Supplementary Information and Figures from The utility of the historical record for assessing the transient climate response to cumulative emissions

The historical observational record offers a way to constrain the relationship between cumulative carbon dioxide emissions and global mean warming. We use a standard detection and attribution technique, along with observational uncertainties to estimate the all-forcing or ‘effective’ transient climate response to cumulative emissions (TCREs) from the observational record. Accounting for observational uncertainty and uncertainty in historical non-CO₂ radiative forcing gives a best-estimate from the historical record of 1.84°C/TtC (1.43–2.37°C/TtC 5–95‰ uncertainty) for the effective TCRE and 1.31°C/TtC (0.88–2.60°C/TtC 5–95‰ uncertainty) for the CO₂-only TCRE. While the best-estimate TCRE lies in the lower half of the IPCC likely range, the high upper bound is associated with the not-ruled-out possibility of a strongly negative aerosol forcing. Earth System Models typically have a higher effective TCRE range when compared like-for-like with the observations over the historical integrations, associated in part with a slight underestimate of diagnosed cumulative emissions relative to the observational best-estimate, a larger ensemble mean-simulated CO₂-induced warming and rapid post-2000 non-CO₂ warming in some ensemble members.This article is part of the themed issue ‘The Paris Agreement: Understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'

Supplementary Information and Figures from The utility of the historical record for assessing the transient climate response to cumulative emissions

The historical observational record offers a way to constrain the relationship between cumulative carbon dioxide emissions and global mean warming. We use a standard detection and attribution technique, along with observational uncertainties to estimate the all-forcing or ‘effective’ transient climate response to cumulative emissions (TCREs) from the observational record. Accounting for observational uncertainty and uncertainty in historical non-CO₂ radiative forcing gives a best-estimate from the historical record of 1.84°C/TtC (1.43–2.37°C/TtC 5–95% uncertainty) for the effective TCRE and 1.31°C/TtC (0.88–2.60°C/TtC 5–95% uncertainty) for the CO₂-only TCRE. While the best-estimate TCRE lies in the lower half of the IPCC likely range, the high upper bound is associated with the not-ruled-out possibility of a strongly negative aerosol forcing. Earth System Models have a higher effective TCRE range when compared like-for-like with the observations over the historical period, associated in part with a slight underestimate of diagnosed cumulative emissions relative to the observational best-estimate, a larger ensemble mean-simulated CO₂-induced warming, and rapid post-2000 non-CO₂ warming in some ensemble members.This article is part of the themed issue ‘The Paris Agreement: Understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'

Mean seasonal and annual anomaly of global precipitation.

<p>Global annual dry-season precipitation (left), wet-season precipitation (middle) and annual precipitation (right) anomaly for 3 different precipitation products (CRU, GPCC, PRE/L) and the ensemble mean. Linear regression models for the ensemble mean are presented as a red line, and individual observation slopes are presented on the bottom left of each plot (m).</p

Wet, dry and seasonal range trends for a 30-yr period (same as Chou et al. [5]) and across half the 20^th century.

<p>Wet, dry and seasonal range trends for a 30-yr period (same as Chou et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190304#pone.0190304.ref005" target="_blank">5</a>]) and across half the 20^th century.</p

Global annual dry-season length (left) and precipitation rate (right).

<p>Legend and methodology follows <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190304#pone.0190304.g001" target="_blank">Fig 1</a>. The data employed is on a monthly resolution, but the figure is displayed in days for a better visualization.</p

Global gridded seasonal precipitation and net primary productivity trends.

<p>Linear gridded trend for the wet- and dry-season precipitation, the seasonal range (wet minus dry) and vegetation net primary productivity (climate-only) for the period 1950–2009.</p

Surface Urban Heat Island Across 419 Global Big Cities

Urban heat island is among the most evident aspects of human impacts on the earth system. Here we assess the diurnal and seasonal variation of surface urban heat island intensity (SUHII) defined as the surface temperature difference between urban area and suburban area measured from the MODIS. Differences in SUHII are analyzed across 419 global big cities, and we assess several potential biophysical and socio-economic driving factors. Across the big cities, we show that the average annual daytime SUHII (1.5 ± 1.2 °C) is higher than the annual nighttime SUHII (1.1 ± 0.5 °C) (<i>P</i> < 0.001). But no correlation is found between daytime and nighttime SUHII across big cities (<i>P</i> = 0.84), suggesting different driving mechanisms between day and night. The distribution of nighttime SUHII correlates positively with the difference in albedo and nighttime light between urban area and suburban area, while the distribution of daytime SUHII correlates negatively across cities with the difference of vegetation cover and activity between urban and suburban areas. Our results emphasize the key role of vegetation feedbacks in attenuating SUHII of big cities during the day, in particular during the growing season, further highlighting that increasing urban vegetation cover could be one effective way to mitigate the urban heat island effect

Surface Urban Heat Island Across 419 Global Big Cities

Urban heat island is among the most evident aspects of human impacts on the earth system. Here we assess the diurnal and seasonal variation of surface urban heat island intensity (SUHII) defined as the surface temperature difference between urban area and suburban area measured from the MODIS. Differences in SUHII are analyzed across 419 global big cities, and we assess several potential biophysical and socio-economic driving factors. Across the big cities, we show that the average annual daytime SUHII (1.5 ± 1.2 °C) is higher than the annual nighttime SUHII (1.1 ± 0.5 °C) (<i>P</i> < 0.001). But no correlation is found between daytime and nighttime SUHII across big cities (<i>P</i> = 0.84), suggesting different driving mechanisms between day and night. The distribution of nighttime SUHII correlates positively with the difference in albedo and nighttime light between urban area and suburban area, while the distribution of daytime SUHII correlates negatively across cities with the difference of vegetation cover and activity between urban and suburban areas. Our results emphasize the key role of vegetation feedbacks in attenuating SUHII of big cities during the day, in particular during the growing season, further highlighting that increasing urban vegetation cover could be one effective way to mitigate the urban heat island effect