1 Evaluation of GPP against FLUXNET data

1.1 Sites selection

Sites used for the model evaluation are shown on the map below. An overview table is in the Appendix (last section of this document).

Figure S1: Geographical distribution of sites selected for the bias evaluation. Sites listed in Table S1 as group 1 are in green, sites of group 2 are in black. The color of land area represents aridity, quantified as the ratio of potential evapotranspiration over precipitation from xxx

1.2 Data processing

Daily data are used from the FLUXNET 2015 Tier 1 dataset, downloaded on 13. November, 2016. We use GPP as the mean of values based on the Nighttime Partitioning and the Daytime Partitioning method, both based on the Variable U-Star Threshold method (variables in the FLUXNET 2015 dataset named GPP_NT_VUT_REF and GPP_DT_VUT_REF). In the FLUXNET 2015 dataset, daily values are sums over half-hourly data. We use only daily values where less than 50% of respective half-hourly data is gap-filled. We further removed data points where the daytime and nighttime methods (GPP_DT_VUT_REF and GPP_NT_VUT_REF, resp.) are inconsistent. I.e., the upper and lower 2.5% quantile of the difference between each method’s GPP quantification. Finally, we removed all negative daily GPP values.

1.3 P-model parameter calibration

The P-model relies on a minimum set of free parameters. Most parameters are given by known physical laws with accurately defined parameters (e.g., viscosity of water) or well-established physiological relationships with independently constrained parameters (e.g., enzyme kinetics of C3 photosynthesis). Two key free parameters are:

  • the ratio of costs for maintaining transpiration versus carboxylation \(\beta\).
  • the quantum yield efficiency \(\varphi_0\).

We applied a value of 146.0 for \(\beta\) based on independent constraints from \(\delta^{13}\)C measurements on leaves (Prentice et al., 2014; Wang Han et al., 2017).

\(\varphi_0\) determines the fraction of absorbed light that can be used by the photosynthetic aparatus for assimilating CO\(_2\). Within the P-model, this parameter acts as a linear scalar between absorbed light and GPP and therefore implies a strong sensitivity of simulated GPP to its valule. Considerable uncertainty resides in the quantification of fAPAR and some inconsistency in terms of its definition. Hence, we consider \(\varphi_0\) as a free parameter that we calibrate, given the fAPAR data product used. Here, we use the MODIS FPAR MCD15A3H data product at 1 km/8 day resolution and extract values for a single pixel surrounding the flux tower location. Calibration was done …

OPEN POINT: I did a such a calibration and got \(\varphi_0 = 0.0579\), as described here. I filtered out data points taken at low temperatures (\(<5^{\circ}C\)) and at low soil moisture (\(<0.4\) relative soil water content). The calibration target was GPP_NT_VUT_REF. Now, I’ve implemented a more flexible and capable calibration function for SOFUN, allowing for multiple parameters to be calibrated simultaneously and accounting for uncertainty in the data. This makes use of different parameter search algorithms implemented in R.

  • How to use uncertainty data from FLUXNET 2015? Just use one the nighttime partitioning and use GPP_NT_VUT_MEAN and GPP_NT_VUT_SE? Or how to account for uncertainty related to the difference between the nighttime and daytime partitioning approach? Thus: how to additionally use GPP_DT_VUT_MEAN and GPP_DT_VUT_SE?

1.4 Metrics

Several performance metrics are calculated for different features of GPP variability. The performance metrics are:

  • R\(^2\)
  • RMSE
  • slope (of regression observed over modelled)
  • bias

The features of variability in GPP, for which model-observation agreement is calculated, are:

  • mean annual values (giving “spatial” correlation)
  • annual anomalies from mean across years
  • daily values, absolute
  • mean across X-day periods, absolute
  • mean seasonal cycle (mean by day of year)
  • daily anomalies from mean seasonal cycle

1.5 Results of evaluation

1.5.1 Overview

library(tidyr)
out_eval$metrics %>% 
  bind_rows() %>% 
  unnest() %>% 
  mutate( level = (out_eval$metrics$gpp$mean_nt_dt_fluxnet2015 %>% names()) ) %>%
  mutate_at( vars(one_of("rsq", "rmse", "slope", "bias")), funs(format(., digits=3))) %>%
  dplyr::select( Level=level, N=nvals, R2=rsq, RMSE=rmse, Slope=slope, Bias=bias) %>%
  knitr::kable( caption="Performance metrics of correlations between modelled and observed values at different temporal aggregation levels, absolute values and anomalies.")
Performance metrics of correlations between modelled and observed values at different temporal aggregation levels, absolute values and anomalies.
Level N R2 RMSE Slope Bias
daily_pooled 262336 0.5966 2.65 0.796 7.07e-01
xdaily_pooled 63282 0.6495 2.38 0.878 6.97e-01
annual_pooled 648 0.5702 488.50 0.949 2.42e+02
monthly_pooled 9134 0.6871 2.16 0.941 7.25e-01
spatial 119 0.5893 505.11 0.956 2.49e+02
anomalies_annual 648 0.0662 173.70 0.374 2.05e+00
meandoy 53641 0.6780 2.09 0.885 7.44e-01
anomalies_daily 262193 0.2525 1.80 0.408 6.62e-03
meanxoy 10755 0.6976 2.01 0.909 7.49e-01
anomalies_xdaily 63282 0.1323 1.47 0.389 9.25e-03

1.5.2 Annual values

Annual values in modelled and simulated GPP can be decomposed into the mean annual GPP per site \(\overline{X}_i\) and its anomaly from the multi-annual mean \(X'_{i,t}\): \[ X_{i,t} = \overline{X}_i + X'_{i,t} \]

1.5.2.1 Spatial correlation

Comparing the multi-annual mean per site in simulated and observed GPP (\(\overline{X}_i\)) yields a “spatial correlation”.

1.5.2.2 Annual GPP anomalies

Comparing annual anomalies (\(X'_{i,t}\)) yields insight into whether the model accurately simulates interannual variability.

1.5.2.3 Combined spatial/annual

The two above can be combined into a single plot. This shows the same as in the SI of our submitted manuscript: Figures S14 and S15 here.

OPEN POINT:

For some sites, this relationship is completely off. I’ll have to have a closer look to check if the data is ok.

Selecting data only for sites used in the SI of our submitted manuscript (Figures S14 and S15 see here), should give exactly the same figure, but it doesn’t (see below). I’ll have to look into what’s going wrong here (thereby hopefully also resolving the open point mentioned above).

1.5.3 Seasonal cycle

The mean seasonal cycle is calculated as the mean by day-of-year (DOY) across multiple years. \[ \overline{X_{\text{DOY}}} = \frac{1}{N_y} \sum_y X_{\text{DOY},y} \]

1.5.3.1 Seasonal cycle by climate zones

More insights are provided by looking at the seasonal cycle explicitly. Plots for each site are given in the Appendix. Aggregating across multiple sites doesn’t make much sense. However, I aggregated across climate zones and distinguishing between respective zones on the northern and southern hemisphere.

The classification of sites into Koeppen-Geiger climate zones is based on Falge et al. 2016. ORNL DAAC, and complemented by extracting information from a global map. A table explaining the Koeppen-Geiger codes is given below.

