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.

Quantifying the Role of Internal Climate Variability in Future Climate Trends

2015 ◽  
Vol 28 (16) ◽  
pp. 6443-6456 ◽  
Author(s):  
David W. J. Thompson ◽  
Elizabeth A. Barnes ◽  
Clara Deser ◽  
William E. Foust ◽  
Adam S. Phillips
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Internal Variability ◽  
Ensemble Member ◽  
Future Climate ◽  
Analytic Model ◽  
Climate Trends ◽  
Internal Climate Variability ◽  
Large Ensembles

Abstract Internal variability in the climate system gives rise to large uncertainty in projections of future climate. The uncertainty in future climate due to internal climate variability can be estimated from large ensembles of climate change simulations in which the experiment setup is the same from one ensemble member to the next but for small perturbations in the initial atmospheric state. However, large ensembles are invariably computationally expensive and susceptible to model bias. Here the authors outline an alternative approach for assessing the role of internal variability in future climate based on a simple analytic model and the statistics of the unforced climate variability. The analytic model is derived from the standard error of the regression and assumes that the statistics of the internal variability are roughly Gaussian and stationary in time. When applied to the statistics of an unforced control simulation, the analytic model provides a remarkably robust estimate of the uncertainty in future climate indicated by a large ensemble of climate change simulations. To the extent that observations can be used to estimate the amplitude of internal climate variability, it is argued that the uncertainty in future climate trends due to internal variability can be robustly estimated from the statistics of the observed climate.

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

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

<p>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 21<sup>st</sup> 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°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.</p>

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.

scholarly journals Timing of human-induced climate change emergence from internal climate variability for hydrological impact studies

2018 ◽  
Vol 49 (2) ◽  
pp. 421-437 ◽  
Author(s):  
Mei-Jia Zhuan ◽  
Jie Chen ◽  
Ming-Xi Shen ◽  
Chong-Yu Xu ◽  
Hua Chen ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Climate Models ◽  
Climate Model ◽  
Climate Trends ◽  
Impact Studies ◽  
Internal Climate Variability ◽  
Temperature And Precipitation ◽  
Hydrological Impact ◽  
Near Term

Abstract This study proposes a method to estimate the timing of human-induced climate change (HICC) emergence from internal climate variability (ICV) for hydrological impact studies based on climate model ensembles. Specifically, ICV is defined as the inter-member difference in a multi-member ensemble of a climate model in which human-induced climate trends have been removed through a detrending method. HICC is defined as the mean of multiple climate models. The intersection between HICC and ICV curves is defined as the time of emergence (ToE) of HICC from ICV. A case study of the Hanjiang River watershed in China shows that the temperature change has already emerged from ICV during the last century. However, the precipitation change will be masked by ICV up to the middle of this century. With the joint contributions of temperature and precipitation, the ToE of streamflow occurs about one decade later than that of precipitation. This implies that consideration for water resource vulnerability to climate should be more concerned with adaptation to ICV in the near-term climate (present through mid-century), and with HICC in the long-term future, thus allowing for more robust adaptation strategies to water transfer projects in China.

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.

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