1. CLIP – Linkages between Land Cover and Regional Climate
Jiaguo Qi presenting on behalf of the LC Group (Alphabetically: Jianjun Ge, David Lusch, Joseph Maitima Jennifer Olson, Robin Reid, Nathan Torbick, Jing Wang, Lijian Yang)
Michigan Sate University
Qi, Lusch, Reid, Maitima,
Olson, Torbick, Ge

2. LC Objectives
To assess land use/cover change impact on regional climate
To better characterize LC as input to regional climate model (RAMS)
To quantify the LC requirement for regional climate modeling. Specifically to assess the effect of land cover classification accuracy on regional climate simulation. This links the human land use to the regional climate
To assess the degree of LCC impact on regional climate
Categorical changes
Continuous changes

3. Data LU/C Biophysical Variables Scales GLC2000, Africover, IGBP, MODIS
LAI, EVI, DEM, Albedo, LST, and Precip. etc.
Scales
Continental – Case Study/Field-Observations

4. Nairobi
LULC products range in data sources, objectives, classification methods

5. Assessments and Developing LULC Scheme
Multiple LULC assessments and evaluations
Strengths and weaknesses
Videography over selected ecological gradients
Q-statistic (uses LAI as ‘evaluator’)
Develop LULC Schemes for models
Create hybrid CLIP Cover
LU from Africover, LC from GLC2000
Crosswalk to LEAF2, biophysical variables

6. LU/C Videography Assessment Sample Points
Aquatic grasslands
Rainfed herbaceous
Bare rock
Urban
LU/C Videography Assessment Sample Points

7. LAI from MODIS
June
2001
March
2001
Sept
2001
Dec
2001

8. Climate Model
In this study, Regional Atmospheric Modeling System (RAMS) Version 4.3 is used (Pielke et al., 1992).
It is fully three-dimensional, nonhydro-static; includes interactive nested grid capabilities, supports various radiation, initialization and boundary condition schemes.

9. Methods RAMS MODEL Simulations RS Biophysical Parameters LULC
Land – Atmosphere Feedback
Crosswalk to Predetermined Defaults
Albedo
Leaf Area Index
Range of Leaf Area Index
Vegetation Fraction
Range of Vegetation Fraction RS
Simulations

10. Land Cover Conversion or Classification Accuracy

11. Experiment Design
Classification error was added to original GLC2000 dataset at random locations and by random predominant (five) classes, which was increased from 0% to 50% at 5% interval
RAMS was run 11 times with different amount of errors in land cover. These runs are called R00, R05, R10, …., R50.

13. Land Cover Change – Continuous Change

14. Leaf Area Index
LAI in RAMS was further improved by incorporating directly monthly 1km MODIS LAI images.
LAI values were reduced to 50% at 5% rate. 11 runs were conducted.
Kain-Fritsch without interior nudging were used.

15. March 15th RAMS new MODIS RAMS default
LAI values (MODIS, RAMS, and MODIS/Interpolation). The default in this slide has already used the GLC2000. What if we use the original OGE?

16. The difference between simulated results from original (0% reduction in LAI) vs those that were reduced at difference percentage. Note that this is the absolute value differences (no negative numbers!)

17. Differences of two time series (maximum differences of the two time series)

18. Phenology at 0o, 5oNorth and 5o South

19. THANK YOU
Questions?

20. DEM