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Day-to-day temperature variability reduces economic growth

Nature Climate Change volume 11, pages 319–325 (2021)Cite this article

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Abstract

Elevated annual average temperature has been found to impact macro-economic growth. However, various fundamental elements of the economy are affected by deviations of daily temperature from seasonal expectations which are not well reflected in annual averages. Here we show that increases in seasonally adjusted day-to-day temperature variability reduce macro-economic growth independent of and in addition to changes in annual average temperature. Combining observed day-to-day temperature variability with subnational economic data for 1,537 regions worldwide over 40 years in fixed-effects panel models, we find that an extra degree of variability results in a five percentage-point reduction in regional growth rates on average. The impact of day-to-day variability is modulated by seasonal temperature difference and income, resulting in highest vulnerability in low-latitude, low-income regions (12 percentage-point reduction). These findings illuminate a new, global-impact channel in the climate–economy relationship that demands a more comprehensive assessment in both climate and integrated assessment models.

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Fig. 1: Day-to-day temperature variability, as measured by deviations of daily temperature from monthly means, can be considerably different for two regions with equal average annual temperature.
Fig. 2: The effect of an extra degree of day-to-day temperature variability on regional growth rates varies geographically.
Fig. 3: The reduction in regional growth rates per extra degree of day-to-day temperature variability (marginal change) is larger in countries with lower GDP per capita (2017 values), since they tend to experience smaller seasonal temperature differences.
Fig. 4: The change in regional growth rates per extra degree of day-to-day temperature variability (marginal change) estimated separately for countries with above- and below-median income per capita.

Data availability

ERA5 data are publicly available from the European Centre for Medium-Range Weather Forecasts (https://www.ecmwf.int/). The 0.5 × 0.5° resolution version used in this analysis and the EWEMBI and WATCH climate datasets are available from the Inter-Sectoral Impact Model Intercomparison Project (https://www.isimip.org/) or from the corresponding author upon request. Source data are provided with this paper. All other data are publicly available at https://doi.org/10.5281/zenodo.4323163 (ref. 63).

Code availability

All code used for analysis and plotting are publicly available at https://doi.org/10.5281/zenodo.4323163 (ref. 63).

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Acknowledgements

We thank S. Lange for his helpful guidance in accessing and using climate data from the Inter-Sectoral Impact Model Intercomparison Project database. We gratefully acknowledge funding from the Volkswagen Foundation. Economic output data are provided by M. Kalkuhl and his team at the Mercator Research Institute on Global Commons and Climate Change.

Author information

Authors and Affiliations

  1. Potsdam Institute for Climate Impact Research, Potsdam, Germany

    Maximilian Kotz, Leonie Wenz, Annika Stechemesser & Anders Levermann

  2. Institute of Physics, Potsdam University, Potsdam, Germany

    Maximilian Kotz, Annika Stechemesser & Anders Levermann

  3. Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany

    Leonie Wenz & Matthias Kalkuhl

  4. Department of Agricultural and Resource Economics, University of California, Berkeley, CA, USA

    Leonie Wenz

  5. Faculty of Economic and Social Sciences, Potsdam University, Potsdam, Germany

    Matthias Kalkuhl

  6. Columbia University, New York, NY, USA

    Anders Levermann

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Contributions

M. Kalkuhl provided the economic data. A.L. proposed the climate measure. M. Kotz processed the climate and economic data. M. Kotz and L.W. designed the regression models. All authors contributed to the interpretation of the results. M. Kotz and L.W. wrote the manuscript, with all authors providing feedback.

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Correspondence to Leonie Wenz.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Shouro Dasgupta and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Marginal effects of day-to-day temperature variability on regional economic growth rates.

Marginal effects of a 1° Celsius increase of day-to-day temperature variability on regional growth rates, as estimated by model (7) in Table 1. Regions which are accustomed to smaller seasonal temperature differences suffer greater marginal losses (greater negative marginal changes) from a 1 degree increase of day-to-day temperature variability. 95% confidence intervals are shown shaded, and the histograms show the distribution of the data used to estimate the model.

Source data

Extended Data Fig. 2 Partitioning of the data by per-capita income.

Partitioning data based on above- and below-median national (a) and regional (b) averages of regional income (GRP) per capita. Data from 2008, the year in which we have best coverage across regions, or the closest year to this for which data are available, are used. Histograms of the distribution of national average (a.i) and regional (b.i) GRP per capita are shown with the median income indicated by a vertical black line. Partitions from (a) are used to estimate the results shown in Fig. 4, those from (b) are used to estimate the results shown in Extend Data Fig. 3.

Extended Data Fig. 3 Marginal effects of high and low-income regions.

Marginal effects of a 1 degree increase of day-to-day temperature variability on regional growth rates, estimated for regions with below- and above-median regional income. When partitioned based on regional-income, the difference between the response of high- and low-income regions is not significant, although above-median income regions generally experience smaller marginal losses. The results shown here are based on the partition shown in Extended Data Fig. 2b and, and the models used to estimate the marginal effects are those shown in Supplementary Table 9.

Source data

Supplementary information

Supplementary Information

Supplementary Sections 1–8, Figs. 1–3 and Tables 1–11.

Source data

Source Data Fig. 1

Daily temperature data for the two regions shown in Fig. 1.

Source Data Fig. 3

Data on national population, GDP per capita and marginal effects for Fig. 3.

Source Data Fig. 4

Data of the central marginal effects and upper and lower limits to the marginal effects for above-median-income and below-median-income countries as shown in Fig. 4.

Source Data Extended Data Fig. 1

Data of the central marginal effects and upper and lower limits to the marginal effects shown in Extended Data Fig. 1.

Source Data Extended Data Fig. 3

Data of the central marginal effects and upper and lower limits to the marginal effects for rich and poor regions as shown in Extended Data Fig. 3.

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Kotz, M., Wenz, L., Stechemesser, A. et al. Day-to-day temperature variability reduces economic growth. Nat. Clim. Chang. 11, 319–325 (2021). https://doi.org/10.1038/s41558-020-00985-5

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