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\listoverridecount0\ls6}{\listoverride\listid714158312\listoverridecount0\ls7}{\listoverride\listid35858763\listoverridecount0\ls8}}{\info{\title Outlier Detection}{\author Dick Olson}{\operator Tim Rhyne}{\creatim\yr2000\mo3\dy28\hr10\min17} {\revtim\yr2000\mo3\dy28\hr10\min17}{\printim\yr2000\mo3\dy15\hr21\min57}{\version2}{\edmins1}{\nofpages5}{\nofwords1954}{\nofchars11140}{\*\company }{\nofcharsws13680}{\vern89}}\margl864\margr864\margt1152\margb1152 \widowctrl\ftnbj\aenddoc\lytprtmet\hyphcaps0\formshade\viewkind4\viewscale90\pgbrdrhead\pgbrdrfoot \fet0\sectd \psz1\sbkodd\linex0\endnhere\sectdefaultcl {\footer \pard\plain \s17\widctlpar\tqc\tx4320\tqr\tx8640\pvpara\phmrg\posxr\posy0\adjustright \fs20\cgrid {\field{\*\fldinst {\cs18 PAGE }}{\fldrslt {\cs18\lang1024 5}}}{\cs18 \par }\pard \s17\ri360\widctlpar\tqc\tx4320\tqr\tx8640\adjustright { \par }}{\*\pnseclvl1\pnucrm\pnstart1\pnindent720\pnhang{\pntxta .}}{\*\pnseclvl2\pnucltr\pnstart1\pnindent720\pnhang{\pntxta .}}{\*\pnseclvl3\pndec\pnstart1\pnindent720\pnhang{\pntxta .}}{\*\pnseclvl4\pnlcltr\pnstart1\pnindent720\pnhang{\pntxta )}} {\*\pnseclvl5\pndec\pnstart1\pnindent720\pnhang{\pntxtb (}{\pntxta )}}{\*\pnseclvl6\pnlcltr\pnstart1\pnindent720\pnhang{\pntxtb (}{\pntxta )}}{\*\pnseclvl7\pnlcrm\pnstart1\pnindent720\pnhang{\pntxtb (}{\pntxta )}}{\*\pnseclvl8 \pnlcltr\pnstart1\pnindent720\pnhang{\pntxtb (}{\pntxta )}}{\*\pnseclvl9\pnlcrm\pnstart1\pnindent720\pnhang{\pntxtb (}{\pntxta )}}\pard\plain \s15\qc\widctlpar\tx11520\adjustright \f1\fs20\cgrid {\b\f0\fs24 Ecosystem Model-Data Intercomparison Workshop \par }\pard \s15\qc\widctlpar\adjustright {\b\f0\fs24 Outlier Detection and Flagging for Class A Sites \par }{\f0\fs24 R.J. Olson, J.M.O. Scurlock, K.R. Johnson, and EMDI Workshop Participants \par March 15, 2000 \par }{\b\f0\fs22 \par }\pard \s15\widctlpar\adjustright {\b\f0\fs22 Summary \par {\pntext\pard\plain\s15 \f3\fs22\cgrid \loch\af3\dbch\af0\hich\f3 \'b7\tab}}\pard \s15\fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlblt\ilvl0\ls7\pnrnot0\pnf3\pnstart1\pnindent360\pnhang{\pntxtb \'b7}}\ls7\adjustright {\f0\fs22 Assigned consistent biome class to Class A sites (162 records, 87 sites) \par {\pntext\pard\plain\s15 \f3\fs22\cgrid \loch\af3\dbch\af0\hich\f3 \'b7\tab}}\pard \s15\fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlblt\ilvl0\ls7\pnrnot0\pnf3\pnstart1\pnindent360\pnhang{\pntxtb \'b7}}\ls7\adjustright {\f0\fs22 Calculated new NPP ensemble values (11 models) \par {\pntext\pard\plain\s15 \f3\fs22\cgrid \loch\af3\dbch\af0\hich\f3 \'b7\tab}}\pard \s15\fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlblt\ilvl0\ls7\pnrnot0\pnf3\pnstart1\pnindent360\pnhang{\pntxtb \'b7}}\ls7\adjustright {\f0\fs22 Calculated new AET ensemble values (5 models) \par {\pntext\pard\plain\s15 \f3\fs22\cgrid \loch\af3\dbch\af0\hich\f3 \'b7\tab}}\pard \s15\fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlblt\ilvl0\ls7\pnrnot0\pnf3\pnstart1\pnindent360\pnhang{\pntxtb \'b7}}\ls7\adjustright {\f0\fs22 Performed outlier analysis on NPP and driver data (11 flags) \par {\pntext\pard\plain\s15 \f3\fs22\cgrid \loch\af3\dbch\af0\hich\f3 \'b7\tab}}\pard \s15\fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlblt\ilvl0\ls7\pnrnot0\pnf3\pnstart1\pnindent360\pnhang{\pntxtb \'b7}}\ls7\adjustright {\f0\fs22 Identified a set of 11 of the 162 (162-11) sites to be excluded, including: \par }\pard \s15\li720\widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\f0\fs22\ul One or more critical flags: \par }{\f0\fs22 11 flagged related to differences with NPP ensemble (bias, NE, MAE) \par }{\f0\fs22\ul Five or more less critical flags: \par }{\f0\fs22 Outside of .05 to .95 percentile by biome \par Outside of .95 CI determined by regressions with AET, PREC, TAVE \par {\pntext\pard\plain\s15 \f3\fs22\cgrid \loch\af3\dbch\af0\hich\f3 \'b7\tab}}\pard \s15\fi-360\li360\ri2736\widctlpar\jclisttab\tx360{\*\pn \pnlvlblt\ilvl0\ls7\pnrnot0\pnf3\pnstart1\pnindent360\pnhang{\pntxtb \'b7}}\ls7\adjustright {\f0\fs22 Recalculated NPP for 81 sites from the 151 NPP records \par }\pard \s15\widctlpar\adjustright {\b\f0\fs22 \par Background \par }{\f0\fs22 \par The Ecosystem Model-Data Intercomparison (EMDI) Workshop was held December 5-8, 1999 in Durham, New Hampshire with 12 modeling groups participating. The EMDI Workshop included a variety of models, including biogeochemical , satellite-driven, detailed process, and DVGM types. Initial results showed general agreement between models and data but with obvious differences that indicate areas for potential data and model improvement. Much of the workshop was devoted to looking at potential outliers and harmonizing some of the driver data, especially land cover or vegetation types. \par \par }{\b\f0\fs22 Goal -}{\f0\fs22 The goal is to produce a consistent set of NPP measurements with associated environmental driver data that can be used for regional model development and validation. \par }\pard\plain \widctlpar\adjustright \fs20\cgrid {\fs22\cgrid0 \par }\pard\plain \s1\keepn\widctlpar\outlinelevel0\adjustright \b\fs22\cgrid {\cgrid0 What comprises an outlier? \par }\pard\plain \widctlpar\adjustright \fs20\cgrid {\fs22\cgrid0 \par For the purposes of this discussion, an outlier is defined initially as a data point considered to be unrepresentative of its location or land cover type, or otherwise \ldblquote difficult\rdblquote to represent in a gen eralized NPP model, such as took part in the EMDI exercise. However, there are also a number of reasons why an NPP data point may be designated an outlier, even when the data themselves are considered quite reasonable and representative of their location . \par \par Our analysis is based upon the complete package of NPP data and model drivers (climatology, soils, land cover type), not just the measured NPP data alone. Thus any problems experienced by the algorithms to produce consistent driver data (e.g. high eleva tion or highly incised topography, failure of the algorithms to represent the influence of the Gulf Stream on northern Scandinavia) may result in a point being labelled an outlier. Some characteristics peculiar to certain sites (e.g. a short growing seas on in \ldblquote cold desert\rdblquote grasslands) also resulted in outlier designation, since it may be hard to generalize the climate for such sites (e.g. mean annual temperature would appear too low in relation to NPP). \par \par The process of synthesis, review and generalization of data sets may also have resulted in some points being designated outliers due to differences in methodologies for estimating NPP. For this first phase of EMDI, the large number of available NPP data allowed the designation of outliers to be fairly lib eral. Future model-data intercomparison may require a more selective and conservatively justified elimination of