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Testing the Efficacy of Climate Forecast Maps as a Means of Communicating with Policy Makers

submitted by

Toru Ishikawa (et al.) Lamont-Doherty Earth Observatory, Columbia University
Author Profile

This is a partially developed activity description. It is included in the collection because it contains ideas useful for teaching even though it is incomplete.

Environmental policy master's students viewed precipitation forecast maps and answered some questions. This activity concerns understanding, use, and visualization of scientific data.
GSA Poster (Acrobat (PDF) 368kB Oct31 03)

Learning Goals

Content/Concepts:

Higher Order Thinking Skills:

Data visualization;
Theory and application of climate forecast;
Comparison of model output (forecast) with observed data

Other Skills:

Concept of probability;
Uncertainty of data

Context

Instructional Level:

Graduate students in the master's program in environmental policy studies

Skills Needed:

Basic map-reading skills

Role of Activity in a Course:

In-class exercise

Data, Tools and Logistics

Required Tools:

N/A

Logistical Challenges:

Some students may have trouble matching the names and places of cities, states, or countries.

Evaluation

Evaluation Goals:

How well students understand climate forecast maps.
How students would evaluate and use climate forecast data in agricultural decision making.
The investigators' goal is to know how to design climate forecast maps that communicate more effectively with policy makers.

Evaluation Techniques:

Students' answers to questions designed to assess their understanding and interpretation of climate forecast maps.

Description

1. Even these qualified and motivated students in the master's program aimed for prospective policy makers failed to interpret some of the forecast maps as the map maker intended: The "efficacy" of the maps as a communication tool was less than one would hope for.

2. They understood observed precipitation maps easily, whereas they had trouble understanding probability forecast maps.

3. It was particularly difficult for them to make a logical inference by referring to and combining two or maps.

4. Most of the students were not inclined to rely on these types of climate forecast data for agricultural decision making.