The inherent uncertainty of precipitation variability, trends, and extremes due to internal variability, with implications for Western US water resources

2021 ◽  
pp. 1-46
Author(s):  
Karen A. McKinnon ◽  
Clara Deser
Keyword(s):  
Water Resource ◽  
Climate Models ◽  
Initial Conditions ◽  
Internal Variability ◽  
Winter Precipitation ◽  
Climate Change Signal ◽  
Monthly Precipitation ◽  
Global Precipitation Climatology Centre ◽  
Large Ensembles ◽  
One Year

AbstractThe approximately century-long instrumental record of precipitation over land reflects a single sampling of internal variability. Thus, the spatiotemporal evolution of the observations is only one realization of `what could have occurred' given the same climate system and boundary conditions, but different initial conditions. While climate models can be used to produce initial-condition large ensembles that explicitly sample different sequences of internal variability, an analogous approach is not possible for the real world. Here, we explore the use of a statistical model for monthly precipitation to generate synthetic ensembles based on a single record. When tested within the context of the NCAR Community Earth System Model version 1 Large Ensemble (CESM1-LE), we find that the synthetic ensemble can closely reproduce the spatiotemporal statistics of variability and trends in winter precipitation over the extended contiguous United States, and that it is difficult to infer the climate change signal in a single record given the magnitude of the variability. We additionally create a synthetic ensemble based on the Global Precipitation Climatology Centre (GPCC) dataset, termed the GPCC-synth-LE; comparison of the GPCC-synth-LE with the CESM1-based ensembles reveals differences in the spatial structures and magnitudes of variability, highlighting the advantages of an observationally-based ensemble. We finally use the GPCC-synth-LE to analyze three water resource metrics in the Upper Colorado River Basin: frequency of dry, wet, and whiplash years. Thirty-one year ‘climatologies’ in the GPCC-synth-LE can differ by over 20% in these key water resource metrics due to sampling of internal variability, and individual ensemble members in the GPCC-synth-LE can exhibit large near-monotonic trends over the course of the last century due to sampling of variability alone.

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 The Emergence and Transient Nature of Arctic Amplification in Coupled Climate Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Marika M. Holland ◽  
Laura Landrum
Keyword(s):  
Sea Ice ◽  
21St Century ◽  
Climate Models ◽  
Internal Variability ◽  
The Arctic ◽  
Arctic Amplification ◽  
Arctic Warming ◽  
Transient Nature ◽  
Coupled Climate Models ◽  
Large Ensembles

Under rising atmospheric greenhouse gas concentrations, the Arctic exhibits amplified warming relative to the globe. This Arctic amplification is a defining feature of global warming. However, the Arctic is also home to large internal variability, which can make the detection of a forced climate response difficult. Here we use results from seven model large ensembles, which have different rates of Arctic warming and sea ice loss, to assess the time of emergence of anthropogenically-forced Arctic amplification. We find that this time of emergence occurs at the turn of the century in all models, ranging across the models by a decade from 1994–2005. We also assess transient changes in this amplified signal across the 21st century and beyond. Over the 21st century, the projections indicate that the maximum Arctic warming will transition from fall to winter due to sea ice reductions that extend further into the fall. Additionally, the magnitude of the annual amplification signal declines over the 21st century associated in part with a weakening albedo feedback strength. In a simulation that extends to the 23rd century, we find that as sea ice cover is completely lost, there is little further reduction in the surface albedo and Arctic amplification saturates at a level that is reduced from its 21st century value.

