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

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.

scholarly journals Anthropogenic climate change versus internal climate variability: Impacts on Alpine snow cover

2020 ◽  
Author(s):  
Fabian Willibald ◽  
Sven Kotlarski ◽  
Adrienne Grêt-Regamey ◽  
Ralf Ludwig
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Snow Cover ◽  
Climate Model ◽  
Regional Climate ◽  
Anthropogenic Climate Change ◽  
Swiss Alps ◽  
Internal Climate Variability ◽  
Annual 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 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.

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

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.

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

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.

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

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.

scholarly journals Impact of anthropogenic climate change on wildfire across western US forests

2016 ◽  
Vol 113 (42) ◽  
pp. 11770-11775 ◽  
Author(s):  
John T. Abatzoglou ◽  
A. Park Williams
Keyword(s):  
Climate Change ◽  
United States ◽  
Climate Variability ◽  
Forest Fire ◽  
Anthropogenic Climate Change ◽  
Natural Climate Variability ◽  
Western Us ◽  
Fire Activity ◽  
Fire Area ◽  
Natural Climate

Increased forest fire activity across the western continental United States (US) in recent decades has likely been enabled by a number of factors, including the legacy of fire suppression and human settlement, natural climate variability, and human-caused climate change. We use modeled climate projections to estimate the contribution of anthropogenic climate change to observed increases in eight fuel aridity metrics and forest fire area across the western United States. Anthropogenic increases in temperature and vapor pressure deficit significantly enhanced fuel aridity across western US forests over the past several decades and, during 2000–2015, contributed to 75% more forested area experiencing high (>1 σ) fire-season fuel aridity and an average of nine additional days per year of high fire potential. Anthropogenic climate change accounted for ∼55% of observed increases in fuel aridity from 1979 to 2015 across western US forests, highlighting both anthropogenic climate change and natural climate variability as important contributors to increased wildfire potential in recent decades. We estimate that human-caused climate change contributed to an additional 4.2 million ha of forest fire area during 1984–2015, nearly doubling the forest fire area expected in its absence. Natural climate variability will continue to alternate between modulating and compounding anthropogenic increases in fuel aridity, but anthropogenic climate change has emerged as a driver of increased forest fire activity and should continue to do so while fuels are not limiting.

scholarly journals Role of Natural Climate Variability in the Detection of Anthropogenic Climate Change Signal for Mean and Extreme Precipitation at Local and Regional Scales

2018 ◽  
Vol 31 (11) ◽  
pp. 4241-4263 ◽  
Author(s):  
Jean-Luc Martel ◽  
Alain Mailhot ◽  
François Brissette ◽  
Daniel Caya
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Extreme Precipitation ◽  
Local Scale ◽  
Anthropogenic Climate Change ◽  
Climate Change Signal ◽  
Total Precipitation ◽  
Natural Climate Variability ◽  
The World ◽  
Natural Climate

Abstract Climate change will impact both mean and extreme precipitation, having potentially significant consequences on water resources. The implementation of efficient adaptation measures must rely on the development of reliable projections of future precipitation and on the assessment of their related uncertainty. Natural climate variability is a key uncertainty component, which can result in apparent decadal trends that may be greater or lower than the long-term underlying anthropogenic climate change trend. The goal of the present study is to assess how natural climate variability affects the ability to detect the climate change signal for mean and extreme precipitation. Annual and seasonal total precipitation are used as indicators of the mean, whereas annual and seasonal maximum daily precipitation are used as indicators of extremes. This is done using the CanESM2 50-member and CESM1 40-member large ensembles of simulations over the 1950–2100 period. At the local scale, results indicate that natural climate variability will dominate the uncertainty for annual and seasonal extreme precipitation going up to the end of the century in many parts of the world. The climate change signal can, however, be reliably detected much earlier at the regional scale for extreme precipitation. In the case of annual and seasonal total precipitation, the climate change signal can be reliably detected at the local scale without resorting to a regional analysis. Nonetheless, natural climate variability can impede the detection of the anthropogenic climate change signal until the middle to late century in many parts of the world for mean and extreme precipitation.

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