Climate Variability Across Scales (CVAS): Understanding and modeling space-time Holocene climate variability

PAGES CVAS Working Group 2nd Workshop
25-27 October 2017
Potsdam, Germany

Climate Variability Across Scales (CVAS): Understanding and modeling space-time Holocene climate variability

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Working Groups

In addition to plenary talks introducing the state of the art of different fields relevant to the scaling of Holocene climate variability, the 2nd CVAS workshop will include extended meetings of topical working groups together with panel discussions bringing about insights and ideas from the individual subgroups to the other participants.


Vertical working groups (1-3)

Following the three main topics of the workshop, three different groups will be formed that shall address the corresponding scientific challenges:

  1. Selecting the best archives to quantify climate variability and understanding their caveats: Data
  2. From paleo-observations to robust and reproducible variability estimates: Analysis tools
  3. Origins and interpretation of the space-time structure of Holocene variability: Models and Theory.

Each of these three groups will work towards establishing inventories of existing benchmark data sets, methods and software solutions, as well as mechanisms, which can serve as zeroth-order drafts for potential future review papers summarizing the essential state of the art of research on Holocene climate variability across scales.


Group 1. Selecting the best paleodata to quantify climate variability and understanding their caveats: Data

Extensive compilations for paleoclimate data exist such as PAGES2K, the data collection underlying Marcott et al., (Science 2013) or the ongoing data collection of the German PALMOD BMBF project (

However, using data for variability estimates is still its infancy and requires different quality criteria than previously applied time-domain analyses such as interpreting and analyzing Holocene temperature trends. This subgroup will therefore close the gap between the existing Holocene data compilations and the needed data compilation optimized to derive Holocene variability.

1.1 Data quality and selection:

For each proxy type, the following questions will be answered:

- What climate variable (or combination of variables) are they recording? How certain are we about this?

- Is there evidence for a time-scale dependency of the climate to proxy relationship, and if yes, in which direction and how can this be constrained or quantified?

- What are the main non-climate effects on the proxy variability? What are their properties: Are they additive or multiplicative? What is their expected time-scale dependency (e.g. are they independent, auto-correlated,...)?

- Is there any specific time-scale one should trust more than another?

- This can be summarized in qualitative indicators, in proxy system models (mid-term) and correction techniques (in collaboration with 2).

1.2 Identify and potentially improve specific case-study datasets:

• Sites with multiple independent observations, or proxy data with extraordinary quality will be identified. These can be sites with multiple proxies measured in the same sediment core or sites with multiple records nearby.

• At these sites, new data assisting the variability estimation might be generated.

Possible outcomes (on the long term):

• Review paper on possibilities and limitations of specific proxy types to derive climate variability

• Database of suitable datasets. To make best use of the existing compilations, this variability database would contain links to suitable datasets in the existing databases with metadata summarizing their quality in terms of variability

• Case-study datasets and potential proposals for new measurements at these sites.


Group 2. From paleo-observations to robust and reproducible variability estimates: Analysis tools

As for Group 1, there is a multitude of methodological challenges that need to be addressed towards obtaining an in-depth understanding of climate variability across scales.

2.1. Inventory and characterization of methodological approaches:

• Which methods are available for studying time scale-specific variability in paleoclimate archives/proxies of different types?

• Which approaches are useful for studying interdependences among variability patterns with different intrinsic scales and their implications for emerging scaling?

• Which are the general demands for the individual methods in terms of time series length and temporal resolution in comparison with the variability scales to be resolved (link to Group 1)?

• How sensitive are the individual methods regarding measurement and timescale uncertainties as well as trends/ultralow-frequency variability?

2.2. Improvements with respect to paleoclimate time series analysis:

• Which methods are applicable to unevenly sampled and time-uncertain time series, and how can existing estimators be modified to work for such data?

• How can information in time, space and scale domain be combined to obtain a holistic picture on scaling properties in paleoclimate variability (correlations, spectral methods, wavelets, etc.)?

Possible outcomes (on the long term):

• Review paper on techniques to estimate time-scale dependent variability and scaling behavior

• Open-Source toolbox with usable code, respectively collection and critical evaluation of existing software packages

Group 3. What is creating the variability and what can we learn from it: Theory

• Can we identify a common framework to describe the variability across scales.

• (How) can we bridge the gap between linear approaches and nonlinear approaches?

• What might be the structural errors or missing components or mechanisms in climate models leading to a different scaling behavior to the observations? How can we distinguish between the different possibilities? • What are the implications from the temporal and spatial scaling estimates of variability for understanding past and predicting future climate? e.g. what is the relationship between of climate sensitivity and climate variability.

• Can we distinguish between internally driven and externally forced variability by their scaling behavior?

Possible outcomes:

• List of testable hypothesis to be used for defining a collaborative research strategy


Horizontal groups (A-C)

Depending on the progress of the “vertical” groups 1-3 during the second day of the workshop, we intend to form new groups A-C for the third workshop day, each of which shall integrate data, method/analysis and theory/model perspectives around a specific research topic.


Group A. Tropical vs. polar (scaling of) Holocene climate variability

Mechanism/Theory: ocean-driven vs. atmosphere-driven variability, climate models

Data: polar vs. tropical records

Methods: different uni- and bivariate time series analysis techniques


Group B. Spatial (covariance) structure of climate variability

Mechanisms/Theory: Diffusive models, wave equations, climate models

Data: Databases and records with good chronology

Methods: Degrees of freedom, effect of time-uncertainty on covariance, EOF-like methods, complex networks


Group C. Origins of (forced vs. internal) Holocene variability

Mechanisms/Theory: EBMs, linear separation of forcing and internal vs. projection on internal modes

Data: Databases and records with good chronology

Methods: fingerprinting,…