scholarly journals How large does a large ensemble need to be?

2020 ◽  
Author(s):  
Sebastian Milinski ◽  
Nicola Maher ◽  
Dirk Olonscheck
Keyword(s):  
North America ◽  
Sea Ice ◽  
Surface Temperature ◽  
Internal Variability ◽  
Forced Response ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ensemble Size ◽  
Large Ensemble ◽  
Large Ensembles

<p>Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool to quantify the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble.</p><p>Here, we introduce an objective method to estimate the required ensemble size. This method can be applied to any given application. We demonstrate its use on the examples that represent typical applications of large ensembles: quantifying the forced response, quantifying internal variability, and detecting a forced change in internal variability.</p><p>We analyse forced trends in global mean surface temperature, local surface temperature and precipitation in the MPI Grand Ensemble (Maher et al., 2019). We find that 10 ensemble members are sufficient to quantify the forced response in historical surface temperature over the ocean, but more than 50 members are necessary over land at higher latitudes. </p><p>Next, we apply our method to identify the required ensemble size to sample internal variability of surface temperature over central North America and over the Niño 3.4 region. A moderate ensemble size of 10 members is sufficient to quantify variability over North America, while a large ensemble with close to 50 members is necessary for the Niño 3.4 region.</p><p>Finally, we use the example of September Arctic sea ice area to investigate forced changes in internal variability. In a strong warming scenario, the variability in sea ice area is increasing because more open water near the coastlines allows for more variability compared to a mostly ice-covered Arctic Ocean (Goosse et al., 2009; Olonscheck and Notz, 2017). We show that at least 5 ensemble members are necessary to detect an increase in sea ice variability in a 1% CO<sub>2</sub> experiment. To also quantify the magnitude of the forced change in variability, more than 50 members are necessary.</p><p>These numbers might be highly model dependent. Therefore, the suggested method can also be used with a long control run to estimate the required ensemble size for a model that does not provide a large number of realisations. Therefore, our analysis framework does not only provide valuable information before running a large ensemble, but can also be used to test the robustness of results based on small ensembles or individual realisations.</p><p><em><strong>References</strong><br>Goosse, H., O. Arzel, C. M. Bitz, A. de Montety, and M. Vancoppenolle (2009), Increased variability of the Arctic summer ice extent in a warmer climate, Geophys. Res. Lett., 36(23), 401–5, doi:10.1029/2009GL040546.</em></p><p><em>Olonscheck, D., and D. Notz (2017), Consistently Estimating Internal Climate Variability from Climate Model Simulations, J Climate, 30(23), 9555–9573, doi:10.1175/JCLI-D-16-0428.1.</em></p><p><em>Milinski, S., N. Maher, and D. Olonscheck (2019), How large does a large ensemble need to be? Earth Syst. Dynam. Discuss., 2019, 1–19, doi:10.5194/esd-2019-70.</em></p>

scholarly journals How large does a large ensemble need to be?

2019 ◽  
Author(s):  
Sebastian Milinski ◽  
Nicola Maher ◽  
Dirk Olonscheck
Keyword(s):  
Surface Temperature ◽  
Internal Variability ◽  
Forced Response ◽  
Ensemble Size ◽  
Large Ensemble ◽  
Acceptable Error ◽  
Substantial Impact ◽  
Temperature And Precipitation ◽  
Large Ensembles ◽  
Local Surface Temperature

Abstract. Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool to quantify the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble. Here, we introduce an objective method to estimate the required ensemble size that can be applied to any given application and demonstrate its use on the examples of global mean surface temperature, local surface temperature and precipitation and variability in the ENSO region and central America. Where possible, we base our estimate of the required ensemble size on the pre-industrial control simulation, which is available for every model. First, we determine how much of an available ensemble size is interpretable without a substantial impact of resampling ensemble members. Then, we show that more ensemble members are needed to quantify variability than the forced response, with the largest ensemble sizes needed to detect changes in internal variability itself. Finally, we highlight that the required ensemble size depends on both the acceptable error to the user and the studied quantity.

scholarly journals Impacts of Arctic Sea Ice on Cold Season Atmospheric Variability and Trends Estimated from Observations and a Multi-model Large Ensemble

2021 ◽  
pp. 1-64
Author(s):  
Yu-Chiao Liang ◽  
Claude Frankignoul ◽  
Young-Oh Kwon ◽  
Guillaume Gastineau ◽  
Elisa Manzini ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
General Circulation ◽  
Cold Season ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Atmospheric Variability ◽  
Large Ensemble ◽  
Near Surface ◽  
Arctic Sea

AbstractTo examine the atmospheric responses to Arctic sea-ice variability in the Northern Hemisphere cold season (October to following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily-varying sea-ice, sea-surface temperature, and radiative forcings prescribed during the 1979-2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multi-model ensemble mean (MMEM) shows decreasing sea-level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea-ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drives a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual co-variability between sea-ice extent in the Barents-Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the co-variability in MMEMs. The interannual sea-ice decline followed by a negative North Atlantic Oscillation-like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea-ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship.

