Abstract: On short (15-year) to mid-term (30-year) time-scales how the Earth's surface temperature evolves can be dominated by internal variability as demonstrated by the global-warming pause or 'hiatus'. In this study, we use six single model initial-condition large ensembles (SMILEs) and the Coupled Model Intercomparison Project 5 (CMIP5) to visualise the role of internal variability in controlling possible observable surface temperature trends in the short-term and mid-term projections from 2019 onwards. We confirm that in the short-term, surface temperature trend projections are dominated by internal variability, with little influence of structural model differences or warming pathway. Additionally we demonstrate that this result is independent of the model-dependent estimate of the magnitude of internal variability. Indeed, and perhaps counter intuitively, in all models a lack of warming, or even a cooling trend could be observed at all individual points on the globe, even under the largest greenhouse gas emissions. The near-equivalence of all six SMILEs and CMIP5 demonstrates the robustness of this result to the choice of models used. On the mid-term time-scale, we confirm that structural model differences and scenario uncertainties play a larger role in controlling surface temperature trend projections than they did on the shorter time-scale. In addition we show that whether internal variability still dominates, or whether model uncertainties and internal variability are a similar magnitude, depends on the estimate of internal variability, which differs between the SMILEs. Finally we show that even out to thirty years large parts of the globe (or most of the globe in MPI-GE and CMIP5) could still experience no-warming due to internal variability.
5. Summary and conclusions
This study is the first to investigate point-wise projected temperature trends across the entire globe in both multiple (six) SMILEs and CMIP5. Hawkins and Sutton (2009) originally demonstrated the changing role of internal variability, model differences and scenario uncertainty on different time-scales. However, they were unable to account for the fact that internal variability in all models is not the same and that this variability itself may change in the future (e.g. Sutton et al 2015,Maher et al 2019, Deser et al 2020). Here, we confirm the results of Hawkins and Sutton (2009) with a more recent generation of climate models and at a higher spatial resolution, using multiple SMILEs and CMIP5 in agreement with Lehner et al (in review 2020). We build on these results, by demonstrating their remarkable robustness and additionally investigating uncertainties due to the differences in internal variability between different models.
We first confirm that on short-term time-scales (15-years) temperature trends are dominated by internal variability. This result is shown to be remarkably robust. There is near-equivalence between the six individual SMILEs and CMIP5, demonstrating that the SMILE results hold when using all available climate models. We find that internal variability dominates projections even when we take the smallest estimate of internal variability available from the SMILEs.
Second we confirm that on mid-term time-scales (30-years) internal variability is still important for driving temperature trends, however in this case both structural model differences and scenario (or pathway) uncertainty also matter, with model differences having the greater importance of the two. Due to the availability of multiple SMILEs we additionally show that the relative importance of internal variability and model differences is dependent on the models representation of internal variability. Model uncertainty is found to be the main driver of mid-term trends when we take a low estimate of internal variability, while with a high estimate, internal variability instead dominates. This result highlights the importance of using multiple SMILEs, with a range of estimates of internal variability in future studies investigating mid-term time-scales and underscores the importance of evaluating not just a model's mean state or forced trend, but also its internal variability.
Due to the difficulty in communicating what internal variability is and its importance in driving the climate that we observe, we have created maps to visualise both the maximum and minimum global and point-wise future trends that could occur on both the short and mid-term time-scales. These maps clearly demonstrate the cooling that could occur under increasing greenhouse gases, caused by internal variability. In the short-term all points on the globe could individually experience cooling or no warming, although in a probabilistic sense they are much more likely to warm. While every grid point can still cool in the future, Sippel et al (2020) have recently demonstrated that climate change is still detectable in the pattern of global temperature anomalies at any given day. We find that even on the mid-term time-scale a large proportion of the globe could by chance still not experience a warming trend due to internal variability, although this result is somewhat model dependent. These maps provide an easy way to visualise the importance of internal variability on both short and mid-term time-scales, and can be used as a tool for understanding what we observe as we observe it over the coming decades.
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