Koeppen-Geiger climate zones.
Code Climate
Am Equatorial monsoon
As Equatorial savannah with dry summer
Aw Equatorial savannah with dry winter
BSh Arid Steppe hot
BSk Arid Steppe cold
BWh Arid desert hot
Cfa Warm temperate fully humid with hot summer
Cfb Warm temperate fully humid with warm summer
Cfc Warm temperate fully humid with cool summer
Csb Temperate/Dry_Summer/Warm_Summer
Cwa Warm temperate with dry winter and hot summer
Cwb Warm temperate with dry winter and warm summer
Dfa Snow with fully humid hot summer
Dfb Snow fully humid warm summer
Dfc Snow fully humid cool summer
Dfd Snow fully humid extremely continental
Dwa Snow dry winter hot summer
Dwb Snow dry winter warm summer
Dwc Snow dry winter cool summer
EF Polar frost
ET Polar tundra

Insights from seasonality analysis by climate zones (results see below, red is the model, black the observations, discussing only climate zones with data from more than three sites):

  • Big problems simulating GPP in the Tropics (Am, Equatorial monsoon): substantial underestimation during most of the year except a short period (Is that the dry period?). Could be related to fAPAR data contaminated by clouds except during the dry period? Unfortunately, only two sites are available in this climate zone.
  • Insufficient GPP decline during dry periods in tropical and hot climates (Aw: Equatorial savannah with dry winter, and BSh: Arid Steppe hot). This is not a surprise. The empirical soil moisture correction is not applied here.
  • Overestimation of GPP in arid steppes (BSk: Arid Steppe cold). Quite robust, seen at 7 sites in total. Probably, model improved by soil moisture correction.
  • In temperate and boreal regions, early-season GPP is consistently overestimated, and late-season in most cases (Cfa: Warm temperate fully humid with hot summer, Cfb: Warm temperate fully humid with warm summer, Dfb: Snow fully humid warm summer, Dfc: Snow fully humid cool summer). It looks like at cold air (or soil?) temperatures, GPP tends to be overestimated.
  • There is a general overestimation of GPP at sites in Cwcb (Warm temperate with dry winter and warm summer).

In brief, there is room for improvement by reducing GPP at low temperatures (sort of found before, but we never dealt with a temperature ramp, and why should we?) and low soil moisture (of course). fAPAR data in the tropics needs to be examined, otherwise, it looks like we have a problem with the Aw sites.

1.5.4 Daily values

Ignoring that we don’t expect a LUE-type linear relationship between absorbed light \(I_{\text{abs}}\) and GPP, we can still evaluate the daily GPP estimated by the P-model: \[ \text{GPP}(d) = \text{LUE}(m|d)\; \times \; I_{\text{abs}}(d) \] Here, \(LUE(m|d)\) is the monthly varying light use efficiency simulated by the P-model using forcing data averaged to monthly means. \(m|d\) refers to the month of a given day.

The correlation is still quite good…

1.5.5 Values aggregated to X days

Aggregated to longer periods, the performance slightly improves. Here, I aggregated modeled and observational data to 5-days periods.

  • There is a pattern: Many simulated data points in the lower range tend to be too high and too low in the higher range. The lower range overestimation will definitely be improved by emprirical soil moisture, and, if applicable, a low-temperature stress function. The underestimation of values in the upper range raise a more fundamental challenge…
  • The fact that the correlation improves when aggregating daily values to 5-daily values might reflect that applying the P-model as a light use efficiency model at the daily time scale violates its basic assumption related to the acclimation time scale. Non-linearities of the light-response curve imply that the ratio of GPP to absorbed light relationship declines with increasing light levels (right?). Day-to-day variations in GPP are mainly driven by light availability (not systematically investigated). Hence, anomalies of daily GPP from its mean seasonal cycle should be larger in the P-model than in the observations. This is the case as the figures below show.

Above figure shows the correlation and the distribution of anomalies in daily GPP from the respective mean seasonal cycle in the observations and in simulated values. Note that the blue lines in the first plot are the regression lines of daily anomalies for each site. The fact that their slope is consistently lower than 1, indicates that the day-to-day variability in simulated GPP is higher than in observed GPP. This is also reflected by the histogram in the second plot (note standard deviation values given in the upper left corner).

When plotting the same for values aggregated to 5-day bins, the standard deviation of simulated anomalies declines strongly and is in better agreement with the observations (see histogram), but the correlation analysis doesn’t suggest a better performance.

OPEN POINT:

Would it be worth exploring this in more detail, e.g. quantifying the relationship between some metric and aggregation level, and ‘some metric’ being e.g. the standard deviation of anomalies, the R\(^2\) of obs. vs. mod., or … ?

1.5.6 Monthly values

Anyways, the obvious next aggregation level is monthly, and the correlation increases further to a stunning R\(^2\) of 0.69.

1.5.7 Functional relationships

Functional relationships seen in the data can be extracted using Artificial Neural Networks (ANN). This is done here. First, a model is fitted to observed GPP with temperature, PPFD, VPD, fAPAR, and soil moisture as predictors. Then, functional relationships are evaluated by using the trained ANN to predict values for a synthetic datasets. The synthetic dataset is generated for each predictor value. E.g. for temperature, a sample of 50 data points is drawn from the empirical distribution of VPD, fAPAR, PPFD, and soil moisture, respectively, and 50 levels of temperature (evenly from 0-40 \(^{\circ}\)C) Then, the dataset is created by all combinations of these variables. Finally, functional relationship between temperature and GPP are assessed by taking the mean across all variable combination for each level of temperature separately. This approach implies that for the evaluation, correlations between predictor variables in the observational dataset are ignored.

In order to improve comparability, ANNs are trained at (P-) modelled and observed GPP, and functional relationships thus derived for observed and modelled. This improves comparability and is preferred here over evaluating the model directly. As always, observations are in black and modelled in red.

There are several points here:

  • This mixes responses at different time scales and within/across sites.
  • The functional relationships based on the ANN look surprisingly “stiff”.
  • The data suggests increasing GPP across the whole temperature range, while the P-model simulates a levelling off. This could be related to the prescribed temperature sensitivity of ecosystem respiration for the flux decomposition.
  • The response to VPD looks surprisingly not like \(\sim1/\sqrt{D}\) in the P-model, but in the observations although the respective equation should force it to look so. Therefore, this must have to do with how the ANNs pick up relationships and the noise and correlation structure in the data.

OPEN POINT:

Colin has shown some plots at the ICDC that apparently Trevor produced. They show something similar as I have tried here, but as I remember, there seemed to be much more fine-structure in the functional relationships. Trevor, how did you do that? Using GAMs (as I remember you saying once)? I don’t understand however, why the ANNs look so “stiff” here…

