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.

Alternate history: A synthetic ensemble of ocean chlorophyll concentrations

2021 ◽  
Author(s):  
Geneviève Elsworth ◽  
Nicole Lovenduski ◽  
Karen McKinnon
Keyword(s):  
Climate Variability ◽  
Internal Variability ◽  
Ensemble Member ◽  
Global Ocean ◽  
Large Ensemble ◽  
Internal Climate Variability ◽  
Surface Ocean ◽  
Large Ensembles ◽  
Long Term Trends

<p>Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) have demonstrated their utility in the study of marine phytoplankton variability. These ESM large ensembles simulate the evolution of multiple alternate realities, each with a different phasing of internal climate variability. However, ESMs may not accurately represent real world variability as recorded via satellite and in situ observations of ocean chlorophyll over the past few decades. Observational records of surface ocean chlorophyll equate to a single ensemble member in the large ensemble framework, and this can cloud the interpretation of long-term trends: are they externally forced, caused by the phasing of internal variability, or both? Here, we use a novel statistical emulation technique to place the observational record of surface ocean chlorophyll into the large ensemble framework. Much like a large initial condition ensemble generated with an ESM, the resulting synthetic ensemble represents multiple possible evolutions of ocean chlorophyll concentration, each with a different phasing of internal climate variability. We further demonstrate the validity of our statistical approach by recreating a ESM ensemble of chlorophyll using only a single ESM ensemble member. We use the synthetic ensemble to explore the interpretation of long-term trends in the presence of internal variability. Our results suggest the potential to explore this approach for other ocean biogeochemical variables.</p>

Quantifying the Role of Internal Climate Variability in Future Climate Trends

2015 ◽  
Vol 28 (16) ◽  
pp. 6443-6456 ◽  
Author(s):  
David W. J. Thompson ◽  
Elizabeth A. Barnes ◽  
Clara Deser ◽  
William E. Foust ◽  
Adam S. Phillips
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Internal Variability ◽  
Ensemble Member ◽  
Future Climate ◽  
Analytic Model ◽  
Climate Trends ◽  
Internal Climate Variability ◽  
Large Ensembles

Abstract Internal variability in the climate system gives rise to large uncertainty in projections of future climate. The uncertainty in future climate due to internal climate variability can be estimated from large ensembles of climate change simulations in which the experiment setup is the same from one ensemble member to the next but for small perturbations in the initial atmospheric state. However, large ensembles are invariably computationally expensive and susceptible to model bias. Here the authors outline an alternative approach for assessing the role of internal variability in future climate based on a simple analytic model and the statistics of the unforced climate variability. The analytic model is derived from the standard error of the regression and assumes that the statistics of the internal variability are roughly Gaussian and stationary in time. When applied to the statistics of an unforced control simulation, the analytic model provides a remarkably robust estimate of the uncertainty in future climate indicated by a large ensemble of climate change simulations. To the extent that observations can be used to estimate the amplitude of internal climate variability, it is argued that the uncertainty in future climate trends due to internal variability can be robustly estimated from the statistics of the observed climate.

The North Atlantic Ocean as a Modulator of Vegetation Greening/Browning in the Northern High Latitudes?

2021 ◽  
Author(s):  
Leonard F. Borchert ◽  
Alexander J. Winkler
Keyword(s):  
Climate Variability ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Internal Variability ◽  
Dominant Mode ◽  
High Latitudes ◽  
Model Simulations ◽  
Internal Climate Variability ◽  
The North ◽  
The North Atlantic

