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AMERICAN METEOROLOGICAL SOCIETY Bulletin of the American Meteorological Society EARLY ONLINE RELEASE This is a preliminary PDF of the author-produced manuscript that has been peer-reviewed and accepted for publication. Since it is being posted so soon after acceptance, it has not yet been copyedited, formatted, or processed by AMS Publications. This preliminary version of the manuscript may be downloaded, distributed, and cited, but please be aware that there will be visual differences and possibly some content differences between this version and the final published version. The DOI for this manuscript is doi: 10.1175/BAMS-D-16-0183.1 The final published version of this manuscript will replace the preliminary version at the above DOI once it is available. If you would like to cite this EOR in a separate work, please use the following full citation: Lewis, S., A. King, and S. Perkins-Kirkpatrick, 2016: Defining a new normal for extremes in a warming world. Bull. Amer. Meteor. Soc. doi:10.1175/BAMS-D-160183.1, in press. © 2016 American Meteorological Society Manuscript (non-LaTeX) Click here to download Manuscript (non-LaTeX) normal_resubmitted.docx Defining a new normal for extremes in a warming world 1 2 Sophie C. Lewisa,d* , Andrew D. Kingb,d and Sarah E. Perkins-Kirkpatrickc,d 3 4 a Fenner School of Environment and Society, The Australian National University, Canberra, ACT, Australia 5 6 b School of Earth Sciences, The University of Melbourne, Parkville, Victoria, Australia 7 8 c Climate Change Research Centre, University of New South Wales, Sydney, UNSW, Australia 9 10 11 d ARC Centre of Excellence for Climate System Science * Corresponding author: Tel: +61 2 6125 2623; email: [email protected] 12 13 1 14 Abstract 15 The term ‘new normal’ has been used in scientific literature and public 16 commentary to contextualise contemporary climate events as an indicator of a 17 changing climate due to enhanced greenhouse warming. A new normal has been 18 used broadly, but tends to be descriptive and ambiguously defined. Here we review 19 previous studies conceptualising this idea of a new climatological normal and argue 20 that this term should be used cautiously and with explicit definition in order to avoid 21 confusion. We provide a formal definition of a new climate normal relative to present 22 based around record-breaking contemporary events and explore the timing of when 23 such extremes become statistically normal in the future model simulations. Applying 24 this method to the record-breaking global average 2015 temperatures as a reference 25 event and a suite of model climate models, we determine that 2015 global annual 26 average temperatures will be the new normal by 2040 in all emissions scenarios. At 27 the regional level, a new normal can be delayed through aggressive greenhouse gas 28 emissions reductions. Using this specific case study to investigate a climatological 29 ‘new normal’, our approach demonstrates the greater value of the concept of a 30 climatological new normal for understanding and communicating climate change 31 when the term is explicitly defined. This approach moves us one step forward to 32 understanding how current extremes will change in the future in a warming world. 33 34 Capsule 35 The term ‘new normal’ is defined and applied to 2015 record-breaking 36 temperatures. A new normal can be useful for understanding and communicating 37 extremes in a changing climate when precisely defined. 38 2 39 1. Background 40 The term ‘a new normal’ has been used to describe various aspects of recently 41 observed climate and weather. This term is widely used in mainstream media reports 42 to succinctly categorise observed extreme weather and climate events as both 43 unusual and influenced, in some regard, by anthropogenic climate change (e.g. 44 Lewis and Perkins-Kirkpatrick 2016; Franz Prein 2016). The use of this terminology 45 that climatological events represent a new normal is also used in scientific literature 46 focused on understanding recent extreme climate events. Trenberth and co-authors 47 (2015) argue 48 “The climate is changing: we have a new normal. The environment in which all 49 weather events occur is not what it used to be. All storms, without exception, are 50 different. Even if most of them look just like the ones we used to have, they are 51 not the same.” 52 Other analyses have attempted to distinguish whether ‘new normal’ climatic 53 conditions have emerged in a specific region (University of Regina. Canadian Plains 54 Research Center 2010; Wood et al. 2013). These studies implicitly define the 55 observational record as the ‘old normal’ and delineate observed contemporary 56 climatological characteristics as the ‘new normal’, which provides a diagnostic of an 57 unprecedented change in climate due to greenhouse warming. However, such 58 terminology tends to be descriptive, and used ambiguously without precise definition 59 in both scientific literature and public commentary on climate change. In this study, 60 we explore the concept of a new normal for climate and propose a framework for its 61 calculation. Specifically, what is meant by a new normal and is this term a useful 62 concept for understanding climatic change? 