Global mean temperature indicators linked to warming levels avoiding climate risks

Many climate change impacts relevant for societies scale with global mean surface air temperature (GMT) rise (Seneviratne et al 2016, UNFCCC 2015b), making it an adequate proxy for the assessment of global climate change risks (Knutti et al 2015). International climate policy has adopted levels of global mean temperature increase to guide global climate action. The most prominent example of such temperature rise levels is the long-term temperature goal of the UN Paris Agreement of 'holding the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels, recognizing that this would significantly reduce the risks and impacts of climate change' (UNFCCC 2015a, Schleussner et al 2016b). The second part of the goal provides highly relevant context as it explicitly links the temperature levels referenced in the Paris Agreement to the assessment of climate risks and impacts.

The adoption of global average temperature levels to avoid climate risks have been informed by a multi-year science-policy process (UNFCCC 2015b), which was predominantly based on the findings of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC 2014). Its products, such as the 'reasons of concern' (O'Neill et al 2017) link various climate risks to levels of GMT increase. The warming levels at which these risks emerge depend on the method that underlies the global average temperature estimation, which ties them to the methods used in the scientific basis of the underlying risk assessment, the AR5. This context is key for scientists to understand how to interpret the Paris long-term temperature goals (Rogelj et al 2017).

With the Paris Agreement in place, international policy has shifted focus from defining its goals to implementing and tracking progress towards their achievement. Monitoring GMT rise has thus become a key component of assessments of whether climate mitigation actions are on track to achieve the Paris temperature goal, for example in terms of carbon budgets (IPCC 2014, Rogelj et al 2016). Clarity on how global mean temperature is assessed is essential for this process. However, there is no single established and agreed-upon method to assess GMT change. In particular, substantial differences emerge between observational-based and model-based GMT products (Richardson et al 2016, Cowtan et al 2015). Here we will assess the implications of discrepancies between different GMT products for our ability to track progress towards the Paris Agreement temperature goal. To this end, we evaluate different methodological approaches to GMT that have been used for policy-relevant statements, carbon budget estimates or for resulting climate impacts.

The IPCC AR5 determines global mean temperature change (hereinafter referred to as GMT_AR5) relative to the 1986–2005 period. Past warming since the 1850–1900 preindustrial reference period is 0.61 °C based on the HadCRUT4 observational dataset (Morice et al 2012). Future warming relative to preindustrial is defined as the sum of past warming and the CMIP5 climate model ensemble mean relative to the 1986–2005 baseline (IPCC 2014). For carbon budget estimates the IPCC AR5 Working Group 1 uses the model-based global mean surface air temperature increase (hereafter GMT_SAT) since the 1861–1880 period from the Coupled Model Intercomparison Project (CMIP5) (IPCC 2013). Note that carbon budgets have been assessed slightly differently in different working groups and subsequent publications (see Rogelj et al (2016) for an overview).

The HadCRUT4 observational GMT product is only based on regions for which observational data exists. Parts of the rapidly warming Arctic, for example, are undersampled (Cowtan et al 2015). Furthermore, surface air temperatures over land and sea ice are blended with sea surface temperatures over the open ocean. In contrast, CMIP5-model-based global mean temperature is derived with global coverage and based on surface air temperatures (SAT) alone. The differences between observational-based and model-based GMT have been shown to introduce considerable differences (Richardson et al 2016, Cowtan et al 2015) and to be partly responsible for discrepancies of the observational record and model projections over the recent decade (Medhaug et al 2017). Correcting for discrepancies between the HadCRUT4 and infilled datasets also affects the warming level of the 1850–1900 period (Richardson et al 2016, Cowtan et al 2015). In the following we will investigate the implications of using non-AR5 GMT products for tracking progress against Paris Agreement warming levels for carbon budgets as well as climate impact indicators.

Based on the method by (Richardson et al 2016, Cowtan et al 2015) we have derived a model-based GMT estimate that has been corrected for masking and blending as in the HadCRUT4 observational record (GMT_blend-mask). We use an ensemble of 32 CMIP5 models forced with the RCP8.5 scenario (see table S1 available at stacks.iop.org/ERL/13/064015/mmedia). For each GCM all runs are averaged to one global mean temperature time series.

This GMT product can be considered a proxy for future observations if the HadCRUT4 approach to derive GMT is continued. Assessments of future GMT could also be rebased to the observational warming record since 1986−2005. This has e.g. been done recently by Millar et al (2017), using human-induced warming until 2015 determined as 0.93 °C based on HadCRUT4 (GMT_M17, Millar et al 2017). The future warming difference for rebased products like GMT_M17 solely depends on the offset to GMT_SAT over the rebase period. An overview of the different GMT products is given in table 1. As other observational datasets project higher warming than HadCRUT4 over this period (Rohde et al 2013, Cowtan and Way 2014, Hansen et al 2010) we also included two sensitivity cases assuming an attributable warming of 1 °C and 1.1 °C until 2015 (Haustein et al 2017) (table S4). Conversions between different GMT products are based on the 20 year running mean values from the CMIP5 models which are closest to 1.5 °C in the source GMT product.

