Disentangling the effects of internal variability and anthropogenic forcing on regional climate trends remains a key challenge with far-reaching implications. Due to its largely unpredictable nature on timescales longer than a decade, internal climate variability limits the accuracy of climate model projections, introduces challenges in attributing past climate changes, and complicates climate model evaluation. Here, we highlight recent advances in climate modeling and physical understanding that have led to novel insights about these key issues. In particular, we synthesize new findings from large-ensemble simulations with Earth system models, observational large ensembles, and dynamical adjustment methodologies, with a focus on European climate.
The climate system is highly variable in both space and time. This variability originates from processes within the coupled ocean–atmosphere–cryosphere–land–biosphere system and from external influences such as solar and orbital cycles, volcanic eruptions, and anthropogenic emissions of greenhouse gases and sulfate aerosols. A primary source of internally generated variability is the atmospheric general circulation, which produces familiar day-to-day and week-to-week weather fluctuations. The non-linear nature of atmospheric dynamics limits predictability to less than a few weeks; beyond this timescale, atmospheric motions may be considered to be random stochastic processes, often termed “weather noise” (e.g., Lorenz, 1963; Leith, 1973; James and James, 1992). It is important to note that such weather noise imparts variability on a continuum of timescales, from sub-monthly to decadal and longer (e.g., Madden, 1975; Deser et al., 2012; Thompson et al., 2015).
Another important source of internally generated variability is the coupling between the ocean and atmosphere. Large-scale air–sea interactions give rise to distinctive patterns (or “modes”) of variability on interannual and longer timescales, including phenomena such as the El Niño–Southern Oscillation (ENSO; Wang et al., 2017), Pacific decadal variability (PDV; Newman et al., 2016), and Atlantic multi-decadal variability (AMV; Zhang et al., 2019). Like the atmospheric general circulation, these coupled modes are governed by non-linear dynamical processes which limit their predictability. For example, forecast skill is generally limited to 1–2 years for ENSO (Jin et al., 2008; DiNezio et al., 2017; Wu et al., 2021), 5 years for PDV (Teng and Branstator, 2011; Meehl et al., 2016; Gordon and Barnes, 2022), and 10 years for AMV (Griffies and Bryan, 1997; Trenary and DelSole, 2016; Yeager et al., 2018). Beyond these predictability time horizons, internally generated variability can be thought of as a roll of the dice, introducing unavoidable uncertainty to climate model projections, especially at local and regional scales (e.g., Deser et al., 2012, 2014, 2020a).
Not only does the unpredictable internal variability cause irreducible uncertainty in future climate projections, but it also confounds interpretation of the historical climate record. For example, internal variability may partially obscure the regional climate response to external forcings including industrial greenhouse gas emissions, stratospheric ozone depletion, and volcanic eruptions (Wallace et al., 2013; Schneider et al., 2015; Lehner et al., 2016; McGraw et al., 2016). In some areas, climate trends driven by internal processes may even outweigh those due to anthropogenic influences over the past 30–60 years (Deser et al., 2012, 2016, 2017a; Wallace et al., 2013; Swart et al., 2015; Lehner et al., 2017). It is important to note that such internally generated multi-decadal trends need not originate from slow processes within the ocean or coupled ocean–atmosphere system; indeed, random fluctuations in the atmospheric circulation independent of oceanic influences have been shown to drive a large fraction of long-term precipitation and temperature trends over North America and Eurasia (Deser et al., 2012; McKinnon and Deser, 2018). The co-existence of internal and anthropogenic factors necessitates a probabilistic approach to detection and attribution of the human contribution to extreme weather events.
