signal in the bottom decile is noisy, however, with some—less significant—negative anomalies in the high latitudes as well. Figure 3. The sam as Figure 1but for the change in: ( a ) top decile (90th percentile); and ( b ) the bottom decile (10th percentile) of the temporal distribution of surface temperature. A 90% significance level is shown as dotted. Climate 2021,9, 66 15 of 22 A slight increase in the local precipitation of 0.02mmday −1 is noticed, despite the local cooling. Pronounced precipitation changes are also simulated outside the target area, especially in the eastern Pacific. Our analysis revealed that the relatively strong local RRM cooling results in a weakening of surface westerly wind and leads to equatorial wind convergence over the central Pacific. This, in turn, leads to a warm Pacific Ocean SST anomaly and enhances the precipitation in the eastern Pacific. The upper-level (200 hPa) anomalies for stream function, wind vector and geopotential height also reveal the dynamic coupling of the troposphere with the stratosphere. In a second step, we studied the feasibility of deploying RRM to mitigate specific harmful weather events that may occur more intensely and more frequently in a warming climate. We chose to target HWs and did so by implementing temporally intermittent RRM, which would presumably lead to less inadvertent effects. The idealized HW suppression scenario assumes accurate predictability of HWs. The results suggest that HWs are mitigated locally with the intermittent implementation of cloud modification by retaining the SAT below the threshold of 32 ◦ C in some cases. Further, the long-term effect of HW suppression shows that the intermittent RRM results in much smaller time-average forcing, surface temperature, or precipitation changes compared to the sustained RRM. However, some regional changes outside the target region are still simulated. This study is illustrative of what RRM may look like and what its consequences could be, which relies on a hypothetical scenario [ 21 ]. Idealized studies like this one are crucial to quantify the regional effect of CE and its consequences on neighboring or more remote regions. Most of the RRM studies have focused so far on the polar [ 22 , 38 , 39 , 59 ] and oceanic regions [ 25 , 43 , 44 ], while RRM studies focusing on continental areas are sparse. Such studies are relevant because different countries and regions of the world have different perspectives on climate change and/or CE. Although it is idealized, our study shows that it would not be appropriate to implement RRM unilaterally, if such RRM technologies become available in the future. Author Contributions: All authors participated in the design of the study. S.D. performed the model simulations, analysis and wrote the paper. All authors assisted in the interpretation of the results and commented on the paper. All authors have read and agreed to the published version of the manuscript. Funding: This study was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) within the Priority Programme SPP1689 “Climate Engineering - Risks, Challenges, Opportunities?” project LEAC-II (GZQU311/10-2 and QU357/3-2). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data that support the findings of this study is available at http: //doi.org/10.5281/zenodo.3956312. (accessed on 1 March 2021). Acknowledgments: The MPI-ESM is developed by the Max Planck Institute for Meteorology, and was run on the facilities of the German Climate Computing Centre (Deutsches Klimarechenzentrum, DKRZ). Conflicts of Interest: The authors declare that they have no conflict of interest.The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Climate 2021,9, 66 16 of 22 Appendix A Figure A1. The geographical location of the RRM region ( 32.5 ◦ N to 47.5 ◦ N, 112.0 ◦ W to 92.0 ◦ W). The blue and the green/brown colors indicate the ocean and the orography, respectively (data sources: [80]). Year1 Year2 Year3 Year4 Year5 Year6 Year28 Year29 Year30 Control Simulation Intermittent Simulation Year1 Year2 Year3 Year4 Year5 Year6 Year28 Year29 Year30 HW1 Year2 HW2 Year3 HW3 Year6 HW15 Year29 HW16 Year30 Figure A2. Schematic representations of the control and intermittent simulations. The long-term consequences of HW suppression is estimated from the 30-year mean control and intermittent simulation. The light blue lines indicate control simulation, dark blue lines indicate year without HWs, dark blue lines with red peaks indicate year with HWs and the green lines indicate year with HW suppression. The dashed red line in the intermittent simulation represents the ensemble part. Climate 2021,9, 66 17 of 22 Figure A3. The changes in sea ice area fraction in the Northern Hemisphere due to sustained RRM: (a) in annual mean; (b) during summer (JJA); and (c) during winter (DJF). Figure A4. For the sustained RRM simulations, seasonal change in SAT: ( a ) for the summer season (JJA); and (b) for the winter season (DJF). Climate 2021,9, 66 18 of 22 Figure A5. For the sustained RRM simulations: ( a ) composite anomalies of the stream function (ms −1 , shaded), wind vector (ms −1 ) and geopotential height (m, red contours are for positive and blue contours for negative anomalies) at 200hPa for conditions in which the standardized SAT in the RRM region greater than − 1.0K; and ( b ) the sam as ( a ), but for conditions in which the standardized SAT in the RRM region is less than −1.0K. Climate 2021,9, 66 19 of 22 (1) 10 days 23 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (2) 10 days 6 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (3) 10 days 4 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (4) 10 days 11 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (5) 10 days 7 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (6) 10 days 6 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (7) 10 days 4 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (8) 10 days 9 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (9) 10 days 6 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (10) 10 days 28 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (11) 10 days 7 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (12) 10 days 23 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (13) 10 days 15 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (14) 10 days 28 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (15) 10 days 14 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM (16) 10 days 21 days 7 days 20 22 24 26 28 30 32 34 36 38 40 HM−start HW HW HM−end Daily maximum surface temperature (C) With HW HWM Figure A6. Time evaluations of the area-averaged daily maximum surface air temperature ( ◦ C) for the simulation with HW (red curve) and with HW suppression (blue curve). References 1. IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013; p. 1535. 2. Boucher, O.; Forster, P.M.; Gruber, N.; Ha-Duong, M.; Lawrence, M.G.; Lenton, T.M.; Maas, A.; Vaughan, N.E. Rethinking climate engineering categorization in the context of climate change mitigation and adaptation. WIREs Clim. Chang. 