2 Appendix

2.1 Sites table

Sites used for evaluation. Lon. is longitude, negative values indicate west longitude; Lat. is latitude, positive values indicate north latitude; Veg. is vegetation type: deciduous broadleaf forest (DBF); evergreen broadleaf forest (EBF); evergreen needleleaf forest (ENF); grassland (GRA); mixed deciduous and evergreen needleleaf forest (MF); savanna ecosystem (SAV); shrub ecosystem (SHR); wetland (WET).
Site Lon. Lat. Period Veg. Clim. N Reference
AR-SLu -66.4598 -33.4648 2009-2011 MF Bwk 430 (Ulke, Gattinoni, and Posse 2015)
AR-Vir -56.1886 -28.2395 2009-2012 ENF Csb 585 (Posse et al. 2016)
AT-Neu 11.3175 47.1167 2002-2012 GRA Dfc 3172 (Wohlfahrt et al. 2008)
AU-Ade 131.1178 -13.0769 2007-2009 WSA Aw 528 (J. Beringer, Hacker, et al. 2011)
AU-ASM 133.2490 -22.2830 2010-2013 ENF BSh 1033 (Cleverly et al. 2013)
AU-Cpr 140.5891 -34.0021 2010-2014 SAV BSk 1360 (Meyer, Kondrlovà, and Koerber 2015)
AU-Cum 150.7225 -33.6133 2012-2014 EBF Cfa 738 (J. Beringer, Hutley, McHugh, Arndt, Campbell, Cleugh, Cleverly, Dios, et al. 2016a)
AU-DaP 131.3181 -14.0633 2007-2013 GRA Aw 1377 (J. Beringer, Hutley, Hacker, et al. 2011a)
AU-DaS 131.3881 -14.1593 2008-2014 SAV Aw 2216 (Hutley et al. 2011)
AU-Dry 132.3706 -15.2588 2008-2014 SAV Aw 1579 (Cernusak et al. 2011)
AU-Emr 148.4746 -23.8587 2011-2013 GRA Bwk 718 (Schroder, Kuske, and Zegelin 2014)
AU-Fog 131.3072 -12.5452 2006-2008 WET Aw 868 (Beringer et al. 2013)
AU-Gin 115.7138 -31.3764 2011-2014 WSA Cwb 935 (J. Beringer, Hutley, McHugh, Arndt, Campbell, Cleugh, Cleverly, De Dios, et al. 2016)
AU-GWW 120.6541 -30.1913 2013-2014 SAV Bwk 646 (Prober et al. 2012)
AU-How 131.1523 -12.4943 2001-2014 WSA Aw NA (Cernusak 2007)
AU-Lox 140.6551 -34.4704 2008-2009 DBF Bsh 271 (Stevens et al. 2011)
AU-RDF 132.4776 -14.5636 2011-2013 WSA Bwh 424 (Bristow et al. 2016)
AU-Rig 145.5759 -36.6499 2011-2014 GRA Cfb 1121 (J. Beringer, Hutley, McHugh, Arndt, Campbell, Cleugh, Cleverly, Dios, et al. 2016b)
AU-Rob 145.6301 -17.1175 2014-2014 EBF Csb 334 (J. Beringer, Hutley, McHugh, Arndt, Campbell, Cleugh, Cleverly, Dios, et al. 2016c)
AU-Stp 133.3502 -17.1507 2008-2014 GRA BSh 1314 (J. Beringer, Hutley, Hacker, et al. 2011b)
AU-TTE 133.6400 -22.2870 2012-2013 OSH BWh 94 (Cleverly et al. 2016)
AU-Tum 148.1517 -35.6566 2001-2014 EBF Cfb 4202 (Leuning et al. 2005)
AU-Wac 145.1878 -37.4259 2005-2008 EBF Cfb 971 (Kilinc et al. 2013)
AU-Whr 145.0294 -36.6732 2011-2014 EBF Cfb 1062 (McHugh et al. 2017)
AU-Wom 144.0944 -37.4222 2010-2012 EBF Cfb 897 (Hinko-Najera et al. 2017)
AU-Ync 146.2907 -34.9893 2012-2014 GRA BSk 384 (Yee et al. 2015)
BE-Bra 4.5206 51.3092 1996-2014 MF Cfb 4463 (Carrara et al. 2004)
BE-Lon 4.7461 50.5516 2004-2014 CRO Cfb 3027 (Moureaux et al. 2006)
BE-Vie 5.9981 50.3051 1996-2014 MF Cfb 5556 (Aubinet et al. 2001)
BR-Sa3 -54.9714 -3.0180 2000-2004 EBF Am 1134 (Wick et al. 2005)
CA-Man -98.4808 55.8796 1994-2008 ENF Dfc 2435 (Dunn et al. 2007)
CA-NS1 -98.4839 55.8792 2001-2005 ENF Dfc 763 (Goulden et al. 2006a)
CA-NS2 -98.5247 55.9058 2001-2005 ENF Dfc 859 (Goulden et al. 2006b)
CA-NS3 -98.3822 55.9117 2001-2005 ENF Dfc 1062 (Goulden et al. 2006c)
CA-NS4 -98.3822 55.9117 2002-2005 ENF Dfc 601 (Goulden et al. 2006d)
CA-NS5 -98.4850 55.8631 2001-2005 ENF Dfc 903 (Goulden et al. 2006e)
CA-NS6 -98.9644 55.9167 2001-2005 OSH Dfc 905 (Goulden et al. 2006f)
CA-NS7 -99.9483 56.6358 2002-2005 OSH Dfc 694 (Goulden et al. 2006g)
CA-Qfo -74.3421 49.6925 2003-2010 ENF Dfc 1795 (Bergeron et al. 2007)
CA-SF1 -105.8176 54.4850 2003-2006 ENF Dfc 513 (Mkhabela et al. 2009a)
CA-SF2 -105.8775 54.2539 2001-2005 ENF Dfc 664 (Mkhabela et al. 2009b)
CA-SF3 -106.0053 54.0916 2001-2006 OSH Dfc 634 (Mkhabela et al. 2009c)
CH-Cha 8.4104 47.2102 2005-2014 GRA Cfb 2876 (Merbold et al. 2014)
CH-Dav 9.8559 46.8153 1997-2014 ENF ET 5293 (Zielis et al. 2014)
CH-Fru 8.5378 47.1158 2005-2014 GRA Cfb 2527 (Imer et al. 2013)
CH-Lae 8.3650 47.4781 2004-2014 MF Cfb 3144 (Etzold et al. 2011)
CH-Oe1 7.7319 47.2858 2002-2008 GRA Cfb 2083 (Ammann et al. 2009)
CH-Oe2 7.7343 47.2863 2004-2014 CRO Cfb 3258 (Dietiker, Buchmann, and Eugster 2010)
CN-Cha 128.0958 42.4025 2003-2005 MF Dwb 804 (Guan et al. 2006)
CN-Cng 123.5092 44.5934 2007-2010 GRA Bsh 1092 (???)
CN-Dan 91.0664 30.4978 2004-2005 GRA ET 619 (Shi et al. 2006)
CN-Din 112.5361 23.1733 2003-2005 EBF Cfa 894 (Yan et al. 2013)
CN-Du2 116.2836 42.0467 2006-2008 GRA Dwb 599 (Chen et al. 2009)
CN-Ha2 101.3269 37.6086 2003-2005 WET ET 892 (???)
CN-HaM 101.1800 37.3700 2002-2004 GRA NA 664 (Kato et al. 2006)
CN-Qia 115.0581 26.7414 2003-2005 ENF Cfa 987 (Wen et al. 2010)
CN-Sw2 111.8971 41.7902 2010-2012 GRA Bsh 224 (Shao et al. 2017)
CZ-BK1 18.5369 49.5021 2004-2008 ENF Dfb 1051 (Acosta et al. 2013)
CZ-BK2 18.5429 49.4944 2004-2006 GRA Dfb 156 (???)
CZ-wet 14.7704 49.0247 2006-2014 WET Cfb 2592 (Dušek et al. 2012)
DE-Akm 13.6834 53.8662 2009-2014 WET Cfb NA (???)
DE-Geb 10.9143 51.1001 2001-2014 CRO Cfb 3779 (Anthoni et al. 2004)
DE-Gri 13.5125 50.9495 2004-2014 GRA Cfb 3354 (Prescher, Grünwald, and Bernhofer 2010a)
DE-Hai 10.4530 51.0792 2000-2012 DBF Cfb 3436 (Knohl et al. 2003)
DE-Kli 13.5225 50.8929 2004-2014 CRO Cfb NA (Prescher, Grünwald, and Bernhofer 2010b)
DE-Lkb 13.3047 49.0996 2009-2013 ENF Cfb 848 (Lindauer et al. 2014)
DE-Obe 13.7196 50.7836 2008-2014 ENF Cfb 1992 (???)
DE-RuR 6.3041 50.6219 2011-2014 GRA Cfb 1178 (Post et al. 2015)
DE-RuS 6.4472 50.8659 2011-2014 CRO Cfb NA (Mauder et al. 2013)
DE-Seh 6.4497 50.8706 2007-2010 CRO Cfb 1026 (Schmidt et al. 2012)
DE-SfN 11.3275 47.8064 2012-2014 WET Cfb 737 (Hommeltenberg et al. 2014)
DE-Spw 14.0337 51.8923 2010-2014 WET Cfb 1323 (???)
DE-Tha 13.5669 50.9636 1996-2014 ENF Cfb 5965 (Grünwald and Bernhofer 2007)
DK-Fou 9.5872 56.4842 2005-2005 CRO Cfb 220 (???)
DK-NuF -51.3861 64.1308 2008-2014 WET ET 870 (Westergaard-Nielsen et al. 2013)
DK-Sor 11.6446 55.4859 1996-2014 DBF Cfb 5514 (Pilegaard et al. 2011)
DK-ZaF -20.5545 74.4814 2008-2011 WET ET 328 (Stiegler et al. 2016)
DK-ZaH -20.5503 74.4732 2000-2014 GRA ET 1652 (Lund et al. 2012)
ES-LgS -2.9658 37.0979 2007-2009 OSH Cwc 778 (Reverter et al. 2010)