outliers, in order to provide a more stringent test of the models. \par \par \par Class A outliers \par \par }\trowd \trgaph108\trleft-108\trbrdrt\brdrs\brdrw30\brdrcf15 \trbrdrb\brdrs\brdrw30\brdrcf15 \clvertalt\clbrdrt\brdrs\brdrw30\brdrcf15 \clbrdrb\brdrs\brdrw15\brdrcf15 \cltxlrtb \cellx503\clvertalt\clbrdrt\brdrs\brdrw30\brdrcf15 \clbrdrb \brdrs\brdrw15\brdrcf15 \cltxlrtb \cellx1336\clvertalt\clbrdrt\brdrs\brdrw30\brdrcf15 \clbrdrb\brdrs\brdrw15\brdrcf15 \cltxlrtb \cellx2202\clvertalt\clbrdrt\brdrs\brdrw30\brdrcf15 \clbrdrb\brdrs\brdrw15\brdrcf15 \cltxlrtb \cellx4190\clvertalt\clbrdrt \brdrs\brdrw30\brdrcf15 \clbrdrb\brdrs\brdrw15\brdrcf15 \cltxlrtb \cellx6469\clvertalt\clbrdrt\brdrs\brdrw30\brdrcf15 \clbrdrb\brdrs\brdrw15\brdrcf15 \cltxlrtb \cellx8748\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 No.\cell Lat.\cell Long.\cell BIOME2 \cell Sitename, Country\cell Comments\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\trowd \trgaph108\trleft-108\trbrdrt\brdrs\brdrw30\brdrcf15 \trbrdrb\brdrs\brdrw30\brdrcf15 \clvertalt\cltxlrtb \cellx503\clvertalt\cltxlrtb \cellx1336 \clvertalt\cltxlrtb \cellx2202\clvertalt\cltxlrtb \cellx4190\clvertalt\cltxlrtb \cellx6469\clvertalt\cltxlrtb \cellx8748\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 1\cell -20.27\cell 148.12\cell grassland\cell Bowen Pertusa, Queensland, Australia \cell relatively warm/wet, but low NPP\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\trowd \trgaph108\trleft-108\trbrdrt\brdrs\brdrw30\brdrcf15 \trbrdrb\brdrs\brdrw30\brdrcf15 \clvertalt\cltxlrtb \cellx503\clvertalt\cltxlrtb \cellx1336 \clvertalt\cltxlrtb \cellx2202\clvertalt\cltxlrtb \cellx4190\clvertalt\cltxlrtb \cellx6469\clvertalt\cltxlrtb \cellx8748\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 2\cell -1.33\cell 36.83\cell grassland\cell Nairobi, Kenya\cell low NPP reported in data synthesis - may actually be higher\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 3\cell 6.33\cell 100.93\cell grassland\cell Hat Yai, Thailand\cell low NPP reported in data synthesis - may actually be higher\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 4\cell 18.00\cell -66.92\cell DBL forest / tropical\cell Guanica, Puerto Rico\cell low NPP reported in data synthesis \cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 5\cell 18.08\cell -76.65\cell EBL forest / tropical\cell John Crow Ridge (Wet Slope), Jamaica\cell low NPP reported in data synthesis\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 6\cell 18.27\cell -65.77\cell EBL forest / tropical\cell Luquillo (Pico del Este), Puerto Rico\cell low NPP, high precipitation (but relatively high elevation)\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\trowd \trgaph108\trleft-108\trbrdrt\brdrs\brdrw30\brdrcf15 \trbrdrb\brdrs\brdrw30\brdrcf15 \clvertalt\clbrdrb\brdrs\brdrw30\brdrcf15 \cltxlrtb \cellx503\clvertalt\clbrdrb\brdrs\brdrw30\brdrcf15 \cltxlrtb \cellx1336\clvertalt\clbrdrb\brdrs\brdrw30\brdrcf15 \cltxlrtb \cellx2202\clvertalt\clbrdrb\brdrs\brdrw30\brdrcf15 \cltxlrtb \cellx4190\clvertalt\clbrdrb\brdrs\brdrw30\brdrcf15 \cltxlrtb \cellx6469\clvertalt\clbrdrb\brdrs\brdrw30\brdrcf15 \cltxlrtb \cellx8748\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 7\cell 29.97\cell 76.80\cell grassland\cell Kurukshetra, India\cell very high NPP (previously fertilized?)