Attribution of high-impact extreme sea surface temperature events

2021 ◽  
Author(s):  
Armineh Barkhordarian ◽  
Johanna Baehr
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Internal Variability ◽  
High Impact ◽  
Sea Surface ◽  
Discrete Events ◽  
Tasman Sea ◽  
Coupled Climate Models ◽  
Large Ensembles

<p>We evaluate whether anthropogenic influence has affected the observed extreme sea surface temperature (SST), defined as discrete events of anomalously warm or cold ocean temperatures, over the last decades. To this end we utilize three large ensembles of coupled climate models and use two methods. The first method analyzes the observed long-term spatiotemporal changes of extreme SST to detect the presence of a signal beyond changes solely due to natural (internal) variability and to attribute the detected changes to external climate drivers. The second method is based on single event attribution, which determines how an external forcing have changed the likelihood of high-impact extreme SST events, such as the north Atlantic cold blob, the northeast Pacific warm blob, Tasman Sea marine heatwave, etc. In this study we further combine observations and model simulations under present and future forcing to assess how internal variability and anthropogenic climate change modulate extreme SST events.</p>

scholarly journals Indo-Pacific Climate Modes in Warming Climate: Consensus and Uncertainty Across Model Projections

2019 ◽  
Vol 5 (4) ◽  
pp. 308-321 ◽  
Author(s):  
Xiao-Tong Zheng
Keyword(s):  
Climate Models ◽  
Internal Variability ◽  
Forced Response ◽  
Future Climate Change ◽  
Climate Modes ◽  
Warming Climate ◽  
Warming Pattern ◽  
Large Ensembles ◽  
Projected Changes ◽  
Climate Mode

Abstract Purpose of Review Understanding the changes in climate variability in a warming climate is crucial for reliable projections of future climate change. This article reviews the recent progress in studies of how climate modes in the Indo-Pacific respond to greenhouse warming, including the consensus and uncertainty across climate models. Recent Findings Recent studies revealed a range of robust changes in the properties of climate modes, often associated with the mean state changes in the tropical Indo-Pacific. In particular, the intermodel diversity in the ocean warming pattern is a prominent source of uncertainty in mode changes. The internal variability also plays an important role in projected changes in climate modes. Summary Model biases and intermodel variability remain major challenges for reducing uncertainty in projecting climate mode changes in warming climate. Improved models and research linking simulated present-day climate and future changes are essential for reliable projections of climate mode changes. In addition, large ensembles should be used for each model to reduce the uncertainty from internal variability and isolate the forced response to global warming.

scholarly journals Probabilistic Concepts in Intermediate-Complexity Climate Models: A Snapshot Attractor Picture

2015 ◽  
Vol 29 (1) ◽  
pp. 259-272 ◽  
Author(s):  
Mátyás Herein ◽  
János Márfy ◽  
Gábor Drótos ◽  
Tamás Tél
Keyword(s):  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Internal Variability ◽  
Co2 Concentration ◽  
Ensemble Average ◽  
Climate Projections ◽  
Intermediate Complexity ◽  
The North

Abstract A time series resulting from a single initial condition is shown to be insufficient for quantifying the internal variability in a climate model, and thus one is unable to make meaningful climate projections based on it. The authors argue that the natural distribution, obtained from an ensemble of trajectories differing solely in their initial conditions, of the snapshot attractor corresponding to a particular forcing scenario should be determined in order to quantify internal variability and to characterize any instantaneous state of the system in the future. Furthermore, as a simple measure of internal variability of any particular variable of the model, the authors suggest using its instantaneous ensemble standard deviation. These points are illustrated with the intermediate-complexity climate model Planet Simulator forced by a CO2 scenario, with a 40-member ensemble. In particular, the leveling off of the time dependence of any ensemble average is shown to provide a much clearer indication of reaching a steady state than any property of single time series. Shifts in ensemble averages are indicative of climate changes. The dynamical character of such changes is illustrated by hysteresis-like curves obtained by plotting the ensemble average surface temperature versus the CO2 concentration. The internal variability is found to be the most pronounced on small geographical scales. The traditionally used 30-yr temporal averages are shown to be considerably different from the corresponding ensemble averages. Finally, the North Atlantic Oscillation (NAO) index, related to the teleconnection paradigm, is also investigated. It is found that the NAO time series strongly differs in any individual realization from each other and from the ensemble average, and climatic trends can be extracted only from the latter.