2014 ,5, 23–35. [CrossRef] 3. MacMartin, D.G.; Wang, W.; Kravitz, B.; Tilmes, S.; Richter, J.H.; Mills, M.J. Timescale for Detecting the Climate Response to Stratospheric Aerosol Geoengineering. J. Geophys. Res. 2019,124, 1233–1247. [CrossRef] 4. Betts, R.A.; Collins, M.; Hemming, D.L.; Jones, C.D.; Lowe, J.A.; Sanderson, M.G. When could global warming reach 4 ◦ C. Phil. Trans. R. Soc. A 2010,369, 67–84. [CrossRef] 5. Battisti, D.; Blackstock, J.J.; Caldeira, K.; Eardley, D.E.; Katz, J.I.; Keith, D.W.; Koonin, S.E.; Patrinos, A.A.N.; Schrag, D.P.; Socolow, R.H. Climate engineering responses to climate emergencies. IOP Conf. Ser. Earth Environ. Sci. 2009,6, 452015. [CrossRef] 6. MacCracken, M. Beyond Mitigation: Potential Options For Counter-Balancing The Climatic And Environmental Consequences Of The Rising Concentrations Of Greenhouse Gases; The World Bank: Washington, DC, USA, 2009; pp. 4938–4943. 7. Dimitrov, R. The Paris Agreement on Climate Change: Behind Closed Doors. Global Environ. Polit. 2016,16, 1–11. [CrossRef] 8. UNFCCC. Adoption of the Paris Agreement, Proposal by the President. 2015. Available online: https://unfccc.int/documents/ 9064 (accessed on 1 March 2021). 9. Keller, D.P.; Feng, E.Y.; Oschlies, A. Potential climate engineering effectiveness and side effects during a high carbon dioxideemission scenario. Nat. Commun. 2014,5, 3304. [CrossRef] 10. Tilmes, S.; Richter, J.H.; Kravitz, B.; MacMartin, D.G.; Mills, M.J.; Simpson, I.R.; Glanville, A.S.; Fasullo, J.T.; Phillips, A.S.; Lamarque, J.F.; et al. CESM1(WACCM) Stratospheric Aerosol Geoengineering Large Ensemble Project. Bull. Amer. Meteor. Soc. 2018,99, 2361–2371. [CrossRef] 11. Corner, A.; Pidgeon, N. Geoengineering, climate change scepticism and the ‘moral hazard’ argument: an experimental study of UK public perceptions. Phil. Trans. R. Soc. A 2014,372. [CrossRef] 12. Quaas, M.F.; Quaas, J.; Rickels, W.; Boucher, O. Are there reasons against open-ended research into solar radiation management? A model of intergenerational decision-making under uncertainty. J. Environ. Econ. Manag. 2017,84, 1–17. [CrossRef] 13. Wigley, T.M.L. A Combined Mitigation/Geoengineering Approach to Climate Stabilization. Science 2006 ,314, 452–454. [CrossRef] 14. Wigley, T.M.L.; Raper, S.C.B. Interpretation of High Projections for Global-Mean Warming. Science 2001 ,293, 451–454. [CrossRef] 15. Rahm, D. Geoengineering Climate Change Solutions: Public Policy issues for National and Global Governance. Humanit. Soc. Sci. Rev. 2018,08, 139–148. 16. Cox, P.; Huntingford, C.; Williamson, M. Emergent constraint on equilibrium climate sensitivity from global temperature variability. Nature 2018,553, 319–322. [CrossRef] [PubMed] 17. Preston, C.J. Ethics and geoengineering: reviewing the moral issues raised by solar radiation management and carbon dioxide removal. WIREs Clim. Chang. 2013,4, 23–37. [CrossRef] 18. Tilmes, S.; Garcia, R.R.; Kinnison, D.E.; Gettelman, A.; Rasch, P.J. Impact of geoengineered aerosols on the troposphere and stratosphere. J. Geophys. Res. 2009,114. [CrossRef] Climate 2021,9, 66 20 of 22 19. Kravitz, B.; Robock, A.; Boucher, O.; Schmidt, H.; Taylor, K.E.; Stenchikov, G.; Schulz, M. The Geoengineering Model Intercomparison Project (GeoMIP). Atmos. Sci. Lett. 2011,12, 162–167. [CrossRef] 20. Jones, A.C.; Haywood, J.M.; Dunstone, N.; Emanuel, K.; Hawcroft, M.K.; Hodges, K.I.; Jones, A. Impacts of hemispheric solar geoengineering on tropical cyclone frequency. Nat. Commun. 