ES-Ln2 -3.4758 36.9695 2009-2009 OSH Cwc 66 (Serrano-Ortiz et al. 2011)
FI-Hyy 24.2950 61.8475 1996-2014 ENF Dfc 5204 (Suni et al. 2003)
FI-Jok 23.5135 60.8986 2000-2003 CRO Dfc 645 (Lohila 2004)
FI-Lom 24.2092 67.9972 2007-2009 WET Dfc 502 (Aurela et al. 2015)
FI-Sod 26.6378 67.3619 2001-2014 ENF Dfc 2660 (Thum et al. 2007)
FR-Fon 2.7801 48.4764 2005-2014 DBF Cfb 2821 (Delpierre et al. 2015)
FR-Gri 1.9519 48.8442 2004-2013 CRO Cfb NA (Loubet et al. 2011)
FR-LBr -0.7693 44.7171 1996-2008 ENF Cfb 3528 (Berbigier, Bonnefond, and Mellmann 2001)
FR-Pue 3.5958 43.7414 2000-2014 EBF Cwb 4678 (Rambal et al. 2004)
GF-Guy -52.9249 5.2788 2004-2014 EBF Am 3695 (Bonal et al. 2008)
IT-BCi 14.9574 40.5238 2004-2014 CRO Cwb NA (Vitale et al. 2015)
IT-CA1 12.0266 42.3804 2011-2014 DBF Cwb 1021 (Sabbatini et al. 2016a)
IT-CA2 12.0260 42.3772 2011-2014 CRO Cwb 998 (Sabbatini et al. 2016b)
IT-CA3 12.0222 42.3800 2011-2014 DBF Cwb 875 (Sabbatini et al. 2016c)
IT-Col 13.5881 41.8494 1996-2014 DBF Cfa 3276 (Valentini et al. 1996)
IT-Cp2 12.3573 41.7043 2012-2014 EBF Cwb 756 (Fares et al. 2014)
IT-Cpz 12.3761 41.7052 1997-2009 EBF Cwb 2560 (Garbulsky et al. 2008)
IT-Isp 8.6336 45.8126 2013-2014 DBF Cfb 557 (Ferréa et al. 2012)
IT-La2 11.2853 45.9542 2000-2002 ENF Cfb 470 (B. Marcolla, Pitacco, and Cescatti 2003a)
IT-Lav 11.2813 45.9562 2003-2014 ENF Cfb 3799 (B. Marcolla, Pitacco, and Cescatti 2003b)
IT-MBo 11.0458 46.0147 2003-2013 GRA Dfb 3203 (Marcolla et al. 2011)
IT-Noe 8.1515 40.6061 2004-2014 CSH Cwb 3041 (Papale et al. 2014)
IT-PT1 9.0610 45.2009 2002-2004 DBF Cfa 813 (Migliavacca et al. 2009)
IT-Ren 11.4337 46.5869 1998-2013 ENF Dfc 3180 (Montagnani et al. 2009)
IT-Ro1 11.9300 42.4081 2000-2008 DBF Cwb NA (Rey et al. 2002)
IT-Ro2 11.9209 42.3903 2002-2012 DBF Cwb 2641 (Tedeschi et al. 2006)
IT-SR2 10.2910 43.7320 2013-2014 ENF Cwb 658 (Hoshika et al. 2017)
IT-SRo 10.2844 43.7279 1999-2012 ENF Cwb 4075 (Chiesi et al. 2005)
IT-Tor 7.5781 45.8444 2008-2014 GRA Dfc 1351 (Galvagno et al. 2013)
JP-MBF 142.3186 44.3869 2003-2005 DBF Dfb 459 (Matsumoto et al. 2008a)
JP-SMF 137.0788 35.2617 2002-2006 MF Cfa 1273 (Matsumoto et al. 2008b)
NL-Hor 5.0713 52.2404 2004-2011 GRA Cfb 2113 (Jacobs et al. 2007)
NL-Loo 5.7436 52.1666 1996-2013 ENF Cfb 5417 (Moors 2012)
NO-Adv 15.9230 78.1860 2011-2014 WET ET 100 (???)
NO-Blv 11.8311 78.9216 2008-2009 SNO ET 62 (Lüers et al. 2014)
RU-Che 161.3414 68.6130 2002-2005 WET Dfc 285 (Merbold, Kutsch, et al. 2009)
RU-Cok 147.4943 70.8291 2003-2014 OSH Dfc 962 (Molen et al. 2007)
RU-Fyo 32.9221 56.4615 1998-2014 ENF Dfb 4397 (Kurbatova et al. 2008)
RU-Ha1 90.0022 54.7252 2002-2004 GRA Dfc 516 (Marchesini et al. 2007)
SD-Dem 30.4783 13.2829 2005-2009 SAV BWh 736 (Ardo et al. 2008)
SN-Dhr -15.4322 15.4028 2010-2013 SAV BWh 631 (Tagesson et al. 2014)
US-AR1 -99.4200 36.4267 2009-2012 GRA Cfa 994 (Raz-Yaseef et al. 2015b)
US-AR2 -99.5975 36.6358 2009-2012 GRA Cfa 875 (Raz-Yaseef et al. 2015c)
US-ARb -98.0402 35.5497 2005-2006 GRA Cfa 408 (Raz-Yaseef et al. 2015a)
US-ARc -98.0400 35.5465 2005-2006 GRA Cfa 478 (Raz-Yaseef et al. 2015d)
US-ARM -97.4888 36.6058 2003-2012 CRO Cfa 2450 (Fischer et al. 2007)
US-Blo -120.6328 38.8953 1997-2007 ENF Cwc 2246 (Goldstein et al. 2000)
US-Cop -109.3900 38.0900 2001-2007 GRA BSk 1017 (Bowling et al. 2010)
US-GBT -106.2397 41.3658 1999-2006 ENF Dfc 533 (Zeller and Nikolov 2000)
US-GLE -106.2399 41.3665 2004-2014 ENF Dfb 2146 (Frank et al. 2014)
US-Ha1 -72.1715 42.5378 1991-2012 DBF Dfb 4810 (Urbanski et al. 2007)
US-KS2 -80.6715 28.6086 2003-2006 CSH Cfa 1258 (Powell et al. 2006)
US-Los -89.9792 46.0827 2000-2014 WET Dfb 2040 (Sulman et al. 2009)
US-Me1 -121.5000 44.5794 2004-2005 ENF Cwc 271 (Irvine, Law, and Hibbard 2007)
US-Me2 -121.5574 44.4523 2002-2014 ENF Cwc 3399 (Irvine et al. 2008)
US-Me6 -121.6078 44.3233 2010-2014 ENF Cwc 1244 (Ruehr, Martin, and Law 2012)
US-MMS -86.4131 39.3232 1999-2014 DBF Cfa 3610 (Dragoni et al. 2011)
US-Myb -121.7651 38.0498 2010-2014 WET Cwb 1111 (Matthes et al. 2014)
US-Ne1 -96.4766 41.1651 2001-2013 CRO Dfa NA (Verma et al. 2005a)
US-Ne2 -96.4701 41.1649 2001-2013 CRO Dfa NA (Verma et al. 2005b)
US-Ne3 -96.4397 41.1797 2001-2013 CRO Dfa NA (Verma et al. 2005c)
US-NR1 -105.5464 40.0329 1998-2014 ENF Dfc 4205 (Monson et al. 2002)
US-ORv -83.0183 40.0201 2011-2011 WET Dfa NA (Morin et al. 2014)
US-PFa -90.2723 45.9459 1995-2014 MF Dfb 4593 (Desai et al. 2015)
US-Prr -147.4876 65.1237 2010-2013 ENF Dfc 515 (Nakai et al. 2013)
US-SRG -110.8277 31.7894 2008-2014 GRA BSk 2099 (R. L. Scott et al. 2015a)
US-SRM -110.8661 31.8214 2004-2014 WSA BSk 3075 (Scott et al. 2009)
US-Syv -89.3477 46.2420 2001-2014 MF Dfb 1977 (Desai et al. 2005)
US-Ton -120.9660 38.4316 2001-2014 WSA Cwb 4235 (Baldocchi et al. 2010)
US-Tw1 -121.6469 38.1074 2012-2014 WET Cwb 554 (Oikawa et al. 2017)
US-Tw2 -121.6433 38.1047 2012-2013 CRO Cwb 285 (Knox et al. 2016)
US-Tw3 -121.6467 38.1159 2013-2014 CRO Cwb 419 (Baldocchi, Sturtevant, and Contributors 2015)
US-Tw4 -121.6414 38.1030 2013-2014 WET Cwb 321 (Baldocchi 2016)
US-Twt -121.6530 38.1087 2009-2014 CRO Cwb 1421 (Hatala et al. 2012)
US-UMB -84.7138 45.5598 2000-2014 DBF Dfb 3970 (Gough et al. 2013a)
US-UMd -84.6975 45.5625 2007-2014 DBF Dfb 2034 (Gough et al. 2013b)
US-Var -120.9507 38.4133 2000-2014 GRA Cwb 2955 (Ma et al. 2007)
US-WCr -90.0799 45.8059 1999-2014 DBF Dfb 2485 (Cook et al. 2004)
US-Whs -110.0522 31.7438 2007-2014 OSH BSk 1561 (R. L. Scott et al. 2015b)
US-Wi0 -91.0814 46.6188 2002-2002 ENF Dfb 175 (Noormets, Chen, and Crow 2007a)
US-Wi3 -91.0987 46.6347 2002-2004 DBF Dfb 353 (Noormets, Chen, and Crow 2007b)
US-Wi4 -91.1663 46.7393 2002-2005 ENF Dfb 568 (Noormets, Chen, and Crow 2007c)
US-Wi6 -91.2982 46.6249 2002-2003 OSH Dfb 175 (Noormets, Chen, and Crow 2007d)
US-Wi9 -91.0814 46.6188 2004-2005 ENF Dfb 291 (Noormets, Chen, and Crow 2007e)
US-Wkg -109.9419 31.7365 2004-2014 GRA BSk 2671 (Scott et al. 2010)
ZA-Kru 31.4969 -25.0197 2000-2010 SAV BSh 2001 (Archibald et al. 2009)
ZM-Mon 23.2528 -15.4378 2000-2009 DBF Aw 625 (Merbold, Ardö, et al. 2009)

