Editing IPCC:AR6/WGI/Chapter-10 (section)

===== 10.3.3.3.2 Tropical phenomena: ENSO teleconnections =====

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Model performance in simulating ENSO characteristics, including ENSO spatial pattern, frequency, asymmetry between warm and cold events, and diversity, is assessed in [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.3|Section 3.7.3]] ). The ability of the recent generation of GCMs and RCMs to adequately simulate ENSO-related teleconnections is reviewed here along with relevant methodological issues (see also Annex IV2.3.2, Figure 3.38 and [[IPCC:Wg1:Chapter:Chapter-3#3.7.3|Section 3.7.3]] ).

[[#Langenbrunner--2013|Langenbrunner and Neelin (2013)]] show that there is little improvement in CMIP5 relative to CMIP3 in amplitude and spatial patterns of the ENSO influence on boreal winter precipitation (spatial pattern correlations against observations are typically less than 0.5). However, the CMIP5 ensemble accurately represents the amplitude of the precipitation response in regions where observed teleconnections are strong. [[#Garcia-Villada--2020|Garcia-Villada et al. (2020)]] found a decline in performance of the representation of simulated ENSO teleconnection patterns for model experiments with fewer observational constraints. They also show that ENSO warm phase (El Niño) teleconnections are better represented than those for the cold phase (La Niña). Individual CMIP5 and CMIP6 models show a good ability to represent the observed teleconnections at aggregated spatial scales ( [[#Power--2018|Power and Delage, 2018]] ; [[IPCC:Wg1:Chapter:Chapter-3#3.7.3|Section 3.7.3]] and Figure 3.38). The evaluation of the atmospheric dynamical linkages is also an important part of the assessment. [[#Hurwitz--2014|Hurwitz et al. (2014)]] showed that CMIP5 models broadly simulate the expected (as seen in the MERRA reanalysis) upper-tropospheric responses to central equatorial Pacific or eastern equatorial Pacific ENSO events in boreal autumn and winter. CMIP5 models also simulate the correct sign of the Arctic stratospheric response, consisting of polar vortex weakening during eastern and central Pacific Niño events and vortex strengthening during both types of La Niña events. In contrast, most CMIP5 models do not capture the observed weakening of the Southern Hemisphere polar vortex in response to central Pacific ENSO events ( [[#Brown--2013|Brown et al., 2013]] ).

In RCMs, the effects of tropical large-scale modes and teleconnections are inherited through the boundary conditions and influenced by the size of the numerical domain. [[#Done--2015|Done et al. (2015)]] and [[#Erfanian--2018|Erfanian and Wang (2018)]] claim that large domains that include source oceanic regions are required to capture the remote influence of teleconnections, although, without spectral nudging, this can lead to biased synoptic-scale patterns ( [[#Prein--2019|Prein et al., 2019]] ). RCMs generally reproduce the regional precipitation responses to ENSO, and can sometimes even improve the representation of these teleconnections compared to the driving reanalysis ( [[#Endris--2013|Endris et al., 2013]] ; [[#Fita--2017|Fita et al., 2017]] ), but the overall performance may depend both on the driving reanalysis or GCM ( [[#Endris--2016|Endris et al., 2016]] ; [[#Chandrasa--2020|Chandrasa and Montenegro, 2020]] ) and on the chosen RCMs ( [[#Whan--2017|Whan and Zwiers, 2017]] ).

New studies since AR5 have shown that model performance assessment regarding ENSO teleconnections remains a difficult challenge due to the different types of ENSO and model errors in ENSO spatial patterns, as well as the strong influence of atmospheric internal variability at mid- to high latitudes ( [[#Coats--2013|Coats et al., 2013]] ; [[#Polade--2013|Polade et al., 2013]] ; [[#Capotondi--2015|Capotondi et al., 2015]] ; [[#Deser--2017c|Deser et al., 2017c]] ; [[#Tedeschi--2017|Tedeschi and Collins, 2017]] ; [[#Garcia-Villada--2020|Garcia-Villada et al., 2020]] ). Another difficulty comes from the non-stationary aspects of teleconnections in both observations and models, raising methodological questions on how best to compare a given model with another model or observations ( [[#Herein--2017|Herein et al., 2017]] ; [[#Perry--2017|Perry et al., 2017]] ; [[#O’Reilly--2018|O’Reilly, 2018]] ; [[#O’Reilly--2019|O’Reilly et al., 2019]] ; [[#Abram--2020|Abram et al., 2020]] ).

There is ''robust evidence'' that an accurate representation of both atmospheric circulation and sea surface temperature (SST) variability are key factors for the realistic representation of ENSO teleconnections in climate models. A robust and thorough evaluation of model performance regarding ENSO teleconnections is a challenging task with many methodological issues related to asymmetry between the warm and cold phases, non-stationarity and time-varying interaction between the Pacific and other ocean basins, signal-to-noise issues in the mid-latitudes and observational uncertainties, particularly for precipitation ( [[#10.2.2.3|Section 10.2.2.3]] ).

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