The prevalence of internal climate variability also complicates model evaluation efforts, since the simulated temporal sequence of (unpredictable) internal variability need not match observations, even if the model's physics are realistic. Furthermore, the brevity of the instrumental record provides only a limited sampling of internal variability, hindering robust model evaluation. Thus, climate models may show an apparent bias with respect to observations, but this could be entirely attributable to sampling issues rather than indicative of a true bias due to incorrect model physics. Apparent model bias due to sampling uncertainty must be kept in mind when assessing the fidelity of simulated modes of internal variability (e.g., Wittenberg, 2009; Deser et al., 2017; Capotondi et al., 2020; Fasullo et al., 2020; McKenna and Maycock, 2021), transient climate sensitivity (Dong et al., 2020; Andrews et al., 2022), and signal-to-noise properties of initial-value predictions and forced responses (e.g., Scaife and Smith, 2018; Smith et al., 2020; Klavans et al., 2021). In particular, even with 100 years of data, sampling uncertainty is a limiting factor for evaluating ENSO properties in climate models, including its global atmospheric teleconnections and associated climate impacts (Deser et al., 2017, 2018; Capotondi et al., 2020) and forced changes thereof (Stevenson et al., 2012; Maher et al., 2018; O'Brien and Deser, 2023). This issue is particularly acute for model assessment of modes of decadal variability such as PDV and AMV due to the paucity of samples in the short instrumental record (Deser and Phillips, 2021; Fasullo et al., 2020).
To overcome the issue of sampling uncertainty, a recent thrust in climate
modeling is to run a large number of simulations (30–100) with the same
coupled model and the same radiative forcing protocol (historical and/or
future scenario) but vary the initial conditions. The initial-condition
variation can be accomplished by introducing a random perturbation to the
atmosphere of the order of the model's numerical rounding-off error (e.g.,
10
Initial-condition large ensembles (LEs for short) have proven to be enormously useful for separating internal variability and forced climate change on regional scales in models and for providing robust sampling of models' internal variability by pooling together all of the ensemble members (e.g., Deser et al., 2012; Kay et al., 2015; Maher et al., 2019; Deser et al., 2020a; Lehner et al., 2020). They have also been used to assess externally forced changes in the characteristics of simulated internal variability, including extreme events for which large sample sizes are crucial (e.g., Tebaldi et al., 2021; O'Brien and Deser, 2023). Additionally, they have served as methodological test beds for evaluating approaches to the detection and attribution of anthropogenic climate change in the (single)
observational record (e.g., Deser et al., 2016; Barnes et al., 2019; Sippel
et al., 2019; Santer et al., 2019; Bonfils et al., 2020; Wills et al., 2020). Until the advent of LEs, it was problematic to identify the sources of model differences in the Coupled Model Intercomparison Project
(CMIP) archives due to the limited number of simulations (generally
Just as in a model LE, the sequence of internal variability in the real world could have unfolded differently. That is, the observational record traces only one of many possible climate histories that could have happened under the same external radiative forcing. For example, El Niño and La Niña events could have occurred in a different set of years, and positive or negative regimes of PDV and AMV could have taken place in different decades. This concept of alternate chronologies, sometimes referred to as the theory of parallel climate realizations (Tél et al., 2020) or the notion of contingency (Gould, 1989), has major implications that call for a reframing of perspective. For example, it means that a single model simulation of the historical period need not match the observed record, even if the model is “perfect” in its physical representation of the real world's climate. However, the statistical characteristics of the model's internal variability must agree with those of the real world, taking into account sampling uncertainty (uncertainty due to limited sampling in the short observational record). Thus, while a single ensemble member need not match observations, the ensemble as a whole should encompass the instrumental data, provided there are enough members to adequately span the range of possible sequences of internal variability (Suarez-Guttierez et al., 2021).
Another implication of the concept of parallel climate realizations is that the climate trends we have experienced are not the only ones that could have occurred under the same radiative forcing conditions. In analogy with a model LE, the observational record is just one “member” of a larger set of possible “members”, each with a different (and largely unpredictable) chronology of internal variability. Although one cannot replay the tape of history, one can construct an observational LE by generating alternate synthetic sequences of internal variability from the instrumental data. Conceptually, this involves removing an estimate of the forced component from the data and then randomizing the residual (internal) variability in time. Importantly, the randomization procedure must be done in a way that preserves the statistical properties of the observed variability including its variance, temporal autocorrelation, and spatial patterns. The resulting synthetic sequences of internal variability derived from the observational record can then be added back to the time-evolving forced response obtained from a climate model LE.
