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**Biogeosciences**
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**Research article**
06 Aug 2020

**Research article** | 06 Aug 2020

Ecosystem physio-phenology revealed using circular statistics

^{1}Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, 07745 Jena, Germany^{2}German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, 04103 Leipzig, Germany^{3}Friedrich Schiller University, Institute of Ecology and Evolution, Philosophenweg 16, 07743 Jena, Germany^{4}Remote Sensing Center For Earth System Research, Leipzig University, 04103 Leipzig, Germany

^{1}Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, 07745 Jena, Germany^{2}German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, 04103 Leipzig, Germany^{3}Friedrich Schiller University, Institute of Ecology and Evolution, Philosophenweg 16, 07743 Jena, Germany^{4}Remote Sensing Center For Earth System Research, Leipzig University, 04103 Leipzig, Germany

**Correspondence**: Daniel E. Pabon-Moreno (dpabon@bgc-jena.mpg.de)

**Correspondence**: Daniel E. Pabon-Moreno (dpabon@bgc-jena.mpg.de)

Abstract

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Quantifying how vegetation phenology responds to climate variability is a key prerequisite to
predicting how ecosystem dynamics will shift with climate change. So far, many studies have focused
on responses of classical phenological events (e.g., budburst or flowering) to climatic variability
for individual species. Comparatively little is known on the dynamics of physio-phenological
events such as the timing of maximum gross primary production (DOY_{GPPmax}),
i.e., quantities that are relevant for understanding terrestrial carbon cycle responses to climate
variability and change. In this study, we aim to understand how DOY_{GPPmax}
depends on climate drivers across 52 eddy covariance (EC) sites in the FLUXNET network for
different regions of the world. Most phenological studies rely on linear methods that cannot be
generalized across both hemispheres and therefore do not allow for deriving general rules that can
be applied for future predictions. One solution could be a new class of circular–linear (here
called circular) regression approaches. Circular regression allows circular variables (in
our case phenological events) to be related to linear predictor variables as climate conditions. As a proof of
concept, we compare the performance of linear and circular regression to recover original
coefficients of a predefined circular model for artificial data. We then quantify the sensitivity
of DOY_{GPPmax} across FLUXNET sites to air temperature, shortwave incoming
radiation, precipitation, and vapor pressure deficit. Finally, we evaluate the predictive power of
the circular regression model for different vegetation types. Our results show that the joint
effects of radiation, temperature, and vapor pressure deficit are the most relevant controlling
factor of DOY_{GPPmax} across sites. Woody savannas are an exception, where the
most important factor is precipitation. Although the sensitivity of the
DOY_{GPPmax} to climate drivers is site-specific, it is possible to generalize
the circular regression models across specific vegetation types. From a methodological point of
view, our results reveal that circular regression is a robust alternative to conventional
phenological analytic frameworks. The analysis of phenological events at the global scale can benefit
from the use of circular statistics. Such an approach yields substantially more robust results
for analyzing phenological dynamics in regions characterized by two growing seasons per year or
when the phenological event under scrutiny occurs between 2 years
(i.e., DOY_{GPPmax} in the Southern Hemisphere).

How to cite

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How to cite.

Pabon-Moreno, D. E., Musavi, T., Migliavacca, M., Reichstein, M., Römermann, C., and Mahecha, M. D.: Ecosystem physio-phenology revealed using circular statistics, Biogeosciences, 17, 3991–4006, https://doi.org/10.5194/bg-17-3991-2020, 2020.

1 Introduction

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Phenology is the study of the timing of biological events that can be observed at either the
organismic level or the ecosystem scale (Lieth, 1974). For the latter, phenology
is the study of some integral behavior across phenological states of the integrated canopy
reflectance captured by remote sensing (Richardson et al., 2009; Zhang et al., 2003)
or vegetation-driven ecosystem–atmosphere CO_{2} exchange fluxes
(Richardson et al., 2010). Ecosystem-scale physio-phenological processes of this kind are
relevant quantities in global biogeochemical cycles and integrate both the seasonal dynamics of
biophysical states (e.g., reflected in the canopy development) and the observed photosynthesis at
the stand level (i.e., gross primary production). Here we are particularly interested in the timing
when ecosystems reach their maximum CO_{2} uptake within a growing
season. Ecosystem physio-phenology is influenced by climate conditions but simultaneously contributes
to the regulation of different micro- and macrometeorological patterns. Physio-phenological cycles
determine the temporal dynamics of land–atmosphere water and energy exchange fluxes. Likewise, the
terrestrial carbon cycle is affected by phenological controls on CO_{2} uptake and release
(Peñuelas et al., 2009).