Acosta, Manuel, Marian Pavelka, Leonardo Montagnani, Werner Kutsch, Anders Lindroth, Radosław Juszczak, and Dalibor Janouš. 2013. “Soil Surface CO2 Efflux Measurements in Norway Spruce Forests: Comparison Between Four Different Sites Across Europe from Boreal to Alpine Forest.” Geoderma 192 (January). Elsevier BV: 295–303. doi:10.1016/j.geoderma.2012.08.027.

Ammann, Christof, Christoph Spirig, Jens Leifeld, and Albrecht Neftel. 2009. “Assessment of the Nitrogen and Carbon Budget of Two Managed Temperate Grassland Fields.” Agriculture, Ecosystems & Environment 133 (3-4). Elsevier BV: 150–62. doi:10.1016/j.agee.2009.05.006.

Anthoni, P. M., A. Knohl, C. Rebmann, A. Freibauer, M. Mund, W. Ziegler, O. Kolle, and E.-D. Schulze. 2004. “Forest and Agricultural Land-Use-Dependent CO2 Exchange in Thuringia, Germany.” Global Change Biology 10 (12). Wiley-Blackwell: 2005–19. doi:10.1111/j.1365-2486.2004.00863.x.

Archibald, S. A., A. Kirton, M. R. van der Merwe, R. J. Scholes, C. A. Williams, and N. Hanan. 2009. “Drivers of Inter-Annual Variability in Net Ecosystem Exchange in a Semi-Arid Savanna Ecosystem, South Africa.” Biogeosciences 6 (2). Copernicus GmbH: 251–66. doi:10.5194/bg-6-251-2009.

Ardo, Jonas, Meelis Molder, Bashir A El-Tahir, and Hatim A M Elkhidir. 2008. “Seasonal Variation of Carbon Fluxes in a Sparse Savanna in Semi Arid Sudan.” Carbon Balance and Management 3 (1). Springer Nature: 7. doi:10.1186/1750-0680-3-7.

Aubinet, M, B Chermanne, M Vandenhaute, B Longdoz, M Yernaux, and E Laitat. 2001. “Long Term Carbon Dioxide Exchange Above a Mixed Forest in the Belgian Ardennes.” Agricultural and Forest Meteorology 108 (4). Elsevier BV: 293–315. doi:10.1016/s0168-1923(01)00244-1.

Aurela, Mika, Annalea Lohila, Juha Pekka Tuovinen, Juha Hatakka, Timo Penttilä, and Tuomas Laurila. 2015. “Carbon dioxide and energy flux measurements in four northern-boreal ecosystems at Pallas.” Boreal Environment Research 20 (4): 455–73.

Baldocchi, Dennis. 2016. “AmeriFlux US-Tw4 Twitchell East End Wetland from 2013-Present.” doi:10.17190/AMF/1246151.

Baldocchi, Dennis, Qi Chen, Xingyuan Chen, Siyan Ma, Gretchen Miller, Youngryel Ryu, Jingfeng Xiao, Rebecca Wenk, and John Battles. 2010. “The Dynamics of Energy, Water, and Carbon Fluxes in a Blue Oak (Quercus Douglasii) Savanna in California.” In Ecosystem Function in Savannas, 135–51. CRC Press. doi:10.1201/b10275-10.

