Editing IPCC:AR6/WGI/Chapter-4 (section)

=== 4.2.3 Sources of Near-term Information ===

<div id="h2-8-siblings" class="h2-siblings"></div>

This subsection describes the three main sources of near-term information used in Chapter 4. These are (i) the projections from the CMIP6 multi-model ensemble introduced in [[#4.2.1|Section 4.2.1]] ( [[#Eyring--2016|Eyring et al., 2016]] ; [[#O’Neill--2016|O’Neill et al., 2016]] ); (ii) observationally constrained projections ( [[#Gillett--2013|Gillett et al., 2013]] ; [[#Stott--2013|Stott et al., 2013]] ); and (iii) the initialized predictions contributed to CMIP6 from the Decadal Climate Prediction Project (DCPP; [[#Boer--2016|Boer et al., 2016]] ). The projections under (i) and the observational constraints under (ii) are used for all time horizons considered in this chapter, whereas the initialized predictions under (iii) are relevant only in the near term.

Observationally constrained projections ( [[#Gillett--2013|Gillett et al., 2013]] , 2021; [[#Shiogama--2016|Shiogama et al., 2016]] ; [[#Ribes--2021|Ribes et al., 2021]] ) use detection and attribution methods to attempt to reach consistency between observations and models and thus provide improved projections of near-term change. Notable advances have been made since AR5, for example the ability to observationally constrain estimates of Arctic sea ice loss for global warming of 1.5°C, 2.0°C, and 3.0°C above pre-industrial levels ( [[#Screen--2017|Screen and Williamson, 2017]] ; [[#Jahn--2018|Jahn, 2018]] ; [[#Screen--2018|Screen, 2018]] ; [[#Sigmond--2018|Sigmond et al., 2018]] ). There is ''high confidence'' that these approaches can reduce the uncertainties involved in such estimates.

The AR5 was the first IPCC report to assess decadal climate predictions initialized from the observed climate state ( [[#Kirtman--2013|Kirtman et al., 2013]] ), and assessed with ''high confidence'' that these predictions exhibit positive skill for near-term average surface temperature information, globally and over large regions, for up to ten years. Substantially more experience in producing initialized decadal predictions has been gained since AR5; the remainder of this subsection assesses the advances made.

Because the ‘memory’ that potentially enables prediction of multi-year to decadal internal variability resides mainly in the ocean, some systems initialize the ocean state only (e.g., [[#Müller--2012|Müller et al., 2012]] ; [[#Yeager--2018|Yeager et al., 2018]] ), whereas others incorporate observed information in the initial atmospheric states (e.g., [[#Pohlmann--2013|Pohlmann et al., 2013]] ; [[#Knight--2014|Knight et al., 2014]] ) or other non-oceanic drivers that provide further sources of predictability ( [[#Alessandri--2014|Alessandri et al., 2014]] ; [[#Weiss--2014|Weiss et al., 2014]] ; [[#Bellucci--2015a|Bellucci et al., 2015a]] ).

Ocean initialization techniques generally use one of two strategies. Under full-field initialization, estimates of observed climate states are represented directly on the model grid. A potential drawback is that predictions initialized using the full-field approach tend to drift toward the biased climate preferred by the model ( [[#Smith--2013a|Smith et al., 2013a]] ; [[#Bellucci--2015b|Bellucci et al., 2015b]] ; [[#Sanchez-Gomez--2016|Sanchez-Gomez et al., 2016]] ; [[#Kröger--2018|Kröger et al., 2018]] ; [[#Nadiga--2019|Nadiga et al., 2019]] ). Such drifts can be as large as, or larger than, the climate anomaly being predicted and may therefore obscure predicted climate anomalies ( [[#Kröger--2018|Kröger et al., 2018]] ) unless corrected for through post-processing. By contrast, anomaly initialization reduces drifts by adding observed anomalies (i.e., deviations from mean climate) to the mean model climate ( [[#Pohlmann--2013|Pohlmann et al., 2013]] ; [[#Smith--2013a|Smith et al., 2013a]] ; [[#Thoma--2015b|Thoma et al., 2015b]] ; [[#Cassou--2018|Cassou et al., 2018]] ), but has the disadvantage that the model state is then further from the real world from the start of the prediction. For both approaches, unrealistic features in the observation data used for initialization may induce unrealistic transient behavior ( [[#Pohlmann--2017|Pohlmann et al., 2017]] ; [[#Teng--2017|Teng et al., 2017]] ; [[#Nadiga--2019|Nadiga et al., 2019]] ), and non-linearity can reduce forecast skill ( [[#Chikamoto--2019|Chikamoto et al., 2019]] ). As yet, neither of the initialization strategies has been clearly shown to be superior ( [[#Hazeleger--2013|Hazeleger et al., 2013]] ; [[#Magnusson--2013|Magnusson et al., 2013]] ; [[#Smith--2013a|Smith et al., 2013a]] ; [[#Marotzke--2016|Marotzke et al., 2016]] ), although such comparisons may be sensitive to the model, region, and details of the initialization and forecast assessment procedures considered ( [[#Polkova--2014|Polkova et al., 2014]] ; [[#Bellucci--2015b|Bellucci et al., 2015b]] ).