The development of statistically based observational LEs is just beginning,
with recent efforts targeting surface climate fields (McKinnon et al., 2017;
McKinnon and Deser, 2018, 2021) and carbon dioxide fluxes across the
air–sea interface (Olivarez et al., 2022). Here, we focus on the work of
McKinnon and Deser (2018, 2021), who constructed an observational LE for
global sea level pressure (SLP) and terrestrial precipitation and
temperature based on
Determining the forced contribution to observed changes in climate remains an ongoing challenge. Most detection and attribution methods rely on climate models to provide a set of spatial and temporal “fingerprints” of forced climate change that are distinct from patterns of internal variability (Hegerl et al., 2007; Santer et al., 2019; Sippel et al., 2019). These model-based fingerprints are then used to assess the proportion of observed climate change that is due to external forcing. However, model shortcomings may limit the accuracy of such methods. Thus, it is also desirable to develop complementary approaches to attribution that do not rely on climate model information. Two such methods, linear inverse modeling (Newman, 2007) and low-frequency pattern analysis (Wills et al., 2020), leverage the assumption that forced climate change evolves slowly compared to the timescales of internal variability. However, decadal shifts in regional anthropogenic aerosol emissions (Deser et al., 2020b; Persad et al., 2018), in addition to decadal changes in solar and volcanic activity and the rate of greenhouse gas rise, present challenges to this assumption and may complicate interpretation of the results.
A complementary, physically based approach to isolating the externally forced response in observations without reliance on climate model information is the technique of dynamical adjustment. This method aims to remove the influence of atmospheric circulation variability from observed temperature and precipitation data, thereby revealing the thermodynamically induced component of observed climate change (Wallace et al., 2013; Smoliak et al., 2015; Deser et al., 2016; Guo et al., 2019). According to the current generation of coupled climate models, the forced component of extratropical atmospheric circulation changes is small relative to internal variability (Deser et al., 2012; Shepherd, 2014). If models are correct in this regard, then dynamical adjustment can be used to parse the relative contributions of internal dynamics and forced thermodynamics to observed climate changes at middle and high latitudes (Wallace et al., 2013; Deser et al., 2016). A variety of dynamical adjustment algorithms has been developed and tested within the framework of a model LE (Deser et al., 2016; Lehner et al., 2017, 2018; Smoliak et al., 2015; Guo et al., 2019; Merrifield et al., 2017; Terray, 2021a; Sippel et al., 2019). These protocols are all based on statistical associations between patterns of SLP and temperature or precipitation deduced from long observational records. Generally, the data are high-pass filtered or detrended so as to avoid aliasing any potential forced component onto the statistical relationships. These procedures generally work best for large-amplitude SLP anomaly patterns and are more effective for temperature than precipitation due to higher levels of noise in the latter (Guo et al., 2019).
We make use of a state-of-the-art 100-member LE conducted with the National
Center for Atmospheric Research (NCAR) Community Earth System Model version 2 (CESM2), described in Rodgers et al. (2021). This publicly available LE
resource is unprecedented for its combination of large ensemble size, high
spatial resolution (approximately 1
For consistency with the 100-member CESM2 LE, we make use of the first 100 members of the observational LE (OBS LE) constructed by McKinnon and Deser (2018) to illustrate the diversity of past 50-year trends consistent with the statistical spatiotemporal properties of internal variability in the observational record. For the purpose of comparing directly to the CESM2 LE, we have added the model's forced trend to the internal trend of each OBS LE member. The OBS LE is based on the Berkeley Earth Surface Temperature (BEST) dataset (Rohde et al., 2013), the Global Precipitation Climatology Centre (GPCC) dataset (Schneider et al., 2014), and the Twentieth Century Reanalysis version 2c (20CR) sea level pressure (SLP) dataset (Compo et al., 2011).