\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\adjustright {\fs24\cgrid0 \par \par }\pard\plain \s15\widctlpar\adjustright \f1\fs20\cgrid {\f0\fs22 \par }\pard\plain \widctlpar\adjustright \fs20\cgrid {\b\fs22 QA Issues -}{\fs22 The ORNL group had reviewed the dat a extensively prior to EMDI by looking at scatter plots and data outside of reasonable limits. However, the Workshop provided the initial model results to compare with the observed NPP data. An ensemble NPP value was calculated for each site as the aver a ge of the 11 Class A (old class 1-2) models (AVIM, BGC, CARAIB, CENTURY, GLO-PEM, GTEC, IBIS, LPJ, PNET, STOMATE, and VECODE). In addition, an ensemble AET (actual evapotranspiration) value was calculated based on the average of AET provided by five of t he models for Class A (CARAIB, CENTURY, IBIS, PNET, GTEC). If the model ensemble NPP value was less than 3 model outputs for Class A, then the model average for that site was dropped. If the model ensemble AET value was less than 3 model outputs for Cla s s A, then the model average for that site was dropped. The EMDI workshop also provided an opportunity to review the classification of the sites, compare observed to predicted NPP, and look at relationships between variables. The specific issues that we hope to address are: \par }\pard\plain \s15\widctlpar\adjustright \f1\fs20\cgrid {\f0\fs22 \par {\pntext\pard\plain\s15 \f3\fs22\cgrid \loch\af3\dbch\af0\hich\f3 \'b7\tab}}\pard \s15\fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlblt\ilvl0\ls6\pnrnot0\pnf3\pnstart1\pnindent360\pnhang{\pntxtb \'b7}}\ls6\adjustright {\f0\fs22 Land cover / Biome Class Consistency- This review was prompted in part by the realization of problems in using the satellite-derived land cover for each site (i.e., often this represented the dominant land cover for a 1x1 km area, not the typical 1x1 m to 1 hectare NPP measurement site). \par {\pntext\pard\plain\s15 \f3\fs22\cgrid \loch\af3\dbch\af0\hich\f3 \'b7\tab}}\pard \s15\fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlblt\ilvl0\ls6\pnrnot0\pnf3\pnstart1\pnindent360\pnhang{\pntxtb \'b7}}\ls6\adjustright {\f0\fs22 Managed sites - In addition, modelers decided to flag and exclude likely heavily managed sites and wetlands from the EMDI comparison. \par {\pntext\pard\plain\s15 \f3\fs22\cgrid \loch\af3\dbch\af0\hich\f3 \'b7\tab}}\pard \s15\fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlblt\ilvl0\ls6\pnrnot0\pnf3\pnstart1\pnindent360\pnhang{\pntxtb \'b7}}\ls6\adjustright {\f0\fs22 Multiple NPP values for a site \endash Some sites have several NPP values, often from several vegetation types. We assume this is often a result of reporting imprecise latitude/longitude coordinates. \par {\pntext\pard\plain\s15 \f3\fs22\cgrid \loch\af3\dbch\af0\hich\f3 \'b7\tab}}\pard \s15\fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlblt\ilvl0\ls6\pnrnot0\pnf3\pnstart1\pnindent360\pnhang{\pntxtb \'b7}}\ls6\adjustright {\f0\fs22 Coordinates \endash Coordinates for sites are reported in a mix of formats, from whole degrees for a set of Russian sites to 4 deci mal places for GPS registered sites. Some of the climate data and model outputs were provided with coordinates rounded off to 2 decimal places. Although we had distributed data with coordinates up to 4 decimal places, we rounded all sites to 2 decimal p laces for consistency. \par }\pard \s15\widctlpar\adjustright {\f0\fs22 \par The power of the statistical-empirical approach is that we can look for patterns within similar groups (i.e., biomes) and look for