From emission scenarios to spatially resolved projections with a chain of computationally efficient emulators: MAGICC (v7.5.1) – MESMER (v0.8.1) coupling

Beusch, Lea; Nicholls, Zebedee; Gudmundsson, Lukas; Hauser, Mathias; Meinshausen, Malte; Seneviratne, Sonia I.

doi:10.5194/gmd-2021-252

Cited by 8 publications

(6 citation statements)

References 41 publications

(63 reference statements)

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“…To this effect, STITCHES can be deployed on available simulations of the target scenario and neighboring scenarios, all potential sources of usable time samples. In this context however we also see promising complementarity with recently developed emulators that focus specifically on estimating the statistical characteristics of an ESM internal variability and randomly generating new realizations of it (Beusch et al, 2020(Beusch et al, , 2022Nath et al, 2022;Quilcaille et al, 2022;Liu et al, 2022).…”

Section: Methodsmentioning

confidence: 85%

STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations

2022

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Abstract. Climate model output emulation has long been attempted to support impact research, mainly to fill in gaps in the scenario space. Given the computational cost of running coupled earth system models (ESMs), which are usually the domain of supercomputers and require on the order of days to weeks to complete a century-long simulation, only a handful of different scenarios are usually chosen to externally force ESM simulations. An effective emulator, able to run on standard computers in times of the order of minutes rather than days could therefore be used to derive climate information under scenarios that were not run by ESMs. Lately, the necessity of accounting for internal variability has also made the availability of initial-condition ensembles, under a specific scenario, important, further increasing the computational demand. At least so far, emulators have been limited to simplified ESM-like output, either seasonal, annual, or decadal averages of basic quantities, like temperature and precipitation, often emulated independently of one another. With this work, we propose a more comprehensive solution to ESM output emulation. Our emulator, STITCHES, uses existing archives of earth system models' (ESMs) scenario experiments to construct ESM-like output under new scenarios or enrich existing initial-condition ensembles, which is what other emulators also aim to do. Importantly, however, STITCHES' output has the same characteristics of the ESM output it sets out to emulate: multivariate, spatially resolved, and high frequency, representing both the forced component and the internal variability around it. STITCHES extends the idea of time sampling – according to which climate outcomes are stratified by the global warming level at which they manifest themselves, irrespective of the scenario and time at which they occur – to the construction of a continuous history of ESM-like output over the whole 21st century, consistent with a 21st-century trajectory of global surface air temperature (GSAT) derived from the scenario that has been chosen as the target of the emulation. STITCHES does so by first splitting the target GSAT trajectory into decade-long windows, then matching each window in turn to a decade-long window within an existing model simulation from the available scenario runs according to its proximity to the target in absolute size of the temperature anomaly and its rate of change. A look-up table is therefore created of a sequence of existing experiment–time-window combinations that, when stitched together, create a GSAT trajectory “similar” to the target. Importantly, we can then stitch together much more than GSAT from these windows, i.e., any output that the ESM has saved for these existing experiment–time-window combinations, at any frequency and spatial scale available in its archive. We show that the stitching does not introduce artifacts in the great majority of cases (we look at temperature and precipitation at monthly frequency and on the native grid of the ESM and at an index of ENSO activity, the Southern Oscillation Index). This is true even if the criteria for the identification of the decades to be stitched together are chosen to work for a smoothed time series of annual GSAT, a result we expect given the larger amount of noise affecting most other variables at finer spatial scales and higher frequencies, which therefore are more “forgiving” of the stitching. We successfully test the method's performance over many ESMs and scenarios. Only a few exceptions surface, but these less-than-optimal outcomes are always associated with a scarcity of the archived simulations from which we can gather the decade-long windows that form the building blocks of the emulated time series. In the great majority of cases, STITCHES' performance is satisfactory according to metrics that reward consistency in trends, interannual and inter-ensemble variance, and autocorrelation structure of the time series stitched together. The method therefore can be used to create ESM-like output according to new scenarios, on the basis of a trajectory of GSAT produced according to that scenario, which could be easily obtained by a simple climate model. It can also be used to increase the size of existing initial-condition ensembles. There are aspects of our emulator that will immediately disqualify it for specific applications, like when climate information is needed whose characteristics result from accumulated quantities over windows of times longer than those used as pieces by STITCHES, droughts longer than a decade for example. But for many applications, we argue that a stitched product can satisfy the climate information needs of impact researchers. STITCHES cannot emulate ESM output from scenarios that result in GSAT trajectories outside of the envelope available in the archive, nor can it emulate trajectories with shapes different from existing ones (overshoots with negative derivative, for example). Therefore, the size and characteristics of the available archives of ESM output are the principal limitations for STITCHES' deployment. Thus, we argue for the possibility of designing scenario experiments within, for example, the next phase of the Coupled Model Intercomparison Project according to new principles, relieved of the need to produce a number of similar trajectories that vary only in radiative forcing strength but more strategically covering the space of temperature anomalies and rates of change.