Baldocchi, Dennis, Cove Sturtevant, and Fluxnet Contributors. 2015. “Does Day and Night Sampling Reduce Spurious Correlation Between Canopy Photosynthesis and Ecosystem Respiration?” Agricultural and Forest Meteorology 207 (July). Elsevier BV: 117–26. doi:10.1016/j.agrformet.2015.03.010.

Berbigier, Paul, Jean-Marc Bonnefond, and Patricia Mellmann. 2001. “CO2 and Water Vapour Fluxes for 2 Years Above Euroflux Forest Site.” Agricultural and Forest Meteorology 108 (3). Elsevier BV: 183–97. doi:10.1016/s0168-1923(01)00240-4.

Bergeron, Onil, Hank A. Margolis, T. Andrew Black, Carole Coursolle, Allison L. Dunn, Alan G. Barr, and Steven C. Wofsy. 2007. “Comparison of Carbon Dioxide Fluxes over Three Boreal Black Spruce Forests in Canada.” Global Change Biology 13 (1). Wiley-Blackwell: 89–107. doi:10.1111/j.1365-2486.2006.01281.x.

Beringer, Jason, Jorg Hacker, Lindsay B. Hutley, Ray Leuning, Stefan K. Arndt, Reza Amiri, Lutz Bannehr, et al. 2011. “SPECIALSavanna Patterns of Energy and Carbon Integrated Across the Landscape.” Bulletin of the American Meteorological Society 92 (11). American Meteorological Society: 1467–85. doi:10.1175/2011bams2948.1.

Beringer, Jason, Lindsay B. Hutley, Jorg M. Hacker, Bruno Neininger, and Kyaw Tha Paw U. 2011a. “Patterns and Processes of Carbon, Water and Energy Cycles Across Northern Australian Landscapes: From Point to Region.” Agricultural and Forest Meteorology 151 (11). Elsevier BV: 1409–16. doi:10.1016/j.agrformet.2011.05.003.

———. 2011b. “Patterns and Processes of Carbon, Water and Energy Cycles Across Northern Australian Landscapes: From Point to Region.” Agricultural and Forest Meteorology 151 (11). Elsevier BV: 1409–16. doi:10.1016/j.agrformet.2011.05.003.

Beringer, Jason, Lindsay B. Hutley, Ian McHugh, Stefan K. Arndt, David Campbell, Helen A. Cleugh, James Cleverly, et al. 2016. “An introduction to the Australian and New Zealand flux tower network - OzFlux.” Biogeosciences 13 (21). Copernicus GmbH: 5895–5916. doi:10.5194/bg-13-5895-2016.

———. 2016a. “An Introduction to the Australian and New Zealand Flux Tower Network OzFlux.” Biogeosciences 13 (21). Copernicus GmbH: 5895–5916. doi:10.5194/bg-13-5895-2016.

———. 2016b. “An Introduction to the Australian and New Zealand Flux Tower Network OzFlux.” Biogeosciences 13 (21). Copernicus GmbH: 5895–5916. doi:10.5194/bg-13-5895-2016.

———. 2016c. “An Introduction to the Australian and New Zealand Flux Tower Network OzFlux.” Biogeosciences 13 (21). Copernicus GmbH: 5895–5916. doi:10.5194/bg-13-5895-2016.

Beringer, Jason, Stephen J. Livesley, Jennifer Randle, and Lindsay B. Hutley. 2013. “Carbon Dioxide Fluxes Dominate the Greenhouse Gas Exchanges of a Seasonal Wetland in the Wetdry Tropics of Northern Australia.” Agricultural and Forest Meteorology 182-183 (December). Elsevier BV: 239–47. doi:10.1016/j.agrformet.2013.06.008.

Bonal, Damien, Alexandre Bosc, Stéphane Ponton, Jean-Yves Goret, Benoît Burban, Patrick Gross, Jean-Marc Bonnefond, et al. 2008. “Impact of Severe Dry Season on Net Ecosystem Exchange in the Neotropical Rainforest of French Guiana.” Global Change Biology 14 (8). Wiley-Blackwell: 1917–33. doi:10.1111/j.1365-2486.2008.01610.x.

Bowling, D. R., S. Bethers-Marchetti, C. K. Lunch, E. E. Grote, and J. Belnap. 2010. “Carbon, Water, and Energy Fluxes in a Semiarid Cold Desert Grassland During and Following Multiyear Drought.” Journal of Geophysical Research 115 (G4). Wiley-Blackwell. doi:10.1029/2010jg001322.

Bristow, M., L. B. Hutley, J. Beringer, S. J. Livesley, A. C. Edwards, and S. K. Arndt. 2016. “Quantifying the Relative Importance of Greenhouse Gas Emissions from Current and Future Savanna Land Use Change Across Northern Australia.” Biogeosciences Discussions, May. Copernicus GmbH, 1–47. doi:10.5194/bg-2016-191.

Carrara, Arnaud, Ivan A. Janssens, Jorge Curiel Yuste, and Reinhart Ceulemans. 2004. “Seasonal Changes in Photosynthesis, Respiration and NEE of a Mixed Temperate Forest.” Agricultural and Forest Meteorology 126 (1-2). Elsevier BV: 15–31. doi:10.1016/j.agrformet.2004.05.002.

Cernusak, Jason Beringer And Lindsay B. Hutley And Nigel J. Tapper And Lucas A. 2007. “Savanna Fires and Their Impact on Net Ecosystem Productivity in North Australia.” Global Change Biology 13 (5). Wiley-Blackwell: 990–1004. doi:10.1111/j.1365-2486.2007.01334.x.

Cernusak, Lucas A., Lindsay B. Hutley, Jason Beringer, Joseph A.M. Holtum, and Benjamin L. Turner. 2011. “Photosynthetic Physiology of Eucalypts Along a Sub-Continental Rainfall Gradient in Northern Australia.” Agricultural and Forest Meteorology 151 (11). Elsevier BV: 1462–70. doi:10.1016/j.agrformet.2011.01.006.

Chen, Shiping, Jiquan Chen, Guanghui Lin, Wenli Zhang, Haixia Miao, Long Wei, Jianhui Huang, and Xingguo Han. 2009. “Energy Balance and Partition in Inner Mongolia Steppe Ecosystems with Different Land Use Types.” Agricultural and Forest Meteorology 149 (11). Elsevier BV: 1800–1809. doi:10.1016/j.agrformet.2009.06.009.

Chiesi, M., F. Maselli, M. Bindi, L. Fibbi, P. Cherubini, E. Arlotta, G. Tirone, G. Matteucci, and G. Seufert. 2005. “Modelling Carbon Budget of Mediterranean Forests Using Ground and Remote Sensing Measurements.” Agricultural and Forest Meteorology 135 (1-4). Elsevier BV: 22–34. doi:10.1016/j.agrformet.2005.09.011.

Cleverly, James, Nicolas Boulain, Randol Villalobos-Vega, Nicole Grant, Ralph Faux, Cameron Wood, Peter G. Cook, Qiang Yu, Andrea Leigh, and Derek Eamus. 2013. “Dynamics of Component Carbon Fluxes in a Semi-Arid Acacia Woodland, Central Australia.” Journal of Geophysical Research: Biogeosciences 118 (3). Wiley-Blackwell: 1168–85. doi:10.1002/jgrg.20101.

Cleverly, James, Derek Eamus, Eva Van Gorsel, Chao Chen, Rizwana Rumman, Qunying Luo, Natalia Restrepo Coupe, et al. 2016. “Productivity and evapotranspiration of two contrasting semiarid ecosystems following the 2011 global carbon land sink anomaly.” Agricultural and Forest Meteorology 220 (April): 151–59. doi:10.1016/j.agrformet.2016.01.086.

