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

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

<p>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 (“control”) 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).</p><p>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°C (1980-2019) would exceed the standard deviation of internally generated variability of a set of `low variability' models by far (> 5σ), 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.</p><p>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’s natural variability estimates are used.</p><p>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.</p><p> </p><p>Bindoff, N.L., et al., 2013. Detection and attribution of climate change: from global to regional. IPCC AR5, WG1, Chapter 10.</p><p>Santer, B.D., et al., 2019. Celebrating the anniversary of three key events in climate change science. <em>Nat Clim Change</em> <strong>9</strong>(3), pp. 180-182.</p>

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.

El Niño variability mediates 21st century growth effects of climate change

2021 ◽  
Author(s):  
Christopher Callahan ◽  
Justin Mankin
Keyword(s):  
Climate Change ◽  
Economic Growth ◽  
Climate Variability ◽  
Climate Model ◽  
Internal Variability ◽  
Climate Impact ◽  
Growth Effects ◽  
Internal Climate Variability ◽  
Mitigation And Adaptation ◽  
Model Ensembles

<p>Understanding the effect of climate change on global economic growth is critical to informing optimal mitigation and adaptation policy. Many recent efforts have been made to empirically quantify the roles of weather and climate in economic growth, but these efforts have generally focused on changes in mean climate rather than changes in climate variability. Climate change is expected to alter modes of climate variability, so fully quantifying the costs of climate change requires both understanding the effects of climate variability on economic growth and constraining how this variability will evolve under forcing. Here we combine historical climate and economic data with multiple climate model ensembles to quantify the economic growth effects of El Niño and examine how these effects evolve in the 21<sup>st</sup> century. Preliminary results show substantial negative effects of El Niño on growth, with historical events reducing growth by >5 percentage points over 5 years in countries whose temperature variability is tightly correlated with ENSO. We then examine how climate change influences El Niño and its growth effects in both multi-model and single-model ensembles, allowing us to isolate the role of internal climate variability in shaping the evolution of ENSO statistics in the 21<sup>st</sup> century. Climate change is generally projected to increase El Niño frequency and thus the resulting growth penalties, but internal variability generates a wide spread of responses, all of which are consistent with the same forcing. These results highlight how internal variability can influence both interannual El Niño occurrence and long-term changes in its statistics, with consequences for future economic growth. Moreover, these results illustrate the range of climate impact trajectories that are consistent with the same emissions, providing critical information for adaptation decision-makers needing to construct robust socioeconomic systems in the face of 21<sup>st</sup> century climate change.</p>

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 Impact of internal variability on climate change for the upcoming decades: analysis of the CanESM2-LE and CESM-LE large ensembles

2019 ◽  
Vol 156 (3) ◽  
pp. 299-314 ◽  
Author(s):  
Gabriel Rondeau-Genesse ◽  
Marco Braun
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Ghg Emissions ◽  
Internal Variability ◽  
Mean Annual Temperature ◽  
Large Ensemble ◽  
Climate Change Effects ◽  
Large Ensembles ◽  
The Mean

Abstract The pace of climate change can have a direct impact on the efforts required to adapt. For short timescales, however, this pace can be masked by internal variability (IV). Over a few decades, this can cause climate change effects to exceed what would be expected from the greenhouse gas (GHG) emissions alone or, to the contrary, cause slowdowns or even hiatuses. This phenomenon is difficult to explore using ensembles such as CMIP5, which are composed of multiple climate models and thus combine both IV and inter-model differences. This study instead uses CanESM2-LE and CESM-LE, two state-of-the-art large ensembles (LE) that comprise multiple realizations from a single climate model and a single GHG emission scenario, to quantify the relationship between IV and climate change over the next decades in Canada and the USA. The mean annual temperature and the 3-day maximum and minimum temperatures are assessed. Results indicate that under the RCP8.5, temperatures within most of the individual large ensemble members will increase in a roughly linear manner between 2021 and 2060. However, members of the large ensembles in which a slowdown of warming is found during the 2021–2040 period are two to five times more likely to experience a period of very fast warming in the following decades. The opposite scenario, where the changes expected by 2050 would occur early because of IV, remains fairly uncommon for the mean annual temperature, but occurs in 5 to 15% of the large ensemble members for the temperature extremes.

