Impact of internal variability on climate change for the upcoming decades: analysis of the CanESM2-LE and CESM-LE large ensembles

Rondeau-Genesse, Gabriel; Braun, Marco

doi:10.1007/s10584-019-02550-2

Cited by 17 publications

(10 citation statements)

References 41 publications

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“…Analysis of observations shows that in the Mediterranean more than half of summer temperature variability can be explained by large-scale atmospheric circulations and sea surface temperatures (Xoplaki et al, 2003). The decrease in winter temperature IAV is suggested to be influenced by changing circulation patterns (Vautard and Yiou, 2009), and a decrease in variability of advected heat due to the decrease in the winter land-ocean temperature gradient (Holmes et al, 2016) and arctic amplification and sea ice loss (Screen, 2014;Sun et al, 2015;Tamarin-Brodsky et al, 2020), even under unchanged circulation variability (Holmes et al, 2016;Tamarin-Brodsky et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe

et al. 2020

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Abstract. For sectors like agriculture, hydrology and ecology, increasing interannual variability (IAV) can have larger impacts than changes in the mean state, whereas decreasing IAV in winter implies that the coldest seasons warm more than the mean. IAV is difficult to reliably quantify in single realizations of climate (observations and single-model realizations) as they are too short, and represent a combination of external forcing and IAV. Single-model initial-condition large ensembles (SMILEs) are powerful tools to overcome this problem, as they provide many realizations of past and future climate and thus a larger sample size to robustly evaluate and quantify changes in IAV. We use three SMILE-based regional climate models (CanESM-CRCM, ECEARTH-RACMO and CESM-CCLM) to investigate downscaled changes in IAV of summer and winter temperature and precipitation, the number of heat waves, and the maximum length of dry periods over Europe. An evaluation against the observational data set E-OBS reveals that all models reproduce observational IAV reasonably well, although both under- and overestimation of observational IAV occur in all models in a few cases. We further demonstrate that SMILEs are essential to robustly quantify changes in IAV since some individual realizations show significant IAV changes, whereas others do not. Thus, a large sample size, i.e., information from all members of SMILEs, is needed to robustly quantify the significance of IAV changes. Projected IAV changes in temperature over Europe are in line with existing literature: increasing variability in summer and stable to decreasing variability in winter. Here, we further show that summer and winter precipitation, as well as the two summer extreme indicators mostly also show these seasonal changes.

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Section: Discussionmentioning

confidence: 99%

Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe

et al. 2020

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“…Kay et al, 2015; Maher et al, 2019). These single member initial condition 55 2 https://doi.large ensembles or SMILEs have become an indispensable tool to concisely represent uncertainty within a model, information that should be considered in a multi-model ensemble context (Rondeau-Genesse and Braun, 2019).The prospect of including SMILE members into a multi-model ensemble highlights another tacit assumption made during multi-model ensemble construction: each member is an independent representation of climate. Though all members of a multimodel ensemble describe the same climate system, differences in performance tend to create a distribution of regional climate 60 change estimates.…”

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A weighting scheme to incorporate large ensembles in multi-model ensemble projections