The eddy covariance (EC) technique allows us to continuously measure the exchange of energy and
matter between ecosystems and the atmosphere (Aubinet et al., 2012). The FLUXNET network collects EC
data for most ecosystems of the world along with other meteorological variables, i.e., radiation,
temperature, precipitation, atmospheric humidity, and often soil moisture
(Baldocchi et al., 2001; Baldocchi, 2020). Particularly relevant to phenological studies
is the seasonal trajectory of gross primary production (GPP), which allows us to derive phenological
transition dates such as start and end of the growing season (e.g., Luo et al., 2018) as
well as the timing of the maximum gross primary production, hereafter as referred to as
DOY_{GPPmax} (Zhou et al., 2016; Peichl et al., 2018; Wang and Wu, 2019).

In this study we focus on understanding how climate variability affects the time when ecosystems
reach their maximum potential for CO_{2} absorption. In order to reach this “optimum state”,
several preconditions must be met during the preceding part of the growing season. So far several
studies have focused on studying the variability of maximum GPP during the growing season
(GPPmax). For instance, Zhou et al. (2017) studied how the variability of annual GPP is
influenced by GPPmax and the start and the end of the growing season. The authors found that GPPmax
is a better explanatory parameter for the interannual variability of annual GPP than the start and
end days of the growing season. Bauerle et al. (2012) studied how photoperiod and
temperature influence plants' photosynthetic capacity for 23 tree species in temperate deciduous
hardwoods, reporting that the photoperiod explains the variability of photosynthetic capacity better
than temperature. So far, to the best of our knowledge, only one study has focused on understanding
the temporal variability of GPPmax; Wang and Wu (2019) used a combination of satellite
remote-sensing and eddy covariance data to explore how DOY_{GPPmax} is controlled
by climatic conditions. The authors reported that higher temperatures advance
DOY_{GPPmax}, while the influence of precipitation and radiation were
biome-dependent. This study had a geographical focus on China; a global approach considering several
ecosystems across the whole latitudinal gradient is still lacking.

The challenge of understanding phenology is generally to characterize a discrete event that repeats periodically. Classically, phenological analyses have been performed using linear regression models (Morente-López et al., 2018; Zhou et al., 2016). Most of these studies analyze ecosystems characterized by one growing season (e.g., temperate or boreal forests) and when the summer is centered around the middle of the calendar year. The existing methods are, however, not sufficiently generic to describe (i) ecosystems in the Southern Hemisphere and (ii) ecosystems with multiple growing seasons per year as is often observed in, for example, semiarid regions.

Figure 1 illustrates the problem of northern vs. southern hemispheric summers from a conceptual point of view, assuming that some discrete event recurs annually, but the timing varies according to some external drivers. We would then need to find a predictive model explaining the interannual variability of phenology, i.e., the probability of this recurrent event in the course of the annual cycle. Figure 1 shows that linear regression models would be inappropriate to predict the day of the year (DOY) of some phenological event in the Southern Hemisphere as the actual target values to predict may alternate between $\mathit{\u2a86}\frac{\mathrm{3}\mathit{\pi}}{\mathrm{2}}$ and $\mathit{\u2a85}\frac{\mathit{\pi}}{\mathrm{2}}$.

In recent years, circular statistics have gained some attention as they offer a solution to problems
of this kind (Morellato et al., 2010; Beyene et al., 2018). Unlike classical
statistics, the predicted variables are expressed in terms of angular directions (degrees or
radians) across a circumference (Fisher, 1995), allowing us to perform statistical
analysis where the data space is not Euclidean. In this framework, point events can be described as
a von Mises distribution (Von Mises, 1918), the equivalent to the normal distribution in
circular statistics. The von Mises distribution is described by two parameters: the mean angular
direction (*μ*) and the concentration parameter (*κ*). Circular–linear regressions (in the
following simply named circular regression) allow us to predict circular responses (e.g., the timing of
phenological events) from other linear variables (Morellato et al., 2010). Given that any
phenological event can be interpreted as an angular direction and should be modeled as such, we
assume that these circular regressions are well suited in this context. Despite this evident
suitability, circular statistics have not yet been extensively applied in the study of phenology and
will therefore be presented here as an alternative to conventional linear techniques.

In this paper, we aim to identify the factors controlling the timing of the maximal seasonal GPP
(DOY_{GPPmax}). The questions we want to answer are as follows: first, can circular
statistics describe and predict DOY_{GPPmax} per vegetation type? This aspect
requires testing the methodological advantages and caveats of circular statistics across hemispheres
in comparison with linear methods. Second, can DOY_{GPPmax} be explained using
cumulative climate conditions? This question needs to consider different possibilities for
generating temporally integrating features. And third, how is DOY_{GPPmax}
affected by the climatic conditions during the growing season? The last question requires a global
cross-site analysis. Based on the findings of these three questions, we then discuss the potential of
circular regressions beyond this specific application case in related phenological problems and
outline future applications.

