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Widespread changes in surface temperature persistence under climate change

Abstract

Climate change has been and will be accompanied by widespread changes in surface temperature. It is clear that these changes include global-wide increases in mean surface temperature and changes in temperature variance that are more regionally-dependent1,2,3. It is less clear whether they also include changes in the persistence of surface temperature. This is important as the effects of weather events on ecosystems and society depend critically on the length of the event. Here we provide an extensive survey of the response of surface temperature persistence to climate change over the twenty-first century from the output of 150 simulations run on four different Earth system models, and from simulations run on simplified models with varying representations of radiative processes and large-scale dynamics. Together, the results indicate that climate change simulations are marked by widespread changes in surface temperature persistence that are generally most robust over ocean areas and arise due to a seemingly broad range of physical processes. The findings point to both the robustness of widespread changes in persistence under climate change, and the critical need to better understand, simulate and constrain such changes.

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Fig. 1: Changes in surface temperature persistence in ESMs.
Fig. 2: Spatially averaged surface temperature persistence.
Fig. 3: Changes in persistence averaged over all four ESMs.
Fig. 4: Changes in persistence in slab-ocean numerical models.
Fig. 5: Changes in persistence in a simplified ‘tropics-world’ simulation.

Data availability

The large-ensemble output is publicly available via the Multi-Model Large Ensemble Archive (MMLEA) at the National Center for Atmospheric Research (https://doi.org/10.1038/s41558-020-0731-2). The output from the gray radiation and RRTMG simulations were provided by Zhihong Tan at the NOAA Geophysical Fluid DynamicsLaboratory; the output from the RCE simulations were provided by Gabor Drotos at the Institute for Cross-Disciplinary Physics and Complex Systems, Palma de Mallorca, Spain. All data used to construct the figures are archived in Figshare (https://doi.org/10.6084/m9.figshare.15078807.v1). All other data that support the findings of the study are available from the corresponding author upon reasonable request.

Code availability

Code that was used in this study is available from the corresponding author upon reasonable request.

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Acknowledgements

We thank A. Philips at NCAR for assistance with the Climate Variability Diagnostics Package analyses of the MMLEA; M. Winton and K. Rodgers for discussion of the GFDL ESM2M output; Z. Tan for providing output from the gray radiation and RRTMG simulations, and comments on the text; G. Drotos for providing the output from the RCE simulations and comments on the text; and F. Lehner, A. Czaja and B. Medeiros for helpful discussions. The large-ensemble output was obtained from the MMLEA produced by the US CLIVAR Working Group on Large Ensembles. We also acknowledge high-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation (NSF). All maps were made using the Proplot Matplotlib wrapper and map projections from the Cartopy Python package. J.L. and D.W.J.T. are supported by the NSF Climate and Large-Scale Dynamics program.

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D.W.J.T. led the writing. J.L. performed the analyses and produced the figures.

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Correspondence to David W. J. Thompson.

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Peer review information Nature thanks Daniel Koll and Christian Franzke for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 The relationship between the autocorrelation and the average length of a warm event.

The 2d density plot of the lag-one autocorrelation and the average length of warm events calculated as a function of grid box in the CESM1 historical output. Warm events are defined as periods when temperatures exceed one standard deviation. Panels (a-d) show results for four sample ensemble members in the CESM1. Each panel includes results from 55296 grid boxes. Data density is found using a Gaussian kernel density estimate.

Extended Data Fig. 2 The ensemble-mean relationship between the autocorrelation and the average length of a warm event.

(a) The 2d density plot of the lag-one autocorrelation and the average length of warm events calculated as a function of grid box in the CESM1 historical output. Results are calculated for each ensemble member and then averaged over all ensemble members. Warm events are defined as periods when temperatures exceed one standard deviation. Each panel includes results from 55296 grid boxes. (b; shading) As in the top panel, but results are averaged over bins that span 0.001 on the abscissa. (b; black line) Results derived from randomly generated red-noise time series with autocorrelation specified on the abscissa. Data density is found using a Gaussian kernel density estimate.

Extended Data Fig. 3 Changes in variance explained by persistence as a function of lag.

