Global Mean Surface Temperature Trends Research Articles

Converged ensemble simulations of climate: possible trends in total solar irradiance cannot explain global warming alone

We address the hypothetical question of whether an increasing total solar irradiance (TSI) trend, without anthropogenic contributions, could be sufficient to explain the ongoing global warming. To this end, the intermediate-complexity climate model PlaSim is used. To consider the total internal variability, we present a set of ensemble simulations, with different forcing histories in TSI and CO2 concentration, that have converged sufficiently tightly to the relevant probability distributions to provide a satisfactory bound on any spurious trend possibly arising from a sampling bias; similar bounds on any other unforced contributions to ensemble mean trends are also estimated. A key point is the consideration, among the forcing histories, the steepest increasing trend in TSI that is still consistent with observations according to a recent study; thereby, we essentially revisit corresponding TSI reconstructions, more than 20 years after their last modeling-based evaluation, by improving the analysis through taking care of all possible sources of error or uncertainty and incorporating data that have become available since then. Without any change in CO2 concentration, our TSI trend (i.e., and upper bound on actual TSI trends) is found to be insufficient to produce outcomes compatible with the observational record in global mean surface temperature (GMST) with a nonnegligible probability. We formalize our statement for quantifiers of GMST trends through evaluating their distributions over the ensemble, and we speculate that the hypothesis about the exclusive role of an increasing TSI remains implausible even beyond our particular model setup. At the same time, if we consider a constant TSI, and the observational record in CO2 concentration is applied as forcing, the simulation results and the recorded GMST match well. While we currently need to leave the question of a precise attribution open, we conclude by pointing out that an attribution of the ongoing global warming to an increasing TSI alone could be made plausible only if a bias in the set of land-based instrumental temperature measurements were increasing more rapidly than commonly estimated; an assessment of the latter possibility is out of the scope of our study, as well as addressing solar forcing mechanisms beyond the effect of TSI.

Role of external forcing in the time-varying trends of global-mean surface temperature under current and future climates

The decadal-scale global-mean surface temperature (GMST) trend fluctuates between rapid-warming, slow-warming and cooling under the combined action of external forcing (EX) and internal variability. However, the principal contributors to the time-varying trends of GMST across decadal scales in current and future climates remain elusive. Here, by leveraging observations and initial-condition large ensembles, we unravel that historical GMST trend fluctuations are predominantly driven by EX on timescales exceeding ∼9 years within the current climate, with anthropogenic and volcanic aerosols being the primary drivers. However, in the warming climate, the significant contribution of EX on decadal scales is projected to decrease. This is primarily due to anthropogenic aerosol mitigation efforts and the absence of unpredictable volcanism. Despite the continuous increase in greenhouse gases, these factors are expected to delay the emergence of EX-dominance to ∼14-year timescale. Our results quantitatively underscore the pivotal role of the EX in modulating GMST trend fluctuations and its potential weakening at decadal scales in the warming climate, indicating that predicting near-term changes in GMST may become less certain in the future.

Is Anthropogenic Global Warming Accelerating?

Abstract Estimates of the anthropogenic effective radiative forcing (ERF) trend have increased by 50% since 2000 (from +0.4 W m−2 decade−1 in 2000–09 to +0.6 W m−2 decade−1 in 2010–19), the majority of which is driven by changes in the aerosol ERF trend, as a result of aerosol emissions reductions. Here we study the extent to which observations of the climate system agree with these ERF assumptions. We use a large ERF ensemble from the IPCC’s Sixth Assessment Report (AR6) to attribute the anthropogenic contributions to global mean surface temperature (GMST), top-of-atmosphere radiative flux, and we use aerosol optical depth observations. The GMST trend has increased from +0.18°C decade−1 in 2000–09 to +0.35°C decade−1 in 2010–19, coinciding with the anthropogenic warming trend rising from +0.19°C decade−1 in 2000–09 to +0.24°C decade−1 in 2010–19. This, as well as observed trends in top-of-atmosphere radiative fluxes and aerosol optical depths, supports the claim of an aerosol-induced temporary acceleration in the rate of warming. However, all three observation datasets additionally suggest that smaller aerosol ERF trend changes are compatible with observations since 2000, since radiative flux and GMST trends are significantly influenced by internal variability over this period. A zero-trend-change aerosol ERF scenario results in a much smaller anthropogenic warming acceleration since 2000 but is poorly represented in AR6’s ERF ensemble. Short-term ERF trends are difficult to verify using observations, so caution is required in predictions or policy judgments that depend on them, such as estimates of current anthropogenic warming trend, and the time remaining to, or the outstanding carbon budget consistent with, 1.5°C warming. Further systematic research focused on quantifying trends and early identification of acceleration or deceleration is required.

Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks

AbstractThe global mean surface temperature (GMST) record exhibits both interannual to multidecadal variability and a long‐term warming trend due to external climate forcing. To explore the predictability of temporary slowdowns in decadal warming, we apply an artificial neural network (ANN) to climate model data from the Community Earth System Model Version 2 Large Ensemble. Here, an ANN is tasked with whether or not there will be a slowdown in the rate of the GMST trend by using maps of ocean heat content (OHC) at the onset. Through a machine learning explainability method, we find the ANN is learning off‐equatorial patterns of anomalous OHC that resemble transitions in the phase of the Interdecadal Pacific Oscillation in order to make slowdown predictions. Finally, we test our ANN on observed historical data, which further reveals how explainable neural networks are useful tools for understanding decadal variability in both climate models and observations.

Understanding Intermodel Diversity When Simulating the Time of Emergence in CMIP5 Climate Models

AbstractUsing a statistical method, we examined the time of emergence (ToE), which was defined as the year when the global mean surface temperature (GMST) trend exceeds the trend explained by internal variability, using 33 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models for 1861–2100. We found that the GMST trend explained by internal variability showed minimal changes even though the GMST trend rapidly increased under the Representative Climate Pathway 8.5 scenario. However, there was large intermodel diversity for the ToEs in CMIP5 climate models. The intensity of Arctic amplification was closely associated with intermodel diversity of the ToEs. A significant difference in the amount of Arctic sea ice extent during the reference period (1861‐1910) played a role in determining whether there was slow or fast simulation of the ToE in CMIP5 climate models. We argue that a slow or fast ToE is influenced by how the climate models initially simulate the sea ice extent during the reference period.

Synchronized tropical Pacific and extratropical variability during the past three decades

Internally generated decadal variability influences global mean surface temperature (GMST), inducing acceleration and slowdown of the warming rate under anthropogenic radiative forcing1–4. While tropical eastern Pacific variability is important for annual-mean GMST2,5–8, the cold ocean–warm land (COWL) pattern9,10 also contributes to continental temperature variability11–13 in the boreal cold season. Although the two contributors are physically independent10,12, here we show that, after the mid-1980s, their decadal components vary in phase by chance to strengthen internal GMST trends, contributing to the early 2000s slowdown and early 2010s acceleration. The synchronized tropical Pacific and COWL variability explains the striking seasonality of the recent slowdown and acceleration during which the GMST trend in the boreal cold season is markedly negative and positive, respectively. Climate models cannot simulate the exact timing of the tropical Pacific and COWL correlations because they are physically independent, random-phased modes of internal variability. Natural decadal variability has a role in global mean surface temperature trends. Observational data and modelling show that since the mid-1980s, the tropical eastern Pacific variability and the cold ocean–warm land pattern have covaried to enhance acceleration and deceleration in warming trends.

Quantifying the irreducible uncertainty in near‐term climate projections

If the Paris agreement at the Conference of Parties 21 is implemented very effectively, greenhouse‐gas emissions might decrease after year 2020. Whether this would lead to identifiable near‐term responses in “iconic” climate quantities of wide scientific and public interest is unclear, because the climate response would be obscured by quasi‐random internal variability. I define the climate response as an increase or decrease in a linear climate trend over the period 2021–2035, compared to 2006–2020, and establish the probability of such a trend change being caused by an assumed policy shift toward emissions reductions after 2020. I quantify the irreducible uncertainty in projecting such a trend change through very large (100‐member) ensembles of the state‐of‐the‐art climate model MPI‐ESM‐LR. Trends in global‐mean surface temperature (GMST) are higher over the period 2021–2035 than over 2006–2020 in one‐third of all realizations in the mitigation scenario RCP2.6, interpreted as implementing the Paris agreement, compared to around one‐half in the no‐mitigation scenario RCP4.5. Mitigation issufficientto cause a GMST trend reduction with a probability of 0.40 andnecessarywith a probability of 0.33. Trend increases in Arctic September sea‐ice area and the Atlantic meridional overturning circulation are caused by the emissions reductions with a probability of only around 0.1. By contrast, emissions reductions arenecessaryfor a trend decrease in upper‐ocean heat content with a probability of over one‐half. Some iconic climate quantities might thus by year 2035 exhibit an identifiable response to a successful Paris agreement but sometimes with low probability, creating a substantial communication challenge.This article is categorized under:Climate Models and Modeling > Knowledge Generation with Models