There is also a wide range of techniques employed to assimilate observed information into models in order to generate suitable initial conditions ( [[#Polkova--2019|Polkova et al., 2019]] ). These range in complexity from simple relaxation towards observed time series of sea surface temperature (SST) ( [[#Mignot--2016|Mignot et al., 2016]] ) or wind stress anomalies ( [[#Thoma--2015a|Thoma et al., 2015a]] , b), to relaxation toward three-dimensional ocean and sometimes atmospheric state estimates from various sources (e.g., [[#Pohlmann--2013|Pohlmann et al., 2013]] ; [[#Knight--2014|Knight et al., 2014]] ; [[#Dunstone--2016|Dunstone et al., 2016]] ), or hybrid relaxation combining surface and tri-dimensional restoring as function of ocean basins and depth ( [[#Sanchez-Gomez--2016|Sanchez-Gomez et al., 2016]] ), to sophisticated data assimilation methods such as the ensemble Kalman filter ( [[#Nadiga--2013|Nadiga et al., 2013]] ; [[#Counillon--2014|Counillon et al., 2014]] , 2016; [[#Msadek--2014|Msadek et al., 2014]] ; [[#Karspeck--2015|Karspeck et al., 2015]] ; [[#Brune--2018|Brune et al., 2018]] ; [[#Cassou--2018|Cassou et al., 2018]] ; [[#Polkova--2019|Polkova et al., 2019]] ), the four-dimensional ensemble-variational hybrid data assimilation ( [[#He--2017|He et al., 2017]] , 2020) and the initialization of sea ice ( [[#Guemas--2016|Guemas et al., 2016]] ; [[#Kimmritz--2018|Kimmritz et al., 2018]] ). In addition, decadal predictions necessarily consist of ensembles of forecasts to quantify uncertainty, as discussed in [[#4.2.1|Section 4.2.1]] . A common way to generate an ensemble is through sets of initial conditions containing small variations that lead to different subsequent climate trajectories. A variety of methods and assumptions has been employed to generate and filter initial-condition ensembles for decadal prediction (e.g., [[#Marini--2016|Marini et al., 2016]] ; [[#Kadow--2017|Kadow et al., 2017]] ). As yet, there is no clear consensus as to which initialization and ensemble generation techniques are most effective, and evaluations of their comparative performance within a single modelling framework are needed ( [[#Cassou--2018|Cassou et al., 2018]] ).

A consequence of model imperfections and resulting model systematic errors is that estimates of these errors must be removed from the prediction to isolate the predicted climate anomaly and the phase of the decadal modes of climate variability ( [[#4.4.3.5|Sections 4.4.3.5]] and [[#4.4.3.6|4.4.3.6]] , and Annex IV, Sections AIV.2.6 and AIV.2.7). Because of the tendency for systematic drifts to occur following initialization, bias corrections generally depend on time since the start of the forecast, often referred to as lead time. In practice, the lead-time-dependent biases are calculated using ensemble retrospective predictions, also known as hindcasts, and recommended basic procedures for such corrections are provided in previous studies ( [[#Goddard--2013|Goddard et al., 2013]] ; [[#Boer--2016|Boer et al., 2016]] ). The biases are also dynamically corrected during hindcasts and predictions by incorporating the multi-year monthly mean analysis increments from the initialization into the initial condition at each integration step ( [[#Wang--2013b|Wang et al., 2013b]] ). Besides mean climate as a function of lead time, further aspects of decadal predictions may be biased, such as the modes of variability (see [[IPCC:Wg1:Chapter:Chapter-3#3.7|Section 3.7]] and Annex IV) upon which drift patterns are projected ( [[#Sanchez-Gomez--2016|Sanchez-Gomez et al., 2016]] ), and additional correction procedures have thus been proposed to remove biases in representing long-term trends ( [[#Kharin--2012|Kharin et al., 2012]] ; [[#Kruschke--2016|Kruschke et al., 2016]] ; [[#Balaji--2018|Balaji et al., 2018]] ; [[#Pasternack--2018|Pasternack et al., 2018]] ), as well as more general dependences of drift on initial conditions ( [[#Fučkar--2014|Fučkar et al., 2014]] ; [[#Pasternack--2018|Pasternack et al., 2018]] ; [[#Nadiga--2019|Nadiga et al., 2019]] ).