3 63 Although a new normal has been applied to describe a diversity of weather and 64 climate phenomena, we focus our specific exploration on a specific set of climate 65 events. Directing our analysis of a new normal to extreme temperatures in the first 66 instance is motivated by several factors. First, extreme weather and climate events 67 occurring on sub-daily to multi-year timescales have significant socio-economic costs 68 and impacts on natural systems. Furthermore, there has been an observed increase 69 in heat extremes on various temporal scales in the observed record (Perkins et al. 70 2012; Coumou and Rahmstorf 2012). As a result, understanding the causes, and 71 potential future changes in the frequency, intensity and spatial extent of temperature 72 anomalies has become an active research direction (National Academies of 73 Sciences, Engineering, Medicine 2016). Extreme temperatures are also appropriate 74 for exploring the concept of a new normal, as this term is popularly applied following 75 extreme or record-breaking temperature events. For example, Australian heatwaves 76 (Perkins and Pitman 2014) and anomalously warm months (Lewis and Perkins- 77 Kirkpatrick 2016), and record-breaking global average temperatures have been 78 referred to as a new normal. In summary, the public and scientific interest in extreme 79 observed temperatures, and the common discussion of such events as indicative of 80 a “new normal” enforces the need for a clear definition of this term. 81 82 Extreme climate events have increasingly been explored from an attribution 83 perspective, now forming the basis for a dedicated annual report investigating the 84 contributing factors to observed extremes (e.g. Peterson et al. 2012). Attribution 85 studies provide insight into the relative contributions of natural and anthropogenic 86 forcings to a specific observed extreme weather or climate event (Stott et al. 2004). 87 The results of event attribution studies are described as potentially useful for 4 88 providing information for preparing for future climatic change. For example, the 89 likelihood of hot Australia summers, such as the record of 2012/2013 was found to 90 have increased fivefold due to anthropogenic forcings, including greenhouse gases 91 (Lewis and Karoly 2013). As a corollary, an increase in the frequency of hot 92 summers is expected under increased greenhouse warming, although such results 93 around future probabilities are not typically within the purview of attribution analyses. 94 A review of attribution approaches states that “By determining the causes of 95 extreme weather events being observed now, robust information can also be 96 provided on the extent to which a specific extreme event is a harbinger of the future, 97 and therefore an impact against which a society, which the recent event has shown 98 to be vulnerable to, may want to develop further resilience” (Stott et al. 2012). 99 However, without a specific focus on providing insight into future projections about 100 the characteristics of contemporary extremes, attribution studies alone are limited in 101 informing adaptive decision-making. 102 103 A further category of climatological studies has attempted to address questions 104 around extremes by posing the idea of ‘time of emergence’ (Mora et al. 2013; King et 105 al. 2015). These studies investigate when the signal of climate change will emerge 106 distinctly from the background noise of climate variability (Hawkins et al. 2014), 107 arguing that the time of emergence (ToE) metric is potentially important for 108 adaptation planning. For example, Mora and co-authors (2013) determined the 109 projected year in which the mean climate of a given location moves to a state 110 continuously outside the bounds of simulated historical variability. Recognising the 111 importance of extremes for societal impacts, King and co-authors (2015) proposed 112 the alternative time of anthropogenic emergence (TAE) and calculate the emergence 5 113 for various extreme indices in historical and future model simulations relative to a 114 quasi-natural state. 115 116 Both attribution and time of emergence approaches have a collective overarching 117 aim of informing adaptation planning. However, these approaches only go part way 118 to doing so. Attribution studies quantify the influence of a specific forcing (e.g. 119 anthropogenic greenhouse gases) on current climate records, but do not specifically 120 address questions around the occurrence of such records in the future, which 121 depends on the nature of future changes in temperatures, both through the rate of 122 warming and higher order changes in the shape of the temperature distributions. 123 Similarly, time of emergence studies have largely focussed on determining when 124 future mean climates can be considered outside the range of historical variability, but 125 typically do not focus on specific events and their future occurrence. Hence, the 126 potential of the information these studies provide for informing decision-making 127 around future planning for potentially high-impact events is inherently limited. 128 129 2. Time of emergence of new normal 130 Previous studies have begun to specifically investigate the incidence of historical 131 heat records in future greenhouse gas emissions scenarios (Christidis et al. 2014). 