The timing of anthropogenic emergence in simulated climate extremes

King, Andrew D.; Donat, Markus G.; Fischer, Erich M.; Hawkins, Ed; Alexander, Lisa V.; Karoly, David J.; Dittus, Andrea J.; Lewis, Sophie C.; Perkins, Sarah E.

doi:10.1088/1748-9326/10/9/094015

Cited by 160 publications

(196 citation statements)

References 37 publications

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“…This pairwise test, based on the individual model ensemble members analysed, allows for robust statements about differences between the two warming levels, even if there is substantial overlap of uncertainty bands in the model ensemble. For GCMs that provide multiple realizations, the distributions are combined for 330 C.-F. Schleussner et al: Climate impacts at 1.5 • C and 2 • C (King et al, 2015). Based on the regionally specific distributions, cumulative density functions (CDF) of changes in the impact indices over the land area of the respective region are derived.…”

Section: Methodsmentioning

confidence: 99%

“…Since this index is derived relative to natural variability over a reference period, the signal again is greatly amplified in tropical regions, where a median WSDI of up to 3 months is projected for Amazonia, East and West Africa and South-East Asia (see Table S2). Given that the WSDI only measures the longest consecutive interval, such an increase can be interpreted as entering a new climate regime for these tropical regions (Diffenbaugh and Scherer, 2011;Mora et al, 2013;King et al, 2015).…”

Section: Heat Extremesmentioning

confidence: 99%

“…Recent decades have seen increasing climate impacts, many of which science is now able to attribute to anthropogenic carbon dioxide emissions and consequent global warming (IPCC, 2013;King et al, 2015). On-going temperature increase will escalate these impacts on ecological and human systems (IPCC, 2014a), which has made climate change a political issue of central importance.…”

Section: Introductionmentioning

confidence: 99%

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Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C

et al. 2016

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Abstract. Robust appraisals of climate impacts at different levels of global-mean temperature increase are vital to guide assessments of dangerous anthropogenic interference with the climate system. The 2015 Paris Agreement includes a two-headed temperature goal: "holding the increase in the global average temperature to well below 2 • C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 • C". Despite the prominence of these two temperature limits, a comprehensive overview of the differences in climate impacts at these levels is still missing. Here we provide an assessment of key impacts of climate change at warming levels of 1.5 • C and 2 • C, including extreme weather events, water availability, agricultural yields, sea-level rise and risk of coral reef loss. Our results reveal substantial differences in impacts between a 1.5 • C and 2 • C warming that are highly relevant for the assessment of dangerous anthropogenic interference with the climate system. For heat-related extremes, the additional 0.5 • C increase in global-mean temperature marks the difference between events at the upper limit of present-day natural variability and a new climate regime, particularly in tropical regions. Similarly, this warming difference is likely to be decisive for the future of tropical coral reefs. In a scenario with an end-of-century warming of 2 • C, virtually all tropical coral reefs are projected to be at risk of severe degradation due to temperature-induced bleaching from 2050 onwards. This fraction is reduced to about 90 % in 2050 and projected to decline to 70 % by 2100 for a 1.5 • C scenario. Analyses of precipitation-related impacts reveal distinct regional differences and hot-spots of change emerge. Regional reduction in median water availability for the Mediterranean is found to nearly double from 9 % to 17 % between 1.5 • C and 2 • C, and the projected lengthening of regional dry spells increases from 7 to 11 %. Projections for agricultural yields differ between crop types as well as world regions. While some (in particular high-latitude) regions may benefit, tropical regions like West Africa, South-East Asia, as well as Central and northern South America are projected to face substantial local yield reductions, particularly for wheat and maize. Best estimate sea-level rise projections based on two illustrative scenarios indicate a 50 cm rise by 2100 relative to year 2000-levels for a 2 • C scenario, and about 10 cm lower levels for a 1.5 • C scenario. In a 1.5 • C scenario, the rate of sea-level rise in 2100 would be reduced by about 30 % compared to a 2 • C scenario. Our findings highlight the importance of regional differentiation to assess both future climate risks and different vulnerabilities to incremental increases in globalmean temperature. The article provides a consistent and comprehensive assessment of existing projections andPublished by Copernicus Publications on behalf of the European Geosciences Union. 328C.-F. Schleussner et al.: Climate impacts at 1.5 • C a...