scholarly journals Attribution of Declining Western U.S. Snowpack to Human Effects

2008 ◽  
Vol 21 (23) ◽  
pp. 6425-6444 ◽  
Author(s):  
David W. Pierce ◽  
Tim P. Barnett ◽  
Hugo G. Hidalgo ◽  
Tapash Das ◽  
Céline Bonfils ◽  
...  
Keyword(s):  
Climate Variability ◽  
Greenhouse Gases ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Formal Model ◽  
Model Estimate ◽  
Economic Implications ◽  
Internal Climate Variability ◽  
Detection And Attribution

Abstract Observations show snowpack has declined across much of the western United States over the period 1950–99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D–A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean–atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D–A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols.

scholarly journals Optimal Detection of Regional Trends Using Global Data

2010 ◽  
Vol 23 (16) ◽  
pp. 4438-4446 ◽  
Author(s):  
Stephen S. Leroy ◽  
James G. Anderson
Keyword(s):  
Climate Change ◽  
Model Uncertainty ◽  
Climate Models ◽  
Region Of Interest ◽  
Internal Variability ◽  
Spatiotemporal Patterns ◽  
Optimal Detection ◽  
Internal Climate Variability ◽  
Climate Signals ◽  
Normally Distributed

Abstract A complete accounting of model uncertainty in the optimal detection of climate signals requires normalization of the signals produced by climate models; however, there is not yet a well-defined rule for the normalization. This study seeks to discover such a rule. The authors find that, to arrive at the equations of optimal detection from a general application of Bayesian statistics to the problem of climate change, it is necessary to assume that 1) the prior probability density function (PDF) for climate change is separable into independent PDFs for sensitivity and the signals’ spatiotemporal patterns; 2) postfit residuals are due to internal variability and are normally distributed; 3) the prior PDF for sensitivity is uninformative; and 4) a continuum of climate models used to estimate model uncertainty gives a normally distributed PDF for the spatiotemporal patterns for the climate signals. This study also finds that the rule for normalization of the signals’ patterns is a simple division of model-simulated climate change in any observable quantity or set of quantities by a change in a single quantity of interest such as regionally averaged temperature or precipitation. With this normalization, optimal detection yields the most probable estimates of the underlying changes in the region of interest due to external forcings. Data outside the region of interest add information that effectively suppresses the interannual fluctuations associated with internal climate variability.

Trends, Variability and Predictive Skill of the Ocean Heat Content in North Atlantic: An Analysis with the EC-Earth3 Model

2021 ◽  
Author(s):  
Teresa Carmo-Costa ◽  
Roberto Bilbao ◽  
Pablo Ortega ◽  
Ana Teles-Machado ◽  
Emanuel Dutra
Keyword(s):  
North Atlantic ◽  
Climate Model ◽  
Decadal Variability ◽  
Heat Content ◽  
Internal Variability ◽  
Ocean Heat Content ◽  
Internal Climate Variability ◽  
The North ◽  
Predictive Skill ◽  
Modeling Uncertainties

