CVAS scientific goals

The overarching goal of a PAGES working group is to examine climate variability in space and time from different perspectives while focusing on centennial and millennial variability. A working group is needed in order to bridge gaps between the modern climate time series and space-time reconstruction techniques, paleo-records and model simulations of past, present and future climate changes.

cvas jigsaw

Image 1: A view of a single climate zone (Baie St. Paul, Quebec, Canada) showing several subsystems including wetlands, ocean (estuary) and atmosphere that are each highly variable over wide ranges of space - time scales. CVAS is trying to understand this climate puzzle.
Image: Shaun Lovejoy, 2015.

The issue will be addressed by gathering together researchers working on climate changes with different expertise and approaches. This includes direct data or proxy data acquisition, statistical data treatment and climate modeling.

A special focus will be given to scaling as a means to combine paleo-time series with observations and simulations. The amplitude of climate variations depends upon the spatial and temporal dimension of the observations as it determines the window of smoothing. Hence, climate-related records – including their uncertainties - are all inextricably related to the spatial and temporal scales of the measurements, whatever they are from direct or indirect observations or model simulations.

In this context, the objectives of the multidisciplinary working group involving specialists of non-linear physics, climatologists and paleoclimatologists, are as follows:

1. To develop statistical and modeling tools for analyzing and comparing time series and spatial distributions focusing on centennial and millennial time scales.

2. To prepare paleoclimate compilations (in both space and time), consistent with respect to their centennial and millennial scale variability, and to properly account for the role of variability in proxy recording on top of climate variability.

3. To better understand, quantify and assess climate variability in time and space, while taking into account intrinsic (often scale dependent) uncertainties.

4. To develop open-source and easy to use software for above data analysis.