2 Methods

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We use 52 EC sites (with at least 7 years of data) located throughout the latitudinal gradient of
the globe from the FLUXNET2015 database (Table A1;
http://fluxnet.fluxdata.org/, last access: 11 July 2019 Pastorello et al., 2020). Each FLUXNET site is identified with an
abbreviation of the country and the name of the place, e.g., the EC tower AU-How, means that it is
located in Howard Springs, Australia. From the dataset we use the GPP data that were derived using
the nighttime partitioning method and considering the threshold of the variable *u*^{∗} to discriminate
values of insufficient turbulence (Reichstein et al., 2005). In order to identify maximum
daily GPP, we compute the quantile 0.9 for each day based on the half-hourly flux observations. As
potential explanatory variables for DOY_{GPPmax} we use the daily air temperature
(Tair), shortwave incoming radiation (SWin), precipitation (Precip), and vapor pressure deficit
(VPD).

Given that the past climate conditions affect the CO_{2} exchange between the atmosphere and the ecosystems
(ecological memory; Liu et al., 2019; Ryan et al., 2015), we assume that an
aggregated form of these climatic variables needs to be considered in the prediction of the
phenological responses. We aggregate the original time series of the Tair, SWin, Precip, and VPD
for each DOY_{GPPmax} using a half-life decay function (Eq. 1),

$$\begin{array}{}\text{(1)}& \u2329{\mathbf{x}}_{t}\u232a={\displaystyle \frac{{\sum}_{i=\mathrm{0}}^{\mathit{\tau}}{x}_{t-i}{w}_{i}}{{\sum}_{i=\mathrm{0}}^{\mathit{\tau}}{w}_{i}}},\end{array}$$

for estimating an exponentially weighted mean of the observation vector
${\mathbf{x}}_{t}=({x}_{t},{x}_{t-\mathrm{1}},\mathrm{\dots},{x}_{t-\mathit{\tau}}{)}^{T}$ at time step *t*. The symbol
〈…〉 denotes the weighted average; *i* indicates the number of days before *t*,
going back to *τ*=365 days. The weight decay is represented by

$$\begin{array}{}\text{(2)}& {w}_{i}={w}_{\mathrm{0}}\mathrm{exp}\left(-i{\displaystyle \frac{\mathrm{ln}\left(\mathrm{2}\right)}{{t}_{\mathrm{1}/\mathrm{2}}}}\right).\end{array}$$

The decay function gives the instantaneous value a weight of 1 (*w*_{0}=1), and all preceding values
receive an exponentially reduced weight as determined by the half-time parameter *t*_{1∕2}. Finally,
we make these variables comparable via centering standardization to unit variance. We perform
a sensitivity analysis, evaluating the effect of the half-time parameter, and identify the optimum as
the value when the variance explained by the circular regression model is at a maximum. The results are
presented in Supplement 1.

Due to the high colinearity between the exponential weighted variables of Tair, SWin, and VPD, we perform a principal component analysis (PCA) on the matrix of variables and FLUXNET sites and retain the leading principal component of these variables as well as precipitation as input for the circular statistics model (Hastie et al., 2009). The results of the PCA are presented in Supplement 2.

Since units of the circular response variable must be in radians or degrees, we transform the days of the year to radians using Eq. (3). For leap years we remove the last day.

$$\begin{array}{}\text{(3)}& \mathrm{rad}=\mathrm{DOY}{\displaystyle \frac{\mathrm{360}}{\mathrm{365}}}{\displaystyle \frac{\mathit{\pi}}{\mathrm{180}}},\end{array}$$

where DOY means day of the year.

A basic circular regression model was proposed by Fisher and Lee (1992) as follows:

$$\begin{array}{}\text{(4)}& y=\mathit{\mu}+\mathrm{2}\phantom{\rule{0.125em}{0ex}}\mathrm{atan}\phantom{\rule{0.125em}{0ex}}\left({\mathit{\beta}}_{i}{x}_{i}\right),\end{array}$$

where *y* is the target variable (i.e., DOY_{GPPmax}) in radians, *μ* is the mean
angular direction of the target variable, *x*_{i} represents the values for the variable *i*, and
*β*_{i} is the regression coefficient. The parameters *μ* and *β* are fitted via the
maximum likelihood method using the reweighted least squares algorithm as proposed by
Green (1984).