scholarly journals Variability of Arctic Sea Ice Thickness Using PIOMAS and the CESM Large Ensemble

2018 ◽  
Vol 31 (8) ◽  
pp. 3233-3247 ◽  
Author(s):  
Zachary Labe ◽  
Gudrun Magnusdottir ◽  
Hal Stern
Keyword(s):  
Sea Ice ◽  
Internal Variability ◽  
Forced Response ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Spatial And Temporal Variability ◽  
Large Ensemble ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract Because of limited high-quality satellite and in situ observations, less attention has been given to the trends in Arctic sea ice thickness and therefore sea ice volume than to the trends in sea ice extent. This study evaluates the spatial and temporal variability in Arctic sea ice thickness using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Additionally, the Community Earth System Model Large Ensemble Project (LENS) is used to quantify the forced response and internal variability in the model. A dipole spatial pattern of sea ice thickness variability is shown in both PIOMAS and LENS with opposite signs of polarity between the East Siberian Sea and near the Fram Strait. As future sea ice thins, this dipole structure of variability is reduced, and the largest interannual variability is found only along the northern Greenland coastline. Under a high-emissions scenario (RCP8.5) projection, average September sea ice thickness falls below 0.5 m by the end of the twenty-first century. However, a regional analysis shows internal variability contributes to an uncertainty of 10 to 20 years for the timing of the first September sea ice thickness less than 0.5 m in the marginal seas.

scholarly journals Late-Twentieth-Century Simulation of Arctic Sea Ice and Ocean Properties in the CCSM4

2012 ◽  
Vol 25 (5) ◽  
pp. 1431-1452 ◽  
Author(s):  
Alexandra Jahn ◽  
Kara Sterling ◽  
Marika M. Holland ◽  
Jennifer E. Kay ◽  
James A. Maslanik ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Sea Ice ◽  
Internal Variability ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Motion Field ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Ensemble Mean

To establish how well the new Community Climate System Model, version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, results from six CCSM4 twentieth-century ensemble simulations are compared here with the available data. It is found that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea ice thickness distribution, fraction of multiyear sea ice, and sea ice edge. The strongest bias exists in the simulated spring-to-fall sea ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea ice extent and the multiyear ice cover is well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea ice extent over 1981–2005 than that observed, two ensemble members show no statistically significant trend over the same period. It is therefore important to compare the observed sea ice extent trend not just with the ensemble mean or a multimodel ensemble mean, but also with individual ensemble members, because of the strong imprint of internal variability on these relatively short trends.

scholarly journals Quantification of the Arctic Sea Ice‐Driven Atmospheric Circulation Variability in Coordinated Large Ensemble Simulations

2020 ◽  
Vol 47 (1) ◽  
Author(s):  
Yu‐Chiao Liang ◽  
Young‐Oh Kwon ◽  
Claude Frankignoul ◽  
Gokhan Danabasoglu ◽  
Stephen Yeager ◽  
...  
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Large Ensemble ◽  
Ensemble Simulations ◽  
Arctic Sea

An Internal Atmospheric Process Determining Summertime Arctic Sea Ice Melting in the Next Three Decades: Lessons Learnt from 5 Large Ensembles and CMIP5 Simulations

2020 ◽  
Author(s):  
Daniel Topal ◽  
Qinghua Ding ◽  
Jonathan Mitchell ◽  
Ian Baxter ◽  
Mátyás Herein ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Cmip5 Models ◽  
Ice Melting ◽  
Surface Cooling ◽  
Atmospheric Process ◽  
Sst Variability ◽  
Arctic Sea ◽  
Large Ensembles

<p>Arctic sea ice melting processes in summer due to internal atmospheric variability have recently received considerable attention. A regional barotropic atmospheric process over Greenland and the Arctic Ocean in summer (June-July-August), featuring either a year-to-year change or a low-frequency trend toward geopotential height rise, has been identified as an essential contributor to September sea ice loss, in both observations and the CESM1 Large Ensemble (CESM-LE) of simulations [1-2]. This local melting is further found to be sensitive to remote sea surface temperature (SST) variability in the East Central Pacific [3]. Here, we utilize five available single-model large ensembles and 31 CMIP5 models’ pre-industrial control simulations to show that the same atmospheric process, resembling the observed one and the one found in the CESM-LE, also dominates internal sea ice variability on interannual to interdecadal time scales in pre-industrial, historical and future scenarios, regardless of the modeling environment. However, all models exhibit limitations in replicating the correct magnitude of the observed local atmosphere-sea ice coupling and its sensitivity to remote tropical SST variability. These biases cast a shadow over models’ credibility in simulating interactions of sea ice variability with the Arctic and global climate systems. Further efforts toward identifying possible causes of these model limitations may provide profound implications for alleviating the biases and improving interannual and decadal time scale sea ice prediction and future sea ice projection.</p><p> </p><p>[1] Ding, Q., and Coauthors, (2017): Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nat. Climate Change, <strong>7</strong>, 289-295.</p><p>[2] Ding, Q., and Coauthors, (2019): Fingerprints of internal drivers of Arctic sea ice loss in observations and model simulations. Nat. Geosci., <strong>12</strong>, 28–33.</p><p>[3] Baxter, I., and Coauthors, (2019): How tropical Pacific surface cooling contributed to accelerated sea ice melt from 2007 to 2012 as ice is thinned by anthropogenic forcing. J. Climate, <strong>32</strong>, 8583–8602 https://doi.org/10.1175/JCLI-D-18-0783.1 </p>