relationships between variables. Even if we could review the original literature reference associated with each study, we would not pickup the potential outliers that were found in this process. \par \par }\pard\plain \s16\widctlpar\adjustright \fs22\cgrid {\b Biomes - }{The outlier process started by reviewing the biome designation for all sites. Twenty-one classes were defined at the EMDI workshop to represent the dat a and needs of the models. They were assigned based on initial biome class, subbiome, species, vegetation type and evaluation by Jonathan Scurlock, Peter Thornton, Mac Post, Bill Parton, Steve DeGrosa and others. Four classes were assigned to heavily ma n aged or sites typically not addressed by regional models. The types included crops, pasture, plantations, and wetlands. These sites will be flagged and generally excluded from the EMDI exercises. 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0200090000001d062100f00001000100bb003402040000002d010100050000001402cc003402050000001302cc003502040000002d010200090000001d062100 f00001000100cc003402040000002d010600040000002d010500040000002701ffff030000001e00040000002d010400050000000102c0c0c000050000000902c0c0c000070000001604cd00340201000100040000002701ffff030000000000}}}}}{ \par }\pard\plain \widctlpar\adjustright \fs20\cgrid {\fs22 \par }\pard\plain \s1\keepn\widctlpar\outlinelevel0\adjustright \b\fs22\cgrid {Classes \par }\pard\plain \widctlpar\adjustright \fs20\cgrid {\fs22 \par At the EMDI Workshop, we decided to rename the groups to Class A (old Class 1 &2), Class B (old Class 3) and Class C (old Regional cells. This analysis covers Class A sites. A similar approach has been used with Class B and will be used with Class C sites. Actually, we have used the biome level information derive d from Class B sites to compare to Class A sites, however, Class C sites will be calculated independently. \par \par }\pard\plain \s1\keepn\widctlpar\outlinelevel0\adjustright \b\fs22\cgrid {Class A Sites \par }\pard\plain \widctlpar\adjustright \fs20\cgrid {\fs22 \par The EMDI NPP and driver data were reviewed to flag potential outliers based on criteria to identify unrepresentative sites or potentia l errors. Each variable was reviewed independently and then in combination with other variables. At the EMDI Workshop, the QA checking was restricted to average NPP at Class A (old Class 12) sites, those for which a complete set of model predictions fro m 7 models were available. In this phase, we reviewed all 162 individual NPP data values that had model predictions from at least 3 models. The final step will be to calculate average NPP for all unique site-biome combinations using the subset of NPP rec ords that pass our flagging criteria. Initially there were 150 Class A sites; however, this set was reduced to 137 unique sites with valid driver data. We expect after the exclusion of outliers that there will be approximately 81 sites remaining. \par \par }\pard\plain \s16\widctlpar\adjustright \fs22\cgrid {The approach included the following tests to set flags: \par }\pard\plain \widctlpar\adjustright \fs20\cgrid {\fs22 \par {\pntext\pard\plain\fs22\cgrid \hich\af0\dbch\af0\loch\f0 1.\tab}}\pard \fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlbody\ilvl0\ls5\pnrnot0\pndec\pnstart1\pnindent360\pnhang{\pntxta .