Relevant interpretations of fitted circular regression models are (1) the sign of the
*β* coefficients, (2) the statistical significance of the coefficients, and (3) the accuracy of
the prediction. Regarding the first point, a negative sign of the coefficient would mean that an
increasing value of the predictor would lead to an earlier DOY_{GPPmax} compared
to the mean angular direction. The inverse would happen when the coefficient is
positive. Figure 2 conceptually illustrates how the
coefficients affect the predictions. Regarding the second aspect, we can state that if a coefficient
is not significant, then its contribution would not be relevant to explaining the phenological
observation. In our case we define the coefficient to be significant if the median of the
distribution of *p* values is less than 0.05. Finally, we can estimate the accuracy of the
prediction using the Jammalamadaka–Sarma (JS) correlation coefficient
(Jammalamadaka and Sarma, 1988). As in any other regression framework, this approach helps us
to quantify the effect of each climate variable on the interannual variability of
DOY_{GPPmax}.

To estimate the relative sensitivity of DOY_{GPPmax} to the leading principal
component representing Tair, SWin, and VPD as well as to Precip, we use the implementation of
Eq. (4) in the R package “circular” (Agostinelli and Lund, 2017). To
increase the robustness of the method we implemented a block bootstrapping per growing season,
generating a model parameter average based on 1000 iterations. In each analysis, we estimate the
accuracy of the model using the JS correlation coefficient.

To assess the performance of linear versus circular regressions we perform an experiment with
simulated data in which we evaluate the accuracy and precision of both approaches to recover original
regression coefficients in a circular setting (Eq. 4). We add noise
generated with a random von Mises distribution with the parameters *n*=100 and *κ*=30 to the
model to ensure that the result follows a normal distribution. We predefined a range of values for
two regression coefficients (${\mathit{\beta}}_{\mathrm{1}}=\langle \mathrm{0.01},\mathrm{\dots},\mathrm{3}\rangle $,
${\mathit{\beta}}_{\mathrm{2}}=\langle \mathrm{0.01},\mathrm{\dots},\mathrm{3}\rangle $). We simulate the variables *x*_{1} and *x*_{2} as
normal distributions with *n*=100; a mean of 10 and 15, respectively; and standard deviations (SDs) of 1
and 2. We evaluate all possible combinations for the regression coefficients 100 times, simulating
different *x*_{1} and *x*_{2}. In each iteration we generate *y* using the setup previously
described, and we recover the original regression coefficients using *y* as a response variable and
*x*_{1} and *x*_{2} as predictors. Finally, we analyze two scenarios: (1) when the target timing
occurs at the beginning of the year (*μ*=0) and (2) when the target timing happens midyear
(*μ*=*π*). The parameters for the entire setup generate realistic data, where the standard
deviation of *y* is not higher than 0.3 rad. An SD of 0.5 rad would be
equivalent to having phenological observations across half a year, which would not be realistic.

To quantify the accuracy of each model per coefficient we estimate the mean absolute error per model and coefficient (Eq. 5). To compare the accuracy between models by coefficient we test the mean absolute errors between models (Eq. 6). To generate a single measure that allows us to compare both coefficients and models we estimate the mean difference accuracy (Eq. 7). The results can be understood as follows: if the difference is higher than 0, the circular model has a higher mean accuracy compared to the linear model and vice versa. To quantify which model has higher precision we estimate the difference between the SD of the mean absolute errors per model for each coefficient (Eq. 8). Finally, we estimate the mean differences of precision between the regression coefficients (Eq. 9), where again if the value is higher than 0, the circular model has a higher mean accuracy than the linear model; the inverse is true if the value is lower than 0.

We estimate regression coefficients for the bootstrap sample $i\in \mathit{\{}\mathrm{1},\mathrm{\dots},m\mathit{\}}$ (*m*=100) for
the regression coefficient *β*_{j}, $j\in \mathit{\{}\mathrm{1},\mathrm{2}\mathit{\}}$, and the model $M\in \mathit{\{}l,c\mathit{\}}$ (denoted as
${\widehat{\mathit{\beta}}}_{j,i}^{M}$). The model accuracy can then be estimated as the mean absolute error of the
estimated regression parameter ${\widehat{\mathit{\beta}}}_{j}^{M}$, $j\in \mathit{\{}\mathrm{1},\mathrm{2}\mathit{\}}$ for the linear model *M*=*l* and
the circular model *M*=*c*:

$$\begin{array}{}\text{(5)}& {\displaystyle}{a}_{M,j}{\displaystyle}={\displaystyle \frac{\mathrm{1}}{m}}\sum _{i=\mathrm{1}}^{m}|{\widehat{\mathit{\beta}}}_{j,i}^{M}-{\mathit{\beta}}_{j}|.\end{array}$$

The difference in accuracy for the coefficient *j* between the circular and the linear model is shown in

$$\begin{array}{}\text{(6)}& {\displaystyle}{\mathit{\delta}}_{a,j}{\displaystyle}={a}_{l,j}-{a}_{c,j}.\end{array}$$

