National Academies Press: OpenBook

Improving the Effectiveness of U.S. Climate Modeling (2001)

Chapter: Appendix E: Climate Modeling Survey: Summary Response

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Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×

Appendix E

Climate Modeling Survey: Summary Responses

42 Responses Received

Note: As a point of reference, there were two unique questionnaires that were sent out to U.S. modeling centers for the purposes of this report. One questionnaire was sent out to large and intermediate centers, and a second questionnaire was sent to small centers. 1 Thus, the ‘coding' after each question, e.g., I6L (large/intermediate), I6S (small), specifies the question number as in the surveys above and whether it was common to both questionnaires, or exclusive to one or the other. In some instances, a question was specific to only one survey as it was believed to be inappropriate to the other category of modeling centers.

  1. What percentage of your modeling activities are devoted to operational versus research purposes? (I6L, I6S)

39 Majority research oriented

3 Majority operations oriented

  • Out of the responses that were majority research oriented, some stated that their research had direct operational relevance.

1  

An example of what is referred to in this document as a small modeling effort is one using a global, stand-alone atmospheric climate model at R15 (~4.5° × 7.5°) resolution; an example of an intermediate effort is one using a global, stand-alone atmospheric climate model at T42 (2.8° × 2.8°) resolution; an example of a large or high-end modeling effort is one using a global, coupled T42 atmospheric / 2° × 2° oceanic model (or finer resolution) for centennial-scale simulations of transient climate change.

Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×
  1. Please describe groups with which you have significant collaboration and briefly describe the nature of this collaboration. (I7L, II1S)

  • From the responses received, there appears to be strong connections between the major centers and academia and vice-versa.

  1. Please provide your opinion on current U.S. climate and weather modeling capabilities relative to overseas efforts. Please describe where differences in capabilities exist and what you feel are the causes for these differences. (II1L, III1S)

U.S. is:

 
 

Ahead

Behind

Comparable

Weather

2

20

6

Climate

1

21

9

Why are there differences?

Underfunded

Understaffed

Lack of computer resources

Lack of common center/coordination

Other statements:

Comparable to other countries at all but high-end

Model development is weak here and overseas

U.S. is ahead in diversity and size of effort

It is more difficult to organize the U.S. effort due to its size and diversity

  1. If you stated that U.S. climate and weather modeling capabilities are behind those of other countries, do you have any suggestions to remedy this deficiency? (II2L, III2S)

7 Increased Funding

8 Shared Infrastructure

18 Enhanced Organization

25 Hardware

8 Adequate brainpower

  1. Do you feel that your modeling effort would be aided by altering the organization of U.S. climate modeling resources? If so, what changes would you recommend be made? (II3S)

6 Yes

5 No

Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×

Observations of the affirmative responses:

  • Too many underfunded, understaffed groups

  • Inadequate links to data collection

  • More emphasis should be placed on the vulnerability of the Earth system to the spectrum of environmental stresses, rather than focus primarily on the effects of greenhouse gases.

  • U.S. should take the lead in the physics of the climate system and its parameterization

  • Devolve computing resources away from computer centers to the users

  • Develop a responsive, interactive computing environment

  • Make it easier to access climate models for climate applications and to long-term model simulation data for analysis

    What additional upgrades would be incorporated if funds were available? (III2bL, V2S)

7 Upgrades for PC clusters

2 More nodes

3 Increased bandwith

7 Increase general computational power

5 Increase disk storage

3 Increase file migration capabilities

1 Purchase Alpha-type workstations

1 Upgrade to parallel vector systems if possible

2 None

6 More processors

  1. Does modeling capacity or capability limit your current activities or does some other factor? Could you make use of additional modeling capacity or capability for additional activities? (III3bL, V3S)

27 Yes

2 No

7 Additional human resources

18 Additional computing capabilities

  1. Is computational time shared with the wider community? If so, how is this interaction organized? (III7L)

9 Yes

12 No

2 Yes, via scientific collaboration

Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×

1 Only within DOD and with DOD funded scientists

3 Sharing is through a queuing system

2 Sharing through proposals for computer time

1 Sharing only within DOE

3 Via allocation process

1 Via output only

  1. Please provide your thoughts on the relative merits and hindrances of running your models on massively parallel processing sys tems relative to parallel vector architectures. (IV1L)

4 Massively parallel architecture is better

18 Parallel vector architecture is better

MPP architecture is better but:

  • There are a lack of compilers for these systems

  • The transfer of code to MPP is not easy

  • Vendors are not ready to supply the needed systems

Other comments:

MPP is harder to use

MPP benefit is that the processing time is cheaper as the cost of the systems and maintenance is less than for parallel vector systems

