Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning

Sippel, Sebastian; Meinshausen, Nicolai; Merrifield, Anna; Lehner, Flavio; Pendergrass, Angeline G.; Fischer, Erich M.; Knutti, Reto

doi:10.1175/jcli-d-18-0882.1

Cited by 76 publications

(87 citation statements)

References 62 publications

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“…There are different ways to achieve this -utilizing the MMLEA as done here is one of them. Alternatively, new techniques to quantify and remove unforced variability from single simulations, such as dynamical adjustment or signal-to-noise maximization can be used (Wallace et al 2012;Smoliak et al 2015;Deser et al 2016;Sippel et al 2019;Wills et al in review) and should provide an improvement over a polynomial fit. Along with a better estimate of the forced response, SMILEs also enable estimating forced changes in variability if a sufficiently large ensemble is available (Milinski et al 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Thanks to their sample size, SMILEs have been applied most successfully to problems of regional detection and attribution (Deser et al 2012;Sanderson et al 2015;Frölicher et al 2016;Rodgers et al 2015;Mankin and Diffenbaugh 2015;Lehner et al 2017aLehner et al , 2018Kumar and Ganguly 2018;Schlunegger et al 2019;Marotzke 2019), extreme and compound events (Fischer et al 2014Schaller et al 2018;Kirchmeier-Young et al 2017), and as testbeds for method development (Lehner et al 2017b;McKinnon et al 2017;Frankignoul et al 2017;Wills et al 2018;Sippel et al 2019;Barnes et al 2019).…”

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confidence: 99%

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Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

Lehner¹,

Deser²,

Maher³

et al. 2020

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130

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Abstract. Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the iconic framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method error

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

Lehner¹,

Deser²,

Maher³

et al. 2020

Preprint

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130

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“…The focus of this study is on FLCs in the central Namib, from which the majority of historical and present-day station measurements stem (e.g., Nagel, 1959;Nieman et al, 1978;Lancaster et al, 1984;Seely and Henschel, 1998;Kaseke et al, 2017;Spirig et al, 2019). To provide a representative measure of the overall FLC cover in the central Namib on a daily basis, FLC occurrence is averaged between 03:00 and 09:00 UTC (local time is UTC +2 h) in the region between 22 and 24 • S and up to 100 km inland.…”

Section: Satellite Observations Of Flcsmentioning

confidence: 99%

Synoptic-scale controls of fog and low-cloud variability in the Namib Desert

et al. 2020

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Abstract. Fog is a defining characteristic of the climate of the Namib Desert, and its water and nutrient input are important for local ecosystems. In part due to sparse observation data, the local mechanisms that lead to fog occurrence in the Namib are not yet fully understood, and to date, potential synoptic-scale controls have not been investigated. In this study, a recently established 14-year data set of satellite observations of fog and low clouds in the central Namib is analyzed in conjunction with reanalysis data in order to identify synoptic-scale patterns associated with fog and low-cloud variability in the central Namib during two seasons with different spatial fog occurrence patterns. It is found that during both seasons, mean sea level pressure and geopotential height at 500 hPa differ markedly between fog/low-cloud and clear days, with patterns indicating the presence of synoptic-scale disturbances on fog and low-cloud days. These regularly occurring disturbances increase the probability of fog and low-cloud occurrence in the central Namib in two main ways: (1) an anomalously dry free troposphere in the coastal region of the Namib leads to stronger longwave cooling of the marine boundary layer, increasing low-cloud cover, especially over the ocean where the anomaly is strongest; (2) local wind systems are modulated, leading to an onshore anomaly of marine boundary-layer air masses. This is consistent with air mass back trajectories and a principal component analysis of spatial wind patterns that point to advected marine boundary-layer air masses on fog and low-cloud days, whereas subsiding continental air masses dominate on clear days. Large-scale free-tropospheric moisture transport into southern Africa seems to be a key factor modulating the onshore advection of marine boundary-layer air masses during April, May, and June, as the associated increase in greenhouse gas warming and thus surface heating are observed to contribute to a continental heat low anomaly. A statistical model is trained to discriminate between fog/low-cloud and clear days based on information on large-scale dynamics. The model accurately predicts fog and low-cloud days, illustrating the importance of large-scale pressure modulation and advective processes. It can be concluded that regional fog in the Namib is predominantly of an advective nature and that fog and low-cloud cover is effectively maintained by increased cloud-top radiative cooling. Seasonally different manifestations of synoptic-scale disturbances act to modify its day-to-day variability and the balance of mechanisms leading to its formation and maintenance. The results are the basis for a new conceptual model of the synoptic-scale mechanisms that control fog and low-cloud variability in the Namib Desert and will guide future studies of coastal fog regimes.

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“…However, the influence of internal variability on climate variables such as surface air temperature (SAT) can be quantified and accounted for in projections of future climate using dynamical adjustment methods (e.g. Deser et al, 2016;Sippel et al, 2019). Additionally, internal variability can be explicitly represented by sets of simulations from the same model, subject to identical forcing, in which members differ only by initial conditions (e.g.…”

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confidence: 99%

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

Merrifield

Brunner

Lorenz

et al. 2019

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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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Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning

Cited by 76 publications

References 62 publications

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

Synoptic-scale controls of fog and low-cloud variability in the Namib Desert

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

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