Finally, the mean difference in accuracy between the linear and the circular model is given by

$$\begin{array}{}\text{(7)}& {\displaystyle}{\mathit{\delta}}_{a}{\displaystyle}={\displaystyle \frac{{\mathit{\delta}}_{a,\mathrm{1}}+{\mathit{\delta}}_{a,\mathrm{2}}}{\mathrm{2}}}.\end{array}$$

The difference in precision for the coefficient *j* between the linear (*l*) and the circular model (*c*) is shown in

$$\begin{array}{}\text{(8)}& {\displaystyle}{\mathit{\delta}}_{p,j}{\displaystyle}={\mathrm{s}}_{l,j}-{\mathrm{s}}_{c,j}.\end{array}$$

The mean difference in precision between the linear and the circular model is given by

$$\begin{array}{}\text{(9)}& {\displaystyle}{\mathit{\delta}}_{p}{\displaystyle}={\displaystyle \frac{{\mathit{\delta}}_{p,\mathrm{1}}+{\mathit{\delta}}_{p,\mathrm{2}}}{\mathrm{2}}},\end{array}$$

where s_{M,j} is the sample SD of the vector $({\widehat{\mathit{\beta}}}_{j,i}^{M}{)}_{i}$, $M\in \mathit{\{}l,c\mathit{\}}$.

The target variable DOY_{GPPmax} is the day of the year when GPP reaches its
maximum during the growing season. Given that different ecosystems present more than one growing
season per year (e.g., semiarid ecosystems), it is necessary to identify the number of growing
seasons per year. To identify the number of growing seasons we apply a fast Fourier transformation
(FFT; Cooley and Tukey, 1965) to the mean seasonal cycle of the GPP time series. The number of
growing seasons is equal to the maximum absolute value of the first four FFT coefficients (excluding
the first one). For each FLUXNET site, we reconstruct the GPP time series, taking the real numbers of
the inverse FFT. We use these reconstructed time series to calculate the expected mean timing of
DOY_{GPPmax} and use this value as a template. To recover the real
DOY_{GPPmax} from the original time series, we define a window around the template
of length inversely proportional to the number of cycles (180 d∕number of growing seasons). To
increase the robustness of the analysis we identify the days with the 10 highest GPP values. These
days are used in the block bootstrapping mentioned above. Finally, since most of the sites are
located in the Northern Hemisphere we expect that, in most cases, DOY_{GPPmax} will
be reached by the middle of the calendar year.

To quantify the contribution of each climate variable, we count the number of sites per vegetation type where the regression coefficient is statistically significant. We perform a leave-one-out cross-validation per vegetation type to evaluate the predictive power of the circular regression using climate conditions. We only consider vegetation types with more than five sites. In this case the standardization of the climate variables is not applied. Finally, we use the mean of the optimum half-time parameter per vegetation type to weigh the climate conditions.

3 Results

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Here, we first report results from simulated data to describe the performance of the circular regression approach compared to a linear model. Second, we compare the performance of circular and linear regression using empirical data. Third, we analyze the sensitivity of DOY_{GPPmax} across vegetation types and climate classes. Finally, we show the results of the predictive power of circular regression per vegetation type.

Figure 3a and c show that for *μ*=0 (DOY_{GPPmax} at the beginning of the year), circular regression has a higher accuracy and precision compared to the linear regression for the entire space of regression coefficient values, with a maximum difference of the order of 0.1 in terms of accuracy and of the order of 1 for precision. For *μ*=*π* (DOY_{GPPmax} midyear) the linear model has a higher accuracy in most of the evaluated space, with a maximum difference of the order of 0.001 compared with the circular regression, while circular regression has a higher precision for most of the regression coefficients of the order of 0.001. These results show that circular regression has a higher precision in recovering the original regression coefficients than linear regression no matter the moment of the year. On the other hand, circular regression has a higher accuracy than the linear model at the beginning of the year. While linear is better midyear, the differences are of the order of 0.001.

To illustrate the method in practice, we compare the circular and linear models using data from two
sites: US-Ha1 (Northern Hemisphere, deciduous broadleaf forest) and AU-How (Southern Hemisphere,
woody savanna). We relate the climate variables with DOY_{GPPmax} (see Methods)
and reconstruct the DOY_{GPPmax} using the linear and circular regression
models. We compare observed and predicted DOY_{GPPmax} using JS correlation for
the circular model and the Pearson product moment for the linear model. For US-Ha1 both methods show similar
performance in predicting DOY_{GPPmax}
(Fig. 4), while for AU-How, the circular model retrieves
the original data better than the linear model, explaining 30 % more of the variance. In the event that the DOY_{GPPmax} is reached at the beginning of the year, linear methods
produce a strong bias that predicts the timing across the entire year
(Fig. 4b).