}}\ls5\adjustright {\fs22 Flags based on values outside of reasonable limits: \par }\pard \li720\widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 \par ANPP_C>2000 gC/m2, BNPP_C>2000, TNPP_C > 3000 \par ANPP_C<50 gC/m2, BNPP_C>50, TNPP_C<50 \par ELEV > 2500 m OR ELEV<-100 m (high or low elevation sites were expected to comprise unrepresentative sub-biomes or present problems with the climate extrapolation algorithm) \par ANPP > .95TNPP, BNPP>.95 TNPP (for a site with both above and below ground components) \par }\pard \widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 \par {\pntext\pard\plain\fs22\cgrid \hich\af0\dbch\af0\loch\f0 2.\tab}}\pard \fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlbody\ilvl0\ls5\pnrnot0\pndec\pnstart1\pnindent360\pnhang{\pntxta .}}\ls5\adjustright {\fs22 Flags based on questionable values. }{ \i\fs22 }{\fs22 \par }\pard \widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 \par }\pard \li720\widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 LAT_DD, LONG_DD \par }\pard \widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 \par {\pntext\pard\plain\fs22\cgrid \hich\af0\dbch\af0\loch\f0 3.\tab}}\pard \fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlbody\ilvl0\ls5\pnrnot0\pndec\pnstart1\pnindent360\pnhang{\pntxta .}}\ls5\adjustright {\fs22 Flags based on NPP values outside of the .05-.95 percentiles for each biome calculated assuming a normal distribution of variables (this replaces our initial rule of using 2 standard deviations): \par }\pard \widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 \par }\pard \li720\widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 ANPP, BNPP, TNPP \par }\pard \widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 \par {\pntext\pard\plain\fs22\cgrid \hich\af0\dbch\af0\loch\f0 4.\tab}}\pard \fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlbody\ilvl0\ls5\pnrnot0\pndec\pnstart1\pnindent360\pnhang{\pntxta .}}\ls5\adjustright {\fs22 Flags based on climate values outside of the .01-.99 percentiles for each biome calculated assuming a normal distribution of variables. In addition, we set some specific limits for some biomes: \par }\pard \widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 \par }\pard \li720\widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 Temp, Precip \par Temp > 6 C for boreal forests \par Precip > 1000 mm for desert and tundra \par Precip < 1000 mm for tropical forest \par }\pard \widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 \par {\pntext\pard\plain\fs22\cgrid \hich\af0\dbch\af0\loch\f0 5.\tab}}\pard \fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlbody\ilvl0\ls5\pnrnot0\pndec\pnstart1\pnindent360\pnhang{\pntxta .}}\ls5\adjustright {\fs22 Flags based on the comparison of measured NPP versus modeled NPP using the average or ensemble value for all avai lable models. The comparison was based on bias (predicted - observed), Normalized Error (predicted - observed) divided by the average (predicted - observed )/2, and Mean Absolute Error (MAE) \endash (predicted - observed) divided by the observed. . Based on the frequency distributions, bias greater than +_ 800 gC/m2, NE ratios greater than +_1.3, and MAE ratios greater than +_ 5 were flagged. \par }\pard \widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 \par }\pard \li720\widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 NPP vs MODEL ensemble \par }\pard \widctlpar{\*\pn \pnlvlcont\ilvl0\ls0\pnrnot0\pndec }\adjustright {\fs22 \par {\pntext\pard\plain\fs22\cgrid \hich\af0\dbch\af0\loch\f0 6.