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WIREs Clim Change
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Characterizing half‐a‐degree difference: a review of methods for identifying regional climate responses to global warming targets

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The Paris Agreement long‐term global temperature goal refers to two global warming levels: well below 2°C and 1.5°C above preindustrial. Regional climate signals at specific global warming levels, and especially the differences between 1.5°C and 2°C, are not well constrained, however. In particular, methodological challenges related to the assessment of such differences have received limited attention. This article reviews alternative approaches for identifying regional climate signals associated with global temperature limits, and evaluates the extent to which they constitute a sound basis for impacts analysis. Four methods are outlined, including comparing data from different greenhouse gas scenarios, sub‐selecting climate models based on global temperature response, pattern scaling, and extracting anomalies at the time of each global temperature increment. These methods have rarely been applied to compare 2°C with 1.5°C, but some demonstrate potential avenues for useful research. Nevertheless, there are methodological challenges associated with the use of existing climate model experiments, which are generally designed to model responses to different levels of greenhouse gas forcing, rather than to model climate responses to a specific level of forcing that targets a given level of global temperature change. Novel approaches may be required to address policy questions, in particular: to differentiate between half degree warming increments while accounting for different sources of uncertainty; to examine mechanisms of regional climate change including the potential for nonlinear responses; and to explore the relevance of time‐lagged processes in the climate system and declining emissions, and the resulting sensitivity to alternative mitigation pathways. WIREs Clim Change 2017, 8:e457. doi: 10.1002/wcc.457 This article is categorized under: Assessing Impacts of Climate Change > Scenario Development and Application
Schematic representation of alternative pathways toward a specified global temperature interval (here 2°C). Each of the stars indicates a 2°C anomaly, but the pathways toward 2°C differ in terms of the rate of warming.
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Four methods for identifying regional climate signals at ΔTg increments. (a) Uncertainty ranges in ΔTg (relative to 1986–2005) for each Representative Concentration Pathway (RCP), from Intergovernmental Panel on Climate Change Fifth Assessment Report SPM6(a). (b) ΔTg time series (relative to 1861–1890) for different model runs, from Betts et al. Model runs which exceed 4°C are highlighted in orange. (c) Schematic illustration of pattern scaling: for each model (shown by different colors), regional climate anomalies are regressed against global temperature, and the gradient used to compute changes per °C (here the example used is regional mean precipitation change over southern Africa, relative to 1980–1999). (d) Schematic illustration of how samples could be extracted at the time each model's smoothed ΔTg time series exceeds 1.5 and 2°C. Two model runs are shown in orange and blue, with ΔTg relative to 1985–1999. The gray lines indicate 1.5 and 2°C, the arrows indicate the year at which these ΔTg increments are exceeded, and the orange and blue shaded areas illustrate the time periods to be sampled, centered around the date that 1.5 and 2°C are exceeded.
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Global mean surface temperature anomaly time series relative to 1985–1999 for 19 Coupled Model Intercomparison Project 3 models run in Special Report for Emissions Scenario A2. Thick black lines illustrate a typical time slice used to analyze regional projections (2075–2100), and the blue shading approximates the range of global temperature anomalies in this time slice. (Source: James, 2013)
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Cumulative density functions (CDFs) of projected regional aggregate changes at 1.5 and 2°C for the global land area between 66°N and 66°S (a) as well as two regions based on Intergovernmental Panel on Climate Change domains: the Mediterranean (b) and Central North America (c), for TXX (annual maximum value of daily maximum temperature) measured relative to standard deviation (σ) over 1986–2005, and CDD (annual maximum number of consecutive dry days for which precipitation is below 1 mm day−1) as change (%) relative to 1986–2005. Based on 11 Coupled Model Intercomparison Project 5 models for TXX and 14 for CDD. The solid lines indicate the median CDFs over the model ensemble and the shading, the respective likely range (that includes 66% of all ensemble members). (Adapted with permission from Ref )
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The map shows the global temperature increase (°C) needed for single locations to undergo a statistically significant change in average summer seasonal surface temperature over a 30‐year time slice, aggregated on a country level. The black line near the color bar denotes the committed global average warming if all atmospheric constituents were fixed at year 2000 levels. The small panels show the interannual summer‐season variability during the base period (1900–1929) (±2 standard deviations shaded in gray) and the multi‐model mean summer surface temperature (red line) of one arbitrarily chosen grid cell within the specific country. The shading in red indicates the 5 and 95% quantiles across all model realizations. (Reprinted with permission from Ref . Copyright 2011 IOP Publishing)
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Regional precipitation change (%) associated with global warming from four different ensembles of climate models: responses to 2 × CO2 in a large perturbed physics ensemble (PPE) (triangles), changes at 1, 2, 3, 4°C, etc. extracted using a time sampling approach from transient experiments from Coupled Model Intercomparison Project (CMIP) 3 [Special Report for Emissions Scenario (SRES) A2], CMIP5 (Representative Concentration Pathway 8.5), and a PPE (SRES A1FI) (purple, blue, and red box plots, respectively). The reference period is preindustrial for the PPEs and 1985–1999 for CMIP. (Reprinted with permission from Ref . Copyright 2014 American Meteorological Society)
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Schematic representation of relationship between emission scenarios, global temperature, regional climate responses, and impacts. (b) and (c) show the uncertainties associated with projections approaches based on Special Report for Emissions Scenario (SRES) and Representative Concentration Pathway (RCP) scenarios. (d) shows the implied uncertainty problem associated with differentiating between 1.5, 2°C, and other ΔTg increments (2.5°C shown here as an example). Limiting to 1.5 or 2°C raises questions associated with emissions pathways to get to these temperatures (the emissions question), as well as impacts associated with these temperatures (the impacts question). Here we focus on the regional climate aspect, highlighted by the orange arrow. (e) highlights different sources of uncertainty and their contribution to regional uncertainty. (Adapted with permission from Ref )
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