Global-mean surface temperature variability: space\u2013time perspective from rotated EOFs

The observed global-mean surface temperature (GST) has been warming in the presence of increasing atmospheric concentration of greenhouse gases, but its rise has not been monotonic. Attention has increasingly been focused on the prominent variations about the linear trend in GST, especially on interdecadal and multidecadal time scales. When the sea-surface temperature (SST) and the land- plus-ocean surface temperature (ST) are averaged globally to yield the global-mean SST (GSST) and the GST, respectively, spatial information is lost. Information on both space and time is needed to properly identify the modes of variability on interannual, decadal, interdecadal and multidecadal time scales contributing to the GSST and GST variability. Empirical Orthogonal Function (EOF) analysis is usually employed to extract the space–time modes of climate variability. Here we use the method of pair-wise rotation of the principal components (PCs) to extract the modes in these time-scale bands and obtain global spatial EOFs that correspond closely with regionally defined climate modes. Global averaging these clearly identified global modes allows us to reconstruct GSST and GST, and in the process identify their components. The results are: Pacific contributes to the global mean variation mostly on the interannual time scale through El Nino-Southern Oscillation (ENSO) and its teleconnections, while the Atlantic contributes strongly to the global mean on the multidecadal time scale through the interhemispheric mode called the Atlantic Multidecadal Oscillation (AMO). The Pacific Decadal Oscillation (PDO) has twice as large a variance as the AMO, but its contribution to GST is only 1/10 that of the AMO because of its compensating patterns of cold and warm SST in northwest and northeast Pacific. Its teleconnection pattern, the Pacific/North America (PNA) pattern over land, is also found to be self-cancelling when globally averaged because of its alternating warm and cold centers. The Interdecadal Pacific Oscillation (IPO) is not a separate mode of variability but contains AMO and PDO. It contributes little to the global mean, and what it contributes is mainly through its AMO component. A better definition of a Pacific low-frequency variability is through the IPO Tripole Index (TPI), using difference of averaged SST in different regions of the Pacific. It also has no contribution to the GSST and GST due to the PDO being its main component.

The relative contributions of tropical Pacific sea surface temperatures and atmospheric internal variability to the recent global warming hiatus

AbstractThe recent slowdown in global mean surface temperature (GMST) warming during boreal winter is examined from a regional perspective using 10‐member initial‐condition ensembles with two global coupled climate models in which observed tropical Pacific sea surface temperature anomalies (TPAC SSTAs) and radiative forcings are specified. Both models show considerable diversity in their surface air temperature (SAT) trend patterns across the members, attesting to the importance of internal variability beyond the tropical Pacific that is superimposed upon the response to TPAC SSTA and radiative forcing. Only one model shows a close relationship between the realism of its simulated GMST trends and SAT trend patterns. In this model, Eurasian cooling plays a dominant role in determining the GMST trend amplitude, just as in nature. In the most realistic member, intrinsic atmospheric dynamics and teleconnections forced by TPAC SSTA cause cooling over Eurasia (and North America), and contribute equally to its GMST trend.

What Caused the Global Surface Warming Hiatus of 1998–2013?

Research into the mechanisms for the global warming slowdown or “hiatus” of 1998–2013 is reviewed here. Observational and modeling studies identify tropical Pacific sea surface temperature variability as a major pacemaker of global mean surface temperature (GMST) change, as corroborated by the GMST increase following a major El Nino event. Specifically, the decadal cooling of the tropical Pacific contributes to the recent global warming hiatus. This tropical Pacific pacemaker effect appears larger for decadal than interannual variability, but the decadal effect remains to be quantified from observations. Our critical review of the literature reveals that the internal and radiatively forced GMST changes are distinct in pattern, energetics, mechanism, and predictability. In contrast to greenhouse gas-induced warming that is spatially uniform in sign and driven by energy perturbations, internal variability in GMST is an order of magnitude smaller than spatial variations, for which ocean-atmosphere interaction is of first-order importance while planetary energetics is not. In fact, decadal variability in GMST is poorly correlated with net radiation at the top of the atmosphere, highlighting the need to distinguish internal and forced GMST change in planetary energy budget. While the planetary energy budget can now be closed observationally over multi-decadal periods, the recent hiatus highlights the need and challenges to measure and quantify decadal changes in both global ocean heat uptake (e.g., for the effect of radiative forcing on the hiatus) and heat redistribution in the ocean. Hiatus research has led to a wide recognition of the importance of internal variability for GMST trends over a decade and longer. The strengthened connection between the climate variability and change communities is an important legacy of hiatus research.