132 One such recent study examined the incidence of historical record summers in the 133 future, determining that historically hot summers will be the norm for large areas 134 globally within the next 20 years (Mueller et al. 2016). This supports previous model- 135 based findings centred on exploring changes in heat regimes. For example, 136 Diffenbaugh and Scherer (2011) previously determined the point in time at which the 137 coolest warm-season of the 21st century became hotter than the hottest warm- 6 138 season of the late 20th century in simulations, indicating a new and permanent 139 climate regime shift. Such approaches extend information provided by event 140 attribution analyses and explicitly indicate that historical temperature records on 141 seasonal timescales occur more frequently under increased greenhouse gas 142 warming. While these future heat records approaches demonstrate that 143 contemporary extremes occur more frequently in the future, they are also limited in 144 providing insight into current extremes broadly. Furthermore, an examination of the 145 widely used concept of a new normal more broadly has not yet been made. 146 147 Our study builds on this set of prior studies in examining the concept of a new 148 normal by combining aspects of attribution, time of emergence and future heat 149 records analytical approaches. We note that from a purely statistical viewpoint, a 150 new normal may be viewed as having limited utility. An extreme event is typically 151 reported as a new normal when its occurrence is considered to have been influenced 152 by anthropogenic warming, and will likely become more frequent under future 153 warming. However, by definition the climate system under the influence of 154 anthropogenic warming is nonstationary and exhibits a nonconstant mean (i.e. a 155 warming trend). Here in a true statistical sense ‘new’ and ‘normal’ are essentially 156 oxymoronic; such extremes cannot be considered as categorical evidence of a 157 distinct state. However, moving beyond semantics, the concept of a new normal is 158 persistent and widely employed for framing and understanding observed weather 159 and climate phenomena. Hence, we instead propose the more precise concept of 160 the time of emergence of a new normal (ToENN). 161 7 162 The time of emergence of a new normal is defined here as having occurred when 163 more than 50% of future anomalies exceed a reference event in magnitude or 164 intensity. This definition can be applied broadly to a diversity of events. We begin by 165 applying this general ToENN framework using the record-breaking global average 166 2015 temperatures as a reference event. When will years as hot as 2015 become 167 the norm? We focus on annual and seasonal-scale events, rather than short 168 duration, high impact extremes such as heatwaves, as such large-scale observed 169 record-breaking events have been widely discussed in the public domain using a 170 ‘new normal’ framing. However, our proposed methodology is intended to be applied 171 to investigating events across spatial and temporal scales. 172 173 3. Case study: 2015 temperatures 174 We investigate the ToENN2015 in Coupled Model Intercomparison Phase 5 175 (CMIP5) climate projections (Taylor et al. 2012). Using a key element of the event 176 attribution approach, we focus on an observed extreme event, defined as the 177 magnitude of the highest global annual-average mean temperature (Tmean) 178 anomaly recorded in the observational record (∆T2015). We combine this attribution- 179 derived focus with a key element of the time of emergence approach to explore the 180 timing of when extreme contemporary annual and seasonal-scale temperatures 181 become statistically normal. We formally define and assess the concept of a ‘new 182 normal’ relative to present based around extreme temperatures, which pose a 183 significant risk of societal impacts (IPCC 2012). 184 185 186 We demonstrate ToENN by using a single realisation from each of 18 models participating in the fifth phase of the Coupled Model Intercomparison Project 8 187 (CMIP5) with temperature data (tas) available for standard historical and 188 historicalNat experiments, and Representative Concentration Pathway (RCP) 189 experiments RCP2.6, RCP4.5, RCP6.0 and RCP8.5 (Taylor et al. 2012) (see Table 190 1). Global mean annual land-only temperature anomalies are calculated for land 191 surface gridboxes relative to each model’s 1961-1990 climatology. Models are 192 regridded onto a uniform 1.5 degree latitude by 1.5 degree longitude horizontal grid. 193 In addition to determining ToENN2015, we also explore the time of emergence of 194 seasonal and regional-scale temperatures. Regional area-mean temperatures are 195 calculated for Australia (50-10°S, 110-155°E), Europe (30-70°N, 10°W-60°E), Asia 196 (10-70°N, 60-170°E) and North America (20-70°N, 160°-50W). Seasonal (DJF and 197 JJA) data are also analysed for regions. Observations are derived as the mean from 198 the GISTEMP (Hansen et al. 2010) and CRUTEM4 (Morice et al. 2012) global 199 temperature datasets (Table 2). 200 201 We apply a model evaluation step demonstrated in CMIP5-based attribution 202 studies, and compare model variability in historical simulations against observed 203 variability (Lewis and Karoly 2013; Lewis et al. 2014). Using a Perkins skill score 204 (Perkins et al. 2007), we assess the similarity of probability density functions (PDFs) 205 of modelled and observed regional-average temperatures. A skill score is 206 determined for each model as a measure of the common area between simulated 207 and observed distributions, which ultimately provides a simple measure of the 208 similarity of models to observations across the entire PDF. We compare each 209 CMIP5 model’s historical realisation to both GISTEMP and CRUTEM4 for annual, 210 DJF and JJA global averages. Models with skill score below 0.5 when compared to 211 either observational dataset for any of these temporal averages are excluded from 9 212 further analysis. This resulted in 11 of 18 models (Table 2) available for further 213 analysis, and a multi-model mean, and 5th and 95th percentile values are calculated 214 for each experiment using these 11 models (Fig. 1). Following this evaluation step, 215 the historical experiment is analysed for years 1976-2005 and the historicalNat 1900- 216 2005. While further CMIP5 models are available than the 11 utilised, we assert that 217 greater confidence in establishing a ToENN occurs when models capture observed 218 variability over the historical period. 