Review of "Anthropogenic climate change versus internal climate variability: Impacts on Alpine snow cover" by Fabian Willibald et al.

Abstract. Snow is a sensitive component of the climate system. In many parts of the world, water, stored as snow, is a vital resource for agriculture, tourism and the energy sector. As uncertainties in climate change assessments are still relatively large, it is important to investigate the interdependencies between internal climate variability and anthropogenic climate change and their impacts on snow cover. We use regional climate model data from a new single model large ensemble with 50 members (ClimEX LE) as driver for the physically based snow model SNOWPACK at eight locations across the Swiss Alps. We estimate the contribution of internal climate variability to uncertainties in future snow trends by applying a Mann-Kendall test for consecutive future periods of different lengths (between 30 and 100 years) until the end of the 21st century. Under RCP8.5, we find probabilities between 15 % and 50 % that there will be no significantly negative trend in future mean snow depths over a period of 50 years. While it is important to understand the contribution of internal climate variability to uncertainties in future snow trends, it is likely that the variability of snow depth itself changes with anthropogenic forcing. We find that relative to the mean, inter-annual variability of snow increases in the future. A decrease of future mean snow depths, superimposed by increases in inter-annual variability will exacerbate the already existing uncertainties that snow-dependent economies will have to face in the future.

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Anthropogenic climate change versus internal climate variability: impacts on snow cover in the Swiss Alps

The Cryosphere ◽

10.5194/tc-14-2909-2020 ◽

2020 ◽

Vol 14 (9) ◽

pp. 2909-2924

Author(s):

Fabian Willibald ◽

Sven Kotlarski ◽

Adrienne Grêt-Regamey ◽

Ralf Ludwig

Keyword(s):

Climate Change ◽

Climate Variability ◽

Snow Cover ◽

Interannual Variability ◽

Climate Model ◽

Regional Climate ◽

Anthropogenic Climate Change ◽

Swiss Alps ◽

Internal Climate Variability ◽

The Future

Abstract. Snow is a sensitive component of the climate system. In many parts of the world, water stored as snow is a vital resource for agriculture, tourism and the energy sector. As uncertainties in climate change assessments are still relatively large, it is important to investigate the interdependencies between internal climate variability and anthropogenic climate change and their impacts on snow cover. We use regional climate model data from a new single-model large ensemble with 50 members (ClimEX LE) as a driver for the physically based snow model SNOWPACK at eight locations across the Swiss Alps. We estimate the contribution of internal climate variability to uncertainties in future snow trends by applying a Mann–Kendall test for consecutive future periods of different lengths (between 30 and 100 years) until the end of the 21st century. Under RCP8.5, we find probabilities between 10 % and 60 % that there will be no significant negative trend in future mean snow depths over a period of 50 years. While it is important to understand the contribution of internal climate variability to uncertainties in future snow trends, it is likely that the variability of snow depth itself changes with anthropogenic forcing. We find that relative to the mean, interannual variability of snow increases in the future. A decrease in future mean snow depths, superimposed by increases in interannual variability, will exacerbate the already existing uncertainties that snow-dependent economies will have to face in the future.

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Relative Importance of Internal Climate Variability versus Anthropogenic Climate Change in Global Climate Change

Journal of Climate ◽

10.1175/jcli-d-20-0424.1 ◽

2021 ◽

Vol 34 (2) ◽

pp. 465-478

Author(s):

Jie Chen ◽

Xiangquan Li ◽

Jean-Luc Martel ◽

François P. Brissette ◽

Xunchang J. Zhang ◽

...