The role of external forcing and internal variability in regulating global mean surface temperatures on decadal timescales

Global mean surface temperature (GMST) shows considerable decadal variations superimposed on a pronounced warming trend, with rapid warming during 1920–1945 and 1977–2000 and warming hiatuses during 1946–1976 and 2001–2013. The prevailing view is that internally generated variations associated with the Interdecadal Pacific Oscillation (IPO) dominate decadal variations in GMST, while external forcing from greenhouse gases and anthropogenic aerosols dominate the long-term trend in GMST over the last hundred years. Here we show evidence from observations and climate models that external forcing largely governs decadal GMST variations in the historical record with internally generated variations playing a secondary role, except during those periods of IPO extremes. In particular, the warming hiatus during 1946–1976 started from a negative IPO but was later dominated by the eruption of Mount Agung in 1963, while the subsequent accelerated warming during 1977–2000 was due primarily to increased greenhouse gas forcing. The most recent warming hiatus apparent in observations occurred largely through cooling from a negative IPO extreme that overwhelmed the warming from external forcing. An important implication of this work is that when the phase of the IPO turns positive, as it did in 2014, the combination of external forcing and internal variability should lead to accelerated global warming. This accelerated warming appears to be underway, with record high GMST in 2014, 2015, and 2016.

Faster Arctic Sea Ice Retreat in CMIP5 than in CMIP3 due to Volcanoes

Abstract The downward trend in Arctic sea ice extent is one of the most dramatic signals of climate change during recent decades. Comprehensive climate models have struggled to reproduce this trend, typically simulating a slower rate of sea ice retreat than has been observed. However, this bias has been widely noted to have decreased in models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) compared with the previous generation of models (CMIP3). Here simulations are examined from both CMIP3 and CMIP5. It is found that simulated historical sea ice trends are influenced by volcanic forcing, which was included in all of the CMIP5 models but in only about half of the CMIP3 models. The volcanic forcing causes temporary simulated cooling in the 1980s and 1990s, which contributes to raising the simulated 1979–2013 global-mean surface temperature trends to values substantially larger than observed. It is shown that this warming bias is accompanied by an enhanced rate of Arctic sea ice retreat and hence a simulated sea ice trend that is closer to the observed value, which is consistent with previous findings of an approximately linear relationship between sea ice extent and global-mean surface temperature. Both generations of climate models are found to simulate Arctic sea ice that is substantially less sensitive to global warming than has been observed. The results imply that much of the difference in Arctic sea ice trends between CMIP3 and CMIP5 occurred because of the inclusion of volcanic forcing, rather than improved sea ice physics or model resolution.

The contribution of greenhouse gases to the recent slowdown in global-mean temperature trends

The recent slowdown in the rate of increase in global-mean surface temperature (GMST) has generated extensive discussion, but little attention has been given to the contribution of time-varying trends in greenhouse gas concentrations. We use a simple model approach to quantify this contribution. Between 1985 and 2003, greenhouse gases (including well-mixed greenhouse gases, tropospheric and stratospheric ozone, and stratospheric water vapour from methane oxidation) caused a reduction in GMST trend of around 0.03–0.05 K decade−1 which is around 18%–25% of the observed trend over that period. The main contributors to this reduction are the rapid change in the growth rates of ozone-depleting gases (with this contribution slightly opposed by stratospheric ozone depletion itself) and the weakening in growth rates of methane and tropospheric ozone radiative forcing. Although CO2 is the dominant greenhouse gas contributor to GMST trends, the continued increase in CO2 concentrations offsets only about 30% of the simulated trend reduction due to these other contributors. These results emphasize that trends in non-CO2 greenhouse gas concentrations can make significant positive and negative contributions to changes in the rate of warming, and that they need to be considered more closely in analyses of the causes of such variations.