Many skill measures exist that describe different aspects of the correspondence between predicted and observed conditions, and no single metric should be considered exclusively. Important aspects of forecast performance captured by different skill measures include: (i) the ability to predict the sign and phases of the main modes of decadal variability and their regional fingerprint through teleconnections; (ii) the typical magnitude of differences between predicted and observed values, forecast reliability and resolution ( [[#Corti--2012|Corti et al., 2012]] ); and (iii) whether the forecast ensemble appropriately represents uncertainty in the predictions. A framework for skill assessment that encompasses each of these aspects of forecast quality has been proposed ( [[#Goddard--2013|Goddard et al., 2013]] ). A new, process-based method to assess forecast skill in decadal predictions is to analyse how well a specific mechanism is represented at each lead time ( [[#Mohino--2016|Mohino et al., 2016]] ).

One additional aspect of forecast quality assessment is that estimated skill can be degraded by errors in observational datasets used for verification, in addition to errors in the predictions ( [[#Massonnet--2016|Massonnet et al., 2016]] ; [[#Ferro--2017|Ferro, 2017]] ; [[#Karspeck--2017|Karspeck et al., 2017]] ; [[#Juricke--2018|Juricke et al., 2018]] ). This suggests that skill may tend to be underestimated, particularly for climate variables whose observational uncertainties are relatively large, and that the predictions themselves may prove useful for assessing the quality of observational datasets ( [[#Massonnet--2019|Massonnet, 2019]] ).

Skill assessmentshave shown that initialized predictions can out-perform their uninitialized counterparts ( [[#Doblas-Reyes--2013|Doblas-Reyes et al., 2013]] ; [[#Meehl--2014|Meehl et al., 2014]] ; [[#Bellucci--2015a|Bellucci et al., 2015a]] ; D.M. [[#Smith--2018|Smith et al., 2018]] , 2019; [[#Yeager--2018|Yeager et al., 2018]] ), although such comparisons are sensitive to the region and variable considered, multi-model predictions are generally more skilful than individual models ( [[#Doblas-Reyes--2013|Doblas-Reyes et al., 2013]] ; D.M. [[#Smith--2013b|Smith et al., 2013b]] , 2019). Considerable skill, especially for temperature, can be attributed to external forcings such as changes in GHG, aerosol concentrations, and volcanic eruptions. On a global scale, this contribution to skill has been found to exceed that from the prediction of internal variability except in the early stages (about one year for global SST) of the forecast (Corti et al., 2015; [[#Sospedra-Alfonso--2020|Sospedra-Alfonso and Boer, 2020]] ; [[#Bilbao--2021|Bilbao et al., 2021]] ), though idealized potential skill measures and observations-based studies suggest that improving the prediction of internal variability could extend this crossover to longer lead times ( [[#Boer--2013|Boer et al., 2013]] ; [[#Årthun--2017|Årthun et al., 2017]] ). In some cases, part of the skill arises from the ability of initialized predictions to capture observed transitions of major modes of climate variability ( [[#Meehl--2016|Meehl et al., 2016]] ) such as the Pacific Decadal Variability (PDV) and the Atlantic Multi-decadal Variability (AMV; see Sections 4.4.3.5 and 4.4.3.6, and Annex IV, Sections AIV.2.6 and AIV.2.7).

Initialized predictions of near-surface temperature are particularly skilful over the North Atlantic, a region of high potential and realized predictability ( [[#Keenlyside--2008|Keenlyside et al., 2008]] ; [[#Pohlmann--2009|Pohlmann et al., 2009]] ; [[#Boer--2013|Boer et al., 2013]] ; [[#Yeager--2017|Yeager and Robson, 2017]] ). Much of this predictability is associated with the North Atlantic subpolar gyre ( [[#Wouters--2013|Wouters et al., 2013]] ), where skill in predicting ocean conditions is typically high ( [[#Hazeleger--2013|Hazeleger et al., 2013]] ; [[#Brune--2020|Brune and Baehr, 2020]] ) and shifts in ocean temperature and salinity potentially affecting surface climate can be predicted up to several years in advance ( [[#Robson--2012|Robson et al., 2012]] ; [[#Hermanson--2014|Hermanson et al., 2014]] ), although such assessments remain challenging due to incomplete knowledge of the state of the ocean during the hindcast evaluation periods ( [[#Menary--2018|Menary and Hermanson, 2018]] ). A substantial improvement of the sub-polar gyre SST prediction is found in CMIP6 models, which is attributed to a more accurate response to the AMOC-related delayed response to volcanic eruptions ( [[#4.4.3|Section 4.4.3]] ; [[#Borchert--2021|Borchert et al., 2021]] ). A significant improvement GSAT prediction skill is also found over some land regions including East Asia ( [[#Monerie--2018|Monerie et al., 2018]] ), Eurasia ( [[#Wu--2019|Wu et al., 2019]] ), Europe ( [[#Müller--2012|Müller et al., 2012]] ; D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ) and the Middle East (D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ).