Keyword(s):

Climate Change ◽

Climate Variability ◽

Global Climate ◽

Regional Climate ◽

Baseline Period ◽

Anthropogenic Climate Change ◽

Boreal Summer ◽

Boreal Winter ◽

Internal Climate Variability ◽

Equatorial Band

AbstractTo better understand the role of internal climate variability (ICV) in climate change impact studies, this study quantifies the importance of ICV [defined as the intermember variability of a single model initial-condition large ensemble (SMILE)] in relation to the anthropogenic climate change (ACC; defined as multimodel ensemble mean) in global and regional climate change using a criterion of time of emergence (ToE). The uncertainty of the estimated ToE is specifically investigated by using three SMILEs to estimate the ICV. The results show that using 1921–40 as a baseline period, the annual mean precipitation ACC is expected to emerge within this century over extratropical regions as well as along the equatorial band. However, ToEs are unlikely to occur, even by the end of this century, over intratropical regions outside of the equatorial band. In contrast, annual mean temperature ACC has already emerged from the temperature ICV for most of the globe. Similar spatial patterns are observed at the seasonal scale, while a weaker ACC for boreal summer (June–August) precipitation and additional ICV for boreal winter (December–February) temperature translate to later ToEs for some regions. In addition, the uncertainty of ToE related to the choice of a SMILE is mostly less than 20 years for annual mean precipitation and temperature. However, it can be as large as 90 years for annual mean precipitation over some regions. Overall, results indicate that the choice of a SMILE is a significant source of uncertainty in the estimation of ToE and results based on only one SMILE should be interpreted with caution.

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A probabilistic framework for quantifying the role of anthropogenic climate change in marine-terminating glacier retreats

10.5194/tc-2021-394 ◽

2022 ◽

Author(s):

John Erich Christian ◽

Alexander A. Robel ◽

Ginny Catania

Keyword(s):

Climate Change ◽

Climate Variability ◽

Probabilistic Approach ◽

Anthropogenic Climate Change ◽

Climate Forcing ◽

Climate Trends ◽

Ice Dynamics ◽

Internal Climate Variability ◽

Dynamic Feedbacks

Abstract. Many marine-terminating outlet glaciers have retreated rapidly in recent decades, but these changes have not been formally attributed to anthropogenic climate change. A key challenge for such an attribution assessment is that if glacier termini are sufficiently perturbed from bathymetric highs, ice-dynamic feedbacks can cause rapid retreat even without further climate forcing. In the presence of internal climate variability, attribution thus depends on understanding whether (or how frequently) these rapid retreats could be triggered by climatic noise alone. Our simulations with idealized glaciers show that in a noisy climate, rapid retreat is a stochastic phenomenon. We therefore propose a probabilistic approach to attribution and present a framework for analysis that uses ensembles of many simulations with independent realizations of random climate variability. Synthetic experiments show that century-scale climate trends substantially increase the likelihood of rapid glacier retreat. This effect depends on the timescales over which ice dynamics integrate forcing. For a population of synthetic glaciers with different topographies, we find that external trends increase the number of large retreats triggered within the population, offering a metric for regional attribution. Our analyses suggest that formal attribution studies are tractable and should be further pursued to clarify the human role in recent ice-sheet change. We emphasize that early-industrial-era constraints on glacier and climate state are likely to be crucial for such studies.

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Natural Decadal Climate Variability and Anthropogenic Climate Change

Natural Decadal Climate Variability ◽

10.1201/9781351052900-8 ◽

2020 ◽

pp. 219-233

Author(s):

Vikram M. Mehta

Keyword(s):

Climate Change ◽

Climate Variability ◽

Anthropogenic Climate Change ◽

Decadal Climate Variability ◽

Decadal Climate

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Impact and uncertainty of climate projections on Alpine catchments using high-resolution models

10.5194/egusphere-egu21-13443 ◽

2021 ◽

Author(s):

Jorge Sebastian Moraga ◽

Nadav Peleg ◽

Simone Fatichi ◽

Peter Molnar ◽

Paolo Burlando

Keyword(s):