219 220 To determine the time that 2015 emerges as the new normal using a multi-model 221 ensemble, we start by calculating ToENN2015 for each model realisation. For this 222 case study, the ToENN future state is the subsequent 20-year period. In order to 223 account adequately for multi-decadal variability (Hawkins et al. 2014), the ToENN in 224 each model realisation is the year that for any subsequent 20-year period, 50% of 225 anomalies exceed the reference event. We take the multi-model average median 226 across the 11-member ensemble (Fig. 2). For global and regional area-mean ToENN 227 calculations, we additionally report a very likely ToENN value if an event has 228 emerged as a new normal in >90% of model realisations. This provides an 229 assessment of the spread of ToENN values in the model realisations. We note that 230 the year for differing reference events will also be different in most cases, such that, 231 for example the hottest DJF in Australia occurred in 2013 and hence ToENN2013 232 would be explored for this event at this location. 233 234 Following Knutti and Sedlácĕk (2012), we apply a measure of robustness (R) to 235 ToENN calculations, which combines measures of ranked probability skill score and 236 the ratio of model spread to the predicted change. R is defined as: 10 R=1-A1/A2, 237 238 where A1 is the integral of the squared area between two cumulative density 239 functions (each RCP model realisation and the multi-model mean) and A2 is the 240 integral of the squared area between two cumulative density functions (the RCP 241 multi-model mean and the historical multi-model mean). A higher robustness scores 242 corresponds to a relative model agreement on sign and magnitude, with R=1 243 representing perfect model agreement, and stippling in Figure 2 indicates robustness 244 of >0.8. 245 246 Return times of heat events in the various regions (Australia, Europe, Asia and 247 North America) are also calculated to allow comparison with previous studies. 248 Following Christidis et al. (2014), return times are based on distributions of 249 temperature anomalies. The probability of exceedance of ∆T2015 is calculated and 250 the return time computed as the inverse of the probability. Return times were 251 estimated using a bootstrap resampling technique, whereby sub-samples of 50% of 252 model data were resampled 10,000 times, with replacement, for each experiment 253 suite and a spread of probability values determined with 90th values percentile 254 presented in this study as the calculated return time. In several cases, the precise 255 return time could not be reliably quantified as such temperatures anomalies were 256 very rare or did not occur in model realisations. In this case, return times are 257 reported as greater than the number of simulated years (N), >N. 258 259 Finally, a more detailed investigation of ToENN uncertainties is made to 260 determine the impact of experimental design on the concept of a new normal for 261 extreme seasonal- and annual-mean temperature anomalies. The ToENN and return 11 262 times calculated here are robust to changes in definition and experimental design. 263 For example, including the full set of 18 CMIP5 models does not impact the 264 emergence patterns of annual and seasonal-scale regional and global temperatures. 265 The ToENN for annual average global-mean temperatures was re-examined using 266 an ensemble defined using multiple realisations from models (in this case three 267 realisations each from MIROC5, CESM-CAM5 and CSIRO-Mk3-6-0). This ensemble 268 produced ToENN values similar to the ensemble constitute by single realisations of 269 11 models, though a new normal tended to occur slightly earlier. 270 271 3. A new normal for extreme temperatures 272 The timeseries of annual average global Tmean shows that the 2015 record 273 occurs outside the simulated range in the historicalNat multi-model ensemble and 274 near the limit of the historical experiment terminating in 2005 (Fig. 1). However, such 275 global anomalies fall well below the multi-model mean for all Representative 276 Concentration Pathway scenarios, indicating ToENN will occur. The time of 277 emergence of a new normal is explored using individual model realisations for the 278 RCP scenarios (Fig. 2), which demonstrates that 2015 emerges as the new normal 279 in all RCP scenarios for >90% of models by 2040. The median time of emergence of 280 2015 as the new norm occurs between 2020 and 2030 under all emissions 281 trajectories. The small contribution of scenario uncertainty in near-term projections 282 has been reported elsewhere (Hawkins and Sutton 2009). The spread of ToENN 283 values in individual model realisations is lowest in the high-end RCP8.5 emissions 284 scenario, where in all models a new normal for ∆T2015 is reached by 2040. 