Cook, Bruce D., Kenneth J. Davis, Weiguo Wang, Ankur Desai, Bradford W. Berger, Ron M. Teclaw, Jonathan G. Martin, et al. 2004. “Carbon Exchange and Venting Anomalies in an Upland Deciduous Forest in Northern Wisconsin, USA.” Agricultural and Forest Meteorology 126 (3-4). Elsevier BV: 271–95. doi:10.1016/j.agrformet.2004.06.008.

Delpierre, Nicolas, Daniel Berveiller, Elena Granda, and Eric Dufrêne. 2015. “Wood Phenology, Not Carbon Input, Controls the Interannual Variability of Wood Growth in a Temperate Oak Forest.” New Phytologist 210 (2). Wiley-Blackwell: 459–70. doi:10.1111/nph.13771.

Desai, Ankur R., Paul V. Bolstad, Bruce D. Cook, Kenneth J. Davis, and Eileen V. Carey. 2005. “Comparing Net Ecosystem Exchange of Carbon Dioxide Between an Old-Growth and Mature Forest in the Upper Midwest, USA.” Agricultural and Forest Meteorology 128 (1-2). Elsevier BV: 33–55. doi:10.1016/j.agrformet.2004.09.005.

Desai, Ankur R., Ke Xu, Hanqin Tian, Peter Weishampel, Jonathan Thom, Dan Baumann, Arlyn E. Andrews, Bruce D. Cook, Jennifer Y. King, and Randall Kolka. 2015. “Landscape-Level Terrestrial Methane Flux Observed from a Very Tall Tower.” Agricultural and Forest Meteorology 201 (February). Elsevier BV: 61–75. doi:10.1016/j.agrformet.2014.10.017.

Dietiker, Dominique, Nina Buchmann, and Werner Eugster. 2010. “Testing the Ability of the DNDC Model to Predict CO2 and Water Vapour Fluxes of a Swiss Cropland Site.” Agriculture, Ecosystems & Environment 139 (3). Elsevier BV: 396–401. doi:10.1016/j.agee.2010.09.002.

Dragoni, Danilo, Hans Peter Schmid, Craig A. Wayson, Henry Potter, C. Susan B. Grimmond, and James C. Randolph. 2011. “Evidence of Increased Net Ecosystem Productivity Associated with a Longer Vegetated Season in a Deciduous Forest in South-Central Indiana, USA.” Global Change Biology 17 (2). Wiley-Blackwell: 886–97. doi:10.1111/j.1365-2486.2010.02281.x.

Dunn, Allison L., Carol C. Barford, Steven C. Wofsy, Michael L. Goulden, and Bruce C. Daube. 2007. “A Long-Term Record of Carbon Exchange in a Boreal Black Spruce Forest: Means, Responses to Interannual Variability, and Decadal Trends.” Global Change Biology 13 (3). Wiley-Blackwell: 577–90. doi:10.1111/j.1365-2486.2006.01221.x.

Dušek, J., H. Čížková, S. Stellner, R. Czerný, and J. Květ. 2012. “Fluctuating Water Table Affects Gross Ecosystem Production and Gross Radiation Use Efficiency in a Sedge-Grass Marsh.” Hydrobiologia 692 (1). Springer Nature: 57–66. doi:10.1007/s10750-012-0998-z.

Etzold, Sophia, Nadine K. Ruehr, Roman Zweifel, Matthias Dobbertin, Andreas Zingg, Peter Pluess, Rudolf Häsler, Werner Eugster, and Nina Buchmann. 2011. “The Carbon Balance of Two Contrasting Mountain Forest Ecosystems in Switzerland: Similar Annual Trends, but Seasonal Differences.” Ecosystems 14 (8). Springer Nature: 1289–1309. doi:10.1007/s10021-011-9481-3.

Fares, S., F. Savi, J. Muller, G. Matteucci, and E. Paoletti. 2014. “Simultaneous Measurements of Above and Below Canopy Ozone Fluxes Help Partitioning Ozone Deposition Between Its Various Sinks in a Mediterranean Oak Forest.” Agricultural and Forest Meteorology 198-199 (November). Elsevier BV: 181–91. doi:10.1016/j.agrformet.2014.08.014.

Ferréa, Chiara, Terenzio Zenone, Roberto Comolli, and Günther Seufert. 2012. “Estimating Heterotrophic and Autotrophic Soil Respiration in a Semi-Natural Forest of Lombardy, Italy.” Pedobiologia 55 (6). Elsevier BV: 285–94. doi:10.1016/j.pedobi.2012.05.001.

Fischer, Marc L., David P. Billesbach, Joseph A. Berry, William J. Riley, and Margaret S. Torn. 2007. “Spatiotemporal Variations in Growing Season Exchanges of CO2, H2O, and Sensible Heat in Agricultural Fields of the Southern Great Plains.” Earth Interactions 11 (17). American Meteorological Society: 1–21. doi:10.1175/ei231.1.

Frank, John M., William J. Massman, Brent E. Ewers, Laurie S. Huckaby, and José F. Negrón. 2014. “Ecosystem CO2/H2O Fluxes Are Explained by Hydraulically Limited Gas Exchange During Tree Mortality from Spruce Bark Beetles.” Journal of Geophysical Research: Biogeosciences 119 (6). Wiley-Blackwell: 1195–1215. doi:10.1002/2013jg002597.

Galvagno, M, G Wohlfahrt, E Cremonese, M Rossini, R Colombo, G Filippa, T Julitta, et al. 2013. “Phenology and Carbon Dioxide Source/Sink Strength of a Subalpine Grassland in Response to an Exceptionally Short Snow Season.” Environmental Research Letters 8 (2). IOP Publishing: 025008. doi:10.1088/1748-9326/8/2/025008.

Garbulsky, Martín F., Josep Peñuelas, Dario Papale, and Iolanda Filella. 2008. “Remote Estimation of Carbon Dioxide Uptake by a Mediterranean Forest.” Global Change Biology 14 (12). Wiley-Blackwell: 2860–7. doi:10.1111/j.1365-2486.2008.01684.x.

Goldstein, A.H., N.E. Hultman, J.M. Fracheboud, M.R. Bauer, J.A. Panek, M. Xu, Y. Qi, A.B. Guenther, and W. Baugh. 2000. “Effects of Climate Variability on the Carbon Dioxide, Water, and Sensible Heat Fluxes Above a Ponderosa Pine Plantation in the Sierra Nevada (CA).” Agricultural and Forest Meteorology 101 (2-3). Elsevier BV: 113–29. doi:10.1016/s0168-1923(99)00168-9.

Gough, Christopher M., Brady S. Hardiman, Lucas E. Nave, Gil Bohrer, Kyle D. Maurer, Christoph S. Vogel, Knute J. Nadelhoffer, and Peter S. Curtis. 2013a. “Sustained Carbon Uptake and Storage Following Moderate Disturbance in a Great Lakes Forest.” Ecological Applications 23 (5). Wiley-Blackwell: 1202–15. doi:10.1890/12-1554.1.

———. 2013b. “Sustained Carbon Uptake and Storage Following Moderate Disturbance in a Great Lakes Forest.” Ecological Applications 23 (5). Wiley-Blackwell: 1202–15. doi:10.1890/12-1554.1.

Goulden, Michael L., Gregory C. Winston, Andrew M. S. McMillan, Marcy E. Litvak, Edward L. Read, Adrian V. Rocha, and J. Rob Elliot. 2006a. “An Eddy Covariance Mesonet to Measure the Effect of Forest Age on Land?atmosphere Exchange.” Global Change Biology 12 (11). Wiley-Blackwell: 2146–62. doi:10.1111/j.1365-2486.2006.01251.x.