\tab}}\pard \fi-360\li360\widctlpar\jclisttab\tx360{\*\pn \pnlvlbody\ilvl0\ls5\pnrnot0\pndec\pnstart1\pnindent360\pnhang{\pntxta .}}\ls5\adjustright {\fs22 Flags based on relationships between variables. Linear regression analysis was performed betwee n NPP and average NPP (from 11 models), NPP and average AET (from 5 models), NPP and precipitation, NPP and temperature, and NPP and ANPP. Points falling outside of the .95 Confidence Interval about the regression line were flagged. \par }\pard \widctlpar\adjustright {\fs22 \par }\pard \li720\widctlpar\adjustright {\fs22 NPP vs AET, NPP vs Prec, NPP vs Tave, NPP vs ANPP_C \par }\pard \li720\ri4896\widctlpar\tx10080\adjustright {\fs22 \par }\pard\plain \s1\keepn\widctlpar\outlinelevel0\adjustright \b\fs22\cgrid {Critical flags \par }\pard\plain \widctlpar\adjustright \fs20\cgrid {\fs22 \par We assume that sites that have multiple flags have inconsistencies between NPP, driver data, and model predictions, and are therefore more likely to be an outlier. This seemed to hold true in plots (see belo w). We also know that some flags are deserve more critical weight as indicators of potential problems. We designated flags by assigning a value of 10 for those critical checks, including NPP inconsistent with model NPP ensemble value, elevation > 2500 m or < -100 m, and either site temperature or precipitation inconsistent with that assigned based on the global climate data. \par The overall flags value was calculated as the sum of the individual flags. Sites with a sum greater that 10 were dropped, and those greater than 4 (all those with at least one major flag) were considered potential for excluding. \par \par {\pntext\pard\plain\fs22\cgrid \hich\af0\dbch\af0\loch\f0 1.\tab}}\pard \fi-360\li720\widctlpar\jclisttab\tx720{\*\pn \pnlvlbody\ilvl0\ls4\pnrnot0\pndec\pnstart1\pnindent360\pnhang{\pntxta .}}\ls4\adjustright {\fs22 Select sites with no flags set or less than 5 noncritical flags (or exclude those with a critical flag set or five or more noncritical flags) \par }\pard \widctlpar\adjustright {\fs22 \par }\pard\plain \s1\keepn\widctlpar\outlinelevel0\adjustright \b\fs22\cgrid {Results \par }\pard\plain \widctlpar\adjustright \fs20\cgrid {\fs22 \par A t otal of 12 appeared to be outliers. We are in the process of reviewing this list. Virtually all the sites identified at the EMDI workshop as outliers were included in this set. If the EMDI group as a whole accepted these as outliers and we recalculate site and biome averages, we would have a total of 161 records for 81 sites. \par \par }\pard\plain \s1\keepn\widctlpar\outlinelevel0\adjustright \b\fs22\cgrid {Plots and Charts \par }\pard\plain \widctlpar\adjustright \fs20\cgrid {\fs22 \par We plotted NPP observed against the modeled NPP ensemble, by AET ensemble, and by latitude with the plot symbols indicating the magnitude of the flags. In genera l, the points that had high flag values appeared to be on the fringe of the cluster of points. As included below, the difference between the old NPP and revised included increases and decreases, generally getting bigger with higher NPP levels. There was a strong relationship of the recalculated model ensemble to be higher at low NPP and lower than observed NPP at higher NPP. Bar charts are presented for the new biome groups. \par \par }{\fs22\cgrid0 The SAS variable names and labels for the flags are: \par \par }\trowd \trgaph108\trleft-108\trbrdrt\brdrs\brdrw10 \trbrdrl\brdrs\brdrw10 \trbrdrb\brdrs\brdrw10 \trbrdrr\brdrs\brdrw10 \trbrdrh\brdrs\brdrw10 \trbrdrv\brdrs\brdrw10 \clvertalt\clbrdrt\brdrs\brdrw10 \clbrdrl\brdrs\brdrw10 \clbrdrb\brdrs\brdrw10 \clbrdrr \brdrs\brdrw10 \cltxlrtb \cellx1620\clvertalt\clbrdrt\brdrs\brdrw10 \clbrdrl\brdrs\brdrw10 \clbrdrb\brdrs\brdrw10 \clbrdrr\brdrs\brdrw10 \cltxlrtb \cellx2430\clvertalt\clbrdrt\brdrs\brdrw10 \clbrdrl\brdrs\brdrw10 \clbrdrb\brdrs\brdrw10 \clbrdrr \brdrs\brdrw10 \cltxlrtb \cellx8640\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 Flag name\cell Flag\cell Flag label\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\trowd \trgaph108\trleft-108\trbrdrt\brdrs\brdrw10 \trbrdrl\brdrs\brdrw10 \trbrdrb\brdrs\brdrw10 \trbrdrr\brdrs\brdrw10 \trbrdrh\brdrs\brdrw10 \trbrdrv\brdrs\brdrw10 \clvertalt\clbrdrt\brdrs\brdrw10 \clbrdrl\brdrs\brdrw10 \clbrdrb\brdrs\brdrw10 \clbrdrr\brdrs\brdrw10 \cltxlrtb \cellx1620\clvertalt\clbrdrt\brdrs\brdrw10 \clbrdrl \brdrs\brdrw10 \clbrdrb\brdrs\brdrw10 \clbrdrr\brdrs\brdrw10 \cltxlrtb \cellx2430\clvertalt\clbrdrt\brdrs\brdrw10 \clbrdrl\brdrs\brdrw10 \clbrdrb\brdrs\brdrw10 \clbrdrr\brdrs\brdrw10 \cltxlrtb \cellx8640\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 ELEV_MXF\cell 10\cell elev >2500, elev < -100*\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 MOD_F\cell 10\cell (npp_est - modcb_av) / ave(npp) > 1*\cell }\pard \widctlpar\intbl\adjustright { \fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 ANPP_P5F\cell 1\cell anpp outsite .05-.95 percentile by biome2\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 BNPP_P5F\cell 1\cell bnpp outsite .05-.95 percentile by biome2\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 TNPP_P5F\cell 1\cell tnpp outsite .05-.95 percentile by biome2\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 ANPPBADF\cell 1\cell anpp >2000gc/m2, anpp>95%tnpp\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright { \fs22\cgrid0 BNPPBADF\cell 1\cell bnpp >2000gc/m2, bnpp>95%tnpp\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 TNPPBADF\cell 1\cell tnpp >3000gc/m2, anppbadf or bnppbadf\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 AET_RGF\cell 1\cell NPP outside .95 CI of NPP=a+b*AET regression\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 PREC_RGF\cell 1\cell NPP outside .95 CI of NPP=a+b*PREC regression\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 TAVE_RGF\cell 1\cell NPP outside .95 CI of NPP=a+b*TAVE regression\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 PRECBADF\cell 1\cell prec outsite .01-.99 percentile by biome2 (or >1000mm in desert)\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\trowd \trgaph108\trleft-108\trbrdrt\brdrs\brdrw10 \trbrdrl\brdrs\brdrw10 \trbrdrb\brdrs\brdrw10 \trbrdrr\brdrs\brdrw10 \trbrdrh\brdrs\brdrw10 \trbrdrv\brdrs\brdrw10 \clvertalt\clbrdrt\brdrs\brdrw10 \clbrdrl\brdrs\brdrw10 \clbrdrb\brdrs\brdrw10 \clbrdrr\brdrs\brdrw10 \cltxlrtb \cellx1620\clvertalt\clbrdrt\brdrs\brdrw10 \clbrdrl\brdrs\brdrw10 \clbrdrb\brdrs\brdrw10 \clbrdrr\brdrs\brdrw10 \cltxlrtb \cellx2430\clvertalt\clbrdrt\brdrs\brdrw10 \clbrdrl \brdrs\brdrw10 \clbrdrb\brdrs\brdrw10 \clbrdrr\brdrs\brdrw10 \cltxlrtb \cellx8640\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 TAVEBADF\cell 1\cell temp outsite .01-.99 percentile by biome2 (>6 in boreal forests)\cell }\pard \widctlpar\intbl\adjustright {\fs22\cgrid0 \row }\pard\plain \s15\nowidctlpar\adjustright \f1\fs20\cgrid {\f0 \par }}