We apply the dynamical adjustment methodology of Deser et al. (2016), based on SLP-constructed circulation analogues, to monthly temperature and precipitation during 1900–2021, using the same observational datasets as in the OBS LE. The reader is referred to Deser et al. (2016), for details of the methodology, and to Lehner et al. (2017, 2018), Guo et al. (2019), and Terray (2021a), for additional applications.
For each ensemble member of the CESM2 and OBS LEs, we form monthly anomalies by subtracting the long-term means for each month individually and then form seasonal averages (December–February) of the monthly anomalies. We compute 50-year trends of the wintertime anomalies using linear least-squares regression analysis. All results shown in this study are original findings.
We begin by illustrating the diversity of winter temperature and precipitation trends over Europe during the past 50 years (1972–2021) in the CESM2 and OBS LEs (Sect. 3.1 and 3.2) and projected for the next 50 years (2022–2071) in the CESM2 LE (Sect. 3.3). We then provide a more quantitative view of the relative contributions of forced climate change and internal variability to past and future climate trends using a variety of signal-to-noise metrics, with comparison between the CESM2 and OBS LEs (Sect. 3.4). We summarize the CESM2 LE results by showing the expected range of trend outcomes in Sect. 3.5. Finally, we apply the technique of dynamical adjustment to estimate the forced component of observed temperature trends (Sect. 3.6) and then use this estimate in conjunction with the OBS LE to produce a purely observational estimate of the plausible range of temperature trend outcomes over the past 60 years (Sect. 3.7).
Winter air temperature trends (
The CESM2 model simulates a wide range of wintertime temperature trend
patterns for the past 50 years due to the combined effects of internal
variability and forced response, as illustrated by the first 28 members of
the LE (Fig. 1). Recall that the only reason that these trend maps are not
identical is because of random differences in internal variability between
the members. While moderate warming is seen over most of the European
continent in the majority of cases, as expected, some members show regions
of considerably greater temperature increase (in excess of 1
As in Fig. 1 but for precipitation (mm d
Like temperature, precipitation trends also vary considerably across ensemble members (Fig. 2). While the ensemble-mean trend shows modest increases in precipitation throughout Europe (except for the southernmost fringes), internal variability can evidently overwhelm the forced response in individual simulations. For example, some members show drying over large parts of the continent, while others depict enhanced wetting in the same regions (compare, for example, members 22 and 28, which show nearly opposite patterns). Observed precipitation trends are generally positive, except over Spain, Portugal, southern France, and other parts of the western Mediterranean (Fig. 2). The observed precipitation increases, while of the same sign as the model's forced response, are approximately twice as large in many areas. Again, the interpretation of the observed trends is ambiguous, since there are individual members that resemble observations (for example, member 1).
As in Fig. 1 but for the observational large ensemble of McKinnon and Deser (2018), with the ensemble mean from the 100-member CESM2 large ensemble. See text for details.
As in Fig. 2 but for the observational large ensemble of McKinnon and Deser (2018), with the ensemble mean from the 100-member CESM2 large ensemble. See text for details.
The individual members of the OBS LE show a qualitatively similar diversity in the 50-year temperature trends as the CESM2 LE (Fig. 3). Like CESM2, some members show weak cooling in some areas, while others show widespread moderate or strong warming. This suggests that the resemblance between the observed trend and the model's forced response may be purely coincidental. Precipitation trends in the OBS LE also display large contrasts between members, similar to CESM2 (Fig. 4). For example, nearly opposite patterns are found between members 6 and 11 (or 8 and 9). Trend amplitudes also vary considerably across the OBS LE, with larger magnitudes in some members (for example, members 3 and 20) compared to others (e.g., members 21 and 13). While no single member of the 28 OBS LE samples shown matches the model's forced trend, member 21, with its relatively muted trends, comes close.
As in Fig. 1 but for the period 2022–2071.