MPP offers more CPU power and memory per dollar spent

Some new models can only be run on MPP

MPP requires longer code development

MPP is not scalable

MPP offers poor system software and is unstable

MPP requires additional personnel

MPP offers poor communication among processors

  1. Do you use models or outputs from other facilities? If so, are any restrictions placed on the models or data? (VI3S)

12 Yes

0 No

Restrictions:

Output is restricted to research collaborators

DOE security restrictions on computing access

No restrictions

Some foreign data is restricted

Some data are restricted due to being in a pre-release state

Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×
  1. How “portable” is your code without experiencing major performance loss? (IV6L)

22 Very portable

2 Somewhat portable

8 Code is custom altered for specific platforms

1 Code works on MPP only

1 Code is portable to VPP and MPP with some limitation

  1. Are you currently planning to (or intending to in the future) convert model codes to run on massively parallel machines? If cur rently converting, what experience do you have with this process? If intending to in the future, what are your plans for doing so? (V5L)

10 Already converted

12 Underway

2 Not underway

  1. Are model results produced by your facility made available to the wider scientific community? If so, are any restrictions placed on the models or data? (VI2S)

10 Yes

0 No

Are model results produced by your facility made available to a wider scientific community? If so, are any restrictions placed on the data? (IV2L)

3 Yes with some restrictions

21 Yes

1 Yes, but only with collaborators

0 No

Additional:

2 More widely distributed if resources were available

1 Yes, through published work

  1. Are the number of staff supporting your efforts sufficient? If not, please describe where improvements are needed. (V2L)

6 Yes

20 No

Staff needed for:

Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×

Data interpretation and analysis

Programmers

Software engineers

Hardware maintenance

Model simulation interpretation

High-performance applications

Are the number of staff supporting your efforts sufficient? If not, please describe where improvements are needed. (VII2S)

4 Yes

8 No

Staff needed for:

Data interpretation and analysis

Programmers

Model developers

  1. Do you feel that your efforts are being limited by access to high-end computing resources? By access to model output from large modeling centers? By availability of diagnostic tools? By any other factors? (VIII1S)

11 Yes

1 No

1 Skilled personnel are not centrally located

1 No long-term strategy

1 Data outputs need to be made more user friendly

1 Satellite data needs to be made more user friendly

9 Access to computing

1 Access to global models

1 Stable funding

Do you feel that your modeling efforts are being limited by lack of sufficient high-end computing resources? By people? By other resources? By any other factors? (VI1L)

26 Yes

27 No

Factors:

17 People

18 Computing

Other factors:

Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×

Lack of well-documented modern model codes

Network bandwidth

Data storage

Stable funding

  1. When staffing positions in the categories listed above, what are the main difficulties, if any, involved (i.e. level of training required, salary requirements). (V3L, VII3S)

1 Research is very specialized

16 Salary is not competitive

10 Finding funding

15 Level of training

7 Difficult to find qualified programmers

1 Navy bureaucracy

3 Difficult to find model developers

1 No difficulty

  1. Please describe any future changes in staffing that are planned. (V4L)

7 None

8 Model/software support

8 Scientist

5 Modeler

1 Hardware

Please describe any future changes in staffing that are planned. (VI4S)

4 None

4 Model/software support

3 Scientist

0 Hardward maintenance

  1. What is your highest priority if some of these limiting factors are removed? (VI2L)

11 Enhanced computing capabilities

8 Enhanced human resources

7 Improved physical performance of the models

1 Build a modeling system infrastructure

4 Increase the number of models

7 Increase model resolution

Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×

1 Develop a high performance regional climate model

1 Adapt model code for parallel systems

1 Perform simulations on non-local systems

1 Additional R& D research funding

  1. Do you feel that future modeling efforts will be hindered by the availability of quality graduate students? If so, what steps would you recommend to remedy this problem? (VI5S)

3 No

5 Yes

Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×
Page 105
Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×
Page 106
Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×
Page 107
Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×
Page 108
Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×
Page 109
Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×
Page 110
Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×
Page 111
Suggested Citation:"Appendix E: Climate Modeling Survey: Summary Response." National Research Council. 2001. Improving the Effectiveness of U.S. Climate Modeling. Washington, DC: The National Academies Press. doi: 10.17226/10087.
×
Page 112
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Information derived from climate modeling has become increasingly important in recent years. More and more we understand that climate variability and change impacts society and that dealing with climate-related disasters, conflicts, and opportunities requires the best possible information about the past, present, and future of the climate system. To this end, Improving the Effectiveness of U.S. Climate Modeling describes ways to improve the efficacy of the U.S. climate modeling enterprise, given the current needs and resources. It discusses enhanced and stable resources for modeling activities, focused and centralized operational activities, how to give researchers access to the best computing facilities, the creation of a common modeling and data infrastructure, and research studies on the socioeconomic aspects of climate and climate modeling.

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