scholarly journals Climate noise effect on uncertainty of hydrological extremes: numerical experiments with hydrological and climate models

2015 ◽  
Vol 369 ◽  
pp. 49-53 ◽  
Author(s):  
A. N. Gelfan ◽  
V. A. Semenov ◽  
Yu. G. Motovilov
Keyword(s):  
Numerical Experiments ◽  
Climate Models ◽  
Hydrological Model ◽  
Climate Model ◽  
Initial Conditions ◽  
Hydrological Regime ◽  
Internal Variability ◽  
Climate Impact ◽  
Noise Effect ◽  
Climate Noise

Abstract. An approach has been proposed to analyze the simulated hydrological extreme uncertainty related to the internal variability of the atmosphere ("climate noise"), which is inherent to the climate system and considered as the lowest level of uncertainty achievable in climate impact studies. To assess the climate noise effect, numerical experiments were made with climate model ECHAM5 and hydrological model ECOMAG. The case study was carried out to Northern Dvina River basin (catchment area is 360 000 km2), whose hydrological regime is characterised by extreme freshets during spring-summer snowmelt period. The climate noise was represented by ensemble ECHAM5 simulations (45 ensemble members) with identical historical boundary forcing and varying initial conditions. An ensemble of the ECHAM5-outputs for the period of 1979–2012 was used (after bias correction post-processing) as the hydrological model inputs, and the corresponding ensemble of 45 multi-year hydrographs was simulated. From this ensemble, we derived flood statistic uncertainty caused by the internal variability of the atmosphere.

scholarly journals Detectability of Decadal Anthropogenic Hydroclimate Changes over North America

2018 ◽  
Vol 31 (7) ◽  
pp. 2579-2597 ◽  
Author(s):  
Honghai Zhang ◽  
Thomas L. Delworth
Keyword(s):  
North America ◽  
Time Scales ◽  
Climate Models ◽  
Signal To Noise Ratio ◽  
Dominant Role ◽  
Internal Variability ◽  
Natural Variability ◽  
Large Ensembles ◽  
Current Century ◽  
Mean State

Regional hydroclimate changes on decadal time scales contain substantial natural variability. This presents a challenge for the detection of anthropogenically forced hydroclimate changes on these spatiotemporal scales because the signal of anthropogenic changes is modest, compared to the noise of natural variability. However, previous studies have shown that this signal-to-noise ratio can be greatly improved in a large model ensemble where each member contains the same signal but different noise. Here, using multiple state-of-the-art large ensembles from two climate models, the authors quantitatively assess the detectability of anthropogenically caused decadal shifts in precipitation-minus-evaporation (PmE) mean state against natural variability, focusing on North America during 2000–50. Anthropogenic forcing is projected to cause detectable (signal larger than noise) shifts in PmE mean state relative to the 1950–99 climatology over 50%–70% of North America by 2050. The earliest detectable signals include, during November–April, a moistening over northeastern North America and a drying over southwestern North America and, during May–October, a drying over central North America. Different processes are responsible for these signals. Changes in submonthly transient eddy moisture fluxes account for the northeastern moistening and central drying, while monthly atmospheric circulation changes explain the southwestern drying. These model findings suggest that despite the dominant role of natural internal variability on decadal time scales, anthropogenic shifts in PmE mean state can be detected over most of North America before the middle of the current century.

scholarly journals Sensitivity of Twentieth-Century Sahel Rainfall to Sulfate Aerosol and CO2 Forcing

2011 ◽  
Vol 24 (19) ◽  
pp. 4999-5014 ◽  
Author(s):  
Duncan Ackerley ◽  
Ben B. B. Booth ◽  
Sylvia H. E. Knight ◽  
Eleanor J. Highwood ◽  
David J. Frame ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Climate Models ◽  
Internal Variability ◽  
Natural Variability ◽  
Severe Drought ◽  
Relative Role ◽  
Coupled Climate Models ◽  
Large Ensembles ◽  
Drying Trend ◽  
Sahel Rainfall