As expected, temperature trends projected for the next 50 years show larger
amplitudes than those for the past 50 years in the CESM2 LE (Fig. 5). This
is due to the fact that the forced (ensemble-mean) component of warming
increases as greenhouse gas emissions accelerate. In most regions, the
forced warming trend increases by approximately 0.2
As in Fig. 2 but for the period 2022–2071.
Forced trends in precipitation are projected to amplify over the next 50 years, with greater wetting over northern Europe and drying over southern
Europe and the Mediterranean (Fig. 6). In addition, the region with a forced
drying trend is projected to expand northward into Spain, Italy, and the
Balkans. While the forced pattern of future drying in the south and
wetting in the north is generally evident in most of the simulations shown,
there are notable differences in amplitude across the members. For example,
member 28 shows precipitation trends in excess of 0.1 mm d
Standard deviation of 50-year trends (1972–2021) across 100
members of the CESM2 large ensemble
In the previous section, we conveyed a qualitative impression of the
possible range of 50-year trends due to the superposition of internal
variability and forced climate change in the CESM2 and OBS LEs. Here, we
provide a more quantitative view, beginning with a comparison of the
standard deviation (
Signal-to-noise ratio of forced trends in winter
Next, we assess the relative magnitude of the forced and internal components
of trends by computing a signal-to-noise ratio defined as the CESM2
ensemble-mean trend divided by the
How much do model biases in the ensemble
As expected, signal-to-noise values are higher for forced trends in the
future than in the past. A total of 97 % of the area of the continent (excluding Iceland and Greenland) shows a signal-to-noise value
The percentage of ensemble members with a positive trend in winter
Another way to view the relative impacts of internal variability and
external forcing on trends is by computing the fraction of ensemble members
at each location that shows a positive trend (e.g., warming or wetting). This
metric conveys the likelihood of having a positive (or negative) trend in
any single ensemble member, which is analogous to the single realization
of the real world. At nearly all locations, more than 95 % of ensemble
members in the CESM2 LE show warming in both the past and future periods,
with slightly lower percentages (85 %–95 %) over western Scandinavia and parts of Great Britain (and
The sign of the trend in any given ensemble member is more uncertain for
precipitation than for temperature. The highest chances (
Taken together, the results shown in Fig. 9 indicate that warming is virtually guaranteed at nearly all locations, both in the past 50 years and the next 50 years, according to the CESM2 LE. However, the sign of the precipitation trend (past and future) is robust only over the northern tier of the continent and only in the future over the Mediterranean region. The model results for past trends are found to be generally credible, as measured against the OBS LE, with some overestimation in north–central Europe.
As the saying goes, “climate is what we expect; weather is what we get”. This adage is also applicable to climate change, where “human-induced climate change is what we expect; internal variability plus human-induced climate change is what we get” (Deser, 2020). Here, we illustrate “what we expect” and the range of “what we get” for past and future 50-year trends in the CESM2 LE, using the ensemble mean for what we expect and two contrasting ensemble members for the range of what we get. We select the contrasting members from the bottom and top 5th percentiles of the distribution of 100-member trends averaged over the European continent for each period separately. This selection criterion is somewhat arbitrary and neither necessarily captures the wide range of trend amplitudes that may occur at a single location or sub-region, nor does it portray the full range of spatial patterns that occur within the ensemble.
A range of outcomes. Trends in winter air temperature (color
shading;
There is a large range in temperature trend outcomes (what we get) for
both the past 50 years and the next 50 years, as depicted by the warm and
cool end-members (Fig. 10). For past trends, the warm end-member
shows temperature increases of 0.9–1.1
As mentioned in Sect. 1.4, previous work has shown that internal variability in the large-scale atmospheric circulation causes much of the member-to-member differences in temperature trends in model LEs. Here, we provide a qualitative indication of the circulation influence by superimposing SLP trends upon the maps in Fig. 10. In the case of past trends, the warm member shows a positive North Atlantic Oscillation (NAO)-like pattern (Hurrell et al., 2003), with negative SLP trends centered near Iceland and positive SLP trends centered over the Mediterranean (Fig. 10b). This SLP pattern is indicative of stronger westerly/southwesterly flow, which brings relatively warm maritime air over the continent. The cool member shows a largely opposite flow configuration (albeit with longitudinal shifts in the SLP centers of action), which advects relatively cold air from the east over the continent (Fig. 10d). In comparison, the forced response shows negligible atmospheric circulation change (Fig. 10c). Striking contrasts in circulation are also found for the future period, with a large positive NAO-like trend pattern in the warm member and a blocking continental high in the cool member (Fig. 10f and h). Future trends in SLP also contain a modest forced component indicative of enhanced westerlies over the continent (Fig. 10g).