The intensity of hot extremes is measured as the annual maximum value of daily maximum temperature (TXx). Following Fischer and Knutti (2014), we derive grid-cell based time averaged differences between the 1986−2005 reference period and model specific 21 year periods with a mean warming above 1986−2005 of 0.89 °C for GMT_AR5 and 1.07 °C for GMT_M17. The mid-years of the 21 year periods are listed in table S5. These differences are aggregated in a spatial probability density function (PDF) over the global land mass and all models area-weighting each grid-cell. The smoothed PDFs are estimated using a weighted Gaussian kernel density estimation method with a bandwidth estimated following 'Silverman's rule'. Sea level rise projections for different warming levels are derived using a component-based approach (Mengel et al 2018) with an updated Antarctic ice sheet contribution (Nauels et al 2017). The updated method emulates a recently proposed and more sensitive Antarctic response to future warming (Deconto and Pollard 2016).

The discrepancies between the different GMT products and the GMT_AR5 are displayed in figure 1. Deviations are smallest for GMT_SAT, as modelled CMIP5 1986–2005 warming since 1861−1880 matches well with the HadCRUT4 reconstruction. This is remarkable, as considerable differences exceeding 0.1 °C between blended-masked GMT and surface air temperature-only products are already apparent for this period (Richardson et al 2016). This–coincidental—close match might be one of the reasons why the methodological difference between observed and modelled GMT products has not risen to larger prominence before. As a result, deviations that result from the mix of products for the AR5 impact appraisals and the AR5 carbon budget estimates are small.

Deviations for the GMT_blend-mask and the GMT_M17 products are more pronounced. A 1.5 °C global mean temperature rise in GMT_AR5 corresponds to a warming of just 1.31 °C in the GMT_blend-mask product (full ensemble range: 0.85 °C−1.77 °C, compare figure 1(a)) and table 2). The difference between GMT_blend-mask and GMT_AR5 is not constant in time (Richardson et al 2016) and increases with increasing warming (figure 1(b)). It is largely introduced by undersampling of fast warming Arctic regions and sea-ice loss. As a result, the future strength of this effect will depend on the emission scenario and will be less pronounced under stringent mitigation scenarios (Richardson et al 2018). A substantial discrepancy between model-based GMT_SAT and GMT_blend-mask is already apparent over the observational record and particularly pronounced in recent decades. Rebasing the reference period as in GMT_M17 introduces a time-invariant offset. In this case, a GMT_AR5 1.5 °C warming corresponds to 1.32 °C (1.06 °C−1.53 °C) increase in GMT_M17.

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Differences between GMT products are sensitive to the choice of method. The difference between GMT products with recent reference periods (GMT_AR5 and GMT_M17) and GMT products referenced against a preindustrial period (GMT_blend-mask and GMT_SAT) depends on the choice of the preindustrial period (Hawkins et al 2017). Setting the preindustrial period to 1850−1900, for example, slightly reduces the difference between GMT_blend-mask and GMT_AR5 (see figure S3). Furthermore, differences depend on the method used to convert between GMT products. For example, basing the conversion into GMT_AR5 on annual mean temperatures within a range of 1.5 °C ± 0.05 °C in the source GMT product (instead of analysing 20 year running mean values close to 1.5 °C) yields slightly lower differences between GMT_AR5 and GMT_M17 and GMT_blend-mask, (see figure S5). Finally, these results depend on the understanding of the 'multi-model mean' (see figure S2). If all available model runs are weighted equally instead of weighting contributions per model, GMT_M17 and GMT_blend-mask would both reach +1.7 °C at the time when GMT_AR5 reaches +1.5 °C (see figure S4). All conversions between GMT products and choices in the method are listed in tables S2 and S3.

The choice of GMT indicators for expressing current and future warming can influence how much carbon emissions are perceived to remain for limiting warming to internationally agreed levels such as 1.5 °C (see table 2). As indicated earlier, international temperature goals have been underpinned by climate risk assessments pegged to GMT_AR5 levels of global mean warming. Warming in the real world, however, is expressed in observation-based indicators. Our GMT_blend-mask time series aims to mimic the limitations of one commonly used indicator (HadCRUT4 (Morice et al 2012)). Consistent with (Schurer et al 2018, Richardson et al 2018) we estimate a mismatch between GMT_AR5 and GMT_blend-mask at the time GMT_AR5 reaches 1.5 °C of about 0.2 °C (GMT_blend-mask is cooler, see table 2). At the moment GMT_AR5 reaches 1.5 °C, the remaining carbon budget for avoiding the assessed impacts of 1.5 °C warming should be effectively zero. However, because of the mismatch between GMT_blend-mask and GMT_AR5, a GMT_blend-mask indicator would continue to suggest a remaining available budget of about 422 Gt CO₂ at that point in time (using an average transient climate response to cumulative emissions of carbon of 1.65 × 10⁻³ °C/Gt C). This amounts to a carbon budget overestimate the size of about 10 years of continued year-2015 emissions. An adjustment of similar size would be required to make recently published carbon budget estimates (GMT_M17, calculated as in Millar et al (2017)) consistent with the assessed warming levels for avoiding global warming risks (table 2).