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Section: Methodsmentioning

confidence: 99%

Section: Heat Extremesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C

et al. 2016

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“…The debate over the existence or length of a global warming hiatus (Lewandowsky et al, 2015;Roberts et al, 2015;Medhaug et al, 2017) and research examining the time of emergence of climatological (Weatherhead et al, 2002;Deser et al, 2012;Hawkins and Sutton, 2012;de Elía et al, 2013;Schurer et al, 2013), meteorological (Giorgi and Bi, 2009;King et al, 2015), chemical (Camalier et al, 2007;Strode and Pawson, 2013;Barnes et al, 2016;Garcia-Menendez et al, 2017), and other sectoral signals (e.g., Monier et al, 2016) embody an accumulation of techniques and strategies for filtering noise (due to natural variability) and maximizing the capability to detect statistically significant signals and trends in noisy data. It is well established that temporal averaging (e.g., Lewandowsky et al, 2015) and spatial averaging (e.g., Frost et al, 2006;Hawkins and Sutton, 2012;Barnes et al, 2016) can enhance signal detection capabilities in atmospheric data.…”

Section: Introductionmentioning

confidence: 99%

Maximizing ozone signals among chemical, meteorological, and climatological variability

et al. 2018

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Abstract. The detection of meteorological, chemical, or other signals in modeled or observed air quality data -such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season -is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical, meteorological, and climatological variabilities (and their interactions) that exist both in space and in time, and which include variability in emissions and surface processes. This can present difficulties for both policymakers and researchers as they attempt to identify the influence or signal of climate trends (e.g., any pauses in warming trends), the impact of enacted emission reductions policies (e.g., United States NO x State Implementation Plans), or an estimate of the mean state of highly variable data (e.g., summertime ozone over the northeastern United States). Here we examine the scale dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the continental US. For signals that are large compared to the meteorological variability (e.g., strong emissions reductions), shorter averaging periods and smaller spatial averaging regions may be sufficient, but for many signals that are smaller than or comparable in magnitude to the underlying meteorological variability, we recommend temporal averaging of 10-15 years combined with some level of spatial averaging (up to several hundred kilometers). If this level of averaging is not practical (e.g., the signal being examined is at a local scale), we recommend some exploration of the spatial and temporal variability to provide context and confidence in the robustness of the rePublished by Copernicus Publications on behalf of the European Geosciences Union.

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“…In recent years, there has been a strong consensus on the changes in climate caused by increased concentrations of anthropogenic greenhouse gas emissions (King et al, 2015;O'Neill et al, 2017;Stocker et al, 2013). Under the pressing circumstances of a warming world, scientific research has focused on estimating the range of changes in the future climate and the effectiveness of different adaptation strategies.…”

Section: Introductionmentioning

confidence: 99%

The effect of GCM biases on global runoff simulations of a land surface model

Papadimitriou

Koutroulis

Grillakis

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

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The timing of anthropogenic emergence in simulated climate extremes

Cited by 160 publications

References 37 publications

Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C

Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C

Maximizing ozone signals among chemical, meteorological, and climatological variability

The effect of GCM biases on global runoff simulations of a land surface model

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The timing of anthropogenic emergence in simulated climate extremes

Cited by 160 publications

References 37 publications

Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C

Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C

Maximizing ozone signals among chemical, meteorological, and climatological variability

The effect of GCM biases on global runoff simulations of a land surface model

Contact Info

Product

Resources

About

Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C

Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C