285 12 286 When framed in terms of return times (Christidis et al. 2014) of future temperature 287 anomalies exceeding ∆T2015, exceedance of this threshold very likely (>90% 288 confidence) has a return period of 1-in-2 years in all RCP scenarios by 2006-2025 289 (Table 3), but occurs infrequently in the historical simulations for 1976-2005. By 290 2026-2045, such a temperature event occurs every year in the higher-end pathways 291 (RCP8.5, RCP6.0 and RCP4.5). While it should be noted that the lack of volcanic 292 eruptions necessarily required within the RCP forcing suite may be important in 293 simulated temperatures the near past and near future, the frequent occurrence of 294 contemporary extremes and emergence of a new normal of 2015 in the early part of 295 the RCP pathways demonstrates rapid warming after the historical experiment 296 finishes in 2005 (see Fig. 1). 297 298 The ToENN for annual-mean temperatures is next explored at lower spatial- 299 scales. The time of emergence of a new normal is first calculated for each model 300 land surface gridbox for annual average temperatures (Fig. 3) using the current 301 maximum temperature anomaly observed for each gridbox (as available up to 2015). 302 An ensemble robustness assessment was also applied based on relative model 303 agreement of sign and magnitude, demonstrating large spatial areas of model 304 agreement on the sign and relative magnitude of simulated temperature changes, 305 with the most notable exceptions over Antarctica where observations are poor, and 306 hence not shown in Figure 3. While all scenarios show that a new normal for annual 307 Tmean occurs for the majority of land surfaces (>70%) before the end of the century, 308 there is a clearer scenario-dependence in the time of emergence of a new normal on 309 the gridbox scale, where the signal-to-noise ratio is likely to be lower than for the 310 global-average. Under aggressive greenhouse gases emission cuts (RCP2.6 13 311 scenario), emergence occurs for 72% of land areas by 2100, but only for 1% of land 312 areas in the first half of the century. In contrast, for the CMIP5 high-level emissions 313 scenario, RCP8.5, which represents our current emissions trajectory (Peters et al. 314 2012), the ToENN is consistently earlier than lower-end emission scenarios, and a 315 robust emergence of a new normal occurs over most land surfaces (98%) by the end 316 of the century and in 12% of locations before 2045 (Fig. 3d). 317 318 In contrast to annual average temperatures, the corresponding ToENN for 319 seasonal Tmean for various regions show a greater sensitivity to the various 320 emissions scenario used in the model experiments (Figures 4-7). We focus on DJF 321 (austral summer/boreal winter) regional-mean temperatures as an example. ToENN 322 occurs comparatively earlier for Australia (Figure 5) than for Northern Hemisphere 323 regional DJF ToENN, which are generally later in the 21st century and a larger model 324 spread is simulated. In Australia, record summer (DJF) temperatures (∆T2013, (Lewis 325 and Karoly 2013)) are a new normal in the majority of model realisations by 2035 in 326 all scenarios. On these shorter temporal and lower spatial scales, the emergence of 327 a new normal can be avoided in low emission pathways in some model realisations. 328 For example, the mean ToENN for DJF temperatures in Europe and Asia, and 329 particularly for North America, occurs later in the 21st century, or not at all, in the 330 RCP2.6 scenario, compared with RCP6.0 or RCP8.5. The scenario dependence of 331 regional ToENN is more complicated in the JJA (boreal winter) season in Asia and 332 Europe, due to the complexity of the emissions trajectories in the RCP in the near- 333 term (Peters et al. 2012). While the RCP2.6 scenario has a lower greenhouse gas 334 forcing than RCP4.5, the near term warming projected is greater and more variable 14 335 due to aerosol contributions that affect these highlighted regions (Chalmers et al. 336 2012). 337 338 This result demonstrates that at the regional-scale, a new normal for 339 contemporary extremes can be avoided for certain seasonal-scale extremes with 340 aggressive greenhouse gas emission reductions. The larger model spread of ToENN 341 on regional and seasonal scales occurs as the signal to noise ratio is lower 342 regionally than for global annual average-temperatures. Furthermore, the temporal 343 and spatial scale dependence of extremes is widely noted in attribution approaches 344 that largely focus on large-scale phenomena such as coherent heatwaves (e.g. Stott 345 et al. 2004; Schär et al. 2004) or continental-scale seasonal heat (e.g. Lewis and 346 Karoly 2014; Knutson et al. 2014). Previous studies investigating the time of 347 emergence of mean and extreme climate indices also identify regional and seasonal- 348 scale differences which are dependent on variability (Mora et al. 2013; King et al. 349 2015). 350 351 4. Communicating changing extremes 352 We have proposed a formal definition of a new normal based on when a 353 particular extreme event emerges as a new normal in a future climate states, which 354 we call the time of emergence of a new normal. We have demonstrated this general 355 framework for a new normal using the case study of the record-breaking global 356 average 2015 temperatures. Based on CMIP5 simulations of alternative future 357 emission scenarios and realisations of multiple contributing models, we show 2015 358 global annual-mean temperature anomalies will emerge as the new normal by 2040 359 at the latest and is unavoidable even in emissions scenarios representing aggressive 15 360 greenhouse gas cuts (i.e. RCP2.6 (Peters et al. 2012)). In contrast, a new normal in 361 local- and regional-scale temperatures can be delayed in low emissions scenarios, 362 suggesting that if greenhouse gas emissions were to fall, the majority of places 363 would benefit, as the extremes of the world today would not become the new normal 364 in the future. This framework can be employed at a multitude of spatial and temporal 365 scales and for a variety of extreme event types in order to understand the 366 occurrence of contemporary extremes of high societal and economic risk in the 367 future. 