<p>Vegetation in the northern high latitudes shows a characteristic pattern of persistent changes as documented by multi-decadal satellite observations. The prevailing explanation that these mainly increasing trends (greening) are a consequence of external CO<sub>2</sub> forcing, i.e., due to the ubiquitous effect of CO2-induced fertilization and/or warming of temperature-limited ecosystems, however does not explain why some areas also show decreasing trends of vegetation cover (browning). We propose here to consider the dominant mode of multi-decadal internal climate variability in the north Atlantic region, the Atlantic Multidecadal Variability (AMV), as the missing link in the explanation of greening and browning trend patterns in the northern high latitudes. Such a link would also imply potential for decadal predictions of ecosystem changes in the northern high latitudes.</p><p>An analysis of observational and reanalysis data sets for the period 1979-2019 shows that locations characterized by greening trends largely coincide with warming summer temperature and increasing precipitation. Wherever either cooling or decreasing precipitation occurs, browning trends are observed over this period. These precipitation and temperature patterns are significantly correlated with a North Atlantic sea surface temperature index that represents the AMV signal, indicating its role in modulating greening/browning trend patterns in the northern high latitudes.</p><p>Using two large ensembles of coupled Earth system model simulations (100 members of MPI-ESM-LR Grand Ensemble and 32 members of the IPSL-CM6A-LR Large Ensemble), we separate the greening/browning pattern caused by external CO<sub>2</sub> forcing from that caused by internal climate variability associated with the AMV. These sets of model simulations enable a clean separation of the externally forced signal from internal variability. While the greening and browning patterns in the simulations do not agree with observations in terms of magnitude and location, we find consistent internally generated greening/browning patterns in both models caused by changes in temperature and precipitation linked to the AMV signal. These greening/browning trend patterns are of the same magnitude as those caused by the external forcing alone. Our work therefore shows that internally-generated changes of vegetation in the northern lands, driven by AMV, are potentially as large as those caused by external CO<sub>2</sub> forcing. We thus argue that the observed pattern of greening/browning in the northern high latitudes could originate from the combined effect of rising CO<sub>2</sub> as well as the AMV.</p>

scholarly journals Interhemispheric asymmetry of climate change projections of boreal winter surface winds in CanESM5 large ensemble simulations

2021 ◽  
Author(s):  
Bin Yu ◽  
Xuebin Zhang ◽  
Guilong Li ◽  
Wei Yu
Keyword(s):  
Climate Variability ◽  
Wind Power ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Boreal Winter ◽  
Interhemispheric Asymmetry ◽  
Large Ensemble ◽  
Internal Climate Variability ◽  
Climate Simulations ◽  
Extreme Wind

Abstract A recent study of future changes in global wind power using an ensemble of ten CMIP5 climate simulations indicated an interhemispheric asymmetry of wind power changes over the 21st century, featured by power decreases across the Northern Hemisphere mid-latitudes and increases across the tropics and subtropics of the Southern Hemisphere. Here we analyze future global projections of surface mean and extreme winds by means of a single-model initial-condition 50-member ensemble of climate simulations generated with CanESM5, the Canadian model participated in CMIP6. We analyze the ensemble mean and spread of boreal winter mean and extreme wind trends over the next half-century (2021-2070) and explore the contribution of internal climate variability to these trends. Surface wind speed is projected to mostly decrease in northern mid-low latitudes and southern mid-latitudes and increase in northern high latitudes and southern tropical and subtropical regions, with considerable regional variations. Large ensemble spreads are apparent, especially with remarkable differences over northern parts of South America and northern Russia. The interhemispheric asymmetry of wind projections is found in most ensemble members, and can be related to large-scale changes in surface temperature and atmospheric circulation. The extreme wind has similar structure of future projections, whereas its reductions tend to be more consistent over northern mid-latitudes. The projected mean and extreme wind changes are attributed to changes in both externally anthropogenic forced and internal climate variability generated components. The spread in wind projections is partially due to large-scale atmospheric circulation variability.

Comparing inter-annual variabilities in three regional single model initial-condition large ensembles (SMILE) over Europe

2020 ◽  
Author(s):  
Fabian von Trentini ◽  
Emma E. Aalbers ◽  
Erich M. Fischer ◽  
Ralf Ludwig
Keyword(s):  
Climate Variability ◽  
Heat Waves ◽  
Internal Variability ◽  
Summer Temperature ◽  
Winter Temperature ◽  
Single Model ◽  
Internal Climate Variability ◽  
Annual Variability ◽  
Temperature And Precipitation ◽  
Large Ensembles

