Data Mining for Climate Change Model Intercomparison and Phenoregions

Transcription

1 Data Mining for Climate Change Model Intercomparison and Phenoregions Forrest M. Hoffman, Jitendra Kumar, and William W. Hargrove Oak Ridge National Laboratory and USDA Forest Service, Eastern Forest Environmental Threat Assessment Center (EFETAC) National Phenology Network (NPN) Seasonal Timing Working Group Meeting August 12 13, 2013

2 Data Mining for Climate Change Model Intercomparison Hoffman et al. (2005)

3 Global LAI for 47 CMIP5 Simulations Compared to MODIS CMIP5 Model Monthly Mean Leaf Area Index ( ) Leaf Area Index (m 2 m 2 ) MODIS level 3 v5 BCC CSM1.1 r1i1p1 BCC CSM1.1 M r1i1p1 BNU ESM r1i1p1 CanESM2 r1i1p1 CanESM2 r2i1p1 CanESM2 r3i1p1 CanESM2 r4i1p1 CanESM2 r5i1p1 CCSM4 r1i1p1 CCSM4 r2i1p1 CCSM4 r3i1p1 CCSM4 r4i1p1 CCSM4 r5i1p1 CCSM4 r6i1p1 CESM1 BGC r1i1p1 CESM1 CAM5 r1i1p1 CESM1 CAM5 r2i1p1 CESM1 CAM5 r3i1p1 CESM1 WACCM r2i1p1 GFDL CM3 r1i1p1 GFDL ESM2G r1i1p1 GFDL ESM2M r1i1p1 HadGEM2 CC r1i1p1 HadGEM2 CC r2i1p1 HadGEM2 CC r3i1p1 HadGEM2 ES r1i1p1 HadGEM2 ES r2i1p1 HadGEM2 ES r3i1p1 HadGEM2 ES r4i1p1 INM CM4 r1i1p1 IPSL CM5A LR r1i1p1 IPSL CM5A LR r2i1p1 IPSL CM5A LR r3i1p1 IPSL CM5A LR r4i1p1 IPSL CM5A MR r1i1p1 IPSL CM5B LR r1i1p1 MIROC5 r1i1p1 MIROC5 r2i1p1 MIROC5 r3i1p1 MIROC ESM CHEM r1i1p1 MIROC ESM r1i1p1 MPI ESM LR r1i1p1 MPI ESM LR r2i1p1 MPI ESM LR r3i1p1 MPI ESM MR r1i1p1 NorESM1 ME r1i1p1 NorESM1 M r1i1p Year (Hoffman et al., in prep.)

4 Zonal LAI for 47 CMIP5 Simulations Compared to MODIS CMIP5 Model DJF Zonal Mean Leaf Area Index ( ) Leaf Area Index (m 2 m 2 ) MODIS level 3 v5 BCC CSM1.1 r1i1p1 BCC CSM1.1 M r1i1p1 BNU ESM r1i1p1 CanESM2 r1i1p1 CanESM2 r2i1p1 CanESM2 r3i1p1 CanESM2 r4i1p1 CanESM2 r5i1p1 CCSM4 r1i1p1 CCSM4 r2i1p1 CCSM4 r3i1p1 CCSM4 r4i1p1 CCSM4 r5i1p1 CCSM4 r6i1p1 CESM1 BGC r1i1p1 CESM1 CAM5 r1i1p1 CESM1 CAM5 r2i1p1 CESM1 CAM5 r3i1p1 CESM1 WACCM r2i1p1 GFDL CM3 r1i1p1 GFDL ESM2G r1i1p1 GFDL ESM2M r1i1p1 HadGEM2 CC r1i1p1 HadGEM2 CC r2i1p1 HadGEM2 CC r3i1p1 HadGEM2 ES r1i1p1 HadGEM2 ES r2i1p1 HadGEM2 ES r3i1p1 HadGEM2 ES r4i1p1 INM CM4 r1i1p1 IPSL CM5A LR r1i1p1 IPSL CM5A LR r2i1p1 IPSL CM5A LR r3i1p1 IPSL CM5A LR r4i1p1 IPSL CM5A MR r1i1p1 IPSL CM5B LR r1i1p1 MIROC5 r1i1p1 MIROC5 r2i1p1 MIROC5 r3i1p1 MIROC ESM CHEM r1i1p1 MIROC ESM r1i1p1 MPI ESM LR r1i1p1 MPI ESM LR r2i1p1 MPI ESM LR r3i1p1 MPI ESM MR r1i1p1 NorESM1 ME r1i1p1 NorESM1 M r1i1p Latitude (degrees) (Hoffman et al., in prep.)

