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

2020 ◽  
Vol 24 (6) ◽  
pp. 3251-3269 ◽  
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.

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.

Interhemispheric asymmetry of surface mean and extreme wind projections in CanESM5 climate change simulations

2021 ◽  
Author(s):  
Bin Yu ◽  
Xuebin Zhang ◽  
Guilong Li ◽  
Wei Yu
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Large Scale ◽  
Surface Wind ◽  
Boreal Winter ◽  
Interhemispheric Asymmetry ◽  
Half Century ◽  
Internal Climate Variability ◽  
Extreme Wind ◽  
The Mean

<p>The internal climate variability contributes to various aspects of climate change projections. This presentation will report results of the ensemble mean and spread of future projections of globally surface mean and extreme winds in boreal winter, based on single model initial-condition simulations forced by the SSP5-8.5 high-emissions scenario from a 50-member ensemble of CanESM5 models. Over the next half century, surface wind is projected to increase in the Northern Hemisphere mid-latitudes and increase in the Southern Hemisphere low-latitudes, an interhemispheric asymmetry feature relevant to large-scale changes in surface temperature and atmospheric circulation. Decreases in the surface extreme wind are clearer than the mean wind in the northern mid-latitudes. Large ensemble spreads are apparent in the mean and extreme wind changes, including spatial pattern and magnitude of the projected trends over the next half century. The internal climate variability generated components of the mean and extreme wind trends exhibit large-scale spatial coherences, and are comparable to the externally anthropogenic forced components of the trends.</p>

scholarly journals Testing Farmers’ Perception of Climate Variability: A Case Study from Kirtipur of Kathmandu Valley

2012 ◽  
pp. 30-34
Author(s):  
Jiban Mani Poudel
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Global Climate ◽  
Climatic Variability ◽  
Global Perspective ◽  
Kathmandu Valley ◽  
Climatic Fluctuations ◽  
Climate Fluctuations ◽  
Global And Local

In the 21st century, global climate change has become a public and political discourse. However, there is still a wide gap between global and local perspectives. The global perspective focuses on climate fluctuations that affect the larger region; and their analysis is based on long-term records over centuries and millennium. By comparison, local peoples’ perspectives vary locally, and local analyses are limited to a few days, years, decades and generations only. This paper examines how farmers in Kirtipur of Kathmandu Valley, Nepal, understand climate variability in their surroundings. The researcher has used a cognized model to understand farmers’ perception on weather fluctuations and climate change. The researcher has documented several eyewitness accounts of farmers about weather fluctuations which they have been observing in a lifetime. The researcher has also used rainfall data from 1970-2009 to test the accuracy of perceptions. Unlike meteorological analyses, farmers recall and their understanding of climatic variability by weather-crop interaction, and events associating with climatic fluctuations and perceptions are shaped by both physical visibility and cultural frame or belief system.DOI: http://dx.doi.org/10.3126/hn.v11i1.7200 Hydro Nepal Special Issue: Conference Proceedings 2012 pp.30-34

The role of conceptual hydrologic model calibration in climate change impact on water resources assessment

2015 ◽  
Vol 7 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Andrijana Todorovic ◽  
Jasna Plavsic
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
High Sensitivity ◽  
Regional Climate Models ◽  
Baseline Period ◽  
Hydrologic Model ◽  
Calibration Period ◽  
Rainfall Runoff Model ◽  
Parameter Values

Assessment of climate change (CC) impact on hydrologic regime requires a calibrated rainfall-runoff model, defined by its structure and parameters. The parameter values depend, inter alia, on the calibration period. This paper investigates influence of the calibration period on parameter values, model efficiency and streamflow projections under CC. To this end, a conceptual HBV-light model of the Kolubara River catchment in Serbia is calibrated against flows observed within 5 consecutive wettest, driest, warmest and coldest years and in the complete record period. The optimised parameters reveal high sensitivity towards calibration period. Hydrologic projections under climate change are developed by employing (1) five hydrologic models with outputs of one GCM–RCM chain (Global and Regional Climate Models) and (2) one hydrologic model with five GCM–RCM outputs. Sign and magnitude of change in projected variables, compared to the corresponding values simulated over the baseline period, vary with the hydrologic model used. This variability is comparable in magnitude to variability stemming from climate models. Models calibrated over periods with similar precipitation as the projected ones may result in less uncertain projections, while warmer climate is not expected to contribute to the uncertainty in flow projections. Simulations over prolonged dry periods are expected to be uncertain.

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