368 369 The results of this specific case study support previous examinations of historical 370 records in future projections under varying emissions scenarios. For example, 371 Mueller and co-authors (2016) examined summer mean temperatures and 372 determined that historically hot summers would be the norm within the next 20 years 373 for half the world’s population. Furthermore, many areas are likely to move into a 374 new seasonal heat regime within the next four decades (Diffenbaugh and Scherer 375 2011). On a regional scale, historically hot summers are projected to be the norm in 376 Eastern China within two decades in the CMIP5 mid-range RCP4.5 scenario (Sun et 377 al. 2014). Later dates were determined by Mora and co-authors’ (2013) analysis, 378 however, this instead focused on a distinct emergence from the range of historical 379 variability, rather than on changing frequency of contemporary extremes in future 380 projections. 381 382 The consistency of these results looking at the increased frequency of historical 383 record-breaking temperatures occurring on regional and seasonal-scales in future 384 projections, together with our ToENN example presented here, collectively 16 385 demonstrate the value of the concept of a new normal. While event attribution 386 studies are increasingly useful for understanding and disentangling the multiple 387 contributing factors to observed extremes, they are limited in providing insight into 388 climate extremes in the future. That is, attribution studies determining that 389 anthropogenic climate change substantially influenced an observed event infer that 390 type of event will occurring more frequently in a future of enhanced warming (Lewis 391 and Karoly 2013) and focus narrowly on the frequency of historical extremes. 392 Alternatively, studies centred on the future occurrence of historical extremes (Mueller 393 et al. 2016; Sun et al. 2014) encompass an assessment of both event frequency and 394 the increasing magnitude of events, relative to a historical baseline. This is 395 demonstrated in Figure 1 where the 2015 record-breaking global-average 396 temperatures are projected to rapidly become cooler than average in the near future. 397 398 Our case study of the 2015 global-average record temperatures also 399 demonstrates the utility of the new normal concept for the communication of climate 400 change, where it is clearly defined. This is one proposal for understanding this 401 concept from a scientific perspective, prompted by the description of observed 402 seasonal- to annual-scale extremes as a ‘new normal’ (Lewis and Perkins-Kirkpatrick 403 2016). Alternative definitions to the time of emergence of a new normal are possible 404 and potentially equally useful. For example, a new normal could alternatively be 405 defined using the variability in observed climates and future climate projections. 406 Other useful explorations could investigate specific classes of events, such as so- 407 called ‘extreme extremes’ like as heatwaves (e.g. Perkins and Pitman 2014) or 408 floods, or holistically examine climatic conditions in a specific region (e.g. Wood et al. 17 409 2013). The generic framework provided here is ideal for expansion to other 410 categories of events and their definition, and for exploration in other model datasets. 411 412 The term ‘new normal’ has been used widely to redress the misplaced perception 413 that climate change impacts will occur only as a future problem, rather than as a 414 influence on present climates (Perkins and Pitman 2014). Framing contemporary 415 phenomena as a new normal readily encapsulates the contribution of climate change 416 to current weather and climate events (Trenberth et al. 2015). However, the use of 417 the term without precise definition in both scientific literature and public commentary 418 on climate change limits its utility. First, the vague use of the term negates the 419 importance of natural climate variability. From a simple perspective, if 2015 is a new 420 normal for global temperatures, what if 2016, 2017 or 2018 are cooler? Second, the 421 use of the term without definition provides limited information about climate change 422 impacts and risks, which are circumvented by a precise description. Applying the 423 new normal definition based on a time of emergence approach and the case study 424 that we offer here, we expect with high confidence that global average temperatures 425 greater than those observed in 2015 will occur at least every second year by 2040. 426 This precisely defined new normal effectively communicates the influence of climate 427 change in the record-breaking temperatures and their changing occurrence in the 428 future. In combination with attribution and time of emergence studies, this approach 429 can help inform adaptation strategic for climate change. 430 431 Acknowledgements 432 This research was supported by ARC DECRAs (160100092 to S.C.L.) and 433 (DE140100952 to S.E.P.