Merrifield

Brunner

Lorenz

et al. 2019

Preprint

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Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs introduce "new" information into a multi-model ensemble by representing region-scale internal variability, but also introduce redundant information, by virtue of a single model being represented by 50-100 outcomes. To preserve the contribution of internal 5 variability and ensure redundancy does not overwhelm uncertainty estimates, a weighting approach is used to incorporate 50members of the Community Earth System Model (CESM1.2.2), 50-members of the Canadian Earth System Model (CanESM2), and 100-members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble. The weight assigned to each multi-model ensemble member is based on the member's ability to reproduce observed climate (performance) and scaled by a measure of redundancy (dependence). Surface air temperature 10 (SAT) and sea level pressure (SLP) diagnostics are used to determine the weights, and relationships between present and future diagnostic behavior are discussed. A new diagnostic, estimated forced trend, is proposed to replace a diagnostic with no clear emergent relationship, 50-year regional SAT trend.The influence of the weighting is assessed in estimates of Northern European winter and Mediterranean summer end-ofcentury warming in the CMIP5 and combined SMILE-CMIP5 multi-model ensembles. The weighting is shown to recover 15 uncertainty obscured by SMILE redundancy, notably in Mediterranean summer. For each SMILE, the independence weight of each ensemble member as a function of the number of SMILE members included in the CMIP5 ensemble is assessed. The independence weight increases linearly with added members with a slope that depends on SMILE, region, and season. Finally, it is shown that the weighting method can be used to guide SMILE member selection if a subsetted ensemble with one member per model is sought. The weight a SMILE receives within a subsetted ensemble depends on which member is used to represent 20 it, reinforcing the advantage of weighting and incorporating all initial condition ensemble members in multi-model ensembles. Projections of regional climate change are both key to climate adaptation policy and fundamentally uncertain due to the nature of the climate system (Deser et al., 2012;Kunreuther et al., 2013). In order to represent regional climate uncertainty to policy-25 makers, scientists often turn to multi-model ensembles to provide a range of plausible outcomes a region may experience (Tebaldi and Knutti, 2007). Uncertainty in a multi-model ensemble is commonly estimated from the ensemble spread, which can be represented e.g., as the 5-95% likely range of the distribution and is usually presented with respect to the ...

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“…It also suggests that the probability of extreme climate events, which are associated with large GMST warming or cooling trends, would be higher in climate models with a large ICV than in those with a low ICV. Therefore, the reason for the frequent occurrence of extreme climate events in recent decades including a surface warming hiatus (Chen et al ., 2012; Coumou and Rahmstorf, 2012; Maher et al ., 2014; Trenberth et al ., 2015; Rondeau‐Genesse and Braun, 2019) should be examined cautiously because we cannot exclude the possibility of a large ICV as the cause. This result also indicates that extreme event studies should use long simulations or ensembles of simulations to clarify their attribution.…”

Section: Resultsmentioning

confidence: 99%

Surface temperature variability in climate models with large and small internal climate variability

Yeh

Hyun

Park

et al. 2021

Quart J Royal Meteoro Soc

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By analyzing large ensemble simulations using the Community Earth System Model (CESM_LE), the Max Planck Institute Earth System Model Grand Ensemble (MPI_GE), and Coupled Model Intercomparison Project phase 5 (CMIP5) climate models, we quantified internal climate variability (ICV) of surface temperature in each model based on the spread of simulated global mean surface temperature from the ensemble mean. Then, we examined the characteristics of simulated surface temperature variability in climate models with large and small ICV in the present climate and in a future climate. Both the CESM_LE and MPI_GE members with large ICVs tended to simulate larger surface temperature variability at low latitudes, including El Niño and Southern Oscillation (ENSO) variability, and larger cooling and warming trends of the global mean surface temperatures than those with small ICVs in the present climate. Similar characteristics were observed in CMIP5 climate models with large and small ICVs in the present climate. This implies that surface temperature variability including extreme climate events should be cautiously examined in climate models with large and small ICVs. On the other hand, the characteristics of surface temperature variability simulated in the CMIP5 climate models with large or small ICVs were similar from the present climate to future climate with magnitude of ICVs. This was in contrast to that simulated in the CESM_LE and MPI_GE, in which the magnitude of ICV changes between the present climate and the future climate. We inferred that these differences between CMIP5 climate models and large ensemble simulations could primarily be attributed to intermodel differences in the CMIP5 climate models, including model physics and parameterizations.

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Impact of internal variability on climate change for the upcoming decades: analysis of the CanESM2-LE and CESM-LE large ensembles

Cited by 17 publications

References 41 publications

Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe

Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe

A weighting scheme to incorporate large ensembles in multi-model ensemble projections

Surface temperature variability in climate models with large and small internal climate variability

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