<p>Single model large ensembles are widely used model experiments to estimate internal climate variability (here: inter-annual variability). The underlying assumption is that the internal variability of the chosen model is a good approximation of the observed natural variability. In this study, for the first time over Europe, we test this assumption based on the comparison of three regional climate model large ensembles (16 members of an EC-EARTH-RACMO ensemble, 21 members of a CESM-CCLM ensemble, 50 members of a CanESM-CRCM ensemble) for four European domains (British Isles, France, Mid-Europe, Alps). Simulated inter-annual variability is evaluated against E-OBS and the inter-annual variability and its future change are compared across the ensembles. Analyses comprise seasonal temperature and precipitation, as well as indicators for dry periods and heat waves. Results show a large consistency of all three ensembles with E-OBS data for most indicators and regions, validating the abilities of these ensembles to represent natural variability on the annual scale. EC-EARTH-RACMO shows the highest inter-annual variability for winter temperature and precipitation, whereas CESM-CCLM shows the highest variability for summer temperature and precipitation, as well as for heatwaves and dry periods. Despite these model differences, the sign of the future changes in internal variability is largely the same in all models: for summer temperature, summer precipitation and the number of heat waves, the internal variability increases, while it decreases for winter temperature. While dry periods reveal a tendency to increase in variability, the changes of winter precipitation remain less conclusive. The overall consistency across single model large ensembles and observations strengthens the concept of large ensembles, and underlines their great potential for understanding and quantifying internal climate variability and its role in climate change dynamics.</p>

scholarly journals Wind speed stilling and its recovery due to internal climate variability

2021 ◽  
Vol 12 (4) ◽  
pp. 1239-1251
Author(s):  
Jan Wohland ◽  
Doris Folini ◽  
Bryn Pickering
Keyword(s):  
Wind Speed ◽  
Climate Variability ◽  
Internal Variability ◽  
Systematic Investigation ◽  
Max Planck ◽  
Near Surface ◽  
Climate Conditions ◽  
Wind Speeds ◽  
Internal Climate Variability ◽  
Long Run

Abstract. Near-surface winds affect many processes on planet Earth, ranging from fundamental biological mechanisms such as pollination to man-made infrastructure that is designed to resist or harness wind. The observed systematic wind speed decline up to around 2010 (stilling) and its subsequent recovery have therefore attracted much attention. While this sequence of downward and upwards trends and good connections to well-established modes of climate variability suggest that stilling could be a manifestation of multidecadal climate variability, a systematic investigation is currently lacking. Here, we use the Max Planck Institute Grand Ensemble (MPI-GE) to decompose internal variability from forced changes in wind speeds. We report that wind speed changes resembling observed stilling and its recovery are well in line with internal climate variability, both under current and future climate conditions. Moreover, internal climate variability outweighs forced changes in wind speeds on 20-year timescales by 1 order of magnitude, despite the fact that smaller, forced changes become relevant in the long run as they represent alterations of mean states. In this regard, we reveal that land use change plays a pivotal role in explaining MPI-GE ensemble-mean wind changes in the representative concentration pathways 2.6, 4.5, and 8.5. Our results demonstrate that multidecadal wind speed variability is of greater relevance than forced changes over the 21st century, in particular for wind-related infrastructure like wind energy.

Inherent Uncertainty Disguises Attribution of Reduced Atmospheric CO2 Growth to Mitigation for up to a Decade

2020 ◽  
Author(s):  
Aaron Spring ◽  
Tatiana Ilyina ◽  
Jochem Marotzke
Keyword(s):  
Climate Variability ◽  
Internal Variability ◽  
Climate Mitigation ◽  
Mitigation Scenario ◽  
Growth Trend ◽  
Emission Reductions ◽  
Internal Climate Variability ◽  
Policy Shift ◽  
Near Term ◽  
Ocean Carbon