Contribution of the Interdecadal Pacific Oscillation to twentieth-century global surface temperature trends

Natural multidecadal climate variability contributes to global mean surface temperature trends. This study quantifies those from the Interdecadal Pacific Oscillation, finding that the largest contributions are during the positive phase, which accelerates warming. Longer-term externally forced trends in global mean surface temperatures (GMSTs) are embedded in the background noise of internally generated multidecadal variability1. A key mode of internal variability is the Interdecadal Pacific Oscillation (IPO), which contributed to a reduced GMST trend during the early 2000s1,2,3. We use a novel, physical phenomenon-based approach to quantify the contribution from a source of internally generated multidecadal variability—the IPO—to multidecadal GMST trends. Here we show that the largest IPO contributions occurred in its positive phase during the rapid warming periods from 1910–1941 and 1971–1995, with the IPO contributing 71% and 75%, respectively, to the difference between the median values of the externally forced trends and observed trends. The IPO transition from positive to negative in the late-1990s contributed 27% of the discrepancy between model median estimates of the forced part of the GMST trend and the observed trend from 1995 to 2013, with additional contributions that are probably due to internal variability outside of the Pacific4 and an externally forced response from small volcanic eruptions5. Understanding and quantifying the contribution of a specific source of internally generated variability—the IPO—to GMST trends is necessary to improve decadal climate prediction skill.

Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown

The global warming slowdown has been attributed to the negative phase of the Pacific Decadal Oscillation. Modelling work simulates this negative phase in response to anthropogenic aerosols, indicating that external forcings may influence natural cycles. The rate of global mean surface temperature (GMST) warming has slowed this century despite the increasing concentrations of greenhouse gases. Climate model experiments1,2,3,4 show that this slowdown was largely driven by a negative phase of the Pacific Decadal Oscillation (PDO), with a smaller external contribution from solar variability, and volcanic and anthropogenic aerosols5,6. The prevailing view is that this negative PDO occurred through internal variability7,8,9,10,11. However, here we show that coupled models from the Fifth Coupled Model Intercomparison Project robustly simulate a negative PDO in response to anthropogenic aerosols implying a potentially important role for external human influences. The recovery from the eruption of Mount Pinatubo in 1991 also contributed to the slowdown in GMST trends. Our results suggest that a slowdown in GMST trends could have been predicted in advance, and that future reduction of anthropogenic aerosol emissions, particularly from China, would promote a positive PDO and increased GMST trends over the coming years. Furthermore, the overestimation of the magnitude of recent warming by models is substantially reduced by using detection and attribution analysis to rescale their response to external factors, especially cooling following volcanic eruptions. Improved understanding of external influences on climate is therefore crucial to constrain near-term climate predictions.

The roles of external forcing and natural variability in global warming hiatuses

Global mean surface temperature (GMST) rising has slowed down since late 1990s, which is referred to as the global warming hiatus. There was another global warming hiatus event during 1940s–1960s. The roles of the external forcing and the natural variability in both global warming hiatuses are explored, using EOF analysis. The first two leading EOF modes of the 5-year running mean global sea surface temperature (SST) reflect the global warming scenario (EOF1) and the interdecadal Pacific oscillation (IPO)-like natural variability (EOF2), respectively. In observation, PC2 was in its positive phase (eastern Pacific cooling) during 1940s–1960s, which contributed to the previous warming hiatus. In addition, GMST trends are found to be negative during late 1950s and 1960s in most of the CMIP5 historical runs, which implies that the external forcing also contributed to the pause in the GMST rising. It is further demonstrated that it is the natural radiative forcing (volcanic forcing) that caused the drop-down of GMST in 1960s. The current global warming hiatus has been attributed to the eastern Pacific cooling/enhanced Pacific trade winds. It is shown that the PC2 switched to its positive phase in late 1990s, and hence the IPO-like natural variability made a contribution to the slowdown of GMST rising in the past decade. It is also found that the EOF1 mode (global warming mode) of the observed SST features a smaller warming in tropical Pacific compared to the Indian Ocean and the tropical Atlantic. Such inter-basin warming contrast, which is attributed to the “ocean thermostat” mechanism, has been suggested to contribute to the intensification of Pacific trade winds since late 1990s as well. Global warming hiatuses are also found in the future projections from CMIP5 models, and the spatial pattern of the SST trends during the warming-hiatus periods exhibits an IPO-like pattern, which resembles the observed SST trends since late 1990s.

Why Does Aerosol Forcing Control Historical Global-Mean Surface Temperature Change in CMIP5 Models?