2017,8, 1382. [CrossRef] 21. Quaas, J.; Quaas, M.F.; Boucher, O.; Rickels, W. Regional climate engineering by radiation management: Prerequisites and prospects. Earth’s Future 2016,4, 618–625. [CrossRef] 22. MacCracken, M.C. The rationale for accelerating regionally focused climate intervention research. Earth’s Future 2016 ,4, 649–657. [CrossRef] 23. Stjern, C.W.; Muri, H.; Ahlm, L.; Boucher, O.; Cole, J.N.S.; Ji, D.; Jones, A.; Haywood, J.; Kravitz, B.; Lenton, A.; et al. Response to marine cloud brightening in a multi-model ensemble. Atmos. Chem. Phys. 2018,18, 621–634. [CrossRef] 24. Aswathy, V.N.; Boucher, O.; Quaas, M.; Niemeier, U.; Muri, H.; Mülmenstädt, J.; Quaas, J. Climate extremes in multi-model simulations of stratospheric aerosol and marine cloud brightening climate engineering. Atmos. Chem. Phys. 2015 ,15, 9593–9610. [CrossRef] 25. Jones, A.; Haywood, J.; Boucher, O. Climate impacts of geoengineering marine stratocumulus clouds. J. Geophys. Res. 2009 ,114, D10106. [CrossRef] 26. Mitchell, D.L.; Finnegan, W. Modification of cirrus clouds to reduce global warming. Environ. Res. Lett. 2009 ,4, 045102. [CrossRef] 27. Barker, T.; Bashmakov, I.; Bernstein, L.; Bogner, J.; Bosch, P.; Dave, R.; Davidson, O.; Fisher, B.; Grubb, M.; Gupta, S.; et al. Technical Summary. In Climate Change 2007: Mitigation; Cambridge University Press: Cambridge, UK, 2007. 28. Mitchell, D.L.; Philip, R.; Dorothea, I.; Greg, M.; Timo, N. Impact of small ice crystal assumptions on ice sedimentation rates in cirrus clouds and GCM simulations. Geophys. Res. Lett. 2008,35. [CrossRef] 29. Robock, A.; Oman, L.; Stenchikov, G.L. Regional climate responses to geoengineering with tropical and Arctic SO 2 injections. J. Geophys. Res. 2008,113, D16101. [CrossRef] 30. Matthews, H.D.; Caldeira, K. Transient climate–carbon simulations of planetary geoengineering. Proc. Natl. Acad. Sci. USA 2007 , 104, 9949–9954. [CrossRef] 31. Carr, W.A.; Preston, C.J.; Yung, L.; Szerszynski, B.; Keith, D.W.; Mercer, A.M. Public engagement on solar radiation management and why it needs to happen now. Clim. Chang. 2013,121, 567–577. [CrossRef] 32. Moreno-Cruz, J.B.; Ricke, K.L.; Keith, D.W. A simple model to account for regional inequalities in the effectiveness of solar radiation management. Clim. Chang. 2012,110, 649–668. [CrossRef] 33. Keith, D.; Parson, E.; Morgan, M.G. Research on global sun block needed now. Nature 2010 ,463, 426–427. [CrossRef] [PubMed] 34. Heckendorn, P.; Weisenstein, D.; Fueglistaler, S.; Luo, B.P.; Rozanov, E.; Schraner, M.; Thomason, L.W.; Peter, T. The impact of geoengineering aerosols on stratospheric temperature and ozone. Environ. Res. Lett. 2009,4, 045108. [CrossRef] 35. MacMartin, D.G.; Kravitz, B.; Tilmes, S.; Richter, J.H.; Mills, M.J.; Lamarque, J.F.; Tribbia, J.J.; Vitt, F. The Climate Response to Stratospheric Aerosol Geoengineering Can Be Tailored Using Multiple Injection Locations. J. Geophys. Res. 2017 ,122, 12574–12590. [CrossRef] 36. MacMartin, D.G.; Kravitz, B. Mission-driven research for stratospheric aerosol geoengineering. Proc. Natl. Acad. Sci. USA 2019 , 116, 1089–1094. [CrossRef] [PubMed] 37. Jones, A.; J, H.; Olivier, B.; Ben, K.; Robock, A. Geoengineering by stratospheric SO 2 injection: results from the Met Office HadGEM2 climate model and comparison with the Goddard Institute for Space Studies ModelE. Atmos. Chem. Phys. 2010 ,10, 5999–6006. [CrossRef] 38. MacCracken, M.C.; Shin, H.J.; Caldeira, K.; Ban-Weiss, G.A. Climate response to imposed solar radiation reductions in high latitudes. Earth Syst. Dyn. 2013,4, 301–315. [CrossRef] 39. Caldeira, K.; Wood, L. Global and Arctic climate engineering: numerical model studies. Phil. Trans. R. Soc. A 2008 ,366, 4039–4056. [CrossRef] [PubMed] 40. Haywood, J.M.; Jones, A.; Bellouin, N.; Stephenson, D. Asymmetric forcing from stratospheric aerosols impacts Sahelian rainfall. Nat. Clim. Chang. 2013,3, 660–665. [CrossRef] 41. Tilmes, S.; Fasullo, J.; Lamarque, J.F.; Marsh, D.R.; Mills, M.; AlterskjÊr, K.; Muri, H.; Kristjánsson, J.E.; Boucher, O.; Schulz, M.; et al. The hydrological impact of geoengineering in the Geoengineering Model Intercomparison Project (GeoMIP). J. Geophys. Res. 2013,118, 11,036–11.058. [CrossRef] 42. Crutzen, P.J. Albedo Enhancement by Stratospheric Sulfur Injections: A Contribution to Resolve a Policy Dilemma? Clim. Chang. 