Reaching 1.5 °C in GMT_M17, or GMT_blend-mask (here considered a proxy for expected observational warming) would correspond to climate risks at higher temperature levels when following the AR5 method. These levels are 1.68 °C for GMT_M17 and 1.71 °C for GMT_blend-mask (see table 2). Several highly vulnerable systems such as tropical coral reefs (Schleussner et al 2016a) or Arctic sea-ice (Screen and Williamson 2017) are very sensitive to small warming increments. Also extreme weather indicators have been found to robustly increase with increasing GMT_SAT (Seneviratne et al 2016) and threshold based indices even in a non-linear fashion (Fischer and Knutti 2015). Figure 2 illustrates how the different GMT products (GMT_AR5 and GMT_M17) lead to different projected changes in global extreme hot day temperatures (TXx, figure 2(a)) and 2100 sea-level rise (figure 2(b)).

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The intensification of extreme hot days is stronger for 1.68 °C GMT_AR5 warming when 1.5 °C is reached in GMT_M17 than for 1.5 °C GMT_AR5 warming. At 1.68 °C GMT_AR5 warming, 40% of the land area experiences an increase in the annual maximum daily temperature of 1 °C relative to 1986−2005, while at 1.5 °C GMT_AR5 warming only 30% of the land area would experience this increase. Similarly, the difference between 1.68 °C and 1.5 °C GMT_AR5 warming could lead to an additional sea level rise of 5 cm in the median for the end of the century, about 10% of the projected median rise for 1.5 °C relative to the 1986−2005 period. Note that future sea level rise exhibits a considerable dependency on the temperature trajectory and projections for Paris Agreement compatible pathways would therefore divert slightly from the stylised estimates presented here (Mengel et al 2018).

Our analysis outlines important differences between different GMT products and illustrates their implications for climate risks assessment. We have shown that by using GMT products other than those used in the IPCC AR5, risks identified for a certain level of global warming in this report would occur at other levels. The quantified discrepancies between observationally derived GMT products and climate change risk levels as expressed in international agreements have important consequences for on-going discussions in the climate policy arena. Climate action is guided by the desire to avoid impacts, not by reaching an imaginary GMT number. If the impacts policy makers aim to avoid (as indicated in the Paris Agreement) will occur at a lower levels in other GMT products, then science needs to communicate this clearly and ideally provide adequate adjustments. In order to limit potential confusion this requires understanding of both, the identified discrepancies between GMT products and the nature of the Paris Agreement temperature goal (Rogelj et al 2017). Indeed, the discrepancy between observed GMT products and the GMT_AR5 will not be easy to reconcile and communicate.

It is important to clarify that our argumentation is not rooted in a scientific reasoning in favour of the IPCC AR5 method that is not without shortcomings and ambiguities. The 1986−2005 reference period, for example, is not free of influences of natural variability (like volcanic eruptions). Climate models used for projecting future warming are not systematically evaluated and may already exhibit substantial deviations compared to observed present-day warming (see figure 1). Furthermore, the effect of different definitions of the 'pre-industrial level' needs to be considered (Hawkins et al 2017). At the same time, scientists will continue to use observed GMT products to assess the state of the climate system. Approaches to assess GMT will be, and should be, updated as our scientific understanding progresses. To ensure the policy relevance of future products in relation to the Paris Agreement and to maintain the agreement's integrity, it is therefore of key importance that different (updated) GMT metrics can be converted into the GMT_AR5 values used at the time, and that full transparency is provided about methods, as we have attempted here. This also relates to other methodological choices in the IPCC AR5 such as the use of multi-model means instead of medians or the 'one-model-one-vote' principle (Flato et al 2013). Diverting from this approach by averaging over all available model runs yields slightly different estimates for the biases between the GMT products (compare figure S4). Under the UNFCCC, climate policy now progresses in quinquennial cycles which include a stocktaking phase and a phase in which governments put forward new proposed actions to limit climate change. If, during the stocktaking phase, current progress and the current state of the Earth system is not assessed with metrics comparable and consistent with the metrics used to define the Paris Agreement long-term temperature goal, the assessment of progress will be imprecise, and, as we have shown, the risk of hitting instead of avoiding some particularly sensitive climate impacts would be increased. In the context of the 1.5 °C and 2 °C global average temperature limits, our results show that following practices based on observational products (Millar et al 2018) would consistently lead to an underestimation of the urgency of emissions reductions (Schurer et al 2018, Richardson et al 2018).

We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison, and the WCRP's Working Group on Coupled Modelling for their roles in making available the CMIP multi-model datasets, which can be accessed through http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html. Support for this dataset is provided by the Office of Science, US Department of Energy. P P and C F S acknowledge support by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (16_II_148_Global_A_IMPACT) and the German Federal Ministry of Education and Research (01LS1613A and 01LN1711A). M M acknowledges support by the AXA Research Fund Postdoctoral Fellowship programme. J R acknowledges support by the Oxford Martin School Visiting Fellowship Programme. The authors thank Kevin Cowtan for providing his scripts for adjusting blended masked biases.