As in Fig. 10 but for precipitation (mm d
The wet and dry end-members also show striking regional contrasts in
both precipitation and circulation (Fig. 11). For example, for past trends,
the wet member shows precipitation increases of 0.2–0.3 mm d
The empirical method of dynamical adjustment introduced in Sect. 1.4 can be used to estimate the circulation-induced component of observed temperature anomalies; this dynamically induced contribution can then be subtracted from the original anomaly to obtain the thermodynamically induced component as a residual. Since this method uses no information from climate models, it provides an independent estimate of the thermodynamic component of observed temperature trends, which can be compared with the forced response simulated by climate model LEs.
Decomposition of
Figure 12 shows the decomposition of observed December–February (DJF) temperature trends into their dynamical and residual thermodynamic contributions. For this example, we have used the 60-year period 1962–2021, when observed SLP trends are more than twice as large as those during 1972–2021 on a per-decade basis (compare SLP contours in Figs. 10a and 12a). Observed SLP trends during the past 60 years show a pronounced positive NAO-like pattern, with maximum negative values of
As in Fig. 12 but for precipitation (mm d
Precipitation is an inherently noisier field than temperature in both time
and space, making it challenging to extract the forced signal via dynamical adjustment; indeed, only one previous study has attempted a dynamical adjustment of observed precipitation trends (Guo et al., 2019). Keeping in mind that the estimate of the circulation-induced component of precipitation trends may be less robust than for temperature, we present the results as a proof of concept. Observed precipitation trends during 1962–2021 are mainly driven by changes in atmospheric circulation, with a small thermodynamic residual component (Fig. 13). This residual component bears some resemblance to the forced response in CESM2, particularly in terms of amplitude (
As in Fig. 10 but for the period 1962–2021. The top row
We conclude by bringing together the results of the observational LE and
dynamical adjustment to produce a fully observationally based estimate of the range of the past 60 years of trends in temperature and precipitation. To the best of our knowledge, this is first time that these two approaches have
been combined. Specifically, we add the internal component of trends from
each member of the OBS LE to the thermodynamic residual trend (the estimate of the observed forced response) obtained from dynamical adjustment. As before, we select two contrasting ensemble members from the tails of the distribution based on Europe-wide averages to illustrate the range of trend outcomes. The warm end-member shows pronounced temperature increases over the northern two-thirds of the continent, with maximum values in excess of 0.9
Precipitation trends in the wet and dry end-members are also similar
between the model and observationally based results (Fig. 15). The wet
members show widespread increases in precipitation over southern and central
Europe (maximum values of 0.2–0.4 mm d
As in Fig. 14 but for precipitation (mm d
Disentangling the effects of internal variability and anthropogenic forcing on regional climate trends remains a long-standing issue in climate sciences. Recent advances in climate modeling and physical understanding have led to new insights about this topic and provided an improved source of information on the future risks of weather extremes associated with human-induced climate change. Here, we have highlighted new findings for European winter climate based on the following complementary tools: Earth system model large-ensemble simulations, an observationally based large ensemble, and an empirical approach for removing the influence of atmospheric circulation variability from observed temperature and precipitation data, which is termed dynamical adjustment.
