scholarly journals “Certain Uncertainty: The Role of Internal Climate Variability in Projections of Regional Climate Change and Risk Management”

2020 ◽  
Vol 8 (12) ◽  
Author(s):  
Clara Deser
Keyword(s):  
Climate Change ◽  
Risk Management ◽  
Climate Variability ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Internal Climate Variability

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.

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 Role of resolution in regional climate change projections over China

2017 ◽  
Vol 51 (5-6) ◽  
pp. 2375-2396 ◽  
Author(s):  
Ying Shi ◽  
Guiling Wang ◽  
Xuejie Gao
Keyword(s):  
Climate Change ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Climate Change Projections

scholarly journals Shallowing of the Svityaz Lake in the context of regional climate change

2020 ◽  
Vol 29 (4) ◽  
pp. 673-683
Author(s):  
Vitalina Fedoniuk ◽  
Maria Khrystetska ◽  
Mykola Fedoniuk ◽  
Ihor Merlenko ◽  
Serhiy Bondarchuk
Keyword(s):  
Climate Change ◽  
Regional Climate ◽  
Correlation Coefficients ◽  
Regional Climate Change ◽  
Water Levels ◽  
Climatic Data ◽  
Ecological Stability ◽  
Air Temperatures

The paper analyzes the dynamics of the main climatic indicators in order to reveal the role of regional and local factors in the current changes in the water content of the Svitiaz Lake (NW Ukraine). The current state of study of the water balance of the lake and the factors that form it are estimated. The main trends for changes in the levels and regime of surface water, groundwater and artesian water in the territory of the Shatsk National Nature Park are identified. Quantitative data characterizing long-term and modern changes in water levels in the lake are presented. Shallowing of 2019 is characterized (the lowest water level over the last 50 years, reduction of the water mirror area by 8%). Based on statistical mathematical and cartographic analysis of climatic data provided by 17 meteorological stations in the region the dynamics of average annual, monthly and seasonal precipitation, evaporation and their spatial distribution were estimated. A significant increase in evaporation during the warm period of the year over the last decades (2000-2018) has been revealed. Changes in the amount and mode of precipitation over 2 long-term periods are estimated. The peculiarities of the dynamics of the main meteorological indicators in 2019 (average monthly and average annual air temperatures, relative humidity, precipitation amounts) were separately analyzed. Values of humidity coefficients and hydrothermal coefficients were calculated. The parts of the region with the lowest values of these indicators, including the catchment area of Lake Svityaz, are outlined and visualized on the map. The significant role of evaporation growth was confirmed given the consistent increase in air temperatures over the last 20 years. Given the Svityaz station data it is also calculated the correlation coefficients of water levels in the lake with the same indicators for the period since 1970. During the period of 2000-2018, a significant increase in the dependence of water levels on the hydrothermal coefficient of Selyaninov was established, which may indicate a decrease in the ecological stability of the lake and its increasing vulnerability to climate change.

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 Assigning responsibility for country-level warming to individual major emitters

2021 ◽  
Author(s):  
Lea Beusch ◽  
Alexander Nauels ◽  
Lukas Gudmundsson ◽  
Carl-Friedrich Schleussner ◽  
Sonia I. Seneviratne
Keyword(s):  
Climate Change ◽  
Regional Climate ◽  
Temperature Response ◽  
Regional Climate Change ◽  
The United States ◽  
The European Union ◽  
Internal Climate Variability ◽  
Country Level ◽  
Global Mean Temperature ◽  
Ipcc Report

<p>Human influence on climate is not usually disentangled in the contribution of single emitters, especially when assessing changes and impacts in individual countries. However, such information could help individual countries understand their role in driving climate change and thus aid them in committing to fair and evidence-based emission reduction targets. Here, we quantify the contribution of single emitters to country-level median warming and extremes based on historical emissions and currently pledged policy targets. Thereby, we focus on the five largest historical emitters – China, the United States of America, the European Union, India, and Russia. While large ensembles are needed for this task, the computational burden of running full Earth System Models (ESMs) renders it impossible to answer our question with actual ESMs. Instead, we combine a physical global mean temperature emulator (Meinshausen et al., 2009) with a statistical spatially-resolved ESM emulator (Beusch et al., 2020) to create millions of temperature field time series. Our setup accounts for three major sources of uncertainty: (i) uncertainty in the global temperature response to greenhouse gas emissions, (ii) uncertainty in the regional response to global warming, (iii) uncertainty due to internal climate variability. </p><p>We find that historically rare hot years (occurring about once every 100 years in pre-industrial times) are expected at least every second year in 89 % (likely range: 71 – 100 %) of all countries by 2030. Without the emissions of the top five emitters over the time period during which policy makers had been informed about the looming anthropogenic climate crisis, i.e., after the first IPCC report of 1990, it would be 40 % (10 – 64 %) of all countries instead. Furthermore, when considering all current and projected emissions until 2030, 8 % (0 – 54 %) of countries are headed towards surpassing 2.0 °C of warming since pre-industrial times by 2030. If all nations followed the same per capita emissions as the USA since the 2015 Paris Agreement, the percentage of countries surpassing 2.0 °C by 2030 would amount to 78 % (24 – 96 %). Generally, northern high latitude countries experience the largest changes in median warming and tropical Africa the largest changes in extremes. Our results emphasize the relevance of individual emitters, and in particular the top five emitters, in driving regional climate change across different time periods.</p><p>Beusch, L., Gudmundsson, L., and Seneviratne, S. I. (ESD, 2020): https://doi.org/10.5194/esd-11-139-2020</p><p>Meinshausen, M., Meinshausen, N., Hare, W. et al. (Nature, 2009): https://doi.org/10.1038/nature08017</p>

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.

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