-K), the ARC Centre of Excellence for Climate System 18 434 Science (grant CE 110001028) and the NCI National Facility. We thank the Bureau 435 of Meteorology, the Bureau of Rural Sciences, and CSIRO for providing AWAP data. 436 We acknowledge the WCRP’s Working Group on Coupled Modelling, which is 437 responsible for CMIP. The U.S. Department of Energy’s PCMDI provides CMIP5 438 coordinating support. 439 440 References 441 442 443 Chalmers, N., E. J. Highwood, E. Hawkins, R. Sutton, and L. J. Wilcox, 2012: Aerosol contribution to the rapid warming of near-term climate under RCP 2.6. Geophysical Research Letters, 39, doi:10.1029/2012GL052848. 444 445 446 Christidis, N., G. S. Jones, and P. A. 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Polar Research, 32, 437–439, doi:10.3402/polar.v32i0.19552. 538 539 21 540 Figure Captions 541 Figure 1 Multi-model mean global temperature change relative to 1961-1990 for 542 CMIP5 historicalNat, historical (1850-2005) and RCP (2006-2100) scenarios. The 543 5th-95th percentile model range is shown for the historical and RCP scenarios. The 544 multi-model mean values area shown in solid lines (left) for each CMIP5 experiment 545 and black dots (right) for means across the entire time period. The horizontal line 546 indicates the global annual-average temperature anomaly observed in 2015 (∆T2015). 547 548 Figure 2 Emergence of new normal for global annual-mean temperatures. Global 549 average temperature changes are shown for each RCP scenario for multi-model 550 mean (and 5th-95th percentile range) relative to 1961-1990. Plot circles indicate the 551 time of emergence of a new normal in each model realisation and the corresponding 552 average annual-mean temperature in the 20-year period centred on the year of 553 emergence. The horizontal line shows the highest observed anomaly (∆T 2015). 554 Vertical lines show the median (>50%) and very likely (>90%) time of emergence of 555 new normal in each scenario for the multi-model ensemble (where n=11). 556 557 Figure 3 Maps of the multi-model median decade of emergence of a new normal for 558 annual average temperatures in each gridbox for various RCP scenarios. Grey 559 stippling marks areas of high robustness (>0.8), where corresponding to a relatively 560 high level of model agreement on sign and magnitude. White areas (where no 561 ToENN value is shown) indicate areas of missing data in observational temperature 562 products and Antarctica is excluded due to poor observational coverage. 563 564 22 565 Figure 4 As for figure 2, but showing the emergence of new normal for Australian 566 DJF temperatures. A black cross at the end of the century represents model 567 realisations where emergence does not occur prior to 2100. In this case, vertical 568 mean and very likely lines are not shown for that experiment (i.e. RCP2.6). In cases 569 where ToENN occurs in between 2090-2100, markers are plotted at the 570 corresponding for the mean temperature over this decade. 571 572 Figure 5 As for figure 4, but showing the emergence of new normal for European 573 DJF temperatures. 574 575 Figure 6 As for figure 4, but showing the emergence of new normal for Asian DJF 576 temperatures. 577 578 Figure 7 As for figure 4, but showing the emergence of new normal for North 579 American DJF temperatures. 580 581 Table 1 Details of CMIP5 experiments analysed. 582 583 Table 2 CMIP5 models and observational datasets analysed. . 584 585 Table 3 Projected return times (years) of global mean annual, DJF and JJA average 586 temperature anomalies exceeding the largest magnitude in the observed record. 587 Return times are expressed as >N where the probability of occurrence of 588 temperatures above this threshold are too small to be accurately estimated, or do not 589 occur at all in N simulated years and as <2 where simulated temperatures greater 23 590 than the most extreme observed are very likely to occur more frequently than in 591 every second projected year. 592 593 24 Manuscript (non-LaTeX) Tables Click here to download Manuscript (non-LaTeX) tables.docx Table 1 Details of CMIP5 experiments analysed. Model Experiment historical historicalNat RCP2.6 RCP4.5 RCP6.0 RCP8.5 Major forcings Anthropogenic (greenhouse gases, aerosols, ozone) and natural (solar, volcanics) Solar, volcanics Anthropogenic (greenhouse gases, aerosols, ozone scenarios) and natural (solar). Radiative forcing reaches a maximum near the middle of the twenty-first century before decreasing to 2.6 W m−2. Anthropogenic (intermediate greenhouse gases, aerosols, ozone scenarios) and natural (solar). Anthropogenic (intermediate greenhouse gases, aerosols, ozone scenarios) and natural (solar) Anthropogenic (greenhouse gases, aerosols, ozone scenarios) and natural (solar). Radiative forcing reaches a level of about 8.5 W m−2 at the end of the century. Table 2 CMIP5 models and observational datasets analysed. . Models bcc-csm1-1 CCSM4 CESM1-CAM5 GFDL-ESM2M GISS-E2-H GISS-E2-R IPSL-CM5A-LR IPSL-CM5A-MR MIROC-ESM MRI-CGCM3 NorESM1-M Observations GISTEMP CRUTEM4 Table 3 Projected return times (years) of global mean annual, DJF and JJA average temperature anomalies exceeding the largest magnitude in the observed record. Return times are expressed as >N where the probability of occurrence of temperatures above this threshold are too small to be accurately estimated, or do not occur at all in N simulated years and as <2 where simulated temperatures greater than the most extreme observed are very likely to occur more frequently than in