Editing IPCC:AR6/WGI/Chapter-1 (section)

==== 1.4.2.1 Climate Variability Can Influence Trends Over Short Periods ====

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Natural variations in both weather and longer time scale phenomena can temporarily mask or enhance any anthropogenic trends (e.g., [[#Deser--2012|Deser et al., 2012]] ; [[#Kay--2015|Kay et al., 2015]] ). These effects are more important on small spatial and temporal scales but can also occur on the global scale (Cross-Chapter Box 3.1).

Since AR5, many studies have examined the role of internal variability through the use of ‘large ensembles’. Each such ensemble consists of many different simulations by a single climate model for the same time period and using the same radiative forcings. These simulations differ only in their phasing of the internal climate variations (also see [[#1.5.4.2|Section 1.5.4.2]] ). A set of illustrative examples using one such large ensemble ( [[#Maher--2019|Maher et al., 2019]] ) demonstrates how variability can influence trends on decadal time scales (Figure 1.13). The long-term anthropogenic trends in this set of climate indicators are clearly apparent when considering the ensemble as a whole (grey shading), and all the individual ensemble members have very similar trends for ocean heat content (OHC), which is a robust estimate of the total energy stored in the climate system (e.g., [[#Palmer--2014|Palmer and McNeall, 2014]] ). However, the individual ensemble members can exhibit very different decadal trends in global surface air temperature (GSAT), UK summer temperatures, and Arctic sea ice variations. More specifically, for a representative 11-year period, both positive and negative trends can be found in all these surface indicators, even though the long-term trend is for increasing temperatures and decreasing sea ice. Periods in which the long-term trend is substantially masked or enhanced for more than 20 years are also visible in these regional examples. This highlights the fact that observations are expected to exhibit short-term trends which are larger or smaller than the long-term trend or that differ from the average projected trend from climate models, especially on continental spatial scales or smaller (Cross-Chapter Box 3.1). The actual observed trajectory can be considered as one realization of many possible alternative worlds that experienced different weather; this is also demonstrated by the construction of ‘observation-based large ensembles’, which are alternate possible realizations of historical observations that retain the statistical properties of observed regional weather (e.g., [[#McKinnon--2018|McKinnon and Deser, 2018]] ).

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'''Figure 1.13 |''' '''Simulated changes in various climate indicators under historical and RCP4.5 scenarios using the MPI ESM Grand Ensemble.''' The grey shading shows the 5–95% range from the 100-member ensemble. The coloured lines represent individual example ensemble members, with linear trends for the 2011–2021 period indicated by the dashed lines. Changes in ocean heat content (OHC) over the top 2000 m represents the integrated signal of global warming '''(left)''' . The '''top row''' shows surface air temperature-related indicators (annual GSAT change and UK summer temperatures) and The '''bottom row''' shows Arctic sea ice-related indicators (annual ice volume and September sea ice extent). For smaller regions and for shorter time-period averages the variability increases and simulated short-term trends can temporarily mask or enhance anthropogenic changes in climate. Data from [[#Maher--2019|Maher et al. (2019)]] . Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

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