Climate Change ◽

High Resolution ◽

Climate Variability ◽

Soil Conditions ◽

Internal Climate Variability ◽

Mountainous Catchments ◽

Precipitation Decrease ◽

Alpine Catchments ◽

Impacts Of Climate Change

Hydrological processes in mountainous catchments will be subject to climate change on all scales, and their response is expected to vary considerably in space. Typical hydrological studies, which use coarse climate data inputs obtained from General Circulation Models (GCM) and Regional Climate Models (RCM), focus mostly on statistics at the outlet of the catchments, overlooking the effects within the catchments. Furthermore, the role of uncertainty, especially originated from natural climate variability, is rarely analyzed. In this work, we quantified the impacts of climate change on hydrological components and determined the sources of uncertainties in the projections for two mostly natural Swiss alpine catchments: Kleine Emme and Thur. Using a two-dimensional weather generator, AWE-GEN-2d, and based on nine different GCM-RCM model chains, we generated high-resolution (2 km, 1 hour) ensembles of gridded climate inputs until the end of the 21st century. The simulated variables were subsequently used as inputs into the fully distributed hydrological model Topkapi-ETH to estimate the changes in hydrological statistics at 100-m and hourly resolutions. Increased temperatures (by 4&#176;C, on average) and changes in precipitation (decrease over high elevations by up to 10%, and increase at the lower elevation by up to 15%) results in increased evapotranspiration rates in the order of 10%, up to a 50% snowmelt, and drier soil conditions. These changes translate into important shifts in streamflow seasonality at the outlet of the catchments, with a significant increase during the winter months (up to 40%) and a reduction during the summer (up to 30%). Analysis at the sub-catchment scale reveals elevation-dependent hydrological responses: mean annual streamflow, as well as high and low flow extremes, are projected to decrease in the uppermost sub-catchments and increase in the lower ones. Furthermore, we computed the uncertainty of the estimations and compared them to the magnitude of the change signal. Although the signal-to-noise-ratio of extreme streamflow for most sub-catchments is low (below 0.5) there is a clear elevation dependency. In every case, internal climate variability (as opposed to climate model uncertainty) explains most of the uncertainty, averaging 85% for maximum and minimum flows, and 60% for mean flows. The results highlight the importance of modelling the distributed impacts of climate change on mountainous catchments, and of taking into account the role of internal climate variability in hydrological projections.

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Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability

Hydrology and Earth System Sciences ◽

10.5194/hess-24-3251-2020 ◽

2020 ◽

Vol 24 (6) ◽

pp. 3251-3269 ◽

Cited By ~ 3

Author(s):

Chao Gao ◽

Martijn J. Booij ◽

Yue-Ping Xu

Keyword(s):

Climate Change ◽

Climate Variability ◽

River Basin ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Internal Climate Variability ◽

Low Flows ◽

Uncertainty Sources ◽

High Flows

Abstract. Projections of streamflow, particularly of extreme flows under climate change, are essential for future water resources management and the development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explored the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, eastern China. ANOVA (analysis of variance) was employed to quantify the contribution of different uncertainty sources from RCPs (representative concentration pathways), GCMs (global climate models) and internal climate variability, using an ensemble of 4 RCP scenarios, 9 GCMs and 1000 simulated realizations of each model–scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and that low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period of 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty becomes more pronounced, particularly for low flows. The findings in this study can help water managers to become more knowledgeable about and get a better understanding of streamflow projections and support decision making regarding adaptations to a changing climate under uncertainty in the Qu River basin.

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Walking, Talking, and Tasting with Winegrowers in Central Ohio and Eastern France: Sensorial Approaches to Making Sense of Climate Change

Practicing Anthropology ◽

10.17730/0888-4552.42.4.27 ◽

2020 ◽

Vol 42 (4) ◽

pp. 27-32

Author(s):

Mark Anthony Arceño

Keyword(s):