2006,77, 211. [CrossRef] 43. Wood, R.; Ackerman, T.P. Defining success and limits of field experiments to test geoengineering by marine cloud brightening. Clim. Chang. 2013,121, 459–472. [CrossRef] 44. Latham, J.; Bower, K.; Choularton, T.; Coe, H.; Connolly, P.; Cooper, G.; Craft, T.; Foster, J.; Gadian, A.; Galbraith, L.; et al. Marine cloud brightening. Phil. Trans. R. Soc. A 2012,370, 4217–4262. [CrossRef] 45. Latham, J. Control of global warming? Nature 1990,347, 339–340. [CrossRef] 46. Twomey, S. The Influence of Pollution on the Shortwave Albedo of Clouds. J. Atmos. Sci. 1977,34, 1149–1152. [CrossRef] 47. Robock, A.; MacMartin, D.G.; Duren, R.; Christensen, M.W. Studying geoengineering with natural and anthropogenic analogs. Clim. Chang. 2013,121, 445–458. [CrossRef] Climate 2021,9, 66 21 of 22 48. Latham, J. Amelioration of global warming by controlled enhancement of the albedo and longevity of low-level maritime clouds. Atmos. Sci. Lett. 2002,3, 52–58. [CrossRef] 49. Kosugi, T. Fail-safe solar radiation management geoengineering. Mitig. Adapt. Strateg. Glob. Chang. 2013 ,18, 1141–1166. [CrossRef] 50. Brovkin, V.; Petoukhov, V.; Claussen, M.; Bauer, E.; Archer, D.; Jaeger, C. Geoengineering climate by stratospheric sulfur injections: Earth system vulnerability to technological failure. Clim. Chang. 2009,92, 243–259. [CrossRef] 51. Parker, A.; Irvine, P.J. The Risk of Termination Shock From Solar Geoengineering. Earth’s Future 2018,6, 456–467. [CrossRef] 52. Duan, L.; Cao, L.; Bala, G.; Caldeira, K. Comparison of the Fast and Slow Climate Response to Three Radiation Management Geoengineering Schemes. J. Geophys. Res. 2018,123, 11980–12001. [CrossRef] 53. Gruber, S.; Blahak, U.; Haenel, F.; Kottmeier, C.; Leisner, T.; Muskatel, H.; Storelvmo, T.; Vogel, B. A process study on thinning of Arctic winter cirrus clouds with high-resolution ICON-ART simulations. J. Geophys. Res. 2019,124. [CrossRef] 54. Muri, H.; Kristjánsson, J.E.; Storelvmo, T.; Pfeffer, M.A. The climatic effects of modifying cirrus clouds in a climate engineering framework. J. Geophys. Res. 2014,119, 4174–4191. [CrossRef] 55. Storelvmo, T.; Boos, W.R.; Herger, N. Cirrus cloud seeding: a climate engineering mechanism with reduced side effects? Phil. Trans. R. Soc. A 2014,372. [CrossRef] [PubMed] 56. Campbell, J.R.; Peterson, D.A.; Marquis, J.W.; Fochesatto, G.J.; Vaughan, M.A.; Stewart, S.A.; Tackett, J.L.; Lolli, S.; Lewis, J.R.; Oyola, M.I.; et al. Unusually Deep Wintertime Cirrus Clouds Observed over the Alaskan Subarctic. Bull. Am. Meteorol. Soc. 2018 , 99, 27–32. [CrossRef] [PubMed] 57. Masunaga, H.; Bony, S. Radiative Invigoration of Tropical Convection by Preceding Cirrus Clouds. J. Atmos. Sci 2018 ,75, 1327–1342. [CrossRef] 58. Storelvmo, T.; Kristjansson, J.E.; Muri, H.; Pfeffer, M.; Barahona, D.; Nenes, A. Cirrus cloud seeding has potential to cool climate. Geophys. Res. Lett. 2013,40, 178–182. [CrossRef] 59. Tilmes, S.; Jahn, A.; Kay, J.E.; Holland, M.; Lamarque, J.F. Can regional climate engineering save the summer Arctic sea ice? Geophys. Res. Lett. 2014,41, 880–885. [CrossRef] 60. Ge, F.; Zhu, S.; Peng, T.; Zhao, Y.; Sielmann, F.; Fraedrich, K.; Zhi, X.; Liu, X.; Tang, W.; Ji, L. Risks of precipitation extremes over Southeast asia: does 1.5◦C or 2◦C global warming make a difference? Environ. Res. Lett. 2019,14, 044015. [CrossRef] 61. Herring, S.C.; Hoerling, M.P.; Peterson, T.C.; Stott, P.A. Explaining Extreme Events of 2013 from a Climate Perspective. Bull. Amer. Meteor. Soc. 2014,95, S1–S104. [CrossRef] 62. Wolf, J.; Adger, W.N.; Lorenzoni, I.; Abrahamson, V.; Raine, R. Social capital, individual responses to heat waves and climate change adaptation: An empirical study of two UK cities. Glob. Environ. Chang. 