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

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.

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

10.5194/esd-2019-67 ◽

2019 ◽

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

Abstract. Single model large ensembles are widely used model experiments to estimate internal climate variability. The underlying assumption is that the internal variability (here: inter-annual variability) of the chosen model is a good approximation of the observed natural (inter-annual) variability. In this study, we test this assumption based on 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-annunal variability and its future change are compared across the ensembles. To the knowledge of the authors, this is the first comparison of regional large ensembles over Europe. Analysis comprises 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. Changes of winter precipitation and dry periods are a bit unclear, with a tendency to increase for dry periods. 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 the role of internal climate variability.

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What can we learn from single model initial-condition large ensembles (SMILEs)? A Comparison of Multiple SMILEs for Precipitation

10.5194/egusphere-egu2020-19202 ◽

2020 ◽

Author(s):

Raul R. Wood ◽

Flavio Lehner ◽

Angeline Pendergrass ◽

Sarah Schlunegger ◽

Keith Rodgers

Keyword(s):

Climate Variability ◽

Internal Variability ◽

Forced Response ◽

Initial Condition ◽

Single Model ◽

Climate Conditions ◽

Internal Climate Variability ◽

Model Response ◽

Large Ensembles ◽

Global Precipitation

Identifying anthropogenic influences on climate amidst the &#8220;noise&#8221; of internal climate variability is a central challenge for the climate research community. In recent years, several modeling groups have produced single-model initial-condition large ensembles (SMILE) to analyze the interplay of the forced climate change and internal climate variability under current and future climate conditions. These simulations help to improve our understanding of climate variability, including extreme events, and can be employed as test-beds for statistical approaches to separate forced and internal components of climate variability.So far, most studies have focused on either an individual or a &#160;limited number of SMILEs. In this work we compare seven large ensembles to disentangle the influence of internal variability and model response uncertainty for multiple precipitation indices (e.g. wettest day of the year, precipitation with a return period of 20 years). What can we learn from intercomparison of SMILEs, how similar are they in terms of spatial patterns and forced response, and what if they aren&#8217;t? How does the forced response of an ensemble of SMILEs compare to the CMIP5 multi-model ensemble? By assessing multiple SMILEs we can identify robust signals for regional and global precipitation properties and revealing anthropogenic responses that are inherent to our current representations of the Earth system.

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Alternate history: A synthetic ensemble of ocean chlorophyll concentrations

10.5194/egusphere-egu21-1375 ◽

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

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.

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Quantifying the Role of Internal Climate Variability in Future Climate Trends

Journal of Climate ◽

10.1175/jcli-d-14-00830.1 ◽

2015 ◽

Vol 28 (16) ◽

pp. 6443-6456 ◽

Cited By ~ 79

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.

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

10.5194/egusphere-egu21-15595 ◽

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

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 CO2 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.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.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 CO2 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 CO2 forcing. We thus argue that the observed pattern of greening/browning in the northern high latitudes could originate from the combined effect of rising CO2 as well as the AMV.

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Understanding drivers of glacier-length variability over the last millennium

The Cryosphere ◽

10.5194/tc-15-1645-2021 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1645-1662

Author(s):

Alan Huston ◽

Nicholas Siler ◽

Gerard H. Roe ◽

Erin Pettit ◽

Nathan J. Steiger

Keyword(s):

Climate Variability ◽

Climate Model ◽

Volcanic Eruptions ◽

Internal Variability ◽

Forced Response ◽

Last Millennium ◽

External Forcing ◽

Climate History ◽

Temperature And Precipitation ◽

Glacier Length

Abstract. Changes in glacier length reflect the integrated response to local fluctuations in temperature and precipitation resulting from both external forcing (e.g., volcanic eruptions or anthropogenic CO2) and internal climate variability. In order to interpret the climate history reflected in the glacier moraine record, the influence of both sources of climate variability must therefore be considered. Here we study the last millennium of glacier-length variability across the globe using a simple dynamic glacier model, which we force with temperature and precipitation time series from a 13-member ensemble of simulations from a global climate model. The ensemble allows us to quantify the contributions to glacier-length variability from external forcing (given by the ensemble mean) and internal variability (given by the ensemble spread). Within this framework, we find that internal variability is the predominant source of length fluctuations for glaciers with a shorter response time (less than a few decades). However, for glaciers with longer response timescales (more than a few decades) external forcing has a greater influence than internal variability. We further find that external forcing also dominates when the response of glaciers from widely separated regions is averaged. Single-forcing simulations indicate that, for this climate model, most of the forced response over the last millennium, pre-anthropogenic warming, has been driven by global-scale temperature change associated with volcanic aerosols.