Climate Change ◽

Climate Variability ◽

Landscape Change ◽

Economic Change ◽

Anthropogenic Climate Change ◽

Change Acceptance ◽

Making Sense ◽

Global Discourse ◽

The Senses ◽

Novel Concept

Abstract Drawing on eighteen months of fieldwork throughout central Ohio, USA, and Alsace, eastern France, I reflect on the importance of relying on more than just my eyes when collecting data. I illustrate examples of how I have felt, heard, smelled, tasted, and now talk about the changes that winegrowers identify in their vineyards, wine cellars, and tasting rooms. Underlying my analysis is a range of winegrowers’ sensibilities when it comes to their attributions of landscape change, acceptance of climate variability, and acknowledgment of anthropogenic climate change. I affirm that it is necessary to look beyond what we observe, as we interpret the collective stories of winegrowers, which are rooted not only in global discourse of climate change but other realities of legislative and economic change. An attunement to the senses, though not in itself a novel concept, remains vital to crafting a holistic picture of which and how livelihoods are changing.

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Robust detection of forced warming in the presence of potentially large climate variability

10.5194/egusphere-egu21-9611 ◽

2021 ◽

Author(s):

Sebastian Sippel ◽

Nicolai Meinshausen ◽

Eniko Székely ◽

Erich Fischer ◽

Angeline G. Pendergrass ◽

...

Keyword(s):

Climate Change ◽

Climate Variability ◽

Climate Models ◽

Climate Model ◽

Decadal Variability ◽

Internal Variability ◽

Warming Trend ◽

Robust Detection ◽

Internal Climate Variability ◽

Detection And Attribution

Warming of the climate system is unequivocal and substantially exceeds unforced internal climate variability. Detection and attribution (D&A) employs spatio-temporal fingerprints of the externally forced climate response to assess the magnitude of a climate signal, such as the multi-decadal global temperature trend, while internal variability is often estimated from unforced (&#8220;control&#8221;) segments of climate model simulations (e.g. Santer et al. 2019). Estimates of the exact magnitude of decadal-scale internal variability, however, remain uncertain and are limited by relatively short observed records, their entanglement with the forced response, and considerable spread of simulated variability across climate models. Hence, a limitation of D&A is that robustness and confidence levels depend on the ability of climate models to correctly simulate internal variability (Bindoff et al., 2013).For example, the large spread in simulated internal variability across climate models implies that the observed 40-year global mean temperature trend of about 0.76&#176;C (1980-2019) would exceed the standard deviation of internally generated variability of a set of `low variability' models by far (> 5&#963;), corresponding to vanishingly small probabilities if taken at face value. But the observed trend would exceed the standard deviation of a few `high-variability' climate models `only' by a factor of about two, thus unlikely to be internally generated but not practically impossible given unavoidable climate system and observational uncertainties. This illustrates the key role of model uncertainty in the simulation of internal variability for D&A confidence estimates.Here we use a novel statistical learning method to extract a fingerprint of climate change that is robust towards model differences and internal variability, even of large amplitude. We demonstrate that externally forced warming is distinct from internal variability and detectable with high confidence on any state-of-the-art climate model, even those that simulate the largest magnitude of unforced multi-decadal variability. Based on the median of all models, it is extremely likely that more than 85% of the observed warming trend over the last 40 years is externally driven. Detection remains robust even if their main modes of decadal variability would be scaled by a factor of two. It is extremely likely that at least 55% of the observed warming trend over the last 40 years cannot be explained by internal variability irrespective of which climate model&#8217;s natural variability estimates are used.Our analysis helps to address this limitation in attributing warming to external forcing and provides a novel perspective for quantifying the magnitude of forced climate change even under uncertain but potentially large multi-decadal internal climate variability. This opens new opportunities to make D&A fingerprints robust in the presence of poorly quantified yet important features inextricably linked to model structural uncertainty, and the methodology may contribute to more robust detection and attribution of climate change to its various drivers.&#160;Bindoff, N.L., et al., 2013. Detection and attribution of climate change: from global to regional. IPCC AR5, WG1, Chapter 10.Santer, B.D., et al., 2019. Celebrating the anniversary of three key events in climate change science.&#160;Nat Clim Change&#160;9(3), pp. 180-182.

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