## 3 Results  
### Validation against In Situ Data  
Average Pearson correlation coefficients (R) [80] are calculated based on biweekly aggregates of \(\tau\) for the period 2007-2014 in comparison to in situ data. Overall, SIF/PAR exhibits an average R = 0.59 for the selection of flux towers, showing individual correlations ranging from R = 0.02 (US-Me2) to R = 0.92 (US-MMS), being statistically significant (p < 0.01) at 22 of the 25 sites. In comparison to individual models, SURFEX (R = 0.56) shows the highest correlations against in situ data; this performance is followed by W3 RA (R = 0.50). The remaining models display lower average correlations, ranging from R = 0.09-0.34. The consistently lower performance of some models is further evaluated by analyzing the time series of T and E\({}_{\text{P}}\) separately against the towers (Figure S2). Results demonstrate that E\({}_{\text{P}}\) estimates from all models are similar, as expected from their common atmospheric forcing, and that they all compare well to the E\({}_{\text{P}}\) retrieved at the towers. As such, most of the model-to-tower disagreements in \(\tau\) result from uncertainties in T: models with higher correlations of T also display a better performance for \(\tau\). Figure 2a represents the results of the site validations separately for each individual tower. The overall higher correlations of SIF/PAR against the tower retrievals of \(\tau\) is evident for 17 of the 25 sites, correlations are greater than the model ensemble average. Consistently, when correlations are grouped by IGBP biome type at the flux tower scale, the highest correlations between SIF/PAR and the in situ \(\tau\) data are found at densely forested sites (Figure S3)--some exceptions are further explored in Section 4, including the US-Me2 evergreen needleleaf forest site. Correlations against in situ data are also explored for months of vegetation growth and decay separately; these periods are defined based on the GPP climatology at each tower. Periods where GPP increases from one month to the next are designated as growing, and those where GPP decreases, designated as decaying. Overall, both SIF/PAR and the models' \(\tau\) capture the dynamics at the towers more realistically during the growing season (Figure 2b). Nonetheless, rankings of model performance across both phases are similar to the results over the entire timeframe shown in Figure 2a. SIF/PAR exhibits the highest average R during the growing period (0.64), and compares better to the in situ \(\tau\) than any of the models. However, during the decaying period, the correlations of SIF/PAR and SURFEX against in situ are comparable (R = 0.53 and R = 0.54, respectively). The remaining models average correlations between 0.30-0.55 during growth and 0.39-0.49 during decay periods. The ability to capture the timing of transpiration throughout the year is critical for LSMs to be able to simulate the effects of land conditions on temperature, precipitation and radiation adequately [22]. This skill is explored in Figure 2c by computing biweekly cross-correlations against in situ \(\tau\) retrievals. For SIF/PAR, the maximum cross-correlation against in situ data occurs at zero lag for 16 of the 25 sites. For the remaining sites, SIF/PAR shows a lag in the order of +/-6 weeks; with the exception of AU-Wom (12 weeks)--results are further discussed in Section 4. Models reflect a wide range of performances, from SURFEX showing maximum cross-correlations at zero lag in a total of 17 of the 25 sites to GLEAM and JULES showing zero lag in only 9 of the 25 sites. Finally, Figure 3 shows individual time series for three sites (US-MMS, US-Ne2, US-SRG). The three flux towers depicted represent a deciduous broadleaf forest (DBF), cropland (CRO) and grassland (GRA). Time series further demonstrate the ability of SIF/PAR to adequately capture seasonal \(\tau\) cycles. SIF/PAR most closely follows the tower \(\tau\) at the DBF site across all seasons compared to the CRO and GRA sites.