Abstract Linear regression is used to examine the relationship between simulated changes in historical global-mean surface temperature (GMST) and global-mean aerosol effective radiative forcing (ERF) in 14 climate models from CMIP5. The models have global-mean aerosol ERF that ranges from −0.35 to −1.60 W m−2 for 2000 relative to 1850. It is shown that aerosol ERF is the dominant factor that determines intermodel variations in simulated GMST change: correlations between aerosol ERF and simulated changes in GMST exceed 0.9 for linear trends in GMST over all periods that begin between 1860 and 1950 and end between 1995 and 2005. Comparison of modeled and observed GMST trends for these time periods gives an inferred global-mean aerosol ERF of −0.92 W m−2. On average, transient climate sensitivity is roughly 40% larger with respect to historical forcing from aerosols than well-mixed greenhouse gases. This enhanced sensitivity explains the dominant effect of aerosol forcing on simulated changes in GMST: it is estimated that 85% of the intermodel variance of simulated GMST change is explained by variations in aerosol ERF, but without the enhanced sensitivity less than half would be explained. Physically, the enhanced sensitivity is caused by a combination of 1) the larger concentration of aerosol forcing in the midlatitudes of the Northern Hemisphere, where positive feedbacks are stronger and transient warming is faster than in the Southern Hemisphere, and 2) the time evolution of aerosol forcing, which levels out earlier than forcing from well-mixed greenhouse gases.

Scale-dependency of the global mean surface temperature trend and its implication for the recent hiatus of global warming.

Studies of the global mean surface temperature trend are typically conducted at a single (usually annual or decadal) time scale. The used scale does not necessarily correspond to the intrinsic scales of the natural temperature variability. This scale mismatch complicates the separation of externally forced temperature trends from natural temperature fluctuations. The hiatus of global warming since 1999 has been claimed to show that human activities play only a minor role in global warming. Most likely this claim is wrong due to the inadequate consideration of the scale-dependency in the global surface temperature (GST) evolution. Here we show that the variability and trend of the global mean surface temperature anomalies (GSTA) from January 1850 to December 2013, which incorporate both land and sea surface data, is scale-dependent and that the recent hiatus of global warming is mainly related to natural long-term oscillations. These results provide a possible explanation of the recent hiatus of global warming and suggest that the hiatus is only temporary.

Forcing, feedback and internal variability in global temperature trends.

Most present-generation climate models simulate an increase in global-mean surface temperature (GMST) since 1998, whereas observations suggest a warming hiatus. It is unclear to what extent this mismatch is caused by incorrect model forcing, by incorrect model response to forcing or by random factors. Here we analyse simulations and observations of GMST from 1900 to 2012, and show that the distribution of simulated 15-year trends shows no systematic bias against the observations. Using a multiple regression approach that is physically motivated by surface energy balance, we isolate the impact of radiative forcing, climate feedback and ocean heat uptake on GMST--with the regression residual interpreted as internal variability--and assess all possible 15- and 62-year trends. The differences between simulated and observed trends are dominated by random internal variability over the shorter timescale and by variations in the radiative forcings used to drive models over the longer timescale. For either trend length, spread in simulated climate feedback leaves no traceable imprint on GMST trends or, consequently, on the difference between simulations and observations. The claim that climate models systematically overestimate the response to radiative forcing from increasing greenhouse gas concentrations therefore seems to be unfounded.

Reconciling warming trends

Any divergence between real-world climate phenomena and prior expectations poses interesting science questions. The case of the apparent slow-down of warming since the record El Nino event in 1997/1998 is no exception. The global mean surface temperature trend was smaller 1 between 1997 and 2013 (0.07±0.08 °C per decade) than over the last 50 years (0.16 ± 0.02 °C per decade), highlighting questions about the mechanisms that regulate decadal variability in the Earth’s temperature. In addition, the warming trend in the most recent 15-year period is near the lower edge of the 5–95% range of projections from a collection of climate models that were part of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Why most of the model simulations suggest more warming than has been observed is a second question that deserves further exploration. Short-term fluctuations in global mean surface temperature anomalies have been a perennial focus of public discussions related to climate change. We should expect to see climate changes, by definition, only in the long-term trends that average over stochastic weather and year-to-year fluctuations such as those associated with the El Nino/Southern Oscillation (ENSO), which favoured a cool La Nina phase in the past few years. On decadal timescales, global mean surface temperatures are expected to vary, too. One cause might be the chaotic internal variability of the coupled system of oceans and atmosphere, for example in the tropical Pacific Ocean 2