<p>On inter-annual time scales the growth rate of atmospheric CO<sub>2</sub> is largely driven by the response of the land and ocean carbon sinks to climate variability. Therefore, climate mitigation in terms of emission reductions can be disguised by internal variability.<br>However, the probability that emission reductions induced by a policy change caused reductions in atmospheric CO<sub>2</sub> growth trend is unclear. <br>We use 100 historical MPI-ESM simulations and interpret mitigation in 2020 as a policy shift from Representative Concentration Pathway 4.5 to 2.5 in a comprehensive causation attribution framework.<br>Here we show that five-year CO<sub>2</sub> trends are higher in 2021-2025 than over 2016-2020 in 30% of all realizations in the mitigation scenario, compared to 52% in the non-mitigation scenario. Therefore, mitigation is sufficient or necessary to cause these trends by 42% or 31%, respectively and therefore far from certain. <br>A stronger increase in atmospheric CO<sub>2</sub> trends despite emission reductions is possible when the global carbon cycle triggered by internal climate variability releases more CO<sub>2</sub> than mitigation saves. Such trends might occur for of up to ten years. Certainty that mitigation causes trend reductions is only reached after ten or fifteen years, respectively of the type of causation.<br>Our analysis showcases the inherent uncertainty of near-term CO<sub>2</sub> projections. Assessments of the efficacy of mitigation in the near term are incomplete without quantitatively considering internal variability.</p>

scholarly journals Imprints of climate forcings in global gridded temperature data

2016 ◽  
Vol 7 (1) ◽  
pp. 231-249 ◽  
Author(s):  
Jiří Mikšovský ◽  
Eva Holtanová ◽  
Petr Pišoft
Keyword(s):  
Climate Variability ◽  
20Th Century ◽  
Southern Oscillation ◽  
Atlantic Multidecadal Oscillation ◽  
Temperature Data ◽  
Data Sets ◽  
Near Surface ◽  
Anthropogenic Forcing ◽  
Internal Climate Variability ◽  
Climate Forcings

Abstract. Monthly near-surface temperature anomalies from several gridded data sets (GISTEMP, Berkeley Earth, MLOST, HadCRUT4, 20th Century Reanalysis) were investigated and compared with regard to the presence of components attributable to external climate forcings (associated with anthropogenic greenhouse gases, as well as solar and volcanic activity) and to major internal climate variability modes (El Niño/Southern Oscillation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Pacific Decadal Oscillation and variability characterized by the Trans-Polar Index). Multiple linear regression was used to separate components related to individual explanatory variables in local monthly temperatures as well as in their global means, over the 1901–2010 period. Strong correlations of temperature and anthropogenic forcing were confirmed for most of the globe, whereas only weaker and mostly statistically insignificant connections to solar activity were indicated. Imprints of volcanic forcing were found to be largely insignificant in the local temperatures, in contrast to the clear volcanic signature in their global averages. Attention was also paid to the manifestations of short-term time shifts in the responses to the forcings, and to differences in the spatial fingerprints detected from individual temperature data sets. It is shown that although the resemblance of the response patterns is usually strong, some regional contrasts appear. Noteworthy differences from the other data sets were found especially for the 20th Century Reanalysis, particularly for the components attributable to anthropogenic forcing over land, but also in the response to volcanism and in some of the teleconnection patterns related to the internal climate variability modes.

scholarly journals Ensemble bias correction of climate simulations: preserving internal variability

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pradeebane Vaittinada Ayar ◽  
Mathieu Vrac ◽  
Alain Mailhot
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Internal Variability ◽  
Ensemble Member ◽  
Climate Change Signal ◽  
Changing Climate ◽  
Uncertainty Model ◽  
Climate Simulations

AbstractClimate simulations often need to be adjusted (i.e., corrected) before any climate change impacts studies. However usual bias correction approaches do not differentiate the bias from the different uncertainties of the climate simulations: scenario uncertainty, model uncertainty and internal variability. In particular, in the case of a multi-run ensemble of simulations (i.e., multiple runs of one model), correcting, as usual, each member separately, would mix up the model biases with its internal variability. In this study, two ensemble bias correction approaches preserving the internal variability of the initial ensemble are proposed. These “Ensemble bias correction” (EnsBC) approaches are assessed and compared to the approach where each ensemble member is corrected separately, using precipitation and temperature series at two locations in North America from a multi-member regional climate ensemble. The preservation of the internal variability is assessed in terms of monthly mean and hourly quantiles. Besides, the preservation of the internal variability in a changing climate is evaluated. Results show that, contrary to the usual approach, the proposed ensemble bias correction approaches adequately preserve the internal variability even in changing climate. Moreover, the climate change signal given by the original ensemble is also conserved by both approaches.

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.

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