scholarly journals Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations

2017 ◽  
Vol 30 (12) ◽  
pp. 4763-4776 ◽  
Author(s):  
Anson H. Cheung ◽  
Michael E. Mann ◽  
Byron A. Steinman ◽  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
...  
Keyword(s):  
Climate Variability ◽  
Surface Temperature ◽  
Time Scales ◽  
Linear Regression Analysis ◽  
Low Frequency ◽  
Semiempirical Method ◽  
Cmip5 Models ◽  
Instrumental Data ◽  
Internal Climate Variability ◽  
Instrumental Record

Low-frequency internal climate variability (ICV) plays an important role in modulating global surface temperature, regional climate, and climate extremes. However, it has not been completely characterized in the instrumental record and in the Coupled Model Intercomparison Project phase 5 (CMIP5) model ensemble. In this study, the surface temperature ICV of the North Pacific (NP), North Atlantic (NA), and Northern Hemisphere (NH) in the instrumental record and historical CMIP5 all-forcing simulations is isolated using a semiempirical method wherein the CMIP5 ensemble mean is applied as the external forcing signal and removed from each time series. Comparison of ICV signals derived from this semiempirical method as well as from analysis of ICV in CMIP5 preindustrial control runs reveals disagreement in the spatial pattern and amplitude between models and instrumental data on multidecadal time scales (>20 yr). Analysis of the amplitude of total variability and the ICV in the models and instrumental data indicates that the models underestimate ICV amplitude on low-frequency time scales (>20 yr in the NA; >40 yr in the NP), while agreement is found in the NH variability. A multiple linear regression analysis of ICV in the instrumental record shows that variability in the NP drives decadal-to-interdecadal variability in the NH, whereas the NA drives multidecadal variability in the NH. Analysis of the CMIP5 historical simulations does not reveal such a relationship, indicating model limitations in simulating ICV. These findings demonstrate the need to better characterize low-frequency ICV, which may help improve attribution and decadal prediction.

scholarly journals Reply to “Comment on ‘Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations’”

2017 ◽  
Vol 30 (23) ◽  
pp. 9773-9782 ◽  
Author(s):  
Anson H. Cheung ◽  
Michael E. Mann ◽  
Byron A. Steinman ◽  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
...  
Keyword(s):  
Low Frequency ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Multimodel Ensemble ◽  
Internal Climate Variability ◽  
Instrumental Record ◽  
Ensemble Mean ◽  
Intercomparison Project ◽  
The Individual

In a comment on a 2017 paper by Cheung et al., Kravtsov states that the results of Cheung et al. are invalidated by errors in the method used to estimate internal variability in historical surface temperatures, which involves using the ensemble mean of simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to estimate the forced signal. Kravtsov claims that differences between the forced signals in the individual models and as defined by the multimodel ensemble mean lead to errors in the assessment of internal variability in both model simulations and the instrumental record. Kravtsov proposes a different method, which instead uses CMIP5 models with at least four realizations to define the forced component. Here, it is shown that the conclusions of Cheung et al. are valid regardless of whether the method of Cheung et al. or that of Kravtsov is applied. Furthermore, many of the points raised by Kravtsov are discussed in Cheung et al., and the disagreements of Kravtsov appear to be mainly due to a misunderstanding of the aims of Cheung et al.

scholarly journals Comment on “Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations”

2017 ◽  
Vol 30 (23) ◽  
pp. 9763-9772 ◽  
Author(s):  
Sergey Kravtsov
Keyword(s):  
Climate Variability ◽  
Low Frequency ◽  
Accurate Estimate ◽  
Cmip5 Models ◽  
Pi Control ◽  
Individual Model ◽  
Internal Climate Variability ◽  
Alternative Methodology ◽  
The Difference ◽  
Model Ensembles

In a recent article, Cheung et al. applied a semiempirical methodology to isolate internal climate variability (ICV) in CMIP5 models and observations. The essence of their methodology is to subtract the scaled CMIP5 multimodel ensemble mean (MMEM) from individual model simulations and from the observed time series of several surface temperature indices. Cheung et al. detected large differences in both the magnitude and spatial patterns of the observed and simulated ICV, as well as large differences between the historical (simulated) ICV and preindustrial (PI) control CMIP5 simulations. Here it is shown that subtraction of the scaled MMEM from CMIP5 historical simulations produces a poor estimate of the modeled ICV due to the difference between the scaled MMEM and a given model’s true forced signal masquerading as ICV. The resulting phase and amplitude errors of the ICV so estimated are large, which compromises most of Cheung et al.’s conclusions pertaining to characterization of ICV in the historical CMIP5 simulations. By contrast, an alternative methodology based on forced signals computed from individual model ensembles produces a much more accurate estimate of the ICV in CMIP5 models, whose magnitude is consistent with the PI control simulations and is much smaller than any of the semiempirical estimates of the observed ICV on decadal and longer time scales.