From the 52 sites analyzed in this study, only one site (ES-LJu) shows bimodal growing seasons (see
Supplement 1.2). As expected, in most cases DOY_{GPPmax} occurs in the middle of
the calendar year (Fig. S6 in the Supplement), reflecting the uneven site distribution in FLUXNET
(Schimel et al., 2015). However, some ecosystems in the Northern Hemisphere do reach
DOY_{GPPmax} at the beginning of the year; these are Mediterranean sites such as
US-Var and ES-LJu. In general terms, most of the sites have an SD between 10 d and 40 d. The
maximal SD is 46.9 d for the AU-Tum site. A detailed table with the mean angular direction and
SD of DOY_{GPPmax} of each site is presented in Sect. S1.2.

For half of the sites, the JS correlation coefficients are between 0.70 and 0.97 (Supplement 1,
Fig. S5), showing that the interannual variability of DOY_{GPPmax} is mainly
explained by the cumulative effect of the climate variables. Nineteen sites have a JS coefficient
of less than 0.7 (DK-Sor, FI-Hyy, US-MMS, DK-ZaH, FR-Pue, US-UMB, AU-Tum, US-Ton, FR-LBr, US-Me2,
IT-Lav, AT-Neu, DE-Gri, IT-MBo, IT-Ro2, US-Wkg, BR-Sa1, FR-Fon, CZ-wet). For ES-LJu the JS
coefficient is 0.77 for the first growing season and 0.78 for the second one (Table S2 in the
Supplement).

We find that air temperature, shortwave incoming radiation, and vapor pressure
deficit appear as the dominant drivers worldwide at 43 of the total sites (84 %; Supplement 3). Precipitation
is the main driver for five sites (AU-How US-Ton ZA-Kru US-SRM US-Wkg; Supplement 3). Interestingly,
precipitation was the most important factor for all the woody savanna sites (Supplement 3). For
three sites (DE-Gri, IT-Ro2, BRSa1), any climatic variable is significant. In terms of the sign of
the coefficients, all the variables are predominantly negative
(Table 1). This means that higher values of radiation, air
temperature, VPD, and precipitation lead to an earlier DOY_{GPPmax}. Individual
sensitivities per site are shown in Supplement 3.

The PCA between shortwave incoming radiation, air temperature, and vapor pressure deficit has the highest frequency of significant correlation coefficients by number of sites for all the vegetation types with the exception of woody savannas (WSAs), where precipitation is shown to be more important for most sites than the dimensionality reduction between Tair, SWin, and VPD (Fig. 5). For closed shrublands (CSHs) and savannas (SAVs), both drivers have the same number of sites where the coefficients are statistically significant.

A special case for understanding the sensitivity of DOY_{GPPmax} to climate variables
is the site “Llano de los Juanes” (ES-LJu), an open shrubland ecosystem in Spain. It is the only
clearly bimodal ecosystem in our study (Fig. 6). In this case precipitation is not
statistically significant, while the combination of Tair, SWin, and VPD is significant for both
seasons. Furthermore, in both growing seasons Tair, SWin, and VPD have a negative coefficient.

The leave-one-out cross-validation for several vegetation types shows that the predictive power of the model for grassland (GRA) and evergreen broadleaf forest (EBF) is −0.3 and −0.31, respectively. For deciduous broadleaf forest (DBF) it is 0.46, and for evergreen needleleaf forest (ENF) it is 0.4, while for mixed forest (MF) the predictive power of the model is 0.88 (Fig. 7).

4 Discussion

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We explored whether circular regression is a suitable tool for analyzing phenological events. Our results suggest that circular regressions can recover predefined coefficients in a set of simulations with higher accuracy and precision than linear regressions. Hence, we would generally suggest that circular regressions may be advantageous when the aim is analyzing the effect of climatic variables on phenological events. We also found cases where the classical linear regression may be either more robust or equally suitable, e.g., when phenological events are reached close to midyear. In the overall view, however, we consider that circular regressions are to be preferred over linear regression for their conceptual capacity to analyze the physio-phenology of ecosystems regardless of the day of the year when an event of interest occurs. This allows us to analyze phenological studies at the global scale regardless of geographic location or the distribution of the observations during the year.

Different phenological models have been developed, ranging from empirical approaches
(Richardson et al., 2013) to process models (Asse et al., 2020) over the last
decades. As we demonstrate here, circular statistics open new opportunities to increase the
robustness of phenological models, allowing us to analyze ecosystems across hemispheres within the same
consistent framework. In fact, the results of the phenological sensitivity of
DOY_{GPPmax} indicate the complexity of ecosystem responses to climate
variability. Our approach provides motivation to integrate circular regressions into more complex
statistical techniques like regression trees, Gaussian processes, or artificial neural networks,
targeting a circular response variable.