every second projected year. ANNUAL HistoricalNat Historical(1976-2005) RCP2.6 RCP4.5 RCP6.0 RCP8.5 DJF HistoricalNat Historical(1976-2005) RCP2.6 RCP4.5 RCP6.0 RCP8.5 JJA HistoricalNat Historical(1976-2005) RCP2.6 RCP4.5 RCP6.0 RCP8.5 20062025 Return Times 2026- 20462045 2065 20662085 20862100 >N 150 2.5 2.5 2.5 2.5 <2 <2 <2 <2 <2 1 1 1 <2 1 1 1 <2 1 1 1 3.2 3.4 3.3 3.3 <2 <2 <2 1 <2 1 1 1 <2 1 1 1 <2 1 1 1 <2 <2 <2 <2 <2 <2 1 1 <2 1 1 1 <2 1 1 1 <2 1 1 1 >N >N >N 75 Rendered Figure 1 Click here to download Rendered Figure fig1.pdf Annual Tmean anomalies (K) RCP8.5 RCP6.0 RCP4.5 RCP2.6 RCP8.5 6 Historical 7 Observations Historical (1850-2005) RCP (2006-2100) HistoricalNat HistoricalNat 8 b. Ensemble range Observations a. Global average Tmean 5 RCP6.0 4 3 RCP4.5 2 1 RCP2.6 ∆ T2015 0 -1 1900 1950 Year 2000 2050 Figure 1 Multi-model mean global temperature change relative to 1961-1990 for CMIP5 historicalNat, historical (1850-2005) and RCP (2006-2100) scenarios. The 5th-95th percentile model range is shown for the historical and RCP scenarios. The multi-model mean values area shown in solid lines (left) for each CMIP5 experiment and black dots (right) for means across the entire time period. The horizontal line indicates the global annual-average temperature anomaly observed in 2015 (∆T2015 Rendered Figure 2 Click here to download Rendered Figure fig2.pdf a. RCP2.6 c. RCP6.0 Annual Tmean anomalies (K) 7 Annual Tmean anomalies (K) 7 6 6 5 5 4 4 3 3 2 2 1 0 1 2020 2040 2060 Year 2080 2100 0 2020 b. RCP4.5 2060 Year 2080 2100 2080 2100 d. RCP8.5 Annual Tmean anomalies (K) 7 Annual Tmean anomalies (K) 7 6 6 5 5 4 4 3 3 2 2 1 0 2040 1 2020 2040 2060 Year 2080 2100 0 2020 2040 2060 Year Figure 2 Emergence of new normal for global annual-mean temperatures. Global average temperature changes are shown for each RCP scenario for multi-model mean (and 5th-95th percentile range) relative to 1961-1990. Plot circles indicate the time of emergence of a new normal in each model realisation and the corresponding average annual-mean temperature in the 20-year period centred on the year of emergence. The horizontal line shows the highest observed anomaly (∆T2015). Vertical lines show the median (>50%) and very likely (>90%) time of emergence of new normal in each scenario for the multi-model ensemble (where n=11). Rendered Figure 3 90 o N Click here to download Rendered Figure fig3.pdf a. RCP2.6 c.RCP6.0 b. RCP4.5 d. RCP8.5 60 o N 30 o N 0 o 30 o S 60 o S 90 o N 60 o N 30 o N 0 o 30 o S 60 o S 00 Af te r 21 5 -2 86 08 20 -2 09 5 5 20 76 -2 66 06 20 -2 56 20 07 5 5 05 -2 20 46 04 -2 36 20 20 26 -2 03 5 5 5 02 -2 16 20 20 06 -2 01 5 (Deacde of Emergence of New Normal) Figure 3 Maps of the multi-model median decade of emergence of a new normal for annual average temperatures in each gridbox for various RCP scenarios. Grey stippling marks areas of high robustness (>0.8), where corresponding to a relatively high level of model agreement on sign and magnitude. White areas (where no ToENN value is shown) indicate areas of missing data in observational temperature products and Antarctica is excluded due to poor observational coverage. Rendered Figure 4 Click here to download Rendered Figure fig4.pdf a. RCP2.6 c. RCP6.0 Australia DJF Tmean anomalies (K) 7 Australia DJF Tmean anomalies (K) 7 6 6 5 5 4 4 3 3 2 2 1 0 1 2020 2040 2060 Year 2080 2100 0 2020 b. RCP4.5 2060 Year 2080 2100 2080 2100 d. RCP8.5 Australia DJF Tmean anomalies (K) 7 Australia DJF Tmean anomalies (K) 7 6 6 5 5 4 4 3 3 2 2 1 0 2040 1 2020 2040 2060 Year 2080 2100 0 2020 2040 2060 Year Figure 4 As for figure 2, but showing the emergence of new normal for Australian DJF temperatures. A black cross at the end of the century represents model realisations where emergence does not occur prior to 2100. In this case, vertical mean and very likely lines are not shown for that experiment (i.e. RCP2.6). In cases where ToENN occurs in between 2090-2100, markers are plotted at the corresponding for the mean temperature over this decade. Rendered Figure 5 Click here to download Rendered Figure fig5.pdf a. RCP2.6 c. RCP6.0 Europe DJF Tmean anomalies (K) 7 Europe DJF Tmean anomalies (K) 7 6 6 5 5 4 4 3 3 2 2 1 0 1 2020 2040 2060 Year 2080 2100 0 2020 b. RCP4.5 2060 Year 2080 2100 2080 2100 d. RCP8.5 Europe DJF Tmean anomalies (K) 7 Europe DJF Tmean anomalies (K) 7 6 6 5 5 4 4 3 3 2 2 1 0 2040 1 2020 2040 2060 Year 2080 2100 0 2020 2040 2060 Year Figure 5 As for figure 4, but showing the emergence of new normal for European DJF temperatures. Rendered Figure 6 Click here to download Rendered Figure fig6.pdf a. RCP2.6 c. RCP6.0 Asia DJF Tmean anomalies (K) 7 Asia DJF Tmean anomalies (K) 7 6 6 5 5 4 4 3 3 2 2 1 0 1 2020 2040 2060 Year 2080 2100 0 2020 b. RCP4.5 2060 Year 2080 2100 2080 2100 d. RCP8.5 Asia DJF Tmean anomalies (K) 7 Asia DJF Tmean anomalies (K) 7 6 6 5 5 4 4 3 3 2 2 1 0 2040 1 2020 2040 2060 Year 2080 2100 0 2020 2040 2060 Year Figure 6 As for figure 4, but showing the emergence of new normal for Asian DJF temperatures. Rendered Figure 7 Click here to download Rendered Figure fig7.pdf a. RCP2.6 c. RCP6.0 N. America DJF Tmean anomalies (K) 7 N. America DJF Tmean anomalies (K) 7 6 6 5 5 4 4 3 3 2 2 1 0 1 2020 2040 2060 Year 2080 2100 0 2020 b. RCP4.5 2060 Year 2080 2100 2080 2100 d. RCP8.5 N. America DJF Tmean anomalies (K) 7 N. America DJF Tmean anomalies (K) 7 6 6 5 5 4 4 3 3 2 2 1 0 2040 1 2020 2040 2060 Year 2080 2100 0 2020 2040 2060 Year Figure 7 As for figure 4, but showing the emergence of new normal for North American DJF temperatures.