Project Overview

For this project to succeed, there are certain steps that must be completed, each with specific data requirements. This is an anticipated chronology of the project, as presented in this website. In most cases, the project followed the outlined procedures reasonably well.

Create a high-resolution climate and ecosystem maps for the modern period.
Correlate modern ecosystem classifications to modern climate data.
Create high-resolution paleoclimate and paleo-ecosystem maps for the historic periods in question.
Assign ecosystem classifications to the paleoclimate data.
Assess the confidence and accuracy of the ecosystem classifications:

1) Create a high-resolution climate and ecosystem maps for the modern period. (Top)

The development of ‘baseline’ requires reliable and complete climate data for the modern era. This is readily available through climate software developed at the University of Alberta (Hamann & Wang, 2005).

Read more about creating baseline data >>>>

Ecosystem classifications will then de applied to this baseline climate data to create high-resolution ecosystem maps for the study area (western North America), based on the generated data from the climate software.

Read more about ecosystem classification data >>>>

2) Correlate modern ecosystem classifications to modern climate data. (Top)

Once acquired, through multivariate statistical analyses similar to correlation calculations, the numerous climate variables can be reduced to the most important, or ‘key’ variables. By associating known ecosystem classifications with these ‘key’ climate variables, it is possible to develop a calibration data set for further ecosystem classifications of novel climate data. Once again, the appropriate application of multivariate statistical methods is critical to developing an accurate classification regime for these areas.

Read more about statistical methods to reduce variables >>>>

3) Create high-resolution paleoclimate and paleo-ecosystem maps for the historic periods in question. (Top)

The creation of paleoclimate and paleo-ecosystem maps will be similar to creation of the modern baseline maps. Climate data for past time periods has been created through global general circulation models (GCMs). These are typically atmospheric and oceanic circulation models of the earth with the ability to predict (or back-predict) detailed climatic conditions.

Prior to assigning ecosystems to this paleoclimate data, certain climatic variables affecting ecosystem distribution may have to be created or extracted from the raw data (eg. mean warmest month temp, snow precipitation, etc.). As with the modern data, the paleoclimate data will also have to be paired to a DEM.

Read more about paleoclimate data >>>>

4) Assign ecosystem classifications to the paleoclimate data. (Top)

Creating ecosystem classifications for the paleoclimate data set can be achieved through a similar statistical procedure that was used with the baseline data. By examining the climatic variables associated with the baseline ecosystem classes, the statistical procedure can then apply the same classifications to the new climate and paleoclimate data.

Read more about statistical methods to classify data >>>>

5) Assess the confidence and accuracy of the ecosystem classifications. (Top)

Once the paleoclimate ecosystem classifications have been made, it would be valuable to produce some indicators of classification confidence--where, based on the statistical analysis, the classifications are most likely correct or have the most potential for error. The ability to map these areas and compare them to the actual classifications and the climate data itself would be valuable for understanding the factors that influence classification accuracy.

Read more about ecosystem classification confidence >>>>

<<<< Back to Home Page | Top of this page | Continue on to Data & Data Management >>>>

References:

Hamann, A. and T. L. Wang (2005). "Models of climatic normals for genecology and climate change studies in British Columbia." Agricultural and Forest Meteorology 128(3-4): 211-221.