Skill for multi-year to decadal precipitation forecasts is generally much lower than for temperature, although one exception is Sahel rainfall ( [[#Sheen--2017|Sheen et al., 2017]] ), due to its dependence on predictable variations in North Atlantic SST through teleconnections (Annex IV; [[#Martin--2014a|Martin and Thorncroft, 2014a]] ). Predictive skill on decadal time scales is found for extratropical storm-tracks and storm density ( [[#Kruschke--2016|Kruschke et al., 2016]] ; [[#Schuster--2019|Schuster et al., 2019]] ), atmospheric blocking ( [[#Schuster--2019|Schuster et al., 2019]] ; [[#Athanasiadis--2020|Athanasiadis et al., 2020]] ), the Quasi-Biennial Oscillation (QBO; [[#Scaife--2014|Scaife et al., 2014]] ; [[#Pohlmann--2019|Pohlmann et al., 2019]] ) and over the tropical oceans (tropical trans-basin variability; [[#Chikamoto--2015|Chikamoto et al., 2015]] ). In addition, decadal predictions with large ensemble sizes are able to predict multi-annual temperature (Peters et al., 2011; [[#Sienz--2016|Sienz et al., 2016]] ; [[#Borchert--2019|Borchert et al., 2019]] ; [[#Sospedra-Alfonso--2020|Sospedra-Alfonso and Boer, 2020]] ), precipitation ( [[#Yeager--2018|Yeager et al., 2018]] ; D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ), and atmospheric circulation ( [[#Smith--2020|Smith et al., 2020]] ) anomalies over certain land regions, although the ensemble-mean magnitudes are much weaker than observed. This discrepancy may be symptomatic of an apparent deficiency in climate models that causes some predictable signal, such as that associated to the North Atlantic Oscillation (NAO; Section AIV.2.1), to be much weaker than in nature ( [[#Eade--2014|Eade et al., 2014]] ; [[#Scaife--2018|Scaife and Smith, 2018]] ; [[#Strommen--2019|Strommen and Palmer, 2019]] ; [[#Smith--2020|Smith et al., 2020]] ), while others, such as that linked to the SAM (Section AIV.2.2), are more consistent with observations ( [[#Byrne--2019|Byrne et al., 2019]] ).

Evidence is accumulating that additional properties of the Earth system relating to ocean variability may be skilfully predicted on multi-annual time scales. These include levels of Atlantic hurricane activity ( [[#Smith--2010|Smith et al., 2010]] ; [[#Caron--2017|Caron et al., 2017]] ), winter sea ice in the Arctic ( [[#Onarheim--2015|Onarheim et al., 2015]] ; [[#Dai--2020|Dai et al., 2020]] ), drought and wildfire ( [[#Chikamoto--2017|Chikamoto et al., 2017]] ; [[#Paxian--2019|Paxian et al., 2019]] ; [[#Solaraju-Murali--2019|Solaraju-Murali et al., 2019]] ), and variations in the ocean carbon cycle including CO <sub>2</sub> uptake (H. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] , 2019; [[#Lovenduski--2019|Lovenduski et al., 2019]] ; [[#Fransner--2020|Fransner et al., 2020]] ) and chlorophyll ( [[#Park--2019|Park et al., 2019]] ).

In summary, despite challenges ( [[#Cassou--2018|Cassou et al., 2018]] ), there is ''high confidence'' that initialized predictions contribute information to near-term climate change for some regions over multi-annual to decadal time scales. Furthermore, there are indications that initialized predictions can constrain near-term projections ( [[#Befort--2020|Befort et al., 2020]] ). The clearest improvements through initialization are seen in the North Atlantic and related phenomena such as hurricane frequency, Sahel and European rainfall. By contrast, there is ''medium'' or ''low confidence'' that uncertainty is reduced for other climate variables.

<div id="4.2.4" class="h2-container"></div>

<span id="pattern-scaling"></span>