The new 100-member CESM2 large ensemble shows that internal climate variability imparts considerable uncertainty to past and future 50-year trends in winter temperature and precipitation over Europe. Such uncertainty is irreducible due to the lack of predictability of the simulated internal variability on decadal timescales. A novel synthetic large ensemble constructed from the statistical characteristics of internal variability in the observational record exhibits quantitatively similar levels of uncertainty in the past 50 years of trends as the CESM2 LE, reinforcing the credibility of the model's internally generated trends. Additionally, the results of our dynamical adjustment procedure applied to observations shows good agreement between the observed thermodynamic residual trend component and the model's forced thermodynamic trend, further underscoring the realism of CESM2. Finally, we have combined internal variability in trends from an observational large ensemble with an observational estimate of the forced trend (the thermodynamic residual component obtained from dynamical adjustment) to show what the observed range of past trends in European temperature and precipitation could have been. Because it does not rely on climate model information, this observationally based range of trend outcomes provides a powerful test for the range of simulated trends in a model large ensemble. To the best of our knowledge, this is the first time that such a synthesis of the two purely observational methods has been undertaken.
Many outstanding questions remain regarding the relative influences of internal climate variability and anthropogenic forcing on regional climate change in models and the real world. Fortunately, promising new tools are being developed to help address these challenges. For example, innovative machine learning methods may be able to improve upon existing techniques for constructing observational large ensembles. Such methods have shown good results as statistical emulators of model-based LEs, but their application to the observational record remains to be pursued (Beusch et al., 2020). Similarly, neural network approaches to dynamical adjustment may offer increased skill compared to conventional methods (Davenport and Diffenbaugh, 2021) but have yet to be applied with the aim of separating forced and internal components of observed trends. Complementary physically based approaches such as linear inverse modeling and low-frequency pattern analysis, mentioned in Sect. 1.4, also offer promise for estimating the forced response in observations without reliance on climate models and should be pursued more widely.
We have relied on the fact that the CESM2 LE (like other models of its class; see Deser et al., 2020a, and references therein) simulates a negligible forced atmospheric circulation trend over the past 50–60 years to interpret our observed dynamical adjustment results (i.e., we have equated the observed dynamically induced trend with the internal component, and the observed thermodynamic residual trend with the forced component). If the model is erroneous in this regard, then our interpretation of our decomposition of observed trends into internal dynamical and forced thermodynamic components is flawed. Indeed, recent work suggests that climate models may be less predictable on seasonal-to-decadal timescales than the real world, particularly in terms of the large-scale extratropical atmospheric circulation (the so-called signal-to-noise paradox; e.g., Scaife et al., 2014; Eade et al., 2014; Scaife and Smith, 2018). But whether the results from such initial-value predictability studies carry over to the models' forced atmospheric circulation responses to anthropogenic emissions remains an open question. Finally, a recent study by Strommen et al. (2022) finds that the inclusion of stochastic parameterizations amplifies the simulated atmospheric circulation response to sea surface temperature and Arctic sea ice anomalies. Such stochastic parameterizations may represent unresolved air–sea coupling processes in coarse-resolution climate models such as CESM2. Emerging efforts to develop mesoscale–eddy-resolving global coupled climate models may provide more definitive answers to this elusive challenge in the near future.
All data used in this study are publicly available, as follows:
CESM2 large ensemble at GPCC precipitation at BEST temperature at ERA5 SLP at
The code used to create the observational large-ensemble and dynamical
adjustment results are publicly available at
CD led the overall effort and wrote the paper. ASP performed some of the calculations and prepared the figures.
The contact author has declared that neither of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the special issue “Interdisciplinary perspectives on climate sciences – highlighting past and current scientific achievements”. It is not associated with a conference.
We acknowledge the efforts of all those who contributed to producing the model simulations and observational datasets used in this study. We thank the reviewers, for their constructive comments and suggestions, Laurent Terray, for providing the dynamical adjustment results, and Karen McKinnon, for providing the observational large ensemble results. The National Center for Atmospheric Research is sponsored by the National Science Foundation.
NCAR is a major facility sponsored by the U.S. National Science Foundation (cooperative agreement no. 1852977).
This paper was edited by Valerio Lembo and reviewed by Tamas Bodai and one anonymous referee.