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Understanding Drivers of Glacier Length Variability Over the Last Millennium

10.5194/tc-2020-230 ◽

2020 ◽

Author(s):

Alan Huston ◽

Nicholas Siler ◽

Gerard H. Roe ◽

Erin Pettit ◽

Nathan J. Steiger

Keyword(s):

Climate Variability ◽

Volcanic Eruptions ◽

Internal Variability ◽

Forced Response ◽

Last Millennium ◽

External Forcing ◽

Climate History ◽

Temperature And Precipitation ◽

Glacier Length ◽

Length Changes

Abstract. Changes in glacier length reflect the integrated response to local fluctuations in temperature and precipitation resulting from both external forcing (e.g., volcanic eruptions or anthropogenic CO2) and internal climate variability. In order to interpret the climate history reflected in the glacier moraine record, therefore, the influence of both sources of climate variability must be considered. Here we study the last millennium of glacier length variability across the globe using a simple dynamic glacier model, which we force with temperature and precipitation time series from a 13-member ensemble of simulations from a global climate model. The ensemble allows us to quantify the contributions to glacier length variability from external forcing (given by the ensemble mean) and internal variability (given by the ensemble spread). Within this framework, we find that internal variability drives most length changes in mountain glaciers that have a response timescale of less than a few decades. However, for glaciers with longer response timescales (more than a few decades) external forcing has a greater influence than internal variability. We further find that external forcing also dominates when the response of glaciers from widely separated regions is averaged. Single-forcing simulations indicate that most of the forced response over the last millennium, pre-anthropogenic warming, has been driven by global-scale temperature change associated with volcanic aerosols.

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Wind speed stilling and its recovery due to internal climate variability

Earth System Dynamics ◽

10.5194/esd-12-1239-2021 ◽

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.

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Separating Internal Variability from the Externally Forced Climate Response

Journal of Climate ◽

10.1175/jcli-d-15-0069.1 ◽

2015 ◽

Vol 28 (20) ◽

pp. 8184-8202 ◽

Cited By ~ 49

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.

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The challenge of estimating high return levels with short records under large internal variability

10.5194/egusphere-egu2020-18105 ◽

2020 ◽

Author(s):

Joel Zeder ◽

Erich M. Fischer

Keyword(s):

Low Frequency ◽

Block Size ◽

Internal Variability ◽

Statistical Modelling ◽

Industrial Control ◽

Temperature And Precipitation ◽

Return Levels ◽

Large Ensembles ◽

Block Sizes ◽

Observational Record

The scientific understanding of changes in climate extremes is mostly limited to moderate definitions of extreme events occurring every few years, due to a lack of long-term observational daily data sets. In order to estimate return levels beyond observed time periods and event magnitudes, extreme events are typically modelled statistically based on extreme value theory. This is challenging since the short observational record may be affected by low-frequency natural internal variability and limits the block size that can be used.Here we test some common assumptions in the statistical modelling of extremes based on indices of climatic extremes (Tx7d, Rx1d, Rx5d) using long pre-industrial control runs and initial-condition large ensembles with thousands of years of model data.The tail of a distribution fitted to temperature and precipitation maxima is known to be highly sensitive to the compliance with statistical assumptions and choices such as the block size. Typically, 1-year block maxima are extracted from observational time series due to short record length. It is unclear whether these maxima are already in the domain of true extremes suitable for an extreme value analysis. Furthermore, the observational record is too short to sample low-frequency regional variability and potential transient changes in the mean climate. Standard uncertainty estimates (confidence intervals and hypothesis tests) are generally not accounting for potential biases introduced by a dominant mode of climate variability or violated modelling assumptions.Based on a 4700-year pre-industrial control simulation and an 84-member ensemble performed with CESM 1.2.2 model, we systematically extend the statistical modelling of temperature and precipitation extremes to larger block-sizes and longer synthetic observational periods. This analysis reveals a considerable influence of climate variability on tail estimates. Furthermore, the use of too small block sizes can induce substantial random as well as systematic biases. Statistical model complexity and thus uncertainty further increases for extremes retrieved from transient large-ensemble members, as non-stationarity has to be accounted for in the model formulation. Thus, the potential of spatial pooling or conditioning on further climatic variables as proxies for a specific climatic mode to derive more robust tail estimates is also evaluated. Findings based on the CESM ensemble are compared with pre-industrial control runs performed with other models in CMIP6 and other initial-condition large ensembles of the CLIVAR large ensemble working group.

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