scholarly journals The Community Climate System Model Version 4

2011 ◽  
Vol 24 (19) ◽  
pp. 4973-4991 ◽  
Author(s):  
Peter R. Gent ◽  
Gokhan Danabasoglu ◽  
Leo J. Donner ◽  
Marika M. Holland ◽  
Elizabeth C. Hunke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Frequency Distribution ◽  
Arctic Sea Ice ◽  
Climate System ◽  
System Model ◽  
The Arctic ◽  
Community Climate System Model ◽  
Climate System Model ◽  
Arctic Sea

The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4°C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.

scholarly journals Internal Variability in Projections of Twenty-First-Century Arctic Sea Ice Loss: Role of the Large-Scale Atmospheric Circulation

2014 ◽  
Vol 27 (2) ◽  
pp. 527-550 ◽  
Author(s):  
Justin J. Wettstein ◽  
Clara Deser
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Wave Train ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
Rossby Wave Train ◽  
Arctic Sea Ice Loss

Abstract Internal variability in twenty-first-century summer Arctic sea ice loss and its relationship to the large-scale atmospheric circulation is investigated in a 39-member Community Climate System Model, version 3 (CCSM3) ensemble for the period 2000–61. Each member is subject to an identical greenhouse gas emissions scenario and differs only in the atmospheric model component's initial condition. September Arctic sea ice extent trends during 2020–59 range from −2.0 × 106 to −5.7 × 106 km2 across the 39 ensemble members, indicating a substantial role for internal variability in future Arctic sea ice loss projections. A similar nearly threefold range (from −7.0 × 103 to −19 × 103 km3) is found for summer sea ice volume trends. Higher rates of summer Arctic sea ice loss in CCSM3 are associated with enhanced transpolar drift and Fram Strait ice export driven by surface wind and sea level pressure patterns. Over the Arctic, the covarying atmospheric circulation patterns resemble the so-called Arctic dipole, with maximum amplitude between April and July. Outside the Arctic, an atmospheric Rossby wave train over the Pacific sector is associated with internal ice loss variability. Interannual covariability patterns between sea ice and atmospheric circulation are similar to those based on trends, suggesting that similar processes govern internal variability over a broad range of time scales. Interannual patterns of CCSM3 ice–atmosphere covariability compare well with those in nature and in the newer CCSM4 version of the model, lending confidence to the results. Atmospheric teleconnection patterns in CCSM3 suggest that the tropical Pacific modulates Arctic sea ice variability via the aforementioned Rossby wave train. Large ensembles with other coupled models are needed to corroborate these CCSM3-based findings.

scholarly journals Going with the floe: tracking CESM Large Ensemble sea ice in the Arctic provides context for ship-based observations

2019 ◽  
Author(s):  
Alice K. DuVivier ◽  
Patricia DeRepentigny ◽  
Marika M. Holland ◽  
Melinda Webster ◽  
Jennifer E. Kay ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Condition ◽  
Model Development ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Large Ensemble ◽  
Community Earth System Model ◽  
Ice Conditions ◽  
Lagrangian Tracking ◽  
Ice Floes

Abstract. In recent decades, Arctic sea ice has shifted toward younger, thinner, seasonal ice regime. Studying and understanding this “New” Arctic will be the focus of a year-long ship campaign beginning in autumn 2019. Lagrangian tracking of sea ice floes in the Community Earth System Model Large Ensemble (CESM-LE) allow for understanding conditions that a floe will experience throughout the calendar year. These model tracks can assist with campaign planning, put into context a single year of observations, and provide guidance on how observations can help with model development. The modelled floe tracks show a Transpolar Drift trajectory is likely, providing guidance for coordinating satellite, airborne, and ground observations. However, there is a smaller possibility of high-risk tracks, including possible melt of the floe before the end of a calendar year. Because of high variability in the melt season sea ice conditions, we recommend in-situ sampling over a large range of ice conditions for a more complete understanding of how ice type or surface condition affect processes. We find that sea ice predictability emerges rapidly during the autumn freeze-up and anticipate that process-based observations during this period may help elucidate the processes leading to this change in predictability. Accurate seasonal cycle comparison of sea ice conditions between point-based observations a model requires the model to use a Lagrangian framework.

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