Anomalous Temperature Regimes during the Cool Season: Long-Term Trends, Low-Frequency Mode Modulation, and Representation in CMIP5 Simulations

2013 ◽  
Vol 26 (22) ◽  
pp. 9061-9076 ◽  
Author(s):  
Rebecca M. Westby ◽  
Yun-Young Lee ◽  
Robert X. Black
Keyword(s):  
United States ◽  
Linear Regression Analysis ◽  
Low Frequency ◽  
The United States ◽  
Cmip5 Models ◽  
Southern United States ◽  
Anomalous Temperature ◽  
Temperature Regimes ◽  
Collective Influence

Abstract During boreal winter, anomalous temperature regimes (ATRs), including cold air outbreaks (CAOs) and warm waves (WWs), provide important societal influences upon the United States. The current study analyzes reanalysis and model data for the period from 1949 to 2011 to assess (i) long-term variability in ATRs, (ii) interannual modulation of ATRs by low-frequency modes, and (iii) the representation of ATR behavior in models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). No significant trends in either WWs or CAOs are identified for the continental United States. On interannual time scales, CAOs are modulated by the (i) North Atlantic Oscillation (NAO) over the U.S. Southeast and (ii) the Pacific–North American (PNA) pattern over the Northwest. WW frequency is modulated by (i) the NAO over the eastern United States and (ii) the combined influence of the PNA, Pacific decadal oscillation (PDO), and ENSO over the southern United States. In contrast to previous studies of seasonal-mean temperature, the influence of ENSO upon ATRs is found to be mainly limited to a modest modulation of WWs over the southern United States. Multiple linear regression analysis reveals that the regional collective influence of low-frequency modes accounts for as much as 50% of interannual ATR variability. Although similar behavior is observed in CMIP5 models, WW (CAO) frequency is typically overestimated (underestimated). All models considered are unable to replicate observed associations between ATRs and the PDO. Further, the collective influence of low-frequency modes upon ATRs is generally underestimated in CMIP5 models. The results indicate that predictions of future ATR behavior are limited by climate model ability to represent the evolving behavior of low-frequency modes of variability.

Impacts of low-frequency internal climate variability and greenhouse warming on the El Niño-Southern Oscillation

2020 ◽  
pp. 1-38
Author(s):  
Benjamin Ng ◽  
Wenju Cai ◽  
Tim Cowan ◽  
Daohua Bi
Keyword(s):  
Standard Deviation ◽  
Climate Variability ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Cmip5 Models ◽  
Surface Warming ◽  
Internal Climate Variability ◽  
Cp El Niño ◽  
Eastern Equatorial Pacific

AbstractThe El Niño-Southern Oscillation (ENSO) is the dominant mode of interannual climate fluctuations with wide-ranging socio-economic and environmental impacts. Understanding the eastern Pacific (EP) and central Pacific (CP) El Niño response to a warmer climate is paramount, yet the role of internal climate variability in modulating their response is not clear. Using large ensembles, we find that internal variability generates a spread in the standard deviation and skewness of these two El Niño types that is similar to the spread of 17 Coupled Model Intercomparison Project phase 5 (CMIP5) models that realistically simulate ENSO diversity. Based on 40 Community Earth System Model Large Ensemble (CESM-LE) and 99 Max Planck Institute for Meteorology Grand Ensemble (MPI-GE) members, unforced variability can explain more than 90% of the historical EP and CP El Niño standard deviation and all of the ENSO skewness spread in the 17 CMIP5 models. Both CESM-LE and the selected CMIP5 models show increased EP and CP El Niño variability in a warmer climate, driven by a stronger mean vertical temperature gradient in the upper ocean and faster surface warming of the eastern equatorial Pacific. However, MPI-GE shows no agreement in EP or CP standard deviation change. This is due to weaker sensitivity to the warming signal, such that when the eastern equatorial Pacific surface warming is faster, the change in upper ocean vertical temperature gradient tends to be weaker. This highlights that individual models produce a different ENSO response in a warmer climate, and that considerable uncertainty within the CMIP5 ensemble may be caused by internal climate variability.