A full understanding of the causes of the severe drought seen in the Sahel in the latter part of the twentieth-century remains elusive some 25 yr after the height of the event. Previous studies have suggested that this drying trend may be explained by either decadal modes of natural variability or by human-driven emissions (primarily aerosols), but these studies lacked a sufficiently large number of models to attribute one cause over the other. In this paper, signatures of both aerosol and greenhouse gas changes on Sahel rainfall are illustrated. These idealized responses are used to interpret the results of historical Sahel rainfall changes from two very large ensembles of fully coupled climate models, which both sample uncertainties arising from internal variability and model formulation. The sizes of these ensembles enable the relative role of human-driven changes and natural variability on historic Sahel rainfall to be assessed. The paper demonstrates that historic aerosol changes are likely to explain most of the underlying 1940–80 drying signal and a notable proportion of the more pronounced 1950–80 drying.

Assessing the role of internal variability in the carbon budgets framework

2020 ◽  
Author(s):  
Katarzyna Tokarska ◽  
Nathan P. Gillett ◽  
Vivek K. Arora ◽  
Roland Séférian
Keyword(s):  
Climate Models ◽  
Internal Variability ◽  
Paris Agreement ◽  
Carbon Uptake ◽  
Carbon Budgets ◽  
Transient Climate Response ◽  
Temperature Component ◽  
One Year ◽  
Global Mean

<p>Carbon budgets are a policy-relevant tool that provides a cap on global total CO<sub>2</sub> emissions to limit global mean warming at the desired level, for example, to meet the Paris Agreement target. Internal variability due to natural fluctuations of the climate system affects the temperature and carbon uptake on land and in the ocean. However, uncertainties arising from internal variability have not been quantified in the Transient Climate Response on Cumulative Emissions (TCRE) framework and related carbon budgets. Here we show that even though land carbon uptake exhibits the highest internal variability, most of the uncertainty in TCRE and carbon budgets arises from the temperature component, in concentration-driven simulations. Resulting remaining carbon budgets for 1.5 and 2.0 °C temperature targets differ even up to ±10 PgC (± 36.7 GtCO2; 5-95% range), due to internal variability, which is approximately equivalent to one year of global annual CO<sub>2</sub> emissions. Our results suggest that calculating carbon budgets directly from climate models’ output does not introduce significant biases in TCRE and remaining carbon budgets due to internal variability. </p>

Which climate models capture the variability and forced response in observed temperatures: a large ensemble comparison

2020 ◽  
Author(s):  
Nicola Maher ◽  
Laura Suarez-Gutierrez ◽  
Sebastian Milinski
Keyword(s):  
Climate Models ◽  
Internal Variability ◽  
Forced Response ◽  
Tropical Eastern Pacific ◽  
The North ◽  
Surface Temperatures ◽  
External Forcings ◽  
Coupled Climate Models ◽  
Large Ensembles ◽  
Adequate Representation

<p>We evaluate how large ensembles of ten coupled climate models represent the observed internal variability and response to external forcings in historical surface temperatures based on a novel methodological framework. This framework allows us to directly attribute whether discrepancies between models and observations arise due to biases in the simulated internal variability or rather in the forced response, without relying on assumptions to separate both signals in the observations. The largest discrepancies occur due to overestimated forced warming in some models during recent decades. The areas where most models, a maximum of nine, adequately simulate observed temperatures are the North Atlantic, Tropical Eastern Pacific, and the Northern Hemisphere land areas. In contrast, none of the models considered offers an adequate representation over the Southern Ocean. Our evaluation shows that CESM-LE, GFDL-ESM2M, and MPI-GE perform best at representing the internal variability and forced response in observed surface temperatures both globally and regionally. </p>

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