## 2 Methods
### Ecosystem Performance Modeling
#### 2.2.1 Actual Performance  
Ecosystem performance (EP), or actual performance, is represented by the growing season integral of the Normalized Difference Vegetation Index (GSN). The Normalized Difference Vegetation Index (NDVI) has been related to chlorophyll [56], nitrogen stress [56], fractional vegetation cover [56], Leaf Area Index (LAI) [56], gross primary production [56], and foliar water potential [56]. NDVI also tracks vegetation dynamics related to phenology [57,58]. Growing season summaries of remotely sensed vegetation indices capture vegetation dynamics through the growing season [57,59,60], including shoulder season dynamics that are sensitive to climate instability [56,57]. Growing season integrals of vegetation indices have been related to or used as relative proxies for total standing biomass [61,62,63], photosynthetic activity [64,65], biological activity [66], productivity [57], forest foliar productivity [67], biomass [62,68], primary production [7,25,27,45,69], fire succession [70], and LAI in deciduous and coniferous boreal forests [71].  
The 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) data, with a temporal (weekly) resolution, capture local variability in productivity and are appropriate for evaluating the impacts of climate change and forest stress. The 250 m expedited MODIS (eMODIS) [72] 7-day NDVI composites were rescaled from the NDVI's \(-1\) to \(1\) range to \(0\) to \(100\), and all negative values (clouds and water) were set to \(0\). Soil and litter effects were minimized by subtracting \(0.09\) from NDVI values [73] and to improve GSN sensitivity to vegetation [69]. These NDVI composites incorporate data quality information related to clouds, view angles, and snow [74] and were temporally smoothed [75] to remove any remaining cloud effects for each growing season, defined as \(1\) April to \(30\) September. Temporal smoothing of NDVI spans temporary drops in NDVI related to residual clouds which can remain after the \(7\)-day maximum NDVI compositing process. A series of linear regressions from a moving temporal window are used to estimate \"fill in\" appropriate NDVI values for periods of temporarily low NDVI values [75]. To capture interannual productivity variations in the shoulder seasons and capture growing season length changes, a constant time window for NDVI averaging was employed (\(1\) April to \(30\) September). The high temporal resolution of the eMODIS data utilized in this study was critical to capturing these patterns [70]. Growing season average NDVI (GSN) with a constant growing season period is algebraically consistent with the sum of NDVI over the growing season and emphasizes the importance of the length of the productive period and the effects of varied shoulder seasons [10]. A varied growing season length used in the average would tend to standardize for growing season length, resulting in potentially similar GSN values for a short growing season and a long growing season with similar weekly NDVI magnitudes during the growing season. By using eMODIS NDVI, the MODIS swath data were directly projected to Alaska Albers Equal Area [74], avoiding geographic distortions associated with the extreme latitudes and longitude in the global sinusoidal projection for this study area [72,76,77].