The geographical location of the FLUXNET2015 sites represents an advantage when capturing the
DOY_{GPPmax} variability at the global scale (Supplement 1, Fig. S6). Most of the
analyzed sites (47) are located in the Northern Hemisphere. Two sites (GF-Guy and BR-Sa1) are
located in the tropical region, and three sites (ZA-Kru, AU-How, AU-Tum) are in the Southern
Hemisphere. However, because of the low number of sites reported in the tropical and southern region
with more than 7 years of data, our understanding of the DOY_{GPPmax}
variability in these regions is still limited. Increasing the number of tropical and Southern
Hemisphere sites should be considered a high priority in the near future to complement our knowledge
about the physio-phenological ecosystem state.

The high values of the JS correlation coefficients for most of the sites demonstrate that the
interannual variability of DOY_{GPPmax} can be explained as the cumulative effect
of the climate variables during the growing season. Sites where it was not possible to explain the
variations in DOY_{GPPmax} with enough confidence (JS correlation <0.7)
might require the incorporation of biotic variables (e.g., species
composition; Peichl et al., 2018) or soil property information that can improve the predictive
power of the model.

Our results suggest that there is no pattern between the DOY_{GPPmax} sensitivity
across vegetation types and climate classes (Sect. Fig. S1.7). In other words, the
DOY_{GPPmax} sensitivity is site-specific, probably produced by the unique
combination of biotic (e.g., species composition, species phenology, species interaction, and
phenotypic plasticity) factors that are not evaluated in our study. Several studies that focused on
ecosystem phenology suggest that species composition plays a fundamental role in ecosystem
physio-phenology of the CO_{2} uptake (Gonsamo et al., 2017; Peichl et al., 2018).

While there is no clear relationship between the DOY_{GPPmax} sensitivity and the
vegetation type, we find a predominant role of the combined effects of shortwave incoming radiation
(SWin), air temperature (Tair), and vapor pressure deficit (VPD) at the global scale on the
DOY_{GPPmax} interannual variability, where for most of the sites these variables
have a negative regression coefficient. This means that if the SWin, Tair, and VPD increase during
the growing season, the DOY_{GPPmax} will be reached earlier. This effect can be
a consequence of DOY_{GPPmax} being reached when SWin and Tair are
at a maximum.

On a global scale, our analysis shows that the combination of air temperature, shortwave incoming
radiation, and vapor pressure deficit as well as precipitation has a negative sign. This means that
if these variables increase during the growing season, the GPPmax will be reached earlier. Our
results are similar to those obtained by Wang and Wu (2019), who were the authors to conclude that
an increase in the temperature produces an earlier DOY_{GPPmax}. This phenomenon
is likely explained by the leaf-out advancing during spring. Nevertheless, there is still no
consensus on whether the increase in temperature will produce an earlier end of the growing
season. Several studies have demonstrated for different vegetation types that when temperature increases,
spring onset is earlier, and autumn senescence is later (Stocker et al., 2013; Linkosalo et al., 2009; Migliavacca et al., 2012; Morin et al., 2010; Post and Forchhammer, 2008), increasing
the length of the growing season and the amount of CO_{2} that is taken up by ecosystems
(Richardson et al., 2013).

Ecosystems with two growing seasons per year represent a very interesting case of the effect of
climate drivers on DOY_{GPPmax} across different growing seasons. In Llano de los
Juanes, Spain (ES-LJu; Fig. 6), DOY_{GPPmax} is reached in the first
growing season, when the rainy season is finishing, while in the second growing season
DOY_{GPPmax} is reached in the middle of the rainy season (data not shown). The
effect of shortwave incoming radiation, temperature, and vapor pressure deficit for both growing
seasons is negative, suggesting that if we increase these variables during the period before, the
DOY_{GPPmax} will happen earlier.

Phenology in Mediterranean ecosystems is mainly controlled by water availability
(Kramer et al., 2000; Luo et al., 2018; Peñuelas et al., 2009). However, our results
suggest that DOY_{GPPmax} is mainly sensitive to SWin, Tair, and VPD. These results
agree with the analysis performed by Gordo and Sanz (2005), who were the authors to evaluate the
phenological sensitivity of Mediterranean ecosystems to temperature and precipitation. They
concluded that temperature was the most important driver. Although water is a limiting factor in
Mediterranean ecosystems, its influence on plant physiology and plant phenology can be completely
different. In terms of physiology, the GPPmax value can decrease, but in terms of phenology,
DOY_{GPPmax} can still be the same.