———. 2006b. “An Eddy Covariance Mesonet to Measure the Effect of Forest Age on Land?atmosphere Exchange.” Global Change Biology 12 (11). Wiley-Blackwell: 2146–62. doi:10.1111/j.1365-2486.2006.01251.x.

———. 2006c. “An Eddy Covariance Mesonet to Measure the Effect of Forest Age on Land?atmosphere Exchange.” Global Change Biology 12 (11). Wiley-Blackwell: 2146–62. doi:10.1111/j.1365-2486.2006.01251.x.

———. 2006d. “An Eddy Covariance Mesonet to Measure the Effect of Forest Age on Land?atmosphere Exchange.” Global Change Biology 12 (11). Wiley-Blackwell: 2146–62. doi:10.1111/j.1365-2486.2006.01251.x.

———. 2006e. “An Eddy Covariance Mesonet to Measure the Effect of Forest Age on Land?atmosphere Exchange.” Global Change Biology 12 (11). Wiley-Blackwell: 2146–62. doi:10.1111/j.1365-2486.2006.01251.x.

———. 2006f. “An Eddy Covariance Mesonet to Measure the Effect of Forest Age on Land?atmosphere Exchange.” Global Change Biology 12 (11). Wiley-Blackwell: 2146–62. doi:10.1111/j.1365-2486.2006.01251.x.

———. 2006g. “An Eddy Covariance Mesonet to Measure the Effect of Forest Age on Land?atmosphere Exchange.” Global Change Biology 12 (11). Wiley-Blackwell: 2146–62. doi:10.1111/j.1365-2486.2006.01251.x.

Grünwald, Thomas, and Christian Bernhofer. 2007. “A Decade of Carbon, Water and Energy Flux Measurements of an Old Spruce Forest at the Anchor Station Tharandt.” Tellus B 59 (3). Informa UK Limited. doi:10.3402/tellusb.v59i3.17000.

Guan, De-Xin, Jia-Bing Wu, Xiao-Song Zhao, Shi-Jie Han, Gui-Rui Yu, Xiao-Min Sun, and Chang-Jie Jin. 2006. “CO2 Fluxes over an Old, Temperate Mixed Forest in Northeastern China.” Agricultural and Forest Meteorology 137 (3-4). Elsevier BV: 138–49. doi:10.1016/j.agrformet.2006.02.003.

Hatala, Jaclyn A., Matteo Detto, Oliver Sonnentag, Steven J. Deverel, Joseph Verfaillie, and Dennis D. Baldocchi. 2012. “Greenhouse Gas (CO2, CH4, H2O) Fluxes from Drained and Flooded Agricultural Peatlands in the Sacramento-San Joaquin Delta.” Agriculture, Ecosystems & Environment 150 (March). Elsevier BV: 1–18. doi:10.1016/j.agee.2012.01.009.

Hinko-Najera, Nina, Peter Isaac, Jason Beringer, Eva van Gorsel, Cacilia Ewenz, Ian McHugh, Jean-François Exbrayat, Stephen J. Livesley, and Stefan K. Arndt. 2017. “Net ecosystem carbon exchange of a dry temperate eucalypt forest.” Biogeosciences 14 (16): 3781–3800. doi:10.5194/bg-14-3781-2017.

Hommeltenberg, J., H. P. Schmid, M. Drösler, and P. Werle. 2014. “Can a Bog Drained for Forestry Be a Stronger Carbon Sink Than a Natural Bog Forest?” Biogeosciences 11 (13). Copernicus GmbH: 3477–93. doi:10.5194/bg-11-3477-2014.

Hoshika, Yasutomo, Silvano Fares, Flavia Savi, Carsten Gruening, Ignacio Goded, Alessandra De Marco, Pierre Sicard, and Elena Paoletti. 2017. “Stomatal conductance models for ozone risk assessment at canopy level in two Mediterranean evergreen forests.” Agricultural and Forest Meteorology 234-235: 212–21. doi:10.1016/j.agrformet.2017.01.005.

Hutley, Lindsay B., Jason Beringer, Peter R. Isaac, Jorg M. Hacker, and Lucas A. Cernusak. 2011. “A Sub-Continental Scale Living Laboratory: Spatial Patterns of Savanna Vegetation over a Rainfall Gradient in Northern Australia.” Agricultural and Forest Meteorology 151 (11). Elsevier BV: 1417–28. doi:10.1016/j.agrformet.2011.03.002.

Imer, D., L. Merbold, W. Eugster, and N. Buchmann. 2013. “Temporal and Spatial Variations of Soil CO2, CH4 and N2O Fluxes at Three Differently Managed Grasslands.” Biogeosciences 10 (9). Copernicus GmbH: 5931–45. doi:10.5194/bg-10-5931-2013.

Irvine, J., B. E. Law, and K. A. Hibbard. 2007. “Postfire Carbon Pools and Fluxes in Semiarid Ponderosa Pine in Central Oregon.” Global Change Biology 13 (8). Wiley-Blackwell: 1748–60. doi:10.1111/j.1365-2486.2007.01368.x.

Irvine, J., B. E. Law, J. G. Martin, and D. Vickers. 2008. “Interannual Variation in Soil CO2 Efflux and the Response of Root Respiration to Climate and Canopy Gas Exchange in Mature Ponderosa Pine.” Global Change Biology 14 (12). Wiley-Blackwell: 2848–59. doi:10.1111/j.1365-2486.2008.01682.x.

Jacobs, C. M. J., A. F. G. Jacobs, F. C. Bosveld, D. M. D. Hendriks, A. Hensen, P. S. Kroon, E. J. Moors, L. Nol, A. Schrier-Uijl, and E. M. Veenendaal. 2007. “Variability of Annual CO2 Exchange from Dutch Grasslands.” Biogeosciences 4 (5). Copernicus GmbH: 803–16. doi:10.5194/bg-4-803-2007.

Kato, Tomomichi, Yanhong Tang, Song Gu, Mitsuru Hirota, Mingyuan Du, Yingnian Li, and Xinquan Zhao. 2006. “Temperature and Biomass Influences on Interannual Changes in CO2 Exchange in an Alpine Meadow on the Qinghai-Tibetan Plateau.” Global Change Biology 12 (7). Wiley-Blackwell: 1285–98. doi:10.1111/j.1365-2486.2006.01153.x.

Kilinc, Musa, Jason Beringer, Lindsay B. Hutley, Nigel J. Tapper, and David A. McGuire. 2013. “Carbon and Water Exchange of the Worlds Tallest Angiosperm Forest.” Agricultural and Forest Meteorology 182-183 (December). Elsevier BV: 215–24. doi:10.1016/j.agrformet.2013.07.003.

Knohl, Alexander, Ernst-Detlef Schulze, Olaf Kolle, and Nina Buchmann. 2003. “Large Carbon Uptake by an Unmanaged 250-Year-Old Deciduous Forest in Central Germany.” Agricultural and Forest Meteorology 118 (3-4). Elsevier BV: 151–67. doi:10.1016/s0168-1923(03)00115-1.

Knox, Sara Helen, Jaclyn Hatala Matthes, Cove Sturtevant, Patricia Y. Oikawa, Joseph Verfaillie, and Dennis Baldocchi. 2016. “Biophysical Controls on Interannual Variability in Ecosystem-Scale CO2and CH4exchange in a California Rice Paddy.” Journal of Geophysical Research: Biogeosciences 121 (3). Wiley-Blackwell: 978–1001. doi:10.1002/2015jg003247.

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