Improved Consistency of Climate Projections over Europe after Accounting for Atmospheric Circulation Variability

Saffioti, Claudio; Fischer, Erich M.; Knutti, Reto

doi:10.1175/jcli-d-16-0695.1

Cited by 14 publications

(25 citation statements)

References 66 publications

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“…A limit of these approaches is that accurate present-day analogs may not always be available. For example, in the Mediterranean area in winter, the response of atmospheric circulation to greenhouse forcing does not project on individual present-day modes of internal atmospheric variability [23,28]. Using clustering algorithms does not necessarily address this issue, as global warming can force a change in the structure of weather regimes between the present and the future climate simulations [26,29].…”

Section: Internal Variability Analogsmentioning

confidence: 99%

Regional Climate Impacts of Future Changes in the Mid–Latitude Atmospheric Circulation: a Storyline View

Zappa

2019

Curr Clim Change Rep

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Purpose of Review Atmospheric circulation exerts a strong control on regional climate and extremes. However, projections of future circulation change remain uncertain, thus affecting the assessment of regional climate change. The purpose of this review is to describe some key cases where regional precipitation and windiness strongly depend on the mid-latitude atmospheric circulation response to warming, and summarise this into alternative plausible storylines of regional climate change. Recent Findings Recent research has enabled to better quantify the importance of dynamical aspects of climate change in shaping regional climate. The cold season precipitation response in Mediterranean-like regions is identified as one of the most susceptible impact-relevant aspects of regional climate driven by mid-latitude circulation changes. A circulation-forced drying might already be emerging in the actual Mediterranean, Chile and southwestern Australia. Increasing evidence indicates that distinct regional changes in atmospheric circulation and European windiness might unfold depending on the interplay of different climate drivers, such as surface warming patterns, sea ice loss and stratospheric changes. Summary The multi-model mean circulation response to warming tends to show washed-out signals due to the lack of robustness in the model projections, with implications for regional changes. To better communicate the information contained within these projections, it is useful to discuss regional climate change conditionally on alternative plausible storylines of atmospheric circulation change. As progress continues in understanding the factors driving the response of circulation to global warming, developing such storylines will provide end-to-end and physically self-consistent descriptions of plausible future unfoldings of regional climate change.

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Section: Internal Variability Analogsmentioning

confidence: 99%

Regional Climate Impacts of Future Changes in the Mid–Latitude Atmospheric Circulation: a Storyline View

Zappa

2019

Curr Clim Change Rep

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“…There have been attempts to separate out the noise components in climate model ensemble signals. For example, Saffioti et al (2017) showed that the removal of atmospheric circulation variability largely decreases the spread of trends in an initial condition ensemble as well as a multi-model ensemble of GCMs. Since there is no better multi-model ensemble available so far (in terms of sampling different models and members), the CORDEX ensemble can be seen as a first order approximation of the uncertainty in a multi-GCM/RCM ensemble.…”

Section: Composition Of the Cordex Ensemblementioning

confidence: 99%

Assessing natural variability in RCM signals: comparison of a multi model EURO-CORDEX ensemble with a 50-member single model large ensemble

2019

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Uncertainties in climate model ensembles are still relatively large. Besides scenario and model response uncertainty, natural variability is another important source of uncertainty. To study regional natural variability on timescales of several decades and more, observations are often too sparse and short. Regional Climate Models (RCMs) can be used to overcome this lack of useful data at high spatial resolutions. In this study, we compare a new 50-member single RCM large ensemble (CRCM5-LE) with an ensemble of 22 EURO-CORDEX models for seasonal temperature and precipitation at 0.11° grid size over Europe-all driven by the RCP 8.5 scenario. This setup allows us to quantify the contribution of natural/modelinternal variability on the total uncertainty of a multi-model ensemble. The variability of climate change signals within the two ensembles is compared for three future periods (2020-2049, 2040-069 and 2070-2099). Results show that the single model spread is usually smaller than the multi-model spread for temperature. Similar variabilities can mostly be found in the near future (and to a lesser extent in the mid future) during winter and spring, especially for northern and central parts of Europe. The contribution of internal variability is generally higher for precipitation. In the near future almost all seasons and regions show similar variabilities. In the mid and far future only fall, summer and spring still show similar variabilites. There is a significant decrease of the contribution of internal variability over time for both variables. However, even in the far future for most regions and seasons 25-75% of the overall variability can be explained by internal variability.

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“…''Dynamical adjustment'' techniques have been developed with the ultimate goal of removing the estimated influence of internal atmospheric circulation variability on target climate variables in the observational record or in models (Wallace et al 2012;Deser et al 2016;Smoliak et al 2015;Saffioti et al 2016). This research followed upon a large suite of earlier studies aiming to understand the relationship between modes of internal atmospheric variability and temperature or precipitation (Wallace et al 1995;Thompson et al 2000;Compo and Sardeshmukh 2010;Foster and Rahmstorf 2011;Iles and Hegerl 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, an improved statistical estimate of the circulation-induced contribution would translate into a higher signal-to-noise ratio in the residual (Smoliak et al 2015). Different dynamical adjustment methods, among them circulation analog techniques (Deser et al 2016;Merrifield et al 2017;Lehner et al 2017) or regression-based approaches (Smoliak et al 2015;Saffioti et al 2016), are different ways to approach the statistical estimation step.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Internal atmospheric variability fundamentally limits predictability of climate and obscures evidence of anthropogenic climate change regionally and on time scales of up to a few decades. Dynamical adjustment techniques estimate and subsequently remove the influence of atmospheric circulation variability on temperature or precipitation. The residual component is expected to contain the thermodynamical signal of the externally forced response but with less circulation-induced noise. Existing techniques have led to important insights into recent trends in regional (hydro-) climate and their drivers, but the variance explained by circulation is often low. Here, we develop a novel dynamical adjustment technique by implementing principles from statistical learning. We demonstrate in an ensemble of Community Earth System Model (CESM) simulations that statistical learning methods, such as regularized linear models, establish a clearer relationship between circulation variability and atmospheric target variables, and need relatively short periods of record for training (around 30 years). The method accounts for, on average, 83% and 78% of European monthly winter temperature and precipitation variability at gridcell level, and around 80% of global mean temperature and hemispheric precipitation variability. We show that the residuals retain forced thermodynamical contributions to temperature and precipitation variability. Accurate estimates of the total forced response can thus be recovered assuming that forced circulation changes are gradual over time. Overall, forced climate response estimates can be extracted at regional or global scales from approximately 3–5 times fewer ensemble members, or even a single realization, using statistical learning techniques. We anticipate the technique will contribute to reducing uncertainties around internal variability and facilitating climate change detection and attribution.

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Improved Consistency of Climate Projections over Europe after Accounting for Atmospheric Circulation Variability

Cited by 14 publications

References 66 publications

Regional Climate Impacts of Future Changes in the Mid–Latitude Atmospheric Circulation: a Storyline View

Regional Climate Impacts of Future Changes in the Mid–Latitude Atmospheric Circulation: a Storyline View

Assessing natural variability in RCM signals: comparison of a multi model EURO-CORDEX ensemble with a 50-member single model large ensemble

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

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