Abstract This study investigates trends, variability and predictive skill of the upper ocean heat content (OHC) in the North Atlantic basin. This is a region where strong decadal variability superimposes the externally forced trends, introducing important differences in the local warming rates, and leading in the case of the Central Subpolar North Atlantic to an overall long-term cooling. Our analysis aims to better understand these regional differences, by investigating how internal and forced variability contribute to local trends, exploring also their role on the local prediction skill. The analysis combines the study of three ocean reanalyses to document the uncertainties related to observations, with two sets of CMIP6 experiments performed with the global coupled climate model EC-Earth3: a historical ensemble to characterise the forced signals; and a retrospective decadal prediction system, to additionally characterise the contributions from internal climate variability. Our results show that internal variability is essential to understand the spatial pattern of North Atlantic OHC trends, contributing decisively to the local trends and providing high levels of predictive skill in the Eastern Subpolar North Atlantic and the Irminger and Iceland Seas, and to a lesser extent in the Labrador Sea. Skill and trends in other areas like the Subtropical North Atlantic, or the Gulf Stream Extension are mostly externally forced. Large observational and modeling uncertainties affect the trends and interannual variability in the Central Subpolar North Atlantic, the only region exhibiting a cooling during the study period, uncertainties that might explain the very poor local predictive skill.

Detection and attribution of seasonal temperature changes in India with climate models in the CMIP5 archive

2015 ◽  
Vol 7 (1) ◽  
pp. 83-102 ◽  
Author(s):  
P. Sonali ◽  
D. Nagesh Kumar
Keyword(s):  
Climate Models ◽  
Internal Variability ◽  
Causative Factor ◽  
Temporal Variations ◽  
Seasonal Temperature ◽  
Temperature Changes ◽  
Model Simulations ◽  
Internal Climate Variability ◽  
Detection And Attribution ◽  
Attribution Analysis

This study analyzes the change in annual and seasonal maximum and minimum temperature (Tmax and Tmin) during the period 1950–2005 (i.e., second half of the 20th century). In-depth analyses have been carried out for all over India as well as for five temperature homogenous regions of India separately. First, the temporal variations of annual and seasonal Tmax and Tmin are analyzed, employing the trend free pre-whitening Mann-Kendall approach. Secondly, it is assessed whether the observations contain significant signals above the natural internal variability determined from a long ‘piControl’ experiment, using Monte Carlo simulation. Thirdly, fingerprint based formal detection and attribution analysis is used to determine the signal strengths of observed and model simulations with respect to different considered experiments. Finally, these signal strengths are compared to attribute the observed changes in Tmax and Tmin to different factors. All the model simulated datasets are retrieved from the CMIP5 archive. It is noticed that the emergence of observed trends is more pronounced in Tmin compared to Tmax. Although observed changes are not solely associated with one specific causative factor, most of the changes in Tmin lie above the bounds of natural internal climate variability.

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 Consistently Estimating Internal Climate Variability from Climate Model Simulations

2017 ◽  
Vol 30 (23) ◽  
pp. 9555-9573 ◽  
Author(s):  
Dirk Olonscheck ◽  
Dirk Notz
Keyword(s):  
Climate Variability ◽  
Sea Ice ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
New Method ◽  
Polar Regions ◽  
Internal Climate Variability ◽  
Ensemble Simulations

This paper introduces and applies a new method to consistently estimate internal climate variability for all models within a multimodel ensemble. The method regresses each model’s estimate of internal variability from the preindustrial control simulation on the variability derived from a model’s ensemble simulations, thus providing practical evidence of the quasi-ergodic assumption. The method allows one to test in a multimodel consensus view how the internal variability of a variable changes for different forcing scenarios. Applying the method to the CMIP5 model ensemble shows that the internal variability of global-mean surface air temperature remains largely unchanged for historical simulations and might decrease for future simulations with a large CO2 forcing. Regionally, the projected changes reveal likely increases in temperature variability in the tropics, subtropics, and polar regions, and extremely likely decreases in midlatitudes. Applying the method to sea ice volume and area shows that their respective internal variability likely or extremely likely decreases proportionally to their mean state, except for Arctic sea ice area, which shows no consistent change across models. For the evaluation of CMIP5 simulations of Arctic and Antarctic sea ice, the method confirms that internal variability can explain most of the models’ deviation from observed trends but often not the models’ deviation from the observed mean states. The new method benefits from a large number of models and long preindustrial control simulations, but it requires only a small number of ensemble simulations. The method allows for consistent consideration of internal variability in multimodel studies and thus fosters understanding of the role of internal variability in a changing climate.

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