Projected timing of perceivable changes in climate extremes for terrestrial and marine ecosystems

Abstract

Human and natural systems have adapted to and evolved within historical climatic conditions. Anthropogenic climate change has the potential to alter these conditions such that onset of unprecedented climatic extremes will outpace evolutionary and adaptive capabilities. To assess whether and when future climate extremes exceed their historical windows of variability within impact-relevant socioeconomic, geopolitical, and ecological domains, we investigate the timing of perceivable changes (time of emergence; TOE) for 18 magnitude-, frequency-, and severity-based extreme temperature (10) and precipitation (8) indices using both multimodel and single-model multirealization ensembles. Under a high-emission scenario, we find that the signal of frequency- and severity-based temperature extremes is projected to rise above historical noise earliest in midlatitudes, whereas magnitude-based temperature extremes emerge first in low and high latitudes. Precipitation extremes demonstrate different emergence patterns, with severity-based indices first emerging over midlatitudes, and magnitude- and frequency-based indices emerging earliest in low and high latitudes. Applied to impact-relevant domains, simulated TOE patterns suggest (a) unprecedented consecutive dry day occurrence in >50% of 14 terrestrial biomes and 12 marine realms prior to 2100, (b) earlier perceivable changes in climate extremes in countries with lower per capita GDP, and (c) emergence of severe and frequent heat extremes well-before 2030 for the 590 most populous urban centers. Elucidating extreme-metric and domain-type TOE heterogeneities highlights the challenges adaptation planners face in confronting the consequences of elevated twenty-first century radiative forcing.

Global Emergence of Anthropogenic Climate Change in Fire Weather Indices

“…Changes in pr during the core fire season exhibit more distinct geographic variability with decreases in the Mediterranean, Amazonia, and southern Africa where FWI signals emerge earliest, and increases across much of the high latitudes of North American and Eurasia where emergence is delayed. Tan, Gan, and Horton (2018) showed similar patterns for the ToE of consecutive dry days across the globe, with increases for most land surfaces except…”

“…First, we present multimodel median changes in the four FWI metrics for the midtwenty-first century (2040-2069) relative to the baseline period ( Figure S4). Tan, Gan, and Horton (2018) showed similar patterns for the ToE of consecutive dry days across the globe, with increases for most land surfaces except Second, we performed a sensitivity analysis to decompose the trends for each of the four meteorological variables used to calculate FWI had on ToE for FWI 95d (supporting information). For comparison, the multimodel median standard deviation calculated over the baseline period shows relatively high interannual variability in FWI metrics across portions of Australia which impede emergence based on a signal-to-noise ratio ( Figure S5).…”

Importance of Framing for Extreme Event Attribution: The Role of Spatial and Temporal Scales

“…Additionally, we consider the emergence of an attribution signal, that is, at what level of GSAT increase a significant change in the probability of an event from including anthropogenic forcings is first detected, and how this depends on spatial and temporal scales. Several analyses have assessed the time of emergence of an anthropogenic signal for temperature Li et al, 2018;Tan et al, 2018) and precipitation (Tan et al, 2018) extremes, but typically, only one spatial and/or one temporal scale is considered. This analysis utilizes a "perfect model" approach to isolate the influence of different spatial and temporal scales on event attribution results using two single-model large ensembles.…”

Time of emergence of novel climates for North American migratory bird populations

“…The most common application of ToE is to examine projected changes in temperature under climate change (Diffenbaugh and Scherer 2011, Mahlstein et al 2011, 2012a, Hawkins and Sutton 2012, Mora et al 2013). There are additional examples that consider other climate change factors such as projected changes in precipitation (Giorgi and Bi 2009, Mahlstein et al 2012b, Douglas 2013, Sui et al 2014, Nguyen et al 2018, projected changes in the frequency and intensity of climate extremes (King et al 2015, Bador et al 2016, Lee et al 2016, Tan et al 2018), projected changes in sea level (Lyu et al 2014), and projected changes in width of the earth's tropical belt (Quan et al 2018). A common method for estimating ToE is to identify the year in which the ratio between climate change and historical climatic variability (signal-to-noise ratio) first crosses a predefined threshold, such as one or two (Hawkins and Sutton 2012).…”