Separating Internal Variability from the Externally Forced Climate Response

2015 ◽  
Vol 28 (20) ◽  
pp. 8184-8202 ◽  
Author(s):  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Michael E. Mann ◽  
Byron A. Steinman
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Low Frequency ◽  
Internal Variability ◽  
Atlantic Multidecadal Oscillation ◽  
Ensemble Member ◽  
Internal Climate Variability ◽  
Climate Simulations ◽  
Scaling Methods ◽  
Synthetic Time Series

Abstract Separating low-frequency internal variability of the climate system from the forced signal is essential to better understand anthropogenic climate change as well as internal climate variability. Here both synthetic time series and the historical simulations from phase 5 of CMIP (CMIP5) are used to examine several methods of performing this separation. Linear detrending, as is commonly used in studies of low-frequency climate variability, is found to introduce large biases in both amplitude and phase of the estimated internal variability. Using estimates of the forced signal obtained from ensembles of climate simulations can reduce these biases, particularly when the forced signal is scaled to match the historical time series of each ensemble member. These so-called scaling methods also provide estimates of model sensitivities to different types of external forcing. Applying the methods to observations of the Atlantic multidecadal oscillation leads to different estimates of the phase of this mode of variability in recent decades.

scholarly journals Climate Variability, Fish, and Fisheries

2006 ◽  
Vol 19 (20) ◽  
pp. 5009-5030 ◽  
Author(s):  
P. Lehodey ◽  
J. Alheit ◽  
M. Barange ◽  
T. Baumgartner ◽  
G. Beaugrand ◽  
...  
Keyword(s):  
Climate Variability ◽  
Time Scales ◽  
Large Scale ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Fish Population ◽  
Marine Ecology ◽  
Fish Populations ◽  
Global Ocean ◽  
Close Link

Abstract Fish population variability and fisheries activities are closely linked to weather and climate dynamics. While weather at sea directly affects fishing, environmental variability determines the distribution, migration, and abundance of fish. Fishery science grew up during the last century by integrating knowledge from oceanography, fish biology, marine ecology, and fish population dynamics, largely focused on the great Northern Hemisphere fisheries. During this period, understanding and explaining interannual fish recruitment variability became a major focus for fisheries oceanographers. Yet, the close link between climate and fisheries is best illustrated by the effect of “unexpected” events—that is, nonseasonal, and sometimes catastrophic—on fish exploitation, such as those associated with the El Niño–Southern Oscillation (ENSO). The observation that fish populations fluctuate at decadal time scales and show patterns of synchrony while being geographically separated drew attention to oceanographic processes driven by low-frequency signals, as reflected by indices tracking large-scale climate patterns such as the Pacific decadal oscillation (PDO) and the North Atlantic Oscillation (NAO). This low-frequency variability was first observed in catch fluctuations of small pelagic fish (anchovies and sardines), but similar effects soon emerged for larger fish such as salmon, various groundfish species, and some tuna species. Today, the availability of long time series of observations combined with major scientific advances in sampling and modeling the oceans’ ecosystems allows fisheries science to investigate processes generating variability in abundance, distribution, and dynamics of fish species at daily, decadal, and even centennial scales. These studies are central to the research program of Global Ocean Ecosystems Dynamics (GLOBEC). This review presents examples of relationships between climate variability and fisheries at these different time scales for species covering various marine ecosystems ranging from equatorial to subarctic regions. Some of the known mechanisms linking climate variability and exploited fish populations are described, as well as some leading hypotheses, and their implications for their management and for the modeling of their dynamics. It is concluded with recommendations for collaborative work between climatologists, oceanographers, and fisheries scientists to resolve some of the outstanding problems in the development of sustainable fisheries.

Decadal modulation of global surface temperature by internal climate variability

2015 ◽  
Vol 5 (6) ◽  
pp. 555-559 ◽  
Author(s):  
Aiguo Dai ◽  
John C. Fyfe ◽  
Shang-Ping Xie ◽  
Xingang Dai
Keyword(s):  
Climate Variability ◽  
Surface Temperature ◽  
Internal Climate Variability ◽  
Global Surface Temperature ◽  
Decadal Modulation ◽  
Global Surface
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