5 Zonal LAI for 47 CMIP5 Simulations Compared to MODIS CMIP5 Model JJA Zonal Mean Leaf Area Index ( ) Leaf Area Index (m 2 m 2 ) MODIS level 3 v5 BCC CSM1.1 r1i1p1 BCC CSM1.1 M r1i1p1 BNU ESM r1i1p1 CanESM2 r1i1p1 CanESM2 r2i1p1 CanESM2 r3i1p1 CanESM2 r4i1p1 CanESM2 r5i1p1 CCSM4 r1i1p1 CCSM4 r2i1p1 CCSM4 r3i1p1 CCSM4 r4i1p1 CCSM4 r5i1p1 CCSM4 r6i1p1 CESM1 BGC r1i1p1 CESM1 CAM5 r1i1p1 CESM1 CAM5 r2i1p1 CESM1 CAM5 r3i1p1 CESM1 WACCM r2i1p1 GFDL CM3 r1i1p1 GFDL ESM2G r1i1p1 GFDL ESM2M r1i1p1 HadGEM2 CC r1i1p1 HadGEM2 CC r2i1p1 HadGEM2 CC r3i1p1 HadGEM2 ES r1i1p1 HadGEM2 ES r2i1p1 HadGEM2 ES r3i1p1 HadGEM2 ES r4i1p1 INM CM4 r1i1p1 IPSL CM5A LR r1i1p1 IPSL CM5A LR r2i1p1 IPSL CM5A LR r3i1p1 IPSL CM5A LR r4i1p1 IPSL CM5A MR r1i1p1 IPSL CM5B LR r1i1p1 MIROC5 r1i1p1 MIROC5 r2i1p1 MIROC5 r3i1p1 MIROC ESM CHEM r1i1p1 MIROC ESM r1i1p1 MPI ESM LR r1i1p1 MPI ESM LR r2i1p1 MPI ESM LR r3i1p1 MPI ESM MR r1i1p1 NorESM1 ME r1i1p1 NorESM1 M r1i1p Latitude (degrees) (Hoffman et al., in prep.)

6 Clustering MODIS NDVI into Phenoregions Hoffman and Hargrove previously used k-means clustering to detect brine scars from hyperspectral data (Hoffman, 2004) and to classify phenologies from monthly climatology and 17 years of 8 km NDVI from AVHRR (White et al., 2005). This data mining approach, using high performance computing, was applied to the entire body of the high resolution MODIS NDVI record for the continental U.S. >80B NDVI values, consisting of 146.4M cells for the CONUS at 250 m resolution with 46 maps per year for 12 years ( ), analyzed using k-means clustering. The annual traces of NDVI for every year and map cell are combined into one 323 GB single-precision binary data set of 46-dimensional observation vectors. Clustering yields 12 maps in which each cell is classified into one of k phenoclasses, and phenoregions form representative prototype annual NDVI traces.

7 50 Phenoregions for year 2011 (Random Colors)

8 50 Phenoregion Prototypes (Random Colors) Phenology Centroid Prototypes (phendump , k = 50) Cluster 31 Cluster 40 Cluster 28 Cluster 25 Cluster 10 Cluster 3 Cluster 2 Cluster 7 Cluster 22 Cluster 6 Cluster 4 Cluster 42 Cluster 5 Cluster 13 Cluster 41 Cluster 39 Cluster 15 Cluster 48 Cluster 21 Cluster 30 NDVI Cluster 11 Cluster 49 Cluster 46 Cluster 32 Cluster 45 Cluster 8 Cluster 47 Cluster 16 Cluster 26 Cluster 38 Cluster 27 Cluster 35 Cluster 29 Cluster 14 Cluster 20 Cluster 50 Cluster 37 Cluster 33 Cluster 12 Cluster 18 Cluster 36 Cluster 34 Cluster 24 Cluster 44 Cluster 1 Cluster 23 Cluster 43 Cluster 19 Cluster 9 Cluster 17 day of year

9 50 Phenoregions Persistence (Random Colors)

10 50 Phenoregions Mode (Random Colors)

11 50 Phenoregions Max Mode (Random Colors)

12 50 Phenoregions Max Mode (Similarity Colors)

13 50 Phenoregions Max Mode (Similarity Colors Legend) Principal Component Loading PC 1 PC 2 PC 3 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time of the year

14 Phenoregions Clearinghouse

15 Acknowledgments This research was sponsored by the U.S. Department of Energy s Biological and Environmental Research (BER) program and the U.S. Department of Agriculture Forest Service, Eastern Forest Environmental Threat Assessment Center (EFETAC). This research used resources of the National Center for Computational Science at Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

16 References F. M. Hoffman. Analysis of reflected spectral signatures and detection of geophysical disturbance using hyperspectral imagery. Master s thesis, University of Tennessee, Department of Physics and Astronomy, Knoxville, Tennessee, USA, Nov F. M. Hoffman, W. W. Hargrove, D. J. Erickson, and R. J. Oglesby. Using clustered climate regimes to analyze and compare predictions from fully coupled general circulation models. Earth Interact., 9(10):1 27, Aug doi: /ei M. A. White, F. Hoffman, W. W. Hargrove, and R. R. Nemani. A global framework for monitoring phenological responses to climate change. Geophys. Res. Lett., 32 (4):L04705, Feb doi: /2004gl