2010,20, 44–52. [CrossRef] 63. Sun, Y.; Zhang, X.; Zwiers, F.W.; Song, L.; Wan, H.; Hu, T.; Yin, H.; Ren, G. Rapid increase in the risk of extreme summer heat in Eastern China. Nat. Clim. Chang. 2014,4, 1082–1085. [CrossRef] 64. Jones, G.S.; Stott, P.A.; Nikolaos, C. Human contribution to rapidly increasing frequency of very warm Northern Hemisphere summers. J. Geophys. Res. 2008,113. [CrossRef] 65. Meehl, G.A.; Tebaldi, C. More Intense, More Frequent, and Longer Lasting Heat Waves in the 21st Century. Science 2004 ,305, 994–997. [CrossRef] [PubMed] 66. Sillmann, J.; Kharin, V.V.; Zhang, X.; Zwiers, F.W.; Bronaugh, D. Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J. Geophys. Res. 2013,118, 1716–1733. [CrossRef] 67. Wang, M.; Yan, X.; Liu, J.; Zhang, X. The contribution of urbanization to recent extreme heat events and a potential mitigation strategy in the Beijing–Tianjin–Hebei metropolitan area. Theor. Appl. Climatol. 2013,114, 407–416. [CrossRef] 68. Keith, D.W.; MacMartin, D.G. A temporary, moderate and responsive scenario for solar geoengineering. Nat. Clim. Chang. 2015 , 5, 201–206. [CrossRef] 69. Irvine, P.; Emanuel, K.; He, J.; Horowitz, L.W.; Vecchi, G.; Keith, D. Halving warming with idealized solar geoengineering moderates key climate hazards. Nat. Clim. Chang. 2019,9, 295–299. [CrossRef] 70. Sugiyama, M.; Arino, Y.; Kosugi, T.; Kurosawa, A.; Watanabe, S. Next steps in geoengineering scenario research: limited deployment scenarios and beyond. Clim. Policy 2018,18, 681–689. [CrossRef] 71. Giorgetta, M.A.; Jungclaus, J.; Reick, C.H.; Legutke, S.; Bader, J.; Böttinger, M.; Brovkin, V.; Crueger, T.; Esch, M.; Fieg, K.; et al. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst. 2013,5, 572–597. [CrossRef] 72. Stevens, B.; Giorgetta, M.; Esch, M.; Mauritsen, T.; Crueger, T.; Rast, S.; Salzmann, M.; Schmidt, H.; Bader, J.; Block, K.; et al. Atmospheric component of the MPI-M Earth System Model: ECHAM6. J. Adv. Model. Earth Syst. 2013,5, 146–172. [CrossRef] 73. Jungclaus, J.H.; Fischer, N.; Haak, H.; Lohmann, K.; Marotzke, J.; Matei, D.; Mikolajewicz, U.; Notz, D.; von Storch, J.S. Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI-Earth system model. J. Adv. Model. Earth Syst. 2013,5, 422–446. [CrossRef] 74. Milinski, S.; Maher, N.; Olonscheck, D. How large does a large ensemble need to be? Earth Syst. Dyn. 2020 ,11, 885–901. [CrossRef] 75. Welch, B.L. The Generalization of ’Student’s’ Problem When Several Different Population Variances Are Involved. Biometrika 1947,34, 28–35. [CrossRef] [PubMed] 76. Boneau, C.A. The effects of violations of assumptions underlying the t test. Psychol. Bull. 1960,57, 49–64. [CrossRef] [PubMed] Climate 2021,9, 66 22 of 22 77. McComiskey, A.; Feingold, G. Quantifying error in the radiative forcing of the first aerosol indirect effect. Geophys. Res. Lett. 2008,35. [CrossRef] 78. Kug, J.S.; Jeong, J.H.; Jang, Y.S.; Kim, B.M.; Folland, C.K.; Min, S.K.; Son, S.W. Two distinct influences of Arctic warming on cold winters over North America and East Asia. Nat. Geosci. 2015,8, 759–762. [CrossRef] 79. Graf, H.; Davide, Z. Central Pacific El Ni no, "the subtropical bridge", and Eurasian climate. J. Geophys. Res. 2012 ,117. [CrossRef] 80. Hastings, D.A.; Dunbar, P.K.; Elphingstone, G.M.; Bootz, M.; Murakami, H.; Maruyama, H.; Masaharu, H.; Holland, P.; Payne, J.; Bryant, N.A.; et al. The Global Land One-kilometer Base Elevation (GLOBE) Digital Elevation Model, Version 1.0; National Oceanic and Atmospheric Administration, National Geophysical Data Center: Boulder, CO, USA, 1999.