The changes in persistence between the “historical” period 1970-1999 and the “future” period 2070-2099 calculated from 40 large-ensembles run on the NCAR CESM1. Warm (red) colours represent an increase in persistence from the Historical to Future periods, while cool (blue) colours represent a decrease in persistence over the same period. Results show the percent changes in the variance explained by the (a) lag 5, (b) lag 10, (c) lag 15, and (d) lag 20-day autocorrelations. That is, they show: \(\frac{{{r}^{2}}_{i,{Future}}}{{{r}^{2}}_{i,{Historical}}}-1\) where r2i denotes the variance explained by the lag i-day autocorrelation. Note that the autocorrelations are calculated first for individual ensemble members and then averaged over all ensembles using the Fisher-z transformation. Stippling indicates grid points where at least 75% of the ensemble members agree on the sign of the change (a likelihood of ~0.1% by chance) and where the ensemble mean results exceed the 95% confidence threshold based on a two-tailed test of the t-statistic. Note that panel (b) is identical to Figure 1a. See Methods for details of the ESM output, analysis, statistical significance, and reproducibility.

Extended Data Fig. 4 Changes in persistence as a function of lag.

The changes in persistence between the “historical” period 1970-1999 and the “future” period 2070-2099 calculated from 40 large-ensembles run on the NCAR CESM1. Warm (cool) colors represent an increase (decrease) in persistence from the historical to future period. Results show the actual changes in the variance explained by the (a) lag 5, (b) lag 10, (c) lag 15, and (d) lag 20-day autocorrelations, not the percent changes as shown in Extended Data Figure 3. That is, they show: \({{r}^{2}}_{i,{Future}}-{{r}^{2}}_{i,{Historical}}\) where r2i denotes the variance explained by the lag i-day autocorrelation. The autocorrelations are calculated first for individual ensemble members and then averaged over all ensembles using the Fisher-z transformation. Stippling indicates grid points where at least 75% of the ensemble members agree on the sign of the change (a likelihood of ~0.1% by chance) and where the ensemble mean results exceed the 95% confidence threshold based on a two-tailed test of the t-statistic. See Methods for details of the ESM output, analysis, statistical significance, and reproducibility.

Extended Data Fig. 5 Testing the robustness of changes in persistence to lag.

(a) The results at lag i on the abscissa indicate the spatial correlation between 1) the spatial map formed as \({{r}^{2}}_{i,{Future}}-{{r}^{2}}_{i,{Historical}}\), where \({{r}^{2}}_{i,{Future}}\) and \({{r}^{2}}_{i,{Historical}}\,\)indicate the variance explained by the lag i-day autocorrelation in the Future and Historical periods, respectively, and the autocorrelations are calculated first for individual ensemble members and then averaged over all ensembles (e.g., the lag i=10 map is shown in Extended Data Figure 4b); and 2) the corresponding map calculated for lag i+1. (b) As in panel (a), but for the spatial correlations between 1) the map formed for lag i and 2) the map formed for lag i=10. Results are based on all members from the CESM1 output.

Extended Data Fig. 6 Climatological-mean autocorrelations of surface temperature in the historical and future periods.

The lag 10-day autocorrelations of surface temperature in large ensembles run on the four indicated ESMs for (top) the 1970-1999 historical period; (bottom) the 2070-2099 future period. The results are derived from (a, e) 40 ensemble members run on the NCAR CESM1, (b, f) 30 ensemble members run on the CSIRO Mk3.6, (c, g) 50 ensemble members run on the CCCma CanESM2, and (d, h) 30 members run on the GFDL ESM2M.

Extended Data Fig. 7 Assessing changes in ENSO in large ensembles run on four ESMs.

Scatter plots of the standard deviation of the monthly mean Nino 3.4 index during the historical period 1970-1999 and the future period 2070-2099 derived from (a) 40 ensemble members run on the NCAR CESM1, (b) 30 ensemble members run on the CSIRO Mk3.6, (c) 50 ensemble members run on the CCCma CanESM2, and (d) 30 members run on the GFDL ESM2M. The black diagonal lines represent the 1:1 line. Dots indicate results from individual ensemble members. The output was obtained from the NCAR CVDP-LE.

Extended Data Fig. 8 Southern Ocean temperatures and SH sea ice extent in large ensembles run on four ESMs.

Monthly mean values of (left) Southern Ocean temperatures; (right) Southern Hemisphere sea ice extent in large ensembles from the indicated ESMs. Results are shown for individual ensemble members and smoothed for display purposes using a 13 month running mean. Results are derived from (a, b) 40 ensemble members run on the NCAR CESM1, (c, d) 30 ensemble members run on the CSIRO Mk3.6, (e, f) 50 ensemble members run on the CCCma CanESM2, and (g, h) 30 members run on the GFDL ESM2M. The output was obtained from the NCAR CVDP-LE.

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Li, J., Thompson, D.W.J. Widespread changes in surface temperature persistence under climate change. Nature 599, 425–430 (2021). https://doi.org/10.1038/s41586-021-03943-z

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