1997: Cloud Structures in Storm Lifecycles

Clouds are a major source of uncertainty in scientists' efforts to predict climate change. The problem lies in large part with the fact that the relationships between cloud properties and atmospheric conditions, while well understood in the microphysical scale of a cloud droplet, are not well known in the large scale of a cloud system. Climate models use representations of microphysical processes to produce large-scale clouds. These representations produce instantaneous cloud fields that are not realistic in their structure and properties. This makes it highly uncertain that, in a climate change scenario, the model clouds will respond appropriately to the changing atmospheric conditions.

A group of scientists at GISS have started a study with the objective to produce relationships between the large-scale cloud properties and structures and the large scale atmospheric conditions. The study concentrates mostly on areas of extratropical storms, since those storms are the primary cloud-makers in the earth's atmosphere. The relationships that will be derived from the data analysis will be used to validate the cloud field of the GISS climate model and to improve the parameterization used by the model to produce clouds. In order to obtain such relationships, several years of satellite-derived cloud data from the International Satellite Cloud Climatology Project (ISCCP) dataset are analyzed and correlated with surface based observations of the atmosphere from the National Meteorological Center (formerly NMC, now NCEP) dataset. Pictures of average cloud and dynamic fields, based on large numbers of storms, are produced and correlated with each other, in order to derive global relations between the properties of clouds and the dynamic conditions in mid-latitude storm areas.

The Cloud Structures in Storm Lifecycles project was created specifically to examine the relationships between cloud properties and dynamic conditions in individual storm cases. The use of averages enables the simultaneous examination of large numbers of cases of storms, but often masks the contrasting behaviors that are extremely important in the interpretation of the results. The examination of individual storm cases will allow these contrasting behaviors to be discerned and provide the means to observe the time evolution of the cloud and dynamic fields through the lifecycles of the storms. This enables us to better understand the physical mechanisms responsible for the cloud-dynamic interactions, and to explain the relationships that are derived from the analysis of the storm composites. By comparing and contrasting the evolution of cloud structures in the observed storms with those found in storms in the climate model, we hope to be able to pinpoint the weaknesses of the model representations dealing with cloud properties.

Educational Aspects

The principal aim and challenge of our curriculum approach is to facilitate the teaching and learning of science using NASA climate research data as course material. We will involve students in long-term research projects relating to climate change using NASA GISS data over the World Wide Web. These research projects will evolve from various investigation questions including: How can atmospheric conditions be inferred from graphical data? How might these atmospheric conditions affect predictions about global warming in the future? How can climate research activities facilitate a better understanding of various physical science concepts?

The curriculum materials based upon this collaborative effort will emphasize the development of science process skills and the integration of science content as a means of better understanding the physical environment. These process skills relate to the Learning Standards for Mathematics, Science, and Technology developed through the New York State Education Department, and include:

• Identifying trends and patterns of change in graphical data
• Making predictions about data when a system element changes
• Identifying cause and effect relationships within systems
• Identifying and explaining possible errors in data
• Using the idea of uncertainty when discussing a system
• Organizing data
• Identifying interactions among components of a system
• Considering the effects of feedback mechanisms on a system
• Making suggestions to improve a given system

Team Products

We will continue to develop a Web-based curriculum package for teaching a course in Earth Science Research at the high school level (grades 9-12). This package will enable students to carry out research projects at their home schools and involve collaboration with NASA scientists and students from other high schools/colleges. Throughout the research process, students will engage in tutorial exercises that provide a conceptual framework for climate research activities.

The Web-based curriculum product will include:

1. Climate change research problem scenarios
2. Weather prediction modules
3. On-line analysis tasks using NASA datasets that relate to existing research problems
4. Background tutorials for basic concepts in atmospheric science
5. Tutorials that guide students through the various stages of conducting and documenting climate change research
6. Data analysis techniques and their application to original scientific research
7. Reporting features for sharing research methods and results with scientists and other students
8. Interface features to integrate analysis and reporting software such as Excel and Microsoft Word

In addition, team members will pursue individual research projects based upon the material available in the Web site.

Primary Responsibilities for Team Members

High School Students

Kwajo / Juan - (Items 1-3) Review Web page modules for climate change scenarios, weather prediction, and on-line data analysis. Propose and design changes.

College Students

Jose - Create Java software applications to facilitate data access and analysis for Web page curriculum materials.

Jeantel - (Items 4, 7) Review software tutorials on science concepts. Propose and design functional software changes. Apply data analysis techniques to existing research data to investigate future project feasibility.

Jericco - (Items 5, 7) Review existing datasets for potential student research projects and assist in research project design. Assist in the design of HTML documents for Web-based research reporting.

High School Faculty

Chris / Robert - (Items 1-8) Design and develop items 1-8 in collaboration with project scientists and students.