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Section: Methodsmentioning

confidence: 85%

STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations

2022

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“…One obvious way in which to apply such approaches to ClimateBench is to marry the simple impulse response models discussed in Section 1 with more complex methods to predict the spatial response. Such an approach has recently been demonstrated for temperature (Beusch et al., 2021) but could conceivably be extended to modeling each of the fields targeted in ClimateBench. A more unified, and ambitious, approach would be to model the ordinary differential equations of the response to a forcing directly in the statistical emulator using either numerical GPs (Raissi et al., 2018) or Fourier neural operators (Li et al., 2020).…”

Section: Discussionmentioning

confidence: 99%

ClimateBench v1.0: A Benchmark for Data‐Driven Climate Projections

Watson-Parris

Rao

Olivié

et al. 2022

J Adv Model Earth Syst

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We introduce the first benchmark for emulation of key spatially resolved climate variables derived from a full complexity Earth System Model • Three baseline emulators are presented which are able to predict regional temperature and precipitation with varying skill • Evaluation metrics and areas for future research are presented to encourage further development of trustworthy data-driven climate emulators

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“…We would like to note that alternative to SMILEs exist. For example, climate emulators 99,100 simulate large sample sizes at low-computational cost; however, despite some exceptions 86 , they are…”

Section: Discussionmentioning

confidence: 99%

“…We used seven SMILEs: CESM1-CAM5 103 (including 40 ensemble members), CSIRO-Mk3-6-0 104 (30), CanESM2 93 (50), EC-EARTH 105 (16), GFDL-CM3 106 (20), GFDL-ESM2M 107 (30), and MPI-GE 108 (100), providing data for the period 1950-2099 (based on the RCP8.5 emission scenario 109 after 2005). We considered the period 1950-1980 as the historical baseline and periods of the same length in a world 2 °C (or 3 °C, in Figure 6) warmer than pre-industrial conditions in 1870-1900 24 .…”

Section: Datamentioning

confidence: 99%

Advancing our understanding of compound weather and climate events via large ensemble model simulations

Bevacqua

Jézéquel²,

Suárez-Gutiérrez³

et al. 2022

Preprint

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Most societally relevant weather impacts result from compound events, that is, rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, we illustrate that robust analyses of compound events – such as frequency and uncertainty analysis under present-day and future conditions, event attribution, exploration of low-probability-high-impact events – require very large sample sizes. In particular, the required sample is much larger than that needed for routinely considered univariate extremes. We demonstrate how large ensemble simulations from multiple climate models are crucial for advancing our understanding of compound events and for constructing robust projections. Importantly, this will avoid providing practitioners with potentially misleading information on climate risks.

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From emission scenarios to spatially resolved projections with a chain of computationally efficient emulators: MAGICC (v7.5.1) – MESMER (v0.8.1) coupling

Cited by 8 publications

References 41 publications

STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations

STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations

ClimateBench v1.0: A Benchmark for Data‐Driven Climate Projections

Advancing our understanding of compound weather and climate events via large ensemble model simulations

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