Complex interactions between climate variables and phenological response and the interspecificity of
the sensitivity at the site level explain in part the poor predictive power of the model for grasslands,
evergreen broadleaf forest, evergreen needleleaf forest, and deciduous broadleaf forests in the
cross-validation analysis (Fig. 7). However, the predictive power for mixed
forest is high, even when the distribution of the latitudinal gradient is not the same for all the
sites. These results reflect the fact that the circular regression model can be extrapolated from different
sites to predict the DOY_{GPPmax} interannual variability. This advantage could
be a way to solve the common criticism that phenological models cannot be extrapolated by only generating
ad hoc hypotheses (Richardson et al., 2013).

5 Conclusions

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In this study we explored the potential of “circular regressions” to explain the physio-phenology of
maximal CO_{2} uptake rates. We conclude that (1) shortwave incoming radiation, temperature,
and vapor pressure deficit are the main drivers of the timing of maximal CO_{2} uptake at
the global scale (precipitation only plays a secondary role, with the exception of woody savannas, where
the most important variable is precipitation), and (2) although the sensitivity of the
DOY_{GPPmax} to the climate drivers is site-specific, it is possible to
extrapolate the circular regression model for different sites with the same vegetation type and
similar latitudes. Finally, we used simulated and empirical data to demonstrate that circular
regression produces more accurate results than linear regression, in particular in cases when data
need to be explored across hemispheres.

Appendix A: FLUXNET sites

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Wohlfahrt et al. (2008)Beringer et al. (2007)Leuning et al. (2005)Carrara et al. (2004)Aubinet et al. (2001)Saleska et al. (2003)Brooks et al. (1997)Merbold et al. (2014)Zielis et al. (2014)Imer et al. (2013)Etzold et al. (2011)Dušek et al. (2012)Prescher et al. (2010)Knohl et al. (2003)Grünwald and Bernhofer (2007)Pilegaard et al. (2011)Lund et al. (2012)Serrano-Ortiz et al. (2009)Suni et al. (2003)Thum et al. (2007)Delpierre et al. (2016)Berbigier et al. (2001)Rambal et al. (2004)Bonal et al. (2008)Valentini et al. (1996)Garbulsky et al. (2008)Marcolla et al. (2003)Marcolla et al. (2011)Marras et al. (2011)Montagnani et al. (2009)Rey et al. (2002)Tedeschi et al. (2006)Chiesi et al. (2005)Moors (2012)van der Molen et al. (2007)Kurbatova et al. (2008)Baker et al. (1999)McDowell et al. (2000)Urbanski et al. (2007)Davis et al. (2003)Treuhaft et al. (2004)Schmid et al. (2000)Monson et al. (2002)Berger et al. (2001)Scott et al. (2008)Desai et al. (2005)Xu and Baldocchi (2003)Curtis et al. (2002)Xu and Baldocchi (2004)Curtis et al. (2002)Emmerich (2003)Archibald et al. (2009)

Code availability

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Code availability.

Code is available under GPL-3 license at https://github.com/dpabon/ecosystem-physio-phenology-repo, last access: 29 July 2020 (https://doi.org/10.5281/zenodo.3921892 Pabon-Moreno et al., 2020).

Data availability

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Data availability.

FLUXNET database is available online at https://fluxnet.fluxdata.org/, last access: 11 July 2019 (https://doi.org/10.1038/s41597-020-0534-3 Pastorello et al., 2020).

Supplement

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Supplement.

The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-3991-2020-supplement.

Author contributions

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Author contributions.

DEPM, TM, MM, and MDM designed the study in collaboration with MR and CR. DEPM conducted the analysis and wrote the manuscript, with substantial contributions from all coauthors.

Competing interests

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Competing interests.

The authors declare that they have no conflict of interest.

Acknowledgements

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Acknowledgements.

We thank the reviewers for their helpful suggestions and Guido Kraemer for his help with the mathematical notation. This project has received funding from the European Union's Horizon 2020 research and innovation program via the TRuStEE project under the Marie Skłodowska-Curie grant agreement no. 721995. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization were carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and the Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, as well as the OzFlux, ChinaFlux, and AsiaFlux offices.

Financial support

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Financial support.

This research has been supported by the H2020 Marie Skłodowska-Curie Actions (TRuStEE grant no. 721995).

The article processing charges for this open-access

publication were covered by the Max Planck Society.

Review statement

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Review statement.

This paper was edited by David Bowling and reviewed by two anonymous referees.

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Short summary

Ecosystem CO_{2} uptake changes in time depending on climate conditions. In this study, we analyze how different climate variables affect the timing when CO_{2} uptake is at a maximum (DOY_{GPPmax}). We found that the joint effects of radiation, temperature, and vapor pressure deficit are the most relevant controlling factors of DOY_{GPPmax} and that if they increase, DOY_{GPPmax} will happen earlier. These results help us to better understand how CO_{2} uptake could be affected by climate change.

Ecosystem CO_{2